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IMPLEMENTASI MANDATORY DESIRABLE INESSENTIAL (MDI) PADA SISTEM POINT OF SALE DI UD. FAZAL TANI BERBASIS ANDROID
The main problem at UD. Fazal Tani lies in the inefficiency of transaction processes and inventory management, which are still carried out manually, often leading to recording errors, delays in reporting, and inaccurate stock data. This study aims to develop and implement an Android-based Point of Sale (POS) system using the Mandatory, Desirable, Inessential (MDI) approach to effectively and efficiently identify and prioritize system feature requirements. The research method employed is qualitative descriptive through observation, interviews, and literature study, while the system development follows the Waterfall method with stages of requirement analysis, design, development, testing, and maintenance. The implementation results show that the developed POS system is capable of handling sales transactions, inventory management, receipt printing, and sales reporting in a more structured and faster manner. Black box testing indicates that all functions operate as expected. The application of the MDI method focuses system development on the main features required by users, reducing the average transaction time from 30 minutes to 4 minutes, achieving an efficiency of 86.67%. Keywords: Point of Sale, Mandatory Desirable Inessential, Information System, Android, Inventory Management.
Copyright (c) 2025 Roya Hefifiya Siregar, Muhamad Alda https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTATION OF HEAVY EQUIPMENT SERVICE MONITORING SYSTEM AT PT. SENTRA KARYA LOGISTIK
The service monitoring system is a monitoring process of the periodic service schedule for vehicle units or heavy equipment with the aim that no heavy equipment misses the periodic service schedule. Currently, the monitoring service system is widely used by large companies engaged in the logistics sector. PT. Sentra Karya Logistik is a company engaged in logistics and heavy equipment rent. PT. Sentra Karya Logistik has several problems in the process of monitoring heavy equipment services. These problems include checking the hours meter (HM) of heavy equipment which is still done manually by asking directly to each operator, recording and searching monitoring service data still using Microsoft Excel so that it takes a long time and there is no notification of heavy equipment that has entered service time so that many heavy equipment often miss their regular service schedule. Based on the above problems, a web-based heavy equipment monitoring service system was created using the waterfall method because this method uses a systematic and sequential approach. With system modeling using the Unified Modeling Language (UML). The purpose of this research is to create a system that can simplify the process of recording and searching monitoring service data and can provide notifications for each heavy equipment that is approaching its periodic service time. The result of this research is the Heavy Equipment Monitoring Service System at PT. Sentra Karya Logistik. Keywords: Heavy Equipment, Monitoring Service, Monitoring Service System, Waterfall Method.
Copyright (c) 2025 Ely Nuryani, Sutanto Sutanto, Huswatun Hasanah, Irma Yunita Ruhiawati, Fikri Nugraha https://creativecommons.org/licenses/by-sa/4.0
GEOGRAPHIC INFORMATION SYSTEM FOR SEARCHING LAUNDRY SERVICES USING THE STEEPEST ASCENT HILL CLIMBING METHOD
Advancements in information technology have driven efficiency across various sectors, including the laundry industry. In Lhokseumawe City, the high number of students and workers has created a significant demand for fast and efficient laundry services. However, the process of finding laundry services is still conducted manually, making it impractical and time-consuming. This study develops a web-based Geographic Information System (GIS) designed to help users easily locate the nearest laundry services with high accuracy and efficiency. The system was developed using the Waterfall model and implements the Steepest Ascent Hill Climbing algorithm to determine the optimal route to the destination. Technologies used include HTML, CSS, React JS, and Express. Data were collected through observations and interviews with students and laundry business owners. The system presents an interactive map with comprehensive information such as location, photos, pricing, and contact details. Based on testing, 91% of users rated the system design as excellent, 91% were satisfied with the ease of search, 93% found the system efficient, and 92% considered it effective in helping them find suitable laundry services. This system not only facilitates public access to laundry services but also helps business owners increase their visibility and reach in the Lhokseumawe area through digital means. Kata Kunci: Geographic Information System, Laundry, Lhokseumawe, React JS, Steepest Ascent Hill Climbing
Copyright (c) 2025 Deny Andrian, Rizky Putra Fhonna, Veri Ilhadi https://creativecommons.org/licenses/by-sa/4.0
EVALUASI EFEKTIVITAS SUPPORT VECTOR MACHINE DAN RANDOM FOREST DALAM KLASIFIKASI ULASAN PENGGUNA APLIKASI STREAMING VIDIO
Perkembangan pesat platform streaming telah menghasilkan banyak ulasan pengguna yang dapat dimanfaatkan sebagai sumber masukan untuk pengembangan aplikasi. Penelitian ini dilakukan untuk mengevaluasi kinerja algoritma Support Vector Machine (SVM) dan Random Forest (RF) dalam mengklasifikasikan sentimen ulasan pengguna terhadap aplikasi Vidio. Sebanyak 1.000 ulasan berbahasa Indonesia dikumpulkan menggunakan teknik web scraping dan diberi label sentimen berdasarkan rating bintang, di mana rating 1–2 dikategorikan sebagai sentimen negatif dan 3–5 sebagai sentimen positif. Data ulasan diproses melalui beberapa tahap preprocessing, seperti pembersihan teks, tokenisasi, penghapusan stopword, dan stemming, sebelum dikonversi menjadi representasi numerik menggunakan metode TF-IDF. Dataset dibagi menjadi 80% data latih dan 20% data uji. Kedua model dilatih dan dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa performa yang lebih unggul diperoleh oleh algoritma SVM, dengan akurasi mencapai 76,11%, dibandingkan dengan RF yang memperoleh akurasi sebesar 71,67%. Selain itu, identifikasi ulasan dengan sentimen negatif juga dilakukan dengan lebih efektif oleh SVM. Temuan ini membuktikan bahwa klasifikasi sentimen ulasan aplikasi Vidio lebih tepat dilakukan menggunakan SVM, sehingga berpotensi mendukung otomatisasi analisis sentimen dan peningkatan kualitas layanan streaming. Hasil ini dapat diimplementasikan dalam sistem dashboard otomatis untuk mendeteksi keluhan pengguna secara real-time, memungkinkan pengembang Vidio meningkatkan pengalaman pengguna dengan respons yang lebih cepat dan tepat. Kata Kunci: Text Classification, User Sentiment, Support Vector Machine, Random Forest, Vidio.
Copyright (c) 2025 Fastabiqul Khusna, Khothibul Umam, Siti Nur'aini, Maya Rini Handayani https://creativecommons.org/licenses/by-sa/4.0
APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS (CNN) FOR HEPATITIS C VIRUS (HCV) DISEASE DETECTION
Hepatitis C (HCV) is a serious liver infection that can progress to cirrhosis or cancer, especially if early diagnosis is neglected. While conventional diagnostic methods such as ELISA are accurate, they are often limited in terms of efficiency and accessibility. This study introduces an innovative approach using a one-dimensional convolutional neural network (1D-CNN) for HCV disease classification, utilizing a Kaggle dataset consisting of 615 patient samples. The methodology includes data preprocessing such as handling missing values ​​and transforming categorical variables to numeric values ​​to ensure data readiness. The 1D-CNN model was trained using the Adam optimizer with ReLU activation functions in the convolution layer and sigmoid in the output layer. Model performance was comprehensively evaluated through accuracy, precision, recall, and F1-score metrics. The results showed that the 1D-CNN model achieved an accuracy of 83.74% on the training data and 81.30% on the testing data after hyperparameter tuning. This improvement is significant compared to the initial accuracy of only around 52%. However, the model exhibits a strong bias towards the majority class (Blood Donor), with very poor performance on minority classes such as Hepatitis, Fibrosis, and Cirrhosis. Nonetheless, this study contributes to the exploration of 1D-CNN for non-image medical data, which is still rarely studied. We conclude that despite the model's potential, further developments such as data balancing are needed to improve the model's overall generalization and accuracy. Keywords: Deep Learning, Disease Classification, Hepatitis C, 1D CNN, Medical Diagnosis
Copyright (c) 2025 Fathorrozi Ariyanto Fathorrozi, Indra Maulana, Moh. Aminollah Hamzah, Aang Kisnu Darmawan https://creativecommons.org/licenses/by-sa/4.0
CROWDFUNDING WEBSITE SECURITY THROUGH POLYGON BLOCKCHAIN TECHNOLOGY IMPLEMENTATION
This study aims to enhance the security and transparency of crowdfunding platforms by implementing Polygon blockchain technology. Traditional crowdfunding systems are often vulnerable to fund mismanagement and lack accountability. To address these issues, a blockchain-based crowdfunding website was developed using smart contracts deployed on the Polygon network. The project follows a software engineering approach using the prototyping development model. The system is built using Solidity for smart contracts and React.js for the frontend, integrated with the MetaMask crypto wallet for authentication and transaction execution. The implementation results show that the system successfully facilitates secure, transparent, and decentralized donation processes. All transactions and fund flows are permanently recorded and verifiable through PolygonScan. User validation is conducted without conventional login systems, relying instead on wallet-based authentication, which reduces the risk of credential theft. Functional testing confirms that all core features perform as expected, including donation program selection, transaction history recording, and MetaMask integration. In conclusion, the developed system successfully achieves the research objective of providing a more secure and trustworthy crowdfunding platform through the application of Polygon blockchain technology. Keywords: blockchain, crowdfunding, data security, MetaMask, smart contract.
Copyright (c) 2025 Rafi Johari, Dr. Viddi Mardiansyah, S.Si., M.T. https://creativecommons.org/licenses/by-sa/4.0
MODEL PREDIKSI SENTIMEN ULASAN MOBILE LEGENDS DI GOOGLE PLAY STORE DAN YOUTUBE BERBASIS PELABELAN OTOMATIS ROBERTA DAN KLASIFIKASI RANDOM FOREST
The rapid development of the mobile gaming industry has led to an increase in users and reviews for various popular titles, including Mobile Legends: Bang Bang. This research aims to analyze user perceptions of the Mobile Legends application through reviews obtained from Google Play Store and YouTube. The methods employed include data crawling, automatic labeling using the RoBERTa model for sentiment classification (positive, negative, and neutral), and modeling with the Random Forest algorithm. The dataset comprises 1,400 data points from Google Play Store and hundreds of data points from YouTube, all of which have undergone preprocessing. Model evaluation uses precision, recall, and f1-score metrics. The test results show that the model can classify reviews quite well, achieving an accuracy of 80% on Google Play Store data and 82% on YouTube data. The model demonstrates high performance in detecting negative and positive reviews, although the accuracy for the neutral class remains low. Overall, the Random Forest-based model is sufficiently reliable in processing user review data, providing insights into public perception of Mobile Legends across different platforms. Keywords: Mobile Legends, Random Forest, Sentiment Analysis, Google Play Store, YouTube
Copyright (c) 2025 Muhammad Rafid Pratama, Maya Rini Handayani, Wenty Dwi Yuniarti, Khothibul Umam https://creativecommons.org/licenses/by-sa/4.0
AN ANALYSIS OF THE FACTORS CONTRIBUTING TO THE FAILURE OF RIDE-HAILING ADOPTION IN DEVELOPING REGIONS THROUGH THE LENS OF THE DIFFUSION OF INNOVATION MODEL: A CASE STUDY OF OKE-JEK IN MANOKWARI REGENCY
This study analyzes the use of the Oke-Jek platform as a ride-hailing service in developing regions. The objective of the study is to identify the factors that hinder the adoption of Oke-Jek as a ride-hailing service in such areas. Currently, the utilization of Oke-Jek remains limited, particularly in regions like West Papua, leading to a research gap regarding the adoption of this service in the area. This study employs the Diffusion of Innovation Theory as its conceptual framework. Data were collected through a Google Forms-based questionnaire, with a total of 112 respondents. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The main findings indicate that the proposed model demonstrates good reliability and validity, with an R² value of 0.628, meaning that Behavioral Intention to Use explains 62% of the variance in the use of Oke-Jek. Furthermore, the results confirm that Compatibility, Relative Advantage, and Complexity are significant factors influencing the adoption of Oke-Jek among respondents. The implications of this research support the development of ride-hailing services in developing regions. These findings can be used to design effective strategies for increasing the adoption of Oke-Jek in West Papua. In addition to providing theoretical insights, this study also offers strategies for the evaluation and optimization of ride-hailing services. Keywords: Developing Regions, Diffusion of Innovation, Partial Least Squares Structural Equation Modeling, Ride-hailing, Technology adoption
Copyright (c) 2025 Roberth Yembise, Dedi Iskandar Inan, Ratna Juita, Muhamad Indra https://creativecommons.org/licenses/by-sa/4.0
PERANCANGAN APLIKASI MONITORING PENGIRIMAN BARANG BERBASIS WEB PADA PT. JAKA OETAMA SENTOSA
delivery process is a crucial factor in supporting the operational effectiveness of a distribution company. PT. Jaka Oetama Sentosa (PT. JOS) still implements manual coordination between warehouses, which results in information delays, potential recording errors, and low work efficiency. This study aims to design and develop a web-based goods delivery monitoring application to improve transparency and information accuracy internally. The method used is Rapid Application Development (RAD), with data collection through observation, interviews, and literature studies. The results of this study include user interface design, database development, and integrated system implementation. The developed application is expected to accelerate information flow, minimize recording errors, and increase the effectiveness of the goods distribution process. These findings contribute to distribution optimization through internal company digitalization. Keywords: Design, Application, Monitoring, Goods Delivery, Website, Rapid Application Development
Copyright (c) 2025 Putri Agustin, Anton Zulkarnain Sianipar, Thomas Budiman https://creativecommons.org/licenses/by-sa/4.0
ANDROID-BASED INFORMATION SYSTEM FOR MONITORING AL-QUR'AN MEMORIZATION AT AL-MADINA ISLAMIC BOARDING SCHOOL, BANJARNEGARA
Al-Madina Islamic Boarding School in Banjarnegara is an Islamic educational institution that conducts an intensive Quran memorization program for its students (santri). However, the current manual method of recording memorization progress poses several challenges, such as slow information dissemination, potential data loss, and difficulties for parents in monitoring their children's development routinely and in real time. These issues result in inefficiencies and a lack of transparency in the memorization evaluation process. To address these challenges, an Android-based Quran memorization monitoring information system was developed using the Rapid Application Development (RAD) approach. RAD was chosen due to its ability to accelerate system development through prototype iterations and active user involvement. The system includes features such as digital memorization logging by instructors (ustaz), automatic notifications to parents via Firebase Cloud Messaging (FCM), and progress reports in the form of downloadable e-report cards (e-Rapor) accessible through the app. The system also integrates digital Quranic pages sourced from the EQuran.id API. The implementation of this system enables a more systematic memorization recording process, facilitates rapid and accurate information delivery, and enhances communication between the boarding school and parents. This development not only addresses administrative issues but also represents a foundational step toward digital transformation in pesantren educational management. Keywords: Islamic boarding school, Qur’an memorization, information system, Android, e-Report, RAD
Copyright (c) 2025 Agasta Pratama Nugraha, Ridho Muktiadi, Abid Yanuar Badharudin, Achmad Fauzan https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK OPTIMASI PEMBERIAN REWARD SISWA SMA
Providing rewards to students is a common strategy to enhance learning motivation and participation in school. However, conventional reward systems often face challenges related to objectivity and the comprehensiveness of assessment criteria. This study aims to apply the K-Nearest Neighbor (KNN) algorithm as an optimization tool for determining reward recipients among high school students. The dataset includes average report card scores, moral values, parental income, number of siblings, and non-academic activity scores. The KNN method was selected for its effectiveness in classifying data based on similarity. The research process involved collecting historical student data, normalizing the data, building the KNN model, and evaluating its performance. The results show that the KNN model achieved an accuracy rate of 91% in classifying students into appropriate reward categories. This indicates that the model can reliably support decision-making in the reward distribution process. In conclusion, applying the KNN algorithm offers a more structured, objective, and transparent approach to student reward allocation, which can enhance the effectiveness of reward programs and promote student development. Keywords: K-Nearest Neighbor, Reward, Classification, Objectivity, Optimization
Copyright (c) 2025 Agus Riyanto, Nurchim, Intan Oktaviani https://creativecommons.org/licenses/by-sa/4.0
SISTEM REKOMENDASI SISWA UNTUK PROGRAM EKSTRAKURIKULER BERBASIS METODE SAW DI SMAN 1 SIMPANG EMPAT
SMA Negeri 1 Simpang Empat is among the secondary schools that participate in secondary education located at Jalan Benteng No 2 Simpang Empat, Simpang Empat District, Asahan Regency, North Sumatra. The extracurricular activities at SMA Negeri 1 Simpang Empat include scouting, student council (OSIS), spiritual organization (ROHIS), volleyball, and Red Cross Youth (PMR). The issue at SMA Negeri 1 Simpang Empat is that there is currently no computerized system available to aid in the process of choosing extracurricular activities for students, which means No technique may be employed to match extracurricular choices with each student's interests and talents. As a result, the selection of extracurricular activities is still done based on personal preference or peer influence without clear guidelines because students tend to choose based on preferences individual or merely following the influence of peers without considering their potential and talents more objectively. This can lead to a mismatch between the chosen extracurricular activities and the students' abilities or interests, making the effectiveness of extracurricular activities in developing their potential less than optimal. To address this issue, a recommendation system SAW, or simple additive weighting, is one method that can be applied., which allows The actual selection process to be conducted objectively. A common name for the SAW method is the weighted summation method. This study's objective is to create an extracurricular recommendation system that can help students choose extracurricular programs based on their interests and talents. The methodology employed in The research methodology used in this study is quantitative. A data-driven system can replace manual processes in the selection of extracurricular activities in a more efficient way and provide more objective results, making it easier for students to determine suitable extracurricular choices. The outcomes of using the SAW technique to the decision support system's implementation can help choose the extracurricular activities that best meet student criteria, based on objective and weighted assessments of students' interests and talents, in order to make decisions that are more precise and measurable. Keywords: Extracurricular Program, Recomendation System, Simple Additive Weighting (SAW).
Copyright (c) 2025 Adelia Klisna Hasibuan, RIKI ANDRI YUSDA, FEBBY MADONNA YUMA https://creativecommons.org/licenses/by-sa/4.0
PEMETAAN LOKASI TOWER TELKOMSEL DI LABUHAN BATU UTARA MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS
The development of information technology has had a significant impact on the telecommunications sector, including in network infrastructure management. Telkomsel as the largest telecommunications service provider in Indonesia faces challenges in tower management in North Labuhan Batu Regency, such as equipment damage, theft, and the impact of natural disasters. The unavailability of a system capable of displaying tower locations accurately and reporting is an obstacle in handling problems in the field. This study aims to build a web-based geographic information system (GIS) to map Telkomsel tower locations in North Labuhan Batu. The development method uses a waterfall that includes needs analysis, system design, implementation, and testing. The system was developed using PHP with the CodeIgniter 3 framework, a MySQL database, and UML design. The results of the study are a WebGIS application that can display tower locations based on coordinates and provides reporting features for maintenance, theft, and damage. The system has two types of users: admins to manage tower data and field technicians to report tower conditions. Test results show that the system runs well and is in accordance with user needs. This system makes the process of finding tower location information more efficient and incident reporting can be done periodically, so it is expected to support Telkomsel's performance in maintaining network infrastructure. Keyword: Geographic Information System, WebGIS, Telecommunication Tower, Telkomsel, North Labuhan Batu Regency
Copyright (c) 2025 Arif Wibowo, Raja Tama Andri Agus, Cecep Maulana https://creativecommons.org/licenses/by-sa/4.0
WORD EMBEDDING ANALYSIS IN SENTIMENT ANALYSIS USING MACHINE LEARNING: A CASE STUDY OF STEAM RPG GAME REVIEWS
User reviews on gaming platforms such as Steam have become a crucial source of information for potential players before making purchasing decisions. Due to the varied nature of user opinions, sentiment analysis is essential for processing and interpreting these reviews. This study investigates the application of sentiment analysis to RPG game reviews on Steam, aiming to assist users by summarizing reviews through sentiment results and providing insights into the general perception of a game. To achieve this, the study applies sentiment analysis using Word2Vec and Support Vector Machine (SVM). It focuses on evaluating the impact of lemmatization during preprocessing and analyzing the performance of Word2Vec in sentiment classification. Word2Vec transforms review text into vector representations that capture semantic relationships, enhancing the model’s ability to understand context. Meanwhile, SVM is chosen as the classifier for its effectiveness in distinguishing between positive and negative reviews and handling high-dimensional data. The system developed uses Word2Vec with 300-dimensional vectors combined with an SVM Polynomial classifier, resulting in the best performance among the tested models. The final model achieves a macro-average F1-score of 88.6%, indicating a strong capability in accurately classifying sentiments in user reviews. These results highlight the potential of combining word embedding and machine learning techniques for analyzing sentiment in gaming platforms. Keywords: sentiment analysis, Word2Vec, SVM, Steam, RPG
Copyright (c) 2025 Ardian Adam Alfarisyi, Mahendra Dwifebri Purbolaksono, Alfian Akbar Gozali https://creativecommons.org/licenses/by-sa/4.0
AUTOMATED EDIBILITY CLASSIFICATION OF MUSHROOMS USING MORPHOLOGY-BASED RANDOM FOREST ALGORITHM.
Mushrooms exhibit high morphological diversity; however, some species are poisonous and harmful if consumed. The physical similarities between edible and poisonous mushrooms often complicate manual identification. This research aims to develop an automated mushroom classification system based on the Random Forest algorithm to distinguish between edible and poisonous mushrooms. It leverages all 21 attributes (18 categorical, 3 numerical) from a compr[1]ehensive Kaggle dataset comprising 61,069 entries. The research methodology follows a modified CRISP-DM workflow, from data collection through to implementation. Crucial data preprocessing steps were extensively performed, including handling data duplicates and imputing missing values (using median and mode). Subsequently, class labels were transformed (edible=0, poisonous=1), and One-Hot Encoding was applied to categorical features for appropriate numerical representation. Numerical features such as cap-diameter and stem-height were normalized using Standard Scaling to balance their contributions. The data was then split 80:20 for training and testing. The Random Forest model was developed with optimal parameters (n_estimators=200, max_depth=15, class_weight="balanced") for efficiency and robustness against class imbalance. Evaluation results demonstrated excellent performance with an overall accuracy of 99.34%, along with balanced precision, recall, and F1-score values of 0.99 for both classes. Feature importance analysis identified stem-width, stem-height, and cap-diameter as the most influential attributes. The learning curve indicated model stability without significant overfitting. Implementation on new mushroom samples also confirmed consistent predictive capability, making this model suitable as an automated decision support system for detecting poisonous mushrooms. Keywords: Musrom, Clasification, Random Forest, Morphology, Machine Learning
Copyright (c) 2025 Muhammad Reza Pahlevi, Imam Tahyudin, Ades Tikaningsih https://creativecommons.org/licenses/by-sa/4.0
PENGEMBANGAN SISTEM E-CATERING BERBASIS WEB TERINTEGRASI WHATSAPP GATEWAY PADA USAHA KATERING KEMBANG DESA NGENTRONG
This study aims to design a web-based e-catering system integrated with WhatsApp Gateway for the Kembang Desa Ngentrong catering company. This system is intended to simplify and streamline the process of ordering, paying, and reporting catering orders online so as to improve operational efficiency and customer satisfaction. The development approach used is Agile Development, which facilitates the system development process iteratively and collaboratively by actively involving stakeholders. The implementation of WhatsApp Gateway as an integrated communication channel facilitates the ordering process by customers directly through the popular WhatsApp application. This system is developed based on web technology with product menu selection features, order confirmation, automatic alerts, and transaction reports that can be accessed by business owners. The results of system testing show that the use of the Agile Development approach can accelerate the development process and produce products that are in accordance with user needs with a validation scale of "Very Eligible." This e-catering system is expected to help the Kembang Desa Ngentrong Catering business manage orders better and improve customer service. Keywords: Agile Development, E-Catering, Kembang Catering, Web Based System, Whatsapp Gateway.
Copyright (c) 2025 Amartya Bambang Kusuma, Joko Iskandar, Yayak Kartika Sari https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTATION OF DATA ANALYTICS USING CRISP-DM METHODOLOGY (CASE STUDY OF BIKE SALES DATA IN EUROPE ON KAGGLE PLATFORM)
This study aims to implement data analytics using the CRISP-DM methodology on a European bicycle sales dataset obtained through the Kaggle platform. Data analytics is one of the important elements in strategic decision-making for companies, especially in the bicycle industry which has unique challenges in understanding consumer preferences and market dynamics. This study uses a qualitative descriptive approach with six main stages, namely: (1) Business Issue Understanding to understand the business context using the SMART method, (2) Data Understanding for initial identification of the dataset, (3) Data Preparation for data cleaning, (4) Exploratory Data Analysis using Python and the Pandas, Matplotlib, and Seaborn libraries, (5) Validation to verify hypotheses and revenue trends using BigQuery, and (6) Visualization and Presentation to present the results in the form of an interactive dashboard using Tableau Public. The results of the study show that the Bikes product category is the largest profit contributor, while Accessories and Clothing have potential that can still be optimized. Customer segmentation analysis shows that the Adult age group (35-64) provides the highest profit contribution. Validation of the revenue analysis shows a stable growth trend since 2013, as well as a profit gap between countries that requires a differentiation strategy. This study emphasizes the importance of implementing data analytics with the CRISP-DM approach to support data-based decision making in the bicycle industry, while providing practical recommendations for more effective marketing and business development strategies. Keyword : CRISP-DM, Data Analytics, Platform Kaggle, SMART Method..
Copyright (c) 2025 Tazkia Ardan, Sulistio Anggara Mujiatno, Bagus Ali Akbar https://creativecommons.org/licenses/by-sa/4.0
ANALISIS PROGRAM MAKAN BERGIZI GRATIS DENGAN SUPPORT VECTOR MACHINE (SVM) PADA APLIKASI X
The Free Nutritious Meal Program (MBG), which represents a priority program of President Prabowo Subianto, has garnered widespread attention from Indonesian society. This program has received sympathy from various groups, including students and informal workers, and has been extensively discussed through social media, particularly on platform X. This research aims to analyze public response in the form of positive and negative sentiment toward the MBG Program based on data from platform X. A total of 1,378 tweets were collected using crawling methods, followed by preprocessing, sentiment labeling using the InSet lexicon dictionary, and feature extraction using three techniques: Term Presence, Bag of Words (BoW), and Term Frequency-Inverse Document Frequency (TF-IDF). Subsequently, sentiment classification was performed using the Support Vector Machine (SVM) algorithm for each feature extraction technique. Classification results demonstrate that the TF-IDF technique achieved the highest accuracy of 77.5%, compared to Term Presence (76.2%) and BoW (75.3%). Validation using K-Fold Cross Validation with five iterations was conducted with imbalanced data handling through the Synthetic Minority Over-sampling Technique (SMOTE) method. In this validation, TF-IDF consistently demonstrated superior performance with an average accuracy of 75.54%, precision of 74.31%, recall of 73.86%, and f1-score of 73.98%. Despite a slight decrease in accuracy following data synthesis, the TF-IDF technique proved to be stable and effective in handling data variation. The superiority of the TF-IDF feature extraction technique is suitable for combination with the SVM algorithm. Keywords: Free Nutritious Meal Program, SVM algorithm, TF-IDF, Platform X, SMOTE Method
Copyright (c) 2025 Sekar Cinta Amaria, Nurtriana Hidayati https://creativecommons.org/licenses/by-sa/4.0
EVALUATION OF INFORMATION SYSTEMS USING COBIT 2019 AT PUSKESMAS (COMMUNITY HEALTH CENTERS) IN DENPASAR CITY
The digitalization of health information systems is crucial for improving service efficiency. DAMAKESMAS, the information system at the Denpasar City Health Office, faces various operational challenges, such as UI/UX limitations, lack of staff training, and suboptimal risk management and system security. This study aims to evaluate the governance maturity level of DAMAKESMAS using the COBIT 2019 framework through a quantitative descriptive method, involving surveys and interviews with 13 respondents from the Denpasar City Health Office. Data collection techniques include observation, interviews, and Likert-scale questionnaires, analyzed using the COBIT 2019 Capability Level method. The results indicate that DAMAKESMAS has reached Level 3 (Defined) in EDM03 (Ensure Risk Managed) with a 100% achievement rate, while the DSS and APO domains show compliance levels ranging from 92.3% to 100%. System evaluation highlights the need for improvements in UI/UX, regular training, enhanced security, and optimized incident management. In conclusion, although DAMAKESMAS has a solid governance foundation, strategic improvements are necessary to enhance its effectiveness, including UI/UX redesign, regular staff training, and strengthening system security and emergency response services. Keywords: Evaluation; IT Governance; COBIT 2019, Health Information System
Copyright (c) 2025 Made Winardana, DEWA GEDE Dewa Gede Hendra Divayana, Gede Indrawan Indrawan https://creativecommons.org/licenses/by-sa/4.0
GAUSSIAN NAÏVE BAYES CLASSIFIER METHOD FOR CLASSIFYING FAST FOOD CONSUMPTION AT THE LEVEL OF OBESITY RISK
The increasing prevalence of obesity, particularly among adolescents, has become a significant public health concern. Main contribution factors are the excessive consumption of fast food, which is often high in calories, fats, sodium, and sugar. This shift in dietary habits is largely leveraged by globalization, which promotes a more convenient yet unhealthy lifestyle. In response to this issue, this study focuses on the designation of fast-food consumption based on nutrition facts, specifically employing data from McDonald’s menu items. The primary goal is to identify consumption patterns and predict whether a particular is at liability of dynamic obesity. This research utilizes the Gaussian Naïve Bayes (GNB) classification algorithm, implemented using the Python programming language. The dataset consists of 500 entries containing nutritional information such as calories, cholesterol, sodium, carbohydrates, sugars, protein, vitamins, calcium, fat, iron, fiber, potassium, minerals, and condition labels indicating obesity risk. The data was carved into training and testing sets, with 20% (100 data points) allocated for testing to assess the conduct of the model. The results of the experiment show that the GNB algorithm achieved an accuracy of 61%. For comparison, other machine learning models were also tested: Random Forest (RF) reach out the accuracy at 68%, succeeded by Multinomial Naïve Bayes at 65%, Support Vector Machine (SVM) and Decision Tree (DT) both at 62%. GNB showed limitations in predicting the TRUE class (indicating obesity risk), with a precision score of 60% and recall of 61%. These results suggest that the dataset distribution and the learning characteristics of GNB contributed to its relatively lower performance. Among the tested models, Random Forest demonstrated the best predictive capability, achieving a TRUE class precision of 76%, significantly outperforming GNB. This indicates that Random Forest is more reliable for classifying obesity risk based on fast food nutritional content. As a direction for future work, improvements can be made by tuning the parameters of the Random Forest model and exploring the application of deep learning techniques to further enhance predictive performance and generalizability. Keywords: Classifier, Gaussian Naive Bayes, Klasifikasi, Preprocessing, Modeling, Good Health and Well-being.
Copyright (c) 2025 Agus Nursikuwagus, Suherman Suherman https://creativecommons.org/licenses/by-sa/4.0
PREDICTION MODEL FOR STUDENTS' ON-TIME GRADUATION USING ALGORITHM SUPPORT VECTOR MACHINE (SVM) BASED PARTICLE SWARM OPTIMIZATION (PSO)
One of the indicators in assessing the tridharma of higher education outcomes and achievements is students' timely graduation and one indicator of the success of the higher education system is timely graduation. However, some students cannot complete their studies on time. In the process of completing the study, several problems emerged, one of which was in completing studies on time, there are problems that arise, such as students who are still repeating because there are grades that have not passed in the course, the Grade Point Average (GPA) is still lacking, the Semester Achievement Index (SAI) is still below the minimum, the total number of semester credit units (total credits) which still have not reached the minimum limit, then the number of active lecture statuses still exceeds 8 semesters, so this problem will have an impact on the accuracy of student study graduate data, where the target performance indicator for graduates is on time The student's target of graduating on time has not been achieved. The factors that cause the students not to graduate on time are not known. In identifying the problem, it was found that the Study Program does not have adequate information regarding the potential for students to graduate on time and the limitations of the study program in assisting students in completing on-time graduation The method used to solve this problem is by creating a prediction model for students' on-time graduation, so that students can receive adequate information regarding the potential for graduating on time. This research aims to create a prediction model for graduating on time using the Support Vector Machine (SVM) method based on Particle Swarm Optimization (PSO) with feature selection. information gain, so that the attributes selected and used are Semester Achievement Index 1, Semester Achievement Index 2, Semester Achievement Index 3, Semester Achievement Index 4, Grade Point Average (GPA) 1, Grade Point Average (GPA) 2, Grade Point Average (GPA) 3, Grade Point Average (GPA) 4, Semester Credit Units 1, and Semester Credit Units 4. The results in this study obtained accuracy values ​​of 0.799, precision 0.851, recall 0.605 and AUC 0.86
Copyright (c) 2025 Syarif Hidayatulloh, Gandung Triyono , Kiki Ari Suwandi kosasih https://creativecommons.org/licenses/by-sa/4.0
ANALISIS METODE MULTI-CRITERIA DECISION MAKING (MCDM) UNTUK PENILAIAN KERUSAKAN DAN KEBUTUHAN PEMULIHAN PASCABENCANA
Indonesia menghadapi risiko tinggi terhadap bencana alam, sehingga penetapan prioritas untuk rehabilitasi dan rekonstruksi pascabencana menjadi hal yang sangat penting. Studi ini mengevaluasi empat metode Multi-Criteria Decision Making (MCDM), yakni ELECTRE, ARAS, SMART, dan CoCoSo, dalam menentukan prioritas penanggulangan bencana berdasarkan data dari Penilaian Kerusakan dan Kerugian (DaLA) serta Penilaian Kebutuhan Pemulihan Manusia (HRNA). Data ini diolah menjadi matriks keputusan dengan enam kriteria utama, menghasilkan peringkat wilayah terdampak untuk masing-masing metode. Validasi melalui korelasi peringkat Spearman telah menghasilkan nilai korelasi sebesar 0,9636 untuk metode penelitian ELECTRE, ARAS, dan SMART, sedangkan metode CoCoSo memiliki korelasi sedikit lebih rendah, yakni 0,9364. Perbedaan ini disebabkan oleh pendekatan algoritma CoCoSo yang menggunakan multi-agregasi, yaitu kombinasi model penjumlahan bobot (WSM) dan perkalian bobot (WPM), yang memengaruhi hasil peringkat. Penelitian ini menyimpulkan bahwa keempat metode layak digunakan dalam pengambilan keputusan pascabencana, meskipun metode berbasis fungsi nilai optimal seperti ARAS cenderung menunjukkan korelasi lebih tinggi. Penelitian juga mendorong pengembangan sistem berbasis teknologi untuk mempercepat dan meningkatkan akurasi proses pengambilan keputusan dalam situasi pascabencana.
Copyright (c) 2025 Dewa Ramadhan Artagautama, Fadhil Naufal Mafino Novian, Agusta Praba Ristadi Pinem https://creativecommons.org/licenses/by-sa/4.0
PERBANDINGAN MODEL PEMBELAJARAN MESIN BERBASIS SMOTE MENINGKATKAN IDENTIFIKASI SISWA BERISIKO DI SEKOLAH MENENGAH PERTAMA
Penelitian ini mengevaluasi efektivitas Teknik Pengambilan Sampel Berlebih Minoritas Sintetis (SMOTE) dalam mengatasi ketidakseimbangan kelas pada kumpulan data pendidikan, dengan fokus pada peningkatan model prediktif untuk mengidentifikasi siswa berisiko di sekolah menengah pertama di Indonesia. Fakta lapangan menunjukkan bahwa tidak semua data yang diambil memiliki ketidakseimbangan kelas. Kinerja model Decision Tree, Random Forest, dan SVM dinilai menggunakan kurva ROC dan metrik AUC sebelum dan setelah penerapan SMOTE. Random Forest menunjukkan peningkatan AUC paling signifikan (0,95→0,99) karena generalisasi ensembel pada data yang seimbang, sementara Decision Tree mengalami peningkatan marjinal (0,94→0,95) dan SVM mengalami trade-off kecil (0,93→0,94) karena sensitivitas terhadap noise sintetis. Semua model mengungguli tebakan acak (AUC>0,93), mengonfirmasi manfaat SMOTE dalam meningkatkan deteksi kelas minoritas untuk aplikasi seperti identifikasi siswa berisiko. Penelitian ini memajukan kerangka kerja praktis untuk memanfaatkan teknik pembelajaran ketidakseimbangan kelas pada dataset dalam pendidikan, menekankan wawasan yang dapat ditindaklanjuti dan meningkatkan hasil pembelajaran melalui pengambilan keputusan berbasis data. Dengan menerapkan teknik seperti SMOTE, sekolah dapat lebih akurat mengidentifikasi siswa berisiko, memungkinkan intervensi dini yang efektif dan alokasi sumber daya yang lebih efisien. Selain itu, penelitian ini mendorong pengembangan kebijakan pendidikan yang inklusif dan adil, mengurangi bias sistemik serta meningkatkan hasil pembelajaran secara keseluruhan. Keywords: Data Mining, Decision Tree C4.5, academic performance, educational classification, determinant factors
Copyright (c) 2025 Hevi Alvina Damayanti, Ucta Pradema Sanjaya https://creativecommons.org/licenses/by-sa/4.0
PERFORMANCE EVALUATION OF LIGHTWEIGHT OBJECT DETECTION MODELS FOR REAL-TIME PERSONAL PROTECTIVE EQUIPMENT DETECTION IN THE CONSTRUCTION SITES
The environment of construction industry was known to have a high risk and high number of occupational accidents and injuries. One of the main causes of the occurrences was the construction workers' negligence in wearing personal protection equipment. Computer vision-based approaches were developed to assist in personal protective equipment adherence to address this issue. Using lightweight machine learning algorithms, object recognition can help to detect if the PPEs are worn correctly. We evaluated performance of YOLOv8-Nano and YOLOv9-Tiny (state of the art lightweight object detection models). Custom dataset was used for training the models and then metrics like F1 score, precision, recall mAP50 and mAP50-95 were used to evaluate both models’ performance. Results found that both models were able to show promising real time detections, but the YOLOv9-Tiny model was able to outperform the YOLOv8-Nano model on many evaluation metrics. Specifically, in terms of mAP, YOLOv8-Nano achieved an mAP50 of 81.48, while YOLOv9-Tiny attained a slightly higher mAP50 of 82.70. Higher efficiency in these parameters will help small industry to enforce PPE adherence monitoring using edge device at a relatively low cost. Lastly, enhanced enforcement of PPE regulations through automated detection system can contribute to improve workplace safety which in turns will lead to less injuries. Keywords: Object Recognition, Computer Vision, Machine Learning, Lightweight, Personal Protective Equipment, YOLO
Copyright (c) 2025 Herman, Sandy Alferro Dion, Andik Yulianto https://creativecommons.org/licenses/by-sa/4.0
PRESERVING THE INDIGENOUS MUSICAL INSTRUMENTS OF PAPUAN USING AUGMENTED REALITY TECHNOLOGY
This study explores the use of augmented reality (AR) technology as a technology enabling the preservation of traditional Papua musical instruments. It aims of understanding factors that influence the adoption of this technology by people living in Papua who are not indigenous Papuans. The use of AR as a tool for cultural preservation is still limited, particularly in the context of Papua, highlighting a research gap concerning the acceptance of AR technology in regions rich in culture but limited in technology adoption. The study employs Design Science Research (DSR) as a research framework. The development and evaluation in the DSR are conducted rigorously and robustly. Once the AR artifact is developed using uniteAR tool, subsequently it is evaluated employing UTAU2 as a theoretical lens. Particularly in the evaluation stage, it involves 115 respondents as participants in data collection. The data is analyzed with partial least square structural equation modeling (PLS-SEM). The main findings indicate that the measurement model has good reliability and validity, with an R² value of 0.8, meaning that Behavioral Intention and Use Behavior explain 80% of the variability in AR technology adoption. The findings also reveal that performance expectancy, effort expectancy, and facilitating conditions are significant factors driving AR technology adoption among respondents. The implications of this study are highly relevant for the development of strategies using AR technology to introduce and preserve traditional Papua musical instruments. These findings can be used by local governments, indigenous communities, and local content developers to design more effective solutions for enhancing AR adoption in Papua, taking into account key factors influencing public behavioral intentions. Thus, this research not only provides theoretical insights into technology adoption but also strengthens the integration of culture and technology in Papua, opening opportunities for more interactive and engaging cultural preservation. Keywords: Technology Adoption, Augmented Reality, Papua Traditional Musical Instruments, PLS-SEM, UTAUT
Copyright (c) 2025 Noor Khosin, Dedi I Inan https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTATION OF FIREBASE IN THE DEVELOPMENT OF ANDROID-BASED QUEUE RESERVATION AND TREATMENT RECORD APPLICATIONS
The development of an Android-based reservation and medical record management app is key to improving customer experience and operational efficiency in the beauty salon industry. This study explores the use of Firebase, offering features like Authentication, Firestore, Cloud Messaging, and Cloud Functions, to create a reliable mobile solution. Firebase enables real-time data management, secure authentication, and efficient notifications, enhancing both salon operations and customer experience. Developed with the Waterfall model and MVVM architecture, the app showed positive results in reservation and medical record management. Users can easily book appointments and access treatment history, with fast data transfer powered by Firebase. With response times of 120 ms for reads and 140 ms for writes, Firebase ensures seamless performance. The app reduced booking time by 79.91% compared to manual methods. Further development is recommended to include staff management and real-time analytics for optimized service and better customer insights.
Copyright (c) 2025 Audy Fitri Ariani, Agung Brastama Putra, Tri Luhur Indayanti Sugata https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTATION OF E-SCM FOR CHIPS STOCK MANAGEMENT
Rumah Keripik Lely Tittiana is a business in the field of selling banana chips. Problems at Rumah Keripik Lely Tittiana include chips stock management experiencing shortages and excess stock due to delays in supplying raw materials by suppliers because ordering is still manual. Excessive stock causes product quality to decrease because it has an expiration period so that it can result in losses. The purpose of this research is to design an E-SCM application system for stock management of chips that experience shortages and excess stock at Lely Tittiana Chips House and implement a web-based E-SCM application using the PHP programming language and MySQL database at Lely Tittiana Chips House. E-SCM includes various parties ranging from suppliers, distributors, manufacturers, to customers with the aim of creating quality products. The research method used in this research is qualitative research method. The results of the implementation of this web-based E-SCM design make stock management more effective in overcoming the problems of excess and shortage of stock. This system helps in monitoring stock availability in real-time, so that decision making can be done more accurately.
Copyright (c) 2025 Sri Wahyuni, Muhammad Ardiansyah Sembiring, Amalia https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTATION OF CRM SYSTEM ON SALES OF COSMETIC SUPPLIES AND ACCESSORIES AT BERKAH JAYA KHALISA STORE
In today's digital era, the development of information technology is increasingly rapid and has a major impact on various sectors, including in the fields of business and trade. Customer Relationship Management (CRM) is a strategy used by companies to manage interactions with customers, with the aim of improving customer relationships, satisfaction, and loyalty. And for the problems that often occur in business owners of the Berkah Jaya Khalisa store, such as in the sales promotion section there is no, the store's income and expenditure reports are also not recorded. The transaction process used by the store still uses traditional methods, for example if there is a purchase of goods that are sold, the store owner must first record it on the invoice for the goods sold. The method used in this study is a qualitative method, namely a method that leads to in-depth understanding, by obtaining data through interviews and observations. The purpose of this study is to increase the efficiency of the sales process and expand the reach of the store's market through a system that can be accessed by customers from various regions, design a web-based CRM system that can facilitate the Berkah Jaya Khalisa Store in managing customer data and Assist sales through the website on the Berkah Jaya Khalisa store in increasing product sales turnover. And for the conclusions that can be drawn through the research that has been done, among others, such as the implementation of the CRM method at the Berkah Jaya Khalisa store, customers find it easier to find information about products and what items are available in the store. And customers can make orders online without having to come directly to the Berkah Jaya Khalisa Store.
Copyright (c) 2025 Putri Indriani, Nuriadi Manurung, Wan Mariatul Kifti https://creativecommons.org/licenses/by-sa/4.0
IMPLEMENTASI ALGORITMA CONDITIONAL RANDOM FIELDS UNTUK PART OF SPEECH TAGGING BAHASA MADURA
Penelitian ini berfokus pada penerapan Conditional Random Fields (CRF) untuk Part of Speech (POS) Tagging dalam bahasa Madura. Mengingat keterbatasan sumber daya pemrosesan bahasa alami (NLP) untuk bahasa daerah, khususnya bahasa Madura, studi ini bertujuan untuk mengembangkan model POS tagging yang akurat. Dataset yang digunakan berisi 73.051 kata yang dikumpulkan dari berbagai sumber, seperti media sosial, artikel, dan percakapan sehari-hari. Data ini melalui tahapan pra-pemrosesan, termasuk pembersihan, tokenisasi, dan pelabelan manual dengan kategori POS yang mencakup 15 jenis tag. Model CRF dilatih menggunakan fitur morfologis dan kontekstual untuk mengenali pola linguistik dalam bahasa Madura. Model ini mencapai akurasi yang kompetitif sebesar 95%, yang menunjukkan kemampuannya dalam menangkap pola linguistik bahasa Madura secara efektif. Model ini berkinerja baik dalam kategori POS umum seperti kata benda (NN), kata kerja (VB), dan kata sifat (JJ), dengan F1-score sebesar 0,96 untuk kata benda dan 0,89 untuk kata kerja. Namun, tantangan muncul pada kategori yang lebih jarang seperti Foreign Word (FW) dan Adverb (RB), terutama disebabkan oleh variasi dialek dan penggunaan kata serapan. Penelitian ini memberikan kontribusi penting dalam pengembangan sumber daya NLP untuk bahasa daerah dan dapat digunakan dalam berbagai aplikasi seperti penerjemahan otomatis, asisten virtual, serta pelestarian bahasa Madura. Penelitian mendatang dimasa depan memperluas dataset dan mengeksplorasi model berbasis neural network untuk lebih meningkatkan kinerja POS tagging. Kata Kunci: Pemrosesan Bahasa Alami, Conditional Random Fields, POS Tagging, Bahasa Madura, Pelestarian Bahasa Daerah.
Copyright (c) 2025 Rizky Sulaiman, Setio Basuki https://creativecommons.org/licenses/by-sa/4.0
ANALISIS DAN PENGEMBANGAN TATA KELOLA TEKNOLOGI INFORMASI PADA KANTOR POS KOTABUMI MENGGUNAKAN COBIT 2019
Analysis of process maturity in various organizational domains shows significant progress in operational effectiveness and management. Assessments were carried out on sub-processes in the MEA, DSS, DSS04, and DSS05 domains, with a focus on financial reporting accuracy, decision making, customer service, risk management, and operational efficiency. The results show that many processes are at a good level of maturity, with some sub-processes reaching the highest level of optimization. Although there are areas for improvement, particularly in internal communications and planning, the organization shows great potential for further development and improved performance in the future. Keywords: Process maturity, operational efficiency, decision making, risk management, customer satisfaction
Copyright (c) 2025 wahyudi, Sutedi https://creativecommons.org/licenses/by-sa/4.0