Jurnal INTI Nusa Mandiri merupakan jurnal yang diperuntukan keilmuan Computer science (Ilmu Komputer). kajian keilmuan mencakupi tentang komputasi, perangkat lunak (software). Ilmu komputer mencakup beragam topik yang berkaitan dengan komputer, mulai dari analisis data science, algoritma sampai subyek yang lebih konkret seperti bahasa pemrograman, perangkat lunak. Ilmu Komputer lebih menekankan pada pemrograman komputer, dan rekayasa perangkat lunak (software), pemrograman science Jurnal INTI Nusa Mandiri terakreditasi SINTA 4 dan memiliki P-ISSN: 0216-6933 (Media Cetak), E-ISSN: 2685-807X (Media Onlie). Jurnal INTI Nusa Mandiri terbit 1 (satu) tahun sebanyak 2 (dua) kali terbit, pada bulan Februari dan Agustus.
Lembaga Penelitian dan Pengabdian Pada Masyarakat
Penulis yang menerbitkan jurnal ini menyetujui ketentuan berikut: 1. Penulis memegang hak cipta dan memberikan hak jurnal mengenai publikasi pertama dengan karya yang dilisensikan secara bersamaan di bawah Creative Commons Attribution 4.0 International License. yang memungkinkan orang lain untuk berbagi karya dengan pengakuan atas karya penulis dan publikasi awal pada jurnal. 2. Penulis dapat memasukkan pengaturan kontrak tambahan yang terpisah untuk distribusi non-eksklusif dari versi jurnal yang diterbitkan (misalnya, mengirimkannya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan atas publikasi awalnya pada Jurnal. 3. Penulis diizinkan dan didorong untuk memposting karya mereka secara online (misalnya, dalam penyimpanan institusional atau di situs web mereka) sebelum dan selama proses pengiriman, karena hal itu dapat menghasilkan pertukaran yang produktif, serta kutipan dari karya yang diterbitkan sebelumnya.
PREDIKSI KATEGORI CURAH HUJAN BERBASIS MACHINE LEARNING UNTUK MENDUKUNG KETAHANAN PANGAN
Rainfall variability significantly influences food security in Central Tapanuli Regency, North Sumatra, a region where agriculture is strongly reliant on climatic patterns. This research evaluates and compares the classification performance of Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Random Forest (RF) for categorizing daily rainfall as supplementary information to strengthen food security. A total of 3,644 daily meteorological records obtained from FL Tobing Meteorological Station spanning 2015 to 2024 were utilized, encompassing seven predictor variables: minimum temperature, maximum temperature, average temperature, mean relative humidity, sunshine duration, peak wind speed, and average wind speed. To mitigate class imbalance, the original six rainfall categories were consolidated into four classes by merging the minority groups. The data were partitioned into training and testing subsets at an 80:20 ratio using stratified sampling, after which the Synthetic Minority Over-sampling Technique (SMOTE) was employed on the training set. Hyperparameter tuning was conducted through Grid Search combined with 5-fold cross-validation, and classification performance was assessed using accuracy, precision, recall, F1-score, and paired t-test analyses. The experimental results indicated that RF delivered superior performance, attaining an accuracy of 51.44% and a weighted F1-score of 0.5036, significantly outperforming both SVM and K-NN (p-value < 0.05). Feature importance analysis revealed that sunshine duration, average temperature, and maximum temperature were the most influential predictors. These outcomes demonstrate that RF holds considerable promise for advancing machine learning-driven rainfall category prediction systems capable of delivering early-stage information for agricultural planting schedules and preparedness against intense rainfall events in Central Tapanuli Regency
Copyright (c) 2026 I Dewa Gede Loka Maheswara, Kanaya Kaizzi Larasati, Muhammad Nur Rizqi, Muhammad Fany Nurwibowo, Yosafat Donni Haryanto http://creativecommons.org/licenses/by-nc/4.0
ANALISIS SENTIMEN INFORMASI GEMPA BUMI BMKG PADA APLIKASI X MENGGUNAKAN SVM DAN RANDOM FOREST
This study analyzes public sentiment towards the dissemination of earthquake information by BMKG through the X application with modeling using the Support Vector Machine (SVM) and Random Forest (RF) algorithms. Data were collected from 2,711 tweets mentioning @infoBMKG using tweet-harvest, then processed through the stages of case folding, cleaning, tokenization, slang normalization, stopword removal, and stemming. Automatic sentiment labeling was performed using a hybrid approach of VADER and InSet Lexicon. Feature representation used TF-IDF (Term Frequency–Inverse Document Frequency) with 1,000 features and data distribution 80% train and 20% validation. The results show that RF achieved an accuracy of 81.92% and SVM 81.17%, with almost identical Macro F1 (RF: 0.7445; SVM: 0.7443). Neutral sentiment indicates informative tweets without emotional content (61.75%), negative sentiment represents the public's emotional response that is not solely intended as a form of negative assessment of BMKG (24.71%), and positive sentiment is an expression of appreciation, gratitude, and hope for the delivery of information (13.54%). SVM excels in cross-validation stability (std ±0.0574) and negative sentiment recall (0.71), making it more suitable for real-time disaster communication monitoring.
Copyright (c) 2026 Arfany Dhimas Muftareza, Reza Okta Pratama, I Dewa Gede Loka Maheswara, Giarno, Agustina Rachmawardani http://creativecommons.org/licenses/by-nc/4.0
PENGEMBANGAN SISTEM PENGADUAN MASYARAKAT BERBASIS WEB PADA KANTOR DESA BABAKAN
The development of information technology encourages village governments to provide public services that are more efficient, transparent, and accessible, including complaint management. In Babakan Village, complaints are still submitted through RT/RW administrators or direct visits to the village office, causing slow responses, difficulties in tracking status, and risks of data loss. This study aims to design and develop SIPEMAS, a web-based public complaint information system that supports online complaint submission, complaint data management, officer responses, and status tracking. The research used the Waterfall software development model consisting of requirement analysis, system design with UML, implementation using PHP with CodeIgniter 3 and MySQL, verification through black-box testing, and maintenance planning. Requirement data were obtained through observation of the existing complaint process, interviews with village officers, and document study. The developed system provides role-based access for citizens and officers, complaint submission with supporting evidence, complaint management, response input, and report recapitulation. Black-box testing on eight main functional scenarios showed valid results for all tested functions (8/8; 100%). A user satisfaction survey showed that 69% of respondents were satisfied with the public complaints system. Therefore, SIPEMAS can support structured complaint management, improve transparency through status tracking, and strengthen the security and documentation of complaint data in Babakan Village.
Copyright (c) 2026 Siti Nurajizah, Rifa Nurafifah Syabaniah, Fani Nurona Cahya, Elin Panca Saputra, Tiara Iswanti Sudrajat, Widi Intan Priyanti, Balqis Mulia Septiany http://creativecommons.org/licenses/by-nc/4.0
TINJAUAN LITERATUR SISTEMATIS KLASIFIKASI CUACA BERBASIS CITRA MENGGUNAKAN METODE DEEP LEARNING
Image-based weather recognition has become an important research area due to its applications in intelligent transportation systems, autonomous vehicles, road condition monitoring, and computer vision. Recent advances in deep learning have significantly improved the automatic recognition of diverse weather conditions. However, the variety of image sources, datasets, deep learning architectures, and weather scenarios makes it challenging to obtain a comprehensive understanding of current research trend. Therefore, this study aims to identify the image sources and datasets used in weather recognition research, analyze dominant deep learning approaches, and investigate the weather conditions most frequently addressed in image-based weather recognition and road weather detection. A Systematic Literature Review (SLR) following the PRISMA 2020 guidelines was conducted using the Scopus database. From an initial set of 603 retrieved articles, 73 studies met the predefined inclusion criteria and were selected for further analysis. The results indicate that public datasets such as DAWN, RTTS, Foggy Cityscapes, nuScenes, WeatherDataset-4, and WeatherNet are among the most frequently used data sources. Convolutional Neural Networks (CNNs) remain the dominant approach, although the adoption of Transformer-based models, Vision Transformers, YOLO, Multimodal Fusion, and Multi-Task Learning has increased considerably in recent years. Furthermore, rain, fog, and snow are the most extensively investigated weather conditions due to their significant impact on visibility and perception system performance. The findings provide a comprehensive overview of recent developments in deep learning-based weather recognition and offer valuable insights and directions for future research.
Copyright (c) 2026 Gilang Andhika Buwana, Mohamad Nurkamal Fauzan http://creativecommons.org/licenses/by-nc/4.0
OPTIMASI HYPERPARAMETER INDOBERT UNTUK ANALISIS SENTIMEN KENDARAAN LISTRIK DENGAN KOMPARASI ALGORITMA MACHINE LEARNING
The presence of electric vehicles has generated diverse public opinions on social media, creating the need for an automated approach to sentiment identification. Transformer-based models such as IndoBERT can capture semantic context more effectively than conventional machine learning methods that rely on feature representations such as TF-IDF, which are less effective in modeling word relationships, particularly in imbalanced datasets. This study aims to analyze public sentiment toward electric vehicles using an IndoBERT model optimized with Grid Search and compare its performance with Naive Bayes and Support Vector Machine (SVM). An experimental method was applied to a dataset of 1,517 Indonesian-language opinions. IndoBERT was fine-tuned using Grid Search by evaluating hyperparameter combinations of epochs (3, 4, and 5), learning rates (2e-5 and 3e-5), and a batch size of 16. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The best IndoBERT configuration was obtained with 5 epochs, a learning rate of 3e-5, and a batch size of 16, achieving 73% accuracy. Although its accuracy matched that of SVM, IndoBERT produced more balanced results, with a macro F1-score of 0.62 and the highest average AUC (0.780), outperforming SVM (0.758) and Naive Bayes (0.736). The novelty of this study lies in optimizing IndoBERT using Grid Search and comparing it with Naive Bayes and SVM based on ROC-AUC for Indonesian-language electric vehicle sentiment analysis. The findings demonstrate that Grid Search optimization enhances IndoBERT's contextual understanding, resulting in superior overall performance.
Copyright (c) 2026 Elly Indrayuni, Acmad Nurhadi http://creativecommons.org/licenses/by-nc/4.0
PERBANDINGAN KINERJA ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI ISPA MENGGUNAKAN DATA KLINIS RUMAH SAKIT
Acute Respiratory Infection (ARI) remains one of the leading causes of morbidity and mortality worldwide, particularly among children and elderly populations. The complexity of ARI clinical symptoms necessitates rapid and accurate diagnostic approaches to support healthcare services. This study compares the performance of five machine learning algorithms, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine algorithms for ARI classification using clinical hospital data. The study employed a quantitative experimental approach, using 521 outpatient clinical records obtained from XYZ Hospital, Jakarta. The research process included data preprocessing, classification model development, model performance evaluation, and statistical analysis using the Kruskal-Wallis test followed by Dunn's Post Hoc Test with Bonferroni correction. Model performance was assessed using accuracy, precision, recall, and F1-score metrics, which were computed using macro averaging due to the imbalanced class distribution. Statistically significant differences were observed among the algorithms across all evaluation metrics (p < 0,001). Effect size analysis using epsilon squared (ε²) indicated large effects for accuracy (ε² = 0.828), precision (ε² = 0.719), recall (ε² = 0.434), and F1-score (ε² = 0.654). The post hoc analysis indicated that Random Forest and Gradient Boosting Machine showed comparable performance and consistently achieved competitive results across evaluation metrics. These findings suggest that ensemble learning methods are better suited to handling the complex clinical data associated with ARI and could help develop decision support systems for early ARI screening. Future studies should incorporate multicenter datasets, hyperparameter optimization, and explainable artificial intelligence techniques to improve model generalizability and interpretability.
Copyright (c) 2026 Irvan Lewenusa, Apriyanto Chandra, Tri Sutrisno http://creativecommons.org/licenses/by-nc/4.0
ANALISIS MULTI-MODEL KOMPARATIF UNTUK DETEKSI DINI KANKER PAYUDARA MENGGUNAKAN EVALUASI ROC-AUC DAN MCC
Breast cancer is one of the leading causes of death in women and relies heavily on early detection to improve the chances of recovery. The main challenges in machine learning-based early detection systems are class imbalance and the limitations of evaluation metrics that often rely solely on accuracy or ROC–AUC. In this context, the Matthews Correlation Coefficient (MCC) offers a more comprehensive assessment because it considers all elements of the confusion matrix. This study analyzes and compares the performance of four classification algorithms: Naive Bayes, Decision Tree, Random Forest, and SVM on the Breast Cancer Wisconsin (Diagnostic) dataset. The dataset was first divided using a stratified train–test split of 80:20 to maintain class proportions. Feature normalization was performed only on the training data to avoid data leakage, then applied to the test data using the same parameters. Furthermore, 5-fold cross-validation was performed on the training data for model evaluation and selection. The results show that SVM provides the best performance with an accuracy of 98.25%, a precision of 1.00, an F1-score of 0.9762, an AUC of 0.9971, and an MCC of 0.9630. Naive Bayes and Random Forest also show excellent performance with AUC values ​​above 0.99 and an MCC of 0.9253, while Decision Tree has a lower performance. Confusion matrix and ROC curve analysis confirm the superiority of SVM in minimizing classification errors. These findings emphasize the importance of a multi-model approach and the use of MCC as a more representative evaluation metric in breast cancer early detection systems.
Copyright (c) 2026 Daning Nur Sulistyowati, Sri Hadianti, Ridan Nurfalah http://creativecommons.org/licenses/by-nc/4.0
FORMULASI MODEL SPASIOTEMPORAL DIGITAL TWIN DALAM SIMULASI DAMPAK KENAIKAN MUKA AIR LAUT
Sea level rise (SLR) due to climate change poses an existential threat to coastal areas, especially in archipelagic countries such as Indonesia. An effective policy response requires simulation tools that can represent spatial and temporal dynamics in an integrated manner. This study formulates and tests a spatiotemporal model based on Digital Twin to simulate the impact of SLR on infrastructure, settlements, and coastal ecosystems. This model combines satellite altimetry data (Sentinel-3), bathymetry data, a 2D hydrodynamic model (Delft3D), and CMIP6 climate projections (SSP2-4.5 and SSP5-8.5) within a cloud-based Digital Twin framework. The case study was conducted in urban coastal areas with high subsidence, which are among the areas with the highest rates of land subsidence in the world. The simulation covers KMA scenarios up to 2050, with a spatial resolution of 5 meters and daily temporal resolution. The results show that in the SSP5-8.5 scenario, up to 68% of urban coastal areas with high subsidence will be below the average sea level in 2050 without intervention. This spatiotemporal model achieved a flood prediction accuracy of 91.3% (based on spatial IoU) compared to historical tidal flood data from 2020–2024. In addition, the system allows for the evaluation of the impact of mitigation infrastructure such as the North Coast Sea Wall (NCICD). This study proves that the formulation of spatiotemporal models in Digital Twin provides a critical foundation. Additionally, the system enables the evaluation of the impact of mitigation infrastructure such as the North Coast Sea Wall (NCICD).
Copyright (c) 2026 Zainul Mufid, Ahmad Gani, Trisna Fajar Prasetyo http://creativecommons.org/licenses/by-nc/4.0
RANCANG BANGUN SISTEM INFORMASI UKK MANDIRI SMK BERBASIS WEB DENGAN INTEGRASI ADDIE DAN SCRUM
The digital transformation of vocational education necessitates competency-based assessment systems that are integrated, accountable, and data-driven. This study develops a web-based UKK Mandiri Information System through an integrative ADDIE–Scrum formulation that operationally maps the relationship between instructional design for competency assessment and iterative software engineering processes. A Research and Development (R&D) approach was employed, involving needs identification through interviews, observations, and document analysis within the implementation context of UKK Mandiri at SMK YPC Tasikmalaya. The system was developed using a three-tier architecture with role-based access control and an automated competency achievement validation mechanism. Testing results demonstrate functional conformance across all core modules, cross-browser compatibility, and baseline security validation. Expert evaluation yielded a mean score of 4.40 (SD = 0.72; α = 0.91; 95% CI [3.89–4.91]), categorized as “Highly Feasible.” The findings indicate improved administrative efficiency, consistency in score calculation, and enhanced traceability of competency assessments, while reinforcing the integration between instructional design and agile software development through a documented and replicable coordinative model
Copyright (c) 2026 Alamsyah Firdaus, Taofik Muhammad, Alfadl Habibie, Imam Amirulloh, Ilpan Rusilpan http://creativecommons.org/licenses/by-nc/4.0
PERANCANGAN SISTEM INFORMASI E-COMMERCE BERBASIS WEBSITE PADA PT. BIMANTARA SAKTI PERSADA MENGGUNAKAN METODE WATERFALL
The development of digital technology encourages companies to implement information systems with the aim of improving operational effectiveness and competitive ability in the market. PT. Bimantara Sakti Persada, which previously conducted sales processes traditionally, faces challenges such as limitations in marketing reach, the possibility of errors in transaction recording, and delays in preparing sales reports. This study aims to design and create a website-based e-commerce information system by utilizing the Waterfall method as a solution to the existing problems. The Waterfall method is implemented through a series of steps that include needs analysis, system design, implementation, testing, and evaluation and maintenance. The designed system provides various functions such as member registration, product management, shopping cart, payment processing, and transaction verification. The system assessment is carried out by conducting functional testing using the Black Box Testing approach as well as testing the utilization of the system by the admin and sales team. The test results show that all system functions operate according to the specified requirements. Based on usage evaluation results, the system is capable of replacing the manual sales process with a more structured computerized system, thereby facilitating transaction recording and the preparation of sales reports. Comparisons of conditions before and after the system's implementation indicate an increase in efficiency in transaction management as well as ease of access to sales information in real time. Thus, the developed web-based e-commerce information system can support PT. Bimantara Sakti Persada's sales process more effectively and in an integrated manner
Copyright (c) 2026 Mohamad Rifa Yusro, Siti Masturoh http://creativecommons.org/licenses/by-nc/4.0
RANCANG BANGUN SISTEM INFORMASI E-BOOKING PENYEWAAN MOBIL BERBASIS WEB MENGGUNAKAN PENDEKATAN OOAD
The manual system employed by PT. ACM Mahadat—a car rental service company—resulted in significant operational issues, including irregular schedules, data recording errors, and slow service, which contrasted sharply with consumer expectations for efficient and secure services. This study aims to design and build a web-based car rental e-booking information system to simplify the reservation process and manage data in real-time. The chosen methodology is Object-Oriented Analysis and Design (OOAD), a systematic approach that concentrates on the concept of objects as the fundamental system elements. The design documentation utilizes Unified Modeling Language (UML) notation, including the Use Case Diagram (functional requirements), Activity Diagram (process flow), Sequence Diagram (object interactions), and Class Diagram (static system structure). The resulting system is designed to be modular and flexible, offering a user-friendly interface for customers to view, select, and book cars easily. It also helps the Admin manage booking and rental data more efficiently, and its core authentication features are validated through Black Box Testing. This implementation is expected to support the digitalization of PT. ACM Mahadat's business processes and enhance the company's competitiveness in the information technology era.
Copyright (c) 2026 Muamar Fallahi, Ridan Nurfalah, Inka Syaifa Fauzia, Samuel Tarigan http://creativecommons.org/licenses/by-nc/4.0
PERANCANGAN SISTEM INFORMASI PENGARSIPAN SURAT MASUK DAN SURAT KELUAR PADA PABAN III/LATGA SOPS TNI
The previously manual system at the Paban III/Latga Sops TNI posed several limitations, such as difficulties in document retrieval, risk of archive loss, and inefficiency in letter management. This study aims to design a structured and computerized information system for archiving incoming and outgoing mail documents. In this research, the software development method used is the Waterfall model, consisting of requirement analysis, system design, implementation, testing, and evaluation phases. The result is a web-based system built using PHP programming language and MySQL database, which effectively supports digital management of incoming and outgoing correspondence in a fast and well-documented manner. The system was tested using Black Box method and user acceptance questionnaires, showing that the application met the functional requirements and is feasible for use in the operational environment of Paban III/Latga Sops TNI.
Copyright (c) 2026 Andri Januar, Yuri Warman, Shinta Oktaviana R http://creativecommons.org/licenses/by-nc/4.0
KLASIFIKASI JENIS IKAN AIR TAWAR MENGGUNAKAN ALGORITMA CNN DAN ARSITEKTUR ALEXNET
Freshwater fish are an important commodity in the fishing industry that requires an accurate classification system. This study aims to develop a freshwater fish classification system using the Convolutional Neural Network (CNN) algorithm with AlexNet architecture, as well as applying data augmentation techniques to improve model accuracy. The dataset used consists of 488 images of five types of freshwater fish, namely catfish, baung fish, tapah fish, juaro fish, and patin fish, which were then augmented into 68,400 images. The model was trained using the Adam optimizer, with a batch size of 16, a learning rate of 1e-5, and 200 epochs. The results of the experiment show that the model achieved a training accuracy of 71.09%, a validation accuracy of 85.00%, and a testing accuracy of 80.29%. Precision reached 0.8310, Recall 0.7909, and F1-score 0.7912, indicating the model's excellent performance in classifying freshwater fish species. This research is expected to support the development of an automatic classification system for the freshwater fisheries industry
Copyright (c) 2026 Ahmad Rizky, Dedy Hermanto http://creativecommons.org/licenses/by-nc/4.0
DETEKSI ULASAN FIKTIF SKINCARE BERBAHASA INDONESIA PADA SITUS FEMALE DAILY MENGGUNAKAN METODE SVM
The increasing popularity of e-commerce and online review platforms has encouraged consumers to rely more heavily on reviews before purchasing skincare products. However, the proliferation of fake reviews presents a new challenge for consumers in making decisions. This study aims to detect fake reviews of skincare products by applying the Support Vector Machine (SVM) algorithm as the main model. Data was taken from the Female Daily website, which contains a collection of reviews of COSRX skincare products. This study applied text mining and data preprocessing techniques to prepare 8,880 unstructured reviews into a format ready for analysis. The SVM model was used to detect genuine and fake reviews based on linguistic patterns in the text. The test results showed excellent accuracy, with the model accurately predicting 5,900 fake reviews and 2,758 genuine reviews, with an error rate of only around 2.5% of the total data. These findings indicate that SVM is an effective method for detecting fake skincare product reviews and has the potential to be implemented as a decision support tool for consumers and online review platforms.
Copyright (c) 2026 Audy Hafiza Bilqis, Farizatul Azna, Dwi Widdi, Nayla Eka Hidayat, Tasya Ramadhinta http://creativecommons.org/licenses/by-nc/4.0
OPTIMALISASI PREDIKSI JARAK TEMPUH KENDARAAN LISTRIK MENGGUNAKAN XGBOOST DAN FEATURE SELECTION RANDOM FOREST
Electric vehicle (EV) adoption is increasing with increasing awareness of clean energy and environmental concerns. However, range anxiety, the uncertainty in estimating driving range, remains a major barrier. This study aims to develop a predictive model for EV range based on technical specifications to provide more accurate estimates and alleviate user concerns. A Machine Learning approach is applied, using Random Forest for feature selection and XGBoost as the primary prediction algorithm. The dataset consists of 478 EV records with 22 attributes, including battery capacity, efficiency, dimensions, and speed. Key features affecting range prediction include battery_capacity_kWh, efficiency_wh_per_km, and height_mm. The XGBoost model demonstrates strong predictive performance with an R² of 0.978, an MAE of 10.555, and an RMSE of 15.180. These results suggest that combining Random Forest and XGBoost offers a promising solution to improve the accuracy of EV range estimation, potentially reducing range anxiety and supporting wider EV adoption
Copyright (c) 2026 M. Rangga Ramadhan Saelan, Riyan Latifahul Hasanah, Siti Fauziah http://creativecommons.org/licenses/by-nc/4.0
CLUSTERING WILAYAH KEMISKINAN MULTIDIMENSI DI INDONESIA MENGGUNAKAN ALGORITMA FUZZY C-MEANS DAN OPTICS
A multidimensional approach to mapping poverty in Indonesia necessitates the use of the Human Development Index (HDI), Poverty Line, and Expenditure per Capita. This study aims to evaluate the performance of two clustering algorithms with distinct paradigms the centroid-based Fuzzy C-Means (FCM) and the density-based OPTICS in profiling poverty across 501 regencies and cities. Experimental results indicate that OPTICS achieved high internal validity, with a Silhouette Score of 0.7301 and a Davies-Bouldin Index (DBI) of 0.329. Significantly, OPTICS identified 94% of the data as noise. This finding reveals a fundamental characteristic: the distribution of socio-economic data in Indonesia is highly heterogeneous and sparse, lacking inherently dense cluster structures. Conversely, FCM, employing a soft clustering approach, successfully accommodates the ambiguity of data boundaries and provides comprehensive segmentation across all regions. Despite yielding lower validity metrics (Silhouette Score 0.3894), FCM was selected as the final model because it satisfies the practical requirements of the application, which demands complete coverage mapping. This study concludes that a soft clustering approach is more applicable than density-based clustering for analyzing highly heterogeneous data such as that found in Indonesia
Copyright (c) 2026 Nicholas Eugene Supardi , Teny Handhayani, Irvan Lewenusa http://creativecommons.org/licenses/by-nc/4.0
IMPLEMENTASI METODE USER CENTERED DESIGN PADA APLIKASI KORAN DIGITAL RADAR KARAWANG
The rapid development of digital technology has required print media to adapt in order to remain relevant and competitive in the information era. Radar Karawang, as a local daily newspaper, needs to undergo digital transformation through the development of an Android-based digital newspaper (e-paper) application. This study aims to design the User Interface (UI) and User Experience (UX) of the Radar Karawang digital newspaper application using the User-Centered Design (UCD) method and to evaluate the usability level of the developed application. The UCD method was implemented through several stages, including user needs analysis, prototype or wireframe design, design evaluation based on user feedback, and application implementation. Data were collected through questionnaires distributed to 13 respondents who represented potential users of the application. Usability evaluation was conducted using the System Usability Scale (SUS) method with a Likert scale to measure ease of use, efficiency, and user satisfaction. The evaluation results show that the Radar Karawang digital newspaper application achieved an average SUS score of 74, which falls into the Acceptable category with a Good adjective rating. Nevertheless, this study has several limitations, particularly related to the limited number of respondents and the use of a single usability evaluation method. Therefore, future research is recommended to involve a larger and more diverse group of respondents and to combine other usability evaluation methods to obtain more comprehensive results
Copyright (c) 2026 Normah Normah, Ainun Zumarniansyah, Agus Echtiyar http://creativecommons.org/licenses/by-nc/4.0
MODEL KLASIFIKASI PENERIMA PROGRAM KARTU INDONESIA PINTAR MENGGUNAKAN METODE XGBOOST
Poverty is one of the major factors contributing to the low quality of education in Indonesia. The Smart Indonesia Program (Program Indonesia Pintar) is a cash assistance program distributed through the Smart Indonesia Card (Kartu Indonesia Pintar/KIP) to support students from economically disadvantaged families, ranging from elementary to higher education levels. This study aims to classify students who are eligible to receive the Smart Indonesia Card using the XGBoost method, with a case study conducted at SMPN 02 Kebun Tebu. The dataset used in this study consists of independent and dependent variables. The independent variables include father’s age, mother’s age, father’s education level, mother’s education level, father’s income, mother’s income, number of family dependents, and students’ academic average scores. The dependent variable is the eligibility status of KIP recipients as the target class. Two classification models were developed using data split ratios of 70:30 and 80:20. The model with a 70:30 data split achieved an accuracy of 0.9048, a precision of 0.9034, a recall of 0.9072, and an F1-score of 0.9053. Meanwhile, the model with an 80:20 data split demonstrated better performance, with an accuracy of 0.9167, a precision of 0.9149, a recall of 0.9189, and an F1-score of 0.9169. The optimal model obtained from this study can be utilized by schools to support policy decision-making in determining eligible Smart Indonesia Card recipients, ensuring that educational assistance is distributed accurately, adequately, and equitably
Copyright (c) 2026 Andi Nurkholis, Styawati Styawati, Susi Susi, Alifah Chairul Munawar http://creativecommons.org/licenses/by-nc/4.0
STUDI KOMPARATIF ALGORITMA C4.5 DAN RANDOM FOREST PADA DIGITALISASI UMKM KABUPATEN TEGAL
Digital transformation has become an essential necessity for Micro, Small, and Medium Enterprises (UMKM) to enhance their competitiveness in the era of Industry 4.0. However, in Tegal Regency, the level of digitalization adoption among MSMEs remains varied and tends to be low, thus requiring further investigation. This study aims to compare the performance of the C4.5 and Random Forest algorithms in classifying the level of digitalization of MSMEs in Tegal Regency. This research employs the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modeling, evaluation, and implementation. Primary data were collected through questionnaires distributed to 100 MSME respondents and processed using RapidMiner. The results indicate that the Random Forest algorithm demonstrates superior performance, achieving an average F1-score of 89.63%, accuracy of 91.43%, while the C4.5 algorithm records an average F1-score of 86.24%, accuracy of 90%. The highest F1-score for both algorithms is observed in the low digitalization category at 95%, which is consistent with the data distribution showing that the majority of MSMEs (59%) fall within this category. This study systematically integrates the CRISP-DM approach from business understanding to model implementation, resulting in a structured data analysis workflow that can be replicated by local governments or future researchers. Another novelty of this study lies in the finding that although Random Forest exhibits better classification performance than C4.5, the majority of MSMEs remain at a low level of digitalization. These results provide practical contributions as a basis for formulating more targeted and sustainable MSME digitalization policies at the local
Copyright (c) 2026 Gita Iftah Royani, Nur Syifa Amelia, Marlina, Fani Nurona Cahya http://creativecommons.org/licenses/by-nc/4.0
PENGELOLAAN DATA PENDISTRIBUSIAN TECHNICAL DAN ELECTRICAL SUPPLY BERBASIS WEB PADA PT.KARUNIA ABADI PADANG (KAP)
This research was motivated by the problems of manual data processing and goods distribution at PT. Karunia Abadi Padang (KAP), which resulted in stock calculation errors, report discrepancies, and lengthy data searches. The purpose of this study is to develop a web-based system to improve the efficiency, accuracy, and speed of the goods distribution data processing process, as well as to minimize errors. The methods used are field research with interviews and direct observation at PT. KAP, as well as literature research with the Waterfall SDLC model approach. The developed system uses PHP and MySQL technology for integrated and real-time data management, enabling online goods distribution. The results of the study show that the implementation of this system reduces data processing and information search time by up to 50%, reduces stock calculation errors, and produces more accurate reports that can be accessed directly. In addition, this system reduces dependence on manual recording, increases time efficiency, and lowers operational costs. The scientific contribution of this research is the development of a methodology for the application of web-based systems in the management of goods distribution in small to medium-sized distribution companies, as well as providing empirical evidence of the positive impact of web-based systems on operational efficiency and error reduction in the context of goods distribution. This research can be used as a reference for the development of similar systems in other distribution companies facing similar problems.
Copyright (c) 2026 Stefani Hardiyanti Putri, Wizra Aulia http://creativecommons.org/licenses/by-nc/4.0
PENINGKATAN AKURASI KNN DALAM PREDIKSI KELULUSAN MAHASISWA MELALUI OPTIMASI PARAMETER PSO
Predicting student graduation is a crucial aspect in supporting academic planning and ensuring timely completion of studies. However, no prior research has specifically applied the integration of K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) for graduation prediction using student data. This study aims to evaluate the effectiveness of combining KNN and PSO in improving classification accuracy. The KNN algorithm is used for classification, while PSO is implemented as a feature selection technique to identify the most relevant attributes. A dataset of 750 student records was processed through data preprocessing and attribute weighting using PSO, followed by model training and evaluation with 10-fold cross-validation. The evaluation results show that the KNN+PSO model improves accuracy from 80.91% to 84.31%, along with increases in precision and recall. These findings indicate that PSO enhances the performance of KNN, particularly in identifying students likely to graduate on time
Copyright (c) 2025 Yessica Fara Desvia, Febryawan Yuda Pratama, Ganda Wijaya http://creativecommons.org/licenses/by-nc/4.0
PERANCANGAN SISTEM SIMPAN PINJAM MENGGUNAKAN OBJECT-ORIENTED SOFTWARE ENGINEERING PADA PT. AUTO PRIMA MAKMUR
PT Auto Prima Makmur Jakarta still faces challenges in managing its savings and loan system, which is currently conducted manually. These problems include recording errors, administrative delays, and a lack of transparency, all of which reduce efficiency, accuracy, and the ability to monitor transactions in real time. This situation also increases the risk of data manipulation and loss, slows down member services, and potentially undermines trust in the company’s credibility. To address these issues, this study aims to design a web-based savings and loan information system that can enhance efficiency, accuracy, security, and transparency in managing employee savings and loans. The proposed system is designed to process transactions, reporting, and monitoring quickly and accurately, while minimizing the risk of errors and data duplication. The development method used is Object-Oriented Software Engineering (OOSE), which emphasizes the use case approach with simple notation yet covers all stages of software engineering. This approach enables the design of a structured, flexible software architecture capable of effectively managing system objects. The analysis results indicate that implementing a web-based system can accelerate savings and loan transactions, simplify reporting and monitoring, and improve data security. In addition, real-time data availability will assist management in making faster and more accurate decisions. Therefore, the implementation of this system will not only digitize the company’s business processes but also improve service quality, strengthen competitiveness, and support the company’s sustainability in facing the challenges of the digital era
Copyright (c) 2025 Moh. Mahar Faisol -, Ridan http://creativecommons.org/licenses/by-nc/4.0
PERACANGAN RINTISAN BISNIS DIGITAL POTOSHARE INDONESIA PLATFORM UNTUK MONETISASI KARYA SENI DIGITAL
Digital Transformation in the creative economy sector has opened up opportunities for industry players, especially photographers, in distributing and monetizing digital artworks more widely. However, many photographers still have limitations in marketing their products, especially because there are no local platforms that focus on and support digital product sales systems such as portfolio management, digital transactions based on these problems, this study aims to design a digital business in the form of a web-based platform called Potoshare Indonesia, which is intended as a digital marketplace that specializes in photography and digital artwork products such as photos, presets, e-books, and building a professional online portfolio. The method used in this study uses the Rational Unified Process (RUP) approach and business modeling using the Business Model Canvas (BMC). The results of this study are the design of digital business applications and strategies that are considered feasible and have great potential for further development. Potoshare Indonesia is expected to be a digital solution that supports the growth of the creative economy in the focused of photography.
Copyright (c) 2026 Firdaus, Muhammad Rizky Dwiputra, Siti Nurlela http://creativecommons.org/licenses/by-nc/4.0
ANALISIS KEPUASAN PENGGUNA WEBSITE LIGA ARLOJI MENGGUNAKAN METODE END USER COMPUTING SATISFACTION (EUCS)
The evolution of information technology plays a significant role in accelerating digital transformation across various sectors, including the wristwatch retail industry. The Liga Arloji website is utilized as a medium for promotion and online transactions, which needs to be evaluated in terms of user satisfaction. This study aims to analyze the level of user satisfaction with the performance of the Liga Arloji website using the End User Computing Satisfaction (EUCS) method, in which the components analyzed include five dimensions: content, accuracy, format, ease of use, and timeliness. The methodology of this study is based on a quantitative approach, using a questionnaire as the data collection instrument distributed to 68 selected respondents. The data were processed using the EUCS calculation formula and tested for validity and reliability using SPSS. Based on the data analysis, the findings show that all EUCS dimensions received scores falling into the “Satisfied” to “Very Satisfied” classification. The content dimension achieved an average score of 4.19, accuracy scored 4.13, format scored 4.18, ease of use scored 4.15, and the timeliness dimension received the highest score of 4.22. These findings indicate that user satisfaction with the website is relatively high, particularly in terms of timely information and presented content, while the system’s reliability and accuracy were rated as moderately satisfactory. This study provides several recommendations, including improving data accuracy and enhancing system reliability. The results are expected to serve as a key reference for the future development of e-commerce-based websites
Copyright (c) 2026 Asni Zalukhu, Siti Nurlela http://creativecommons.org/licenses/by-nc/4.0
PENGEMBANGAN CHATBOT TELEGRAM FAQ LAYANAN ICT MENGGUNAKAN ALGORITMA RANDOM FOREST DAN METODE WORD2VEC
In today's digital era, chatbots have become an essential tool for businesses to improve interaction with customers. A responsive and efficient chatbot can help customer service agents be happier, improve customer satisfaction, and resolve issues faster. The study aims to create a Telegram-based chatbot that uses the Random Forest algorithm and the Word2Vec method to answer questions about ICT services. The development was carried out by collecting a dataset of questions and answers from FAQs (Frequently Asked Questions) of ICT services. Then, the Random Forest algorithm is used to classify the questions. In addition, the Word2Vec method is used to create vector representations of words in questions and answers. This improves the chatbot's ability to understand complex questions. The test results show that the chatbot gets an accuracy of 91.28%, a precision of 93.56%, a recall of 91.28% and an F1-Score of 91.42% and can provide relevant and accurate answers to user questions. Therefore, the development of this chatbot using the Random Forest algorithm and the Word2Vec method can be an effective solution to improve customer service in the field of ICT services
Copyright (c) 2025 Muhammad Arif Setiyawan, Erina Divaa Kenoya http://creativecommons.org/licenses/by-nc/4.0
PENENTUAN PRIORITAS PENGEMBANGAN DESA WISATA RINTISAN KOTA PURWAKARTA MENGGUNAKAN METODE MULTI ATTRIBUTE UTILITY THEORY
A Tourism Village can be understood as a village that organizes tourism activities due to the tourist attractions arising from the characteristics of the local community’s life, including various attractions available within the village itself. Each tourism village needs to be supported by tourist attractions, accessibility, and amenities, which include the potential of cultural tourism as well as natural tourism. The purpose of this research is to design and develop a Decision Support System for Determining the Priority of Pioneer Tourism Village Development using the Multi Attribute Utility Theory (MAUT) method. The urgency of this research lies in the need for DISPORAPARBUD Purwakarta to have a tool to assist in making strategic decisions related to the development of pioneer tourism villages. The methodology used in developing this system is the Waterfall model. The system is designed using PHP with the CodeIgniter 3 framework, MySQL as the database, and Unified Modeling Language (UML) for system modeling. The main criteria used in the MAUT calculation include public facilities, homestay management, local crafts, local arts, and local food. The results of the calculation using the MAUT method show that Batu Nunggal Margaluyu Tourism Village ranks first with a preference value of 100, Sasanakerta Tourism Village ranks second with a value of 96, and Sumbersari Tourism Village ranks third with a value of 90. Therefore, these three tourism villages are the best recommendations to be prioritized in the development of tourism villages in Purwakart
Copyright (c) 2025 Lia Fitriyanti, Mochzen Gito Resmi, Dede Irmayanti http://creativecommons.org/licenses/by-nc/4.0
REKOMENDASI PEKERJAAN BIDANG EKONOMI : SISTEM REKOMENDASI MENGGUNAKAN CONTENT BASED
The recommendation system was developed to assist students of the Institut Teknologi dan Bisnis Widya Gama Lumajang, particularly those from the Faculty of Economics and Business, in determining their preferred career options. This system helps students by providing various job references that match their individual criteria. The data was collected from a tracer study, which includes information such as academic grades, non-academic achievements, job positions, company names, salaries received. From the total dataset, 1,120 records were deemed valid and used in the research process. The aim of this research is to assist students by providing job recommendations based on similar criteria between current students and alumni. The method applied in this study is quantitative experimental research based on data mining, with the main approach being Content-Based filtering and the MLP (Multi-Layer Perceptron) Classifier algorithm. The data was split into two parts: 65% for training and 35% for testing. This division aims to allow the model to learn from most of the data while also being tested for accuracy using unfamiliar data. The recommendation model was developed using the MLP Classifier algorithm with a hidden_layer_size configuration of 100 neurons and a max_iter of 200 iterations. For the initial test, 10 sample data points were used to evaluate the model’s performance. During training, the loss value was monitored to assess how well the model understood the data and adjusted its internal weights. With this configuration, the system is expected to provide accurate job recommendations based on the user’s profile and academic history.
Copyright (c) 2025 Abdur Rouf, Hasyim Asy’ari , Maysas Yafi Urrohman, Febriane Devi Rahmawati http://creativecommons.org/licenses/by-nc/4.0
PERANCANGAN APLIKASI TIKET IT HELPDESK UNTUK MENINGKATKAN EFEKTIVITAS LAYANAN TEKNOLOGI INFORMASI
Effective and responsive Information Technology (IT) services play a crucial role in supporting organizational operations. However, in practice, ASPAN Company still faces challenges in recording, monitoring, and resolving IT incidents due to the absence of an integrated system. This study aims to design an IT Helpdesk ticketing application that enhances the effectiveness of IT services through a systematic, fast, and measurable incident management process. The research adopts the waterfall methodology and qualitative data collection using a case study approach through direct observation involving IT staff and service users. The result of this study is a web-based application that enables users to create incident report tickets, track ticket status, and provide feedback on the services received. The system also provides performance reports that are useful for service evaluation. The implementation results demonstrate a significant improvement in IT service performance, where the average incident resolution time decreased from 2.5 hours to 1.5 hours. Furthermore, the system facilitates real-time ticket tracking, historical issue management, and structured performance reporting to support IT service evaluation. With the implementation of the IT Helpdesk ticketing application, the IT issue-handling process becomes more efficient, well-documented, and ultimately improves the overall quality of Information Technology services within ASPAN company
Copyright (c) 2025 veron vaskarian karwur, Ramdha, Alvi, Fahmi, rocci, Riyan, Siti Nurhasanah Nugraha http://creativecommons.org/licenses/by-nc/4.0
EVALUASI PENERIMAAN MAHASISWA TERHADAP APLIKASI AKADEMIK MOBILE: PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM)
Mobile applications are widely used in educational environments to accelerate various academic and administrative tasks. Their presence has enhanced service effectiveness, expedited decision-making, and improved the digital campus ecosystem. This study was conducted to evaluate the acceptance level of MyNusa Student, a mobile-based academic application for students. The Technology Acceptance Model (TAM) framework was employed in this research to assess students’ acceptance of the MyNusa Student application. A total of 238 respondents, all registered students using the application, provided data for this study. Data analysis was carried out using Structural Equation Modeling (SEM) with a Partial Least Squares (PLS) approach to examine the relationships among variables: Perceived Ease of Use, Perceived Usefulness, Attitude Toward Using, Behavioral Intention to Use, and Actual Usage. The results indicated that all relationships among variables were statistically significant. The most influential relationship was observed between Perceived Ease of Use and Perceived Usefulness, followed by the relationship between Attitude Toward Using and Behavioral Intention to Use, and subsequently, Actual Usage. The findings suggest that the primary elements influencing students’ positive perceptions of the application—which in turn affect their intention and actual usage patterns—are their evaluations of its usefulness and utility. The practical implications highlight the need for continuous improvement in usability and utility aspects, with a focus on enhancing ease of use, optimizing core features such as real-time data updates, and improving technical as well as system security aspects.
Copyright (c) 2025 Muji Ernawati, Eni Heni Hermaliani, Evita Fitri, Siti Nurhasanah Nugraha http://creativecommons.org/licenses/by-nc/4.0
RANCANG BANGUN APLIKASI PENGELOLAAN PPH 21 PADA CV.ECS CONSULTING SERVICES DENGAN PENDEKATAN RAD
Income Tax (PPh) 21 often poses a challenge for companies and employees in managing tax payments efficiently and accurately. CV. ECS Consulting Service, which currently uses Microsoft Excel for PPh 21 calculations, faces risks of errors and time-consuming manual processes. This research aims to develop a web-based PPh 21 calculation application using the Rapid Application Development (RAD) method tailored to the company’s needs. Data collection methods include direct observation and literature study, while the application development method adopts RAD, which is iterative and responsive to changing requirements. The scope of the research includes user interface design, development of calculation algorithms, employee data processing, and ensuring data security. The application was tested using Black Box testing to ensure all features function properly, User Acceptance Testing (UAT) to assess whether it meets user needs, and performance testing to evaluate the website’s speed and stability. Black Box testing was conducted on six cases, and UAT was carried out directly with users. The results showed that the application passed all tests and met the required functionalities. Performance testing also indicated that the system is fairly stable, although further improvement is needed for long-term use or during high traffic.
Copyright (c) 2025 Aji Yunisyaputra, Syarif Hidayatulloh http://creativecommons.org/licenses/by-nc/4.0