TECHNO Nusa Mandiri: Journal of Computing and Information Technology is a journal published by LPPM Universitas Nusa Mandiri. The TECHNO Nusa Mandiri: Journal of Computing and Information Technology was originally intended to accommodate scientific papers made by Informatics Engineering lecturers. TECHNO Nusa Mandiri Journal has ISSN: 1978-2136 (Print Media) and 2527-676X (Online Media). The TECHNO Nusa Mandiri: Journal of Computing and Information Technology have the accredited National Journal status is accredited by the Indonesian Ministry of Research and Higher Education at the Sinta S4 level, in accordance with Decree on Strengthening SK Research and Development Number 21 / E / KPT / 2018 which has been in effect since July 9, 2018, for 5 years. Source: Risbang Ristekdikti.go.id. This journal is Rank 4 Accreditation Certificate (S4), Accreditation is valid for 5 years. Starting from Vol. 13, No. 1 the Year 2016 to Vol. 17, No. 1 the Year 2020. Journal of TECHNO Nusa Mandiri, re-accreditation remains at Rank 4 (SINTA 4), starting Vol. 16 No. 2 of 2019 based on the Decree of the Minister of Research and Technology / National Research and Innovation Agency Number 85/M/ KPT/2020, April 1, 2020
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The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license. The copyright in each article belongs to the author. Authors retain all their rights to published works, not limited to the rights set out on this page. The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC). Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri); The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author. If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.
ANALYSIS OF THE CANTEEN INFORMATION SYSTEM AT AN-NAWAWI ISLAMIC BOARDING SCHOOL USING PIECES
The canteen at An-Nawawi Modern Islamic Boarding School Bogor plays a crucial role in supporting students' daily activities; however, its current information system is not optimally integrated to handle the growing student population and operational complexity. This study aims to analyze the problems within the canteen's information system to identify priority areas for improvement. The methodology employs a quantitative analysis using the PIECES framework, which evaluates six variables: Performance, Information, Economy, Control, Efficiency, and Service. Data was collected by distributing a questionnaire to respondents via Google Forms. The research findings show that among the six variables, the Control variable obtained the lowest average score (563.4), indicating a significant weakness in the supervision and data security aspects of the current system. Therefore, it is concluded that the main priority for future development should be focused on strengthening the control aspects to create a more secure, reliable, and well-managed canteen information system.
Copyright (c) 2025 Nurajijah Nurajijah, Syarif Hidayatulloh http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZED DEEP AUTOENCODER WITH L1 REGULARIZATION AND DROPOUT FOR ANOMALY DETECTION IN 6G NETWORK SLICING
The increasing complexity of 6G network slicing introduces new challenges in identifying abnormal behavior within highly virtualized and dynamic network infrastructures. This study aims to address the anomaly detection problem in 6G slicing environments by comparing the performance of three models: a supervised random forest classifier, a basic unsupervised autoencoder, and an optimized deep autoencoder enhanced with L1 regularization and dropout techniques. The optimized autoencoder is trained to reconstruct normal data patterns, with anomaly detection performed using a threshold- based reconstruction error approach. Reconstruction errors are evaluated across different percentile thresholds to determine the optimal boundary for classifying abnormal behavior. All models are tested on a publicly available 6G Network Slicing Security dataset. Results show that the optimized autoencoder outperforms both the baseline autoencoder and the random forest in terms of anomaly sensitivity. Specifically, the optimized model achieves an F1- score of 0.1782, a recall of 0.2095, and an accuracy of 0.714. These results indicate that introducing regularization and dropout significantly improves the ability of autoencoders to generalize and isolate anomalies, even in highly imbalanced datasets. This approach provides a lightweight and effective solution for unsupervised anomaly detection in next- generation network environments.
Copyright (c) 2025 Valencia Claudia Jennifer Kaunang, Nur Alamsyah, Titan Parama Yoga, Acep Hendra, Budiman Budiman http://creativecommons.org/licenses/by-nc/4.0
EVALUATION OF USER PERCEPTIONS AND SATISFACTION THROUGH SENTIMENT ANALYSIS NEWS APPLICATIONS WITH NAIVE BAYES
The development of digital technology has driven the transformation of mass media into online news platforms such as Detikcom, Kompas.id, and CNN Indonesia. Competition among these news applications has created the need to evaluate user perceptions of service quality. This study aims to analyze user sentiment toward the three news applications based on reviews from the Google Play Store. The methods employed include web scraping, text pre-processing, labeling using the IndoBERT model, feature extraction with the TF-IDF method, and sentiment classification with the Naive Bayes algorithm. To address class imbalance in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the Naive Bayes model achieved high accuracy, namely 88.5% for Kompas.id, 88.8% for Detikcom, and 90.8% for CNN Indonesia. The analysis also revealed that positive reviews are more dominant, although recurring criticisms were identified regarding advertisements and technical performance of the applications. The use of Generative AI further assisted in automatically summarizing opinions and sentiment patterns. These findings provide valuable insights for developers in enhancing user experience and refining the features of digital news applications
Copyright (c) 2025 Aldiansyah Kusuma Kusuma, Diaz Aditya Yudha, Muhammad Bahril Afwa, Hanafi Eko Darono http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZING PHARMACEUTICAL DISTRIBUTION IN PUBLIC HEALTH CENTERS USING FUZZY C-MEANS CLUSTERING
Efficient drug distribution is fundamental to ensuring the quality of public healthcare services. However, health departments often face challenges with imbalances between drug demand and available supply. This study addresses this issue by applying the Fuzzy C-Means (FCM) clustering algorithm to categorize drug demand levels across 16 public health centers (puskesmas) in Langkat Regency, Indonesia, from 2021 to 2023. Using historical data from 2,400 drug records, the analysis identified five distinct demand clusters: Very Low, Low, Medium, High, and Very High. The results revealed a significant disparity in drug needs, with the "Very High" demand cluster dominating (51.29% of data) in centers like Besitang and Tanjung Selamat, driven by high morbidity rates. In contrast, other clusters were less prevalent, such as the "Low" demand cluster, which was primarily concentrated in the Gebang health center. These findings, visualized using t-SNE plots, highlight significant regional variations in pharmaceutical needs. This data-driven clustering provides a robust framework for the Langkat District Health Office to develop more targeted, efficient, and equitable drug distribution strategies, ultimately improving healthcare service delivery.
Copyright (c) 2025 Syahfitri Nurahma, Eva Darnila, Fajriana http://creativecommons.org/licenses/by-nc/4.0
NAVIGATING ETHICAL LONG-TERM PERSONAL DATA STORAGE: PRIVACY, SECURITY, REGULATORY CHALLENGES, AND SOCIETAL IMPACT REVIEW
Digital data storage is now fundamental to modern society, facilitating the collection and utilization of vast information resources. Yet, the exponential growth of stored data has intensified ethical concerns, particularly regarding long-term retention. This multidisciplinary review synthesizes literature on the ethical implications of long-term data storage, focusing on privacy, security, data ownership, accessibility, sustainability, and societal impact. The study analyzes ethical frameworks encompassing deontology, privacy by design, fair information practices, data minimization, and accountability while examining the responsibilities of organizations, individuals, regulators, technology providers, and ethics committees in upholding ethical standards. Key findings reveal that the ethical landscape of long-term data storage is highly complex and demands a collaborative, multi-stakeholder approach. The review concludes by recommending the integration of robust security measures, enhanced user control and consent mechanisms, sustainable storage technologies, and governance frameworks prioritizing fairness, non-discrimination, and the public good. These recommendations aim to guide stakeholders toward responsible and ethical data stewardship, ensuring that digital data storage practices remain trustworthy and sustainable amid ongoing technological and societal change
Copyright (c) 2025 Simeon Ayoade Adedokun, Theophilus Adedayo Adedokun http://creativecommons.org/licenses/by-nc/4.0
LAND COVER CHANGE PREDICTION USING CELLULAR AUTOMATA AND MARKOV CHAIN MODELS
This research examines the impact of land use change on mobility. Spatial problems arise due to increased activity, population, and transportation in the same space, necessitating the development of modeling strategies. This aligns with SDG 11 on cities and settlements, as well as the PRN's focus on transportation innovation. The urgency of this research lies in its adaptive and sustainable spatial prediction efforts aimed at controlling future land use. This study aims to analyze land use change patterns using the Cellular Automata Markov Chain (CA-Markov) model in Kupang City until 2043. CA-Markov simulations efficiently evaluate land cover changes and movement. The quantitative research method was conducted based on spatial predictions and spatial configuration. Quantum GIS (QGIS) and GeoSOS-FLUS were used to obtain results from each stage. There are three research stages. First, identification of land cover (land use in 2018 and 2023), driving factors (distance to settlements, airports, highways, elevation, slope, slope orientation, rainfall, population density), and conservation areas. Second, standardisation of spatial data. Third, land cover prediction using GeoSOS software (five-year prediction) to identify patterns of land use change. These findings emphasize the importance of using CA-Markov-based spatial predictions as a foundation for adaptive spatial planning to control land-use conversion and maintain sustainable spatial connectivity in Kupang City until 2043.
Copyright (c) 2025 Amandus Jong Tallo, Maria Gratiana Yudith Tallo , Antonius Leonardo Antjak, Maria Imanuela Doko, Maria Anita Christanti Lodang http://creativecommons.org/licenses/by-nc/4.0
ASSESSING SMPIT AJIMUTU GLOBAL INSANI WEBSITE QUALITY USING THE WEBQUAL 4.0 METHOD
Digitalization in the world of education encourages schools to have quality websites to provide online information and learning services. This study aims to measure the quality of the SMPIT Ajimutu Global Insani website using the Webqual 4.0 method, which involves three main dimensions: usability quality, information quality, and service interaction quality. This research method involves a survey of 50 respondents consisting of teachers, students, and parents of students. Data were analyzed descriptively using a Likert scale to evaluate the level of user satisfaction. The results showed that the information quality dimension had the highest score (4.2), followed by service interaction quality (4.0), while usability quality scored the lowest (3.8). These findings indicate that the website content is relevant, but navigation and interface design need improvement. Recommendations are given to improve the quality of the website, including optimizing interactive features and adding multimedia content. The implementation of the results of this study is expected to support the digital transformation of schools more effectively.
Copyright (c) 2025 Andi Saryoko, Faruq Aziz, Instianti Eliyana, Elin Panca Saputra, Bagas Eka Saputra http://creativecommons.org/licenses/by-nc/4.0
A LIGHTWEIGHT AND PRACTICAL PIPELINE FOR CROSS-PROJECT DEFECT PREDICTION USING METRIC-BASED LEARNING
Cross-Project Defect Prediction (CPDP) addresses the scarcity of defect data in new software projects by transferring knowledge from existing ones. However, domain shift between projects remains a major challenge. This study introduces a lightweight and practical CPDP pipeline based on traditional metric features, integrating domain adaptation (CORAL, TCA, TCA+), feature selection, and resampling techniques. Through 120 configurations evaluated on multiple PROMISE datasets, we found that combining TCA or TCA+ with Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN) consistently improved F1-Score and Recall on imbalanced datasets. LightGBM demonstrated the most stable performance across projects, while Logistic Regression yielded the highest MCC in specific cases. Principal Component Analysis (PCA) visualizations supported the effectiveness of domain alignment. The proposed pipeline offers a reproducible, cost-efficient alternative to deep learning models and provides actionable insights for defect prediction in resource-constrained environments.
Copyright (c) 2025 Novia Heriyani, Agus Subekti http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION OF MACHINE LEARNING ALGORITHMS IN THE CLASSIFICATION OF VECTOR-BORNE DISEASES
Developing a predictive model is the objective of this study, focusing on vector-borne diseases using various machine learning methods, including Random Forest (RF), Logistic Regression (LR), k-nearest Neighbors (kNN), Tree (DT), and XGBoost. The main goal is to use oversampling techniques like SMOTE and Random Oversampling to correct the dataset's class imbalance. The dataset was obtained from Kaggle and literature references published in Frontiers in Ecology and Evolution (Endo and Amarasekare 2022), consisting of approximately 9,490 entries with environmental, demographic, and clinical attributes. Dengue Fever is one of the diseases that this study focuses on. Aedes aegypti mosquitoes spread it, and it is a significant health risk in tropical areas. The DT and XGBoost models had the highest accuracy, at 99.2%. Logistic Regression and Random Forest also did well, with 99.1% accuracy. KNN did well, too, but with a lower recall, at 99.0%. The ROC curve gave a complete picture of how well each model classified things. These findings indicate that when combined with proper data handling, machine learning models can significantly improve early detection of vector-borne diseases and support more accurate and timely decision-making in public health interventions.
Copyright (c) 2025 Sukrul Ma’mun, Eni Heni Hermaliani http://creativecommons.org/licenses/by-nc/4.0
DESIGN OF WEB-BASED CAR RENTAL INFORMATION SYSTEM USING EXTREME PROGRAMMING AT CV. NUGROHO
This study discusses the development of a web-based car rental information system for CV. Nugroho Trans Surabaya using the Extreme Programming (XP) methodology. The system was designed to address issues in the previous manual rental process, such as transaction recording on paper, which was prone to errors and delays in data management. The developed system includes key features such as car booking, fleet data management, rental confirmation, and payment integration. Testing was conducted through various methods, including performance testing, usability testing, and security testing. Performance testing using PageSpeed Insights in desktop mode showed the following scores: performance 93, accessibility 84, best practices 93, and SEO 82. Meanwhile, testing with GTmetrix yielded a performance score of 96%, a structure score of 72%, a Largest Contentful Paint (LCP) time of 909 ms, and a fully interactive time of 1.3 seconds, indicating excellent speed and interface stability. In terms of security, testing with Pentest Tools indicated an overall medium risk level, with 1 medium risk finding, 5 low-risk findings, and 13 informational findings, and no high-risk vulnerabilities. The application of the XP method enabled adaptive system development tailored to user needs and iterative changes. This system has proven to increase the company’s operational efficiency by up to 40%, based on faster transaction completion times compared to the manual system. However, some limitations remain, such as user interface constraints and suboptimal integration of online payment channels. For future research, it is recommended to improve user experience, optimize the mobile interface, enhance server security protection, and expand system features to support the broader business growth of CV. Nugroho Trans Surabaya
Copyright (c) 2025 Ramadhan Nugroho Satrio Wibowo, Anton Anton http://creativecommons.org/licenses/by-nc/4.0
EARLY DETECTION OF ROT IN THAI PAPAYA (CARICA PAPAYA) USING THE K-NN METHOD
Determining the category of a plant or fruit involves several criteria. One of the easiest methods to use is morphological criteria, which entails studying the external structure that can be directly observed. However, this approach cannot be regarded as a fixed standard since people's interpretations may vary. To address this, a system was developed to assess the ripeness of Thai papaya fruit, utilizing image processing and the K-Nearest Neighbor (KNN) method. This study analyzes a data set to detect rotten papaya fruit, which is expected to help consumers recognize papaya fruit that is purchased in a perfectly ripe condition, not ripe with certain parts that are rotting. The indicator used to determine the category is the color of the skin of Thai Papaya fruit with an ROI of 600 pixels x 300 pixels by finding the mean RGB value and then calculating it using the Euclidean distance formula. From the results of these calculations, it is expected to get a classification using K-Nearest Neighbor (KNN) to get an image pattern of the level of rottenness on the surface of the papaya. Therefore, by improving the RGB image eliminating noise in the papaya image, and using the K-NN classification of the image pattern obtained from the research results from the sampling data, an accuracy level of 80% was obtained with a range of mean R values: 130,671-169,630, mean G: 106,891-131,895, and mean B: 61,119-100,776 which came from 120 data.
Copyright (c) 2025 Agus Prayitno http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZATION OF COVID-19 DETECTION THROUGH TRANSFER LEARNING ON CHEST X-RAY IMAGES
The urgent need for rapid and reliable identification of COVID-19 cases has highlighted the importance of auxiliary diagnostic tools. Chest X-ray imaging serves as a key resource in clinical settings, yet manual interpretation remains susceptible to inter-observer variability and diagnostic delays. This study introduces an optimized deep learning framework based on transfer learning to enhance the detection of COVID-19 from chest X-ray images. The aim is to improve classification accuracy and operational efficiency using pre-trained models tailored for radiographic analysis. We applied transfer learning with fine-tuning to convolutional neural networks pre-trained on large-scale image datasets. The models were adapted and evaluated on a curated collection of chest X-rays representing COVID-19 positive and negative cases. The proposed model achieved a test accuracy of 99% with a loss of 0.15, indicating high diagnostic performance and robustness in distinguishing COVID-19 cases from other pulmonary conditions. Transfer learning offers a viable and efficient strategy for COVID-19 screening using chest X-rays. This approach has the potential to support frontline clinical decision-making and scale public health response during outbreaks.
Copyright (c) 2025 LEBEAU YAMBA MUAMBA, Albert Ntumba Nkongolo, Matendo Mabela Esther, Mbuyi Mpumbu Ben http://creativecommons.org/licenses/by-nc/4.0
ANALYSIS OF CUSTOMER SATISFACTION WITH LIVIN BY MANDIRI VIRTUAL CREDIT CARD FEATURES
Credit cards are a payment method that replaces cash, enabling transactions at various locations that accept them. Credit cards can be physical or virtual, with virtual cards facilitating transactions without the need for a physical card. Bank Mandiri offers virtual credit cards to support digital transactions, providing benefits such as discounts and cashback. Although Bank Mandiri experienced a decline in credit card usage during the pandemic, there has been a post-pandemic upward trend influenced by the features of the virtual credit card in Livin' by Mandiri. This is attributed to customer satisfaction, which has become a key factor in this increase. However, as of now, there has been no research conducted on customer satisfaction regarding the virtual credit card feature in Livin' by Mandiri. Therefore, this study aims to analyze customer satisfaction levels with the virtual credit card feature using the PIECES Method (Performance, Information, Economy, Control, Efficiency, and Service). To support the theoretical framework of this issue, relevant journal reviews and data collection through questionnaires were conducted, with the population size based on Bank Mandiri's annual reports for 2022–2023. The analysis of the questionnaire data, employing the PIECES method and the Likert Scale, revealed the highest scores in the categories of Performance and Service, both scoring 4.20. The Efficiency category scored 4.19, while the Information and Control categories each scored 4.15. The lowest score was observed in the Economy category, with a score of 4.13. Therefore, it can be concluded that customers are satisfied.
Copyright (c) 2025 Iman Ary Hartono, Onny Marleen, Meilani Basaria Siregar http://creativecommons.org/licenses/by-nc/4.0
SELECTION OF MUARA ABU BEACH SAFETY SYSTEM IN KUPANG CITY USING ANALYTICAL HIERARCHY PROCESS METHOD
Kupang City has a large coastal area, most of them live so close to the coastline that there is no longer a coastal buffer zone. One of the beaches close to the settlement is Muara Abu beach, which is located in Oesapa Barat Village, Kelapa Lima Sub-district, Kupang City. This research uses the Analytical Hierarchy Process (AHP) method with the following objectives are Establishing a coastal safety system at Muara Abu beach location based on the decision results of the AHP method used optimally and Analyze the coastal safety system using the AHP method. The criteria used in the selection of coastal safety systems are Waves (history, vulnerability, probability, and threat), Erosion (Shoreline change, scouring at the foot of the building, length of eroded beach), Abrasion (Width of abraded beach, length of abraded beach), Sedimentation (Length of closed estuary, percentage of estuary opening, and influence of sedimentation) and Environment (Sea water quality, coral reefs, mangroves). And the alternative system chosen is structural coastal protection, namely Seawall, Groin and Jetty. The results of calculations with the AHP method show the priority scale for securing Muara Abu Beach can be sorted as follows are Jetty is 46.53%, Seawall is 33.37% and Groin is 20.10% The selection of coastal safety systems using the AHP method provides objective results in determining the best alternative. Jetty is the main solution recommended to be implemented in Muara Abu Beach. Further research is recommended to examine the effectiveness of Jetty implementation in the long term.
Copyright (c) 2025 Dian Johannis http://creativecommons.org/licenses/by-nc/4.0
SENTIMENT ANALYSIS OF PUBLIC OPINION ON TRANSPORTATION SERVICES IN INDONESIA USING MACHINE LEARNING
This study analyzes public sentiment towards transportation services in Indonesia through social media using Naïve Bayes and Support Vector Machine (SVM) algorithms. Data was collected from Twitter using an API with transportation-related keywords over a three-month period. The analysis results indicate that 93.5% of the opinions are neutral, 3.5% are positive, and 3% are negative. The dominance of neutral sentiment suggests potential dataset imbalance or user hesitation in expressing strong opinions. SVM achieved a higher accuracy (100%) compared to Naïve Bayes (92%), which may be influenced by dataset limitations or the model's validation method. Data preprocessing involved several steps, including tokenization, stopword removal, stemming, lemmatization, and handling of missing data to ensure cleaner and more structured text input. These findings highlight the potential of sentiment analysis for transportation policy improvements, providing insights for policymakers and transport service providers. Future research should address data balancing and broader dataset usage to enhance the robustness of findings and support better decision-making in the transportation sector.
Copyright (c) 2025 Fina Sifaul Nufus, Windu Gata http://creativecommons.org/licenses/by-nc/4.0
SENTIMENT ANALYSIS OF JAKLINGKO APP REVIEWS USING MACHINE LEARNING AND LSTM
Application-based transportation services have rapidly developed in recent years, with various studies indicating that service quality and user experience play a crucial role in the adoption of this technology. Previous research has analyzed user satisfaction with digital transportation applications, highlighting factors such as ease of use, service reliability, and the effectiveness of fare systems. This study aims to analyze user sentiment toward the JakLingko application to assess satisfaction levels and identify aspects that need improvement. Utilizing a dataset of 200 user reviews, this research applies data preprocessing techniques to clean and organize the information before performing sentiment classification. The machine learning models used include Naïve Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Long Short-Term Memory (LSTM), categorizing sentiment into positive, negative, and neutral. The analysis results indicate a dominance of negative sentiment in user reviews, reflecting a significant level of dissatisfaction with the application. This highlights major challenges in the implementation of transportation applications, potentially affecting public adoption and trust in the service. Therefore, besides providing insights into user perceptions, this study also proposes improvement strategies aimed at enhancing features and the overall user experience. Given the high proportion of negative sentiment, this research emphasizes the importance of improving the accuracy of sentiment analysis models to generate deeper and more precise insights. These findings can serve as a foundation for designing policies and strategies to improve application-based transportation services, ultimately enhancing service quality and expanding user adoption.
Copyright (c) 2025 Maghfiroh Maulani, Windu Gata http://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE OF THE DELTA MODULATION SYSTEM WITH VARIOUS DELTA STEP SIZES
Delta Modulation Systems are widely used in Analog-to-Digital Converter (ADC) systems. This research aims to determine the optimal delta step size that can be achieved in a Delta Modulation system, as the system's performance is highly influenced by the delta step size. The method used involves simulations with MATLAB to identify the optimal delta step value. The performance of a Delta Modulation system is greatly influenced by the Delta step size. The optimal value in this study was achieved at a Delta step size of 0.4 with the smallest error, namely MSE = 0.1186. If the Delta step size is smaller or larger than this optimal value, the MSE increases. When the frequency of the input signal increases, the Delta step size needs to be increased to follow the changes in the input signal. Otherwise, the MSE will also increase, a phenomenon known as Slope-overload Distortion. Granular Noise occurs when the input signal changes very slowly or is almost constant, while the step size is too large, resulting in a high MSE. To overcome this problem, a dynamic Delta step size is needed, adjusted to the frequency changes of the input signal. Such a system with a dynamic Delta step size is known as Adaptive Delta Modulation.
Copyright (c) 2025 Djadjat Sudaradjat, Andi Rosano http://creativecommons.org/licenses/by-nc/4.0
IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS
This article presents an analysis of agricultural yields in the Democratic Republic of Congo (DRC) using machine learning algorithms. The study is based on around 30,000 records covering several years of agricultural production. Each record includes variables such as seed type, climatic conditions (temperature, rainfall and humidity), soil characteristics (pH, nutrients), farming practices (fertilizer use, irrigation) and yields obtained. The data comes from a variety of sources, including METTELSAT, the World Meteorological Organization (WMO) and WorldClim for climate data, and the DRC Ministry of Agriculture and the FAO for soil and agricultural data. The algorithms evaluated include linear regression, random forest regression, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The performance of the algorithms is measured using metrics such as MSE, MAE, RMSE, R² Score and MAPE on three separate case studies (Farm A, Farm B and Farm C). The results show that artificial neural networks (ANNs) perform best, with MSE ranging from 600 to 850, MAE from 12 to 17, RMSE from 24.49 to 29.15, R² Score from 0.92 to 0.95, and MAPE from 8.5% to 10.7%. Next came GBM, random forest regression, SVM and finally linear regression. These results highlight the potential of machine learning algorithms to improve agricultural yield forecasts in the DRC.
Fiston Chrisnovic Balanganayi Kabutakapua
Copyright (c) 2025 Rodolphe Nsimba Malumba, Mardochee Longo Kayembe, Fiston Chrisnovic Balanganayi Kabutakapua, Bopatriciat Boluma Mangata, Trésor MAZAMBI KILONGO, Rufin Tabiaki Tandele, Emmanuel Ntanyungu Ndizieye, Parfum Bukanga Christian http://creativecommons.org/licenses/by-nc/4.0
OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING
MPOX (Monkeypox) has become a significant global health concern, requiring accurate forecasting for effective outbreak management. This study improves MPOX case prediction using Facebook Prophet with hyperparameter optimization. The dataset consists of global MPOX case records collected over time. Data preprocessing includes missing value imputation, normalization, and aggregation. Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. The model is then optimized by fine-tuning seasonality mode, changepoint prior scale, and growth model. The results show that hyperparameter tuning significantly enhances forecasting accuracy. The optimized model reduces MSE from 541,844.77 to 320,953.34 and RMSE from 736.10 to 566.53, demonstrating improved precision. The model also captures trend shifts and seasonal fluctuations more effectively. In conclusion, this study confirms that tuning Facebook Prophet improves epidemic forecasting, making it a reliable tool for MPOX monitoring. Future research should integrate external factors, such as vaccination rates and mobility data, to further refine predictions.
Copyright (c) 2025 Nur Alamsyah, Venia Restreva Danestiara, Budiman Budiman, Reni Nursyanti, Elia Setiana, Acep Hendra http://creativecommons.org/licenses/by-nc/4.0
FEATURE SELECTION COMPARATIVE PERFORMANCE FOR UNSUPERVISED LEARNING ON CATEGORICAL DATASET
In the era of big data, Knowledge Discovery in Databases (KDD) is vital for extracting insights from extensive datasets. This study investigates feature selection for clustering categorical data in an unsupervised learning context. Given that an insufficient number of features can impede the extraction of meaningful patterns, we evaluate two techniques—Chi-Square and Mutual Information—to refine a dataset derived from questionnaires on college library visitor characteristics. The original dataset, containing 24 items, was preprocessed and partitioned into five subsets: one via Chi-Square and four via Mutual Information using different dependency thresholds (a low-mid-high scheme and dynamic quartile thresholds: Q1toMax, Q2toMax, and Q3toMax). K-Means clustering was applied across nine variations of K (ranging from 2 to 10), with clustering performance assessed using the silhouette score and Davies-Bouldin Index (DBI). Results reveal that while the Mutual Information approach with a Q3toMax threshold achieves an optimal silhouette score at K=7, it retains only 4 features—insufficient for comprehensive analysis based on domain requirements. Conversely, the Chi-Square method retains 18 features and yields the best DBI at K=9, better capturing the intrinsic characteristics of the data. These findings underscore the importance of aligning feature selection techniques with both clustering quality and domain knowledge, and highlight the need for further research on optimal dependency threshold determination in Mutual Information.
Copyright (c) 2025 Rachmad Fitriyanto, Mohamad Ardi http://creativecommons.org/licenses/by-nc/4.0
DIGITALIZATION OF HR AT ONIP: INFORMATION SYSTEMS URBANIZATION AND STRATEGIC ALIGNMENT AS KEY LEVERS
This article examines the digitalization of human resources (HR) at the Office National d’Identification de la Population (ONIP) in the Democratic Republic of Congo (DRC), emphasizing the pivotal role of information systems (IS) urbanization and strategic alignment as key levers. Using a qualitative methodology that combines semi-structured interviews with 15 stakeholders (HR managers, IT specialists, directors) and process analysis, we demonstrate the following outcomes: 40% reduction in HR processing time (from 7 to 4.2 days), 30% decrease in data entry errors through administrative task automation, 29% optimization of annual IT expenditures (from 120,000 to 85,000 USD), Increase in employee satisfaction scores from 58% to 82% (based on an internal survey of 200 employees). These results, derived from the implementation of a secure and modular HR information system (HRIS), underscore the efficacy of a structured approach in a fragile context. The article contributes to the literature on HR digital transformation in the African public sector by proposing a reproducible framework grounded in IS interoperability and collaborative governance.
Copyright (c) 2025 Evariste SINDANI, Pierre Kafunda Katalay, Simon Ntumba Badibanga, Eugène Mbuyi Mukendi http://creativecommons.org/licenses/by-nc/4.0
PREDICTION OF HAJJ PILGRIMS' HEALTH RISK USING K-NN, DECISION TREE, CROSS VALIDATION, AND SMOTE
The background of this study is predicting the health risk levels of hajj pilgrims, which is a significant challenge in improving healthcare services during the pilgrimage. This research contributes by systematically evaluating several machine learning techniques and applying SMOTE to balance the dataset, as opposed to previous studies that relied on single-model classification approaches. The data analyzed includes 5,000 health records of pilgrims, covering various attributes such as age, gender, medical history, and disease diagnosis, sourced from the Siskohat database of the Directorate General of Hajj and Umrah Management. The results show that Cross-Validation (Logistic Regression) achieved the highest accuracy (87.9%) after applying SMOTE, outperforming Decision Tree (86.4%) and K-NN (83.1%). These findings highlight that SMOTE significantly enhances recall, ensuring better identification of high-risk patients. The implications of these results contribute to hajj health management by providing a robust predictive framework that improves early risk detection and medical resource allocation, while also demonstrating a novel approach to handling imbalanced healthcare datasets.
Copyright (c) 2025 Widi Astuti, Fajar Sarasati http://creativecommons.org/licenses/by-nc/4.0
DESIGNING A WEB-BASED RESTAURANT RESERVATION INFORMATION SYSTEM WITH REQUIREMENT PROTOTYPING METHOD
The development of information technology is proliferating, along with the increasing human need for fast, precise, and accurate information. The role of information technology in supporting the business world will make it easier for business people to run their business. One of the business fields that can implement information technology is restaurants. Restaurant management is gradually changing from using conventional methods with manual recording to being more systematic through digital devices. With digital devices, restaurant managers can more efficiently record table reservations and food menu orders. On the consumer side, it is also easier because they can make reservations and food menus from anywhere via a computer or smartphone device connected to the internet. In this research, a web-based application will be created that is used as a means to record table reservations and order food menus with the case studies at Wisata Kampung Kemiri Jember. The design and creation of the website at Wisata Kampung Kemiri Jember uses the requirement prototyping software development method. For the testing process, the Black Box Testing method is used to test all features on the website that have been running according to their functions. To test the user experience, the User Acceptance Testing (UAT) method was used by distributing questionnaires. Through the implementation of this website-based table reservation and food ordering system, it is hoped that work efficiency can be improved to optimize customer satisfaction
Copyright (c) 2025 Raymond Sutjiadi, Titasari Rahmawati, Ariel Kristianto, Frederick Theo Kanessa http://creativecommons.org/licenses/by-nc/4.0
DEVELOPMENT OF THE ODOO SYSTEM FOR THE EMPLOYEE PERFORMANCE APPRAISAL SYSTEM IN HRM MODULE
In organizations, such as companies, performance evaluation aims to assess, motivate, and improve employee performance. Human resource development is achieved by recognizing employees' potential. At PT.HM, there is currently no system for employee performance evaluation. The process is done manually by distributing Excel files for self-assessment, which are then forwarded for further evaluation. Performance evaluations are critical for measuring individual performance, serving as a basis for rewards and career paths. This research aims to develop an appraisal system in the Odoo HRM module using the NineBox Matrix, as PT.HM requires employee mapping across nine categories. The research includes four stages: problem identification, data collection, data analysis, and system design. The performance appraisal system will be integrated into the Odoo system already in use at the company for HRM. Data collection was done through direct observation at PT.HM. The system's development benefits HR Managers, Personal Managers, and employees by automating performance and competency assessments. It calculates scores based on predefined weights configured by HR Managers and provides a career path for employee promotions. The system is tested through black-box testing, yielding a 100% success rate, and User Acceptance Testing (UAT), also achieving a 100% success rate, as expected.
Copyright (c) 2025 Reizki Jihan Melani, Dina Anggraini, Widiastuti Widiastuti http://creativecommons.org/licenses/by-nc/4.0
ARCHITECTURAL DESIGN USING THE ZACHMAN FRAMEWORK AT MINING EQUIPMENT INDUSTRY
The mining equipment industry requires the effective integration of Information Systems (IS) and Information Technology (IT) into its business processes to achieve a competitive advantage. This study focuses on Enterprise Architecture (EA) planning to align IS/IT implementation with the company's vision and mission. The Zachman Framework is utilized to map the organization’s systems comprehensively, considering six perspectives and addressing 5W+1H (What, Why, When, Where, Who, and How). The research methodology includes data collection through interviews with key stakeholders and observations of core and supporting business activities. These data are analyzed using the Value Chain to assess the current state of the organization. The findings reveal gaps in the existing business processes and the misalignment of IS/IT initiatives with the organization’s goals. Based on these analyses, the study develops an Enterprise Architecture design that proposes a structured approach to IS/IT implementation. The result of this research is a detailed proposal for the development of a tailored application to optimize business processes, improve operational efficiency, and ensure better alignment between IS/IT initiatives and organizational objectives. This study provides practical recommendations for the mining equipment industry to enhance its competitive edge through strategic IS/IT integration.
Copyright (c) 2025 Francka Sakti Lee, Bryan Arvin http://creativecommons.org/licenses/by-nc/4.0
ARTIFICIAL LEARNING BASED ON KERNEL SVM FOR THE PREDICTION OF CARDIOVASCULAR DISEASE HYPERTENSION
Hypertension, a critical risk factor for cardiovascular diseases, requires accurate early detection for effective management. This study examines the application of kernel-based Support Vector Machines (SVM) for predicting hypertension, utilizing advanced machine learning techniques to address the complex, non-linear relationships inherent in healthcare data. By employing various kernel functions, such as the radial basis function (RBF) and polynomial kernels, the study aims to enhance the model's ability to capture and interpret the nuanced patterns associated with hypertension risk. The research utilizes a diverse dataset that includes demographic, physiological, and lifestyle variables, applying kernel SVM to predict hypertension outcomes. Performance is evaluated through rigorous cross-validation, with metrics including accuracy, precision, recall, and F1-score. The findings indicate that kernel SVMs significantly outperform traditional linear models, offering superior prediction accuracy and robustness. This study highlights the potential of advanced machine learning methods in improving early detection and personalized risk assessment for hypertension, ultimately supporting more effective management strategies and better cardiovascular health outcomes.
Copyright (c) 2025 Patient MUSUBAO SWAMBI, Albert Ntumba Nkongolo, Pierre Kafunda Katalay, Rostin Mabela Matendo Makengo, Eugène Mbuyi Mukendi http://creativecommons.org/licenses/by-nc/4.0
QUALITY ANALYSIS OF THE ELECTRONIC GOVERNMENT PROCUREMENT ORDER SYSTEM USING WEBQUAL AND EUCS METHODS
Within district of Hulu Sungai Selatan there is a website for the community to offer goods or services, namely SIOPEN, SIOPEN functions as a forum for the HSS community and umkm to offer their goods or services to the local government. The goal of this research is to evaluate SIOPEN's quality using the Webqual approach and users' degree of satisfaction using the EUCS approach. The Webqual approach, which has three dimensions information quality, usability, and interaction service was employed in this research together with the EUCS approach. The EUCS approach has some variables in partikular ease of use, accuracy, format, content, and timeliness. The approach of gathering information data through respondents via sending out online questionnaires to SIOPEN users and determining the sample using the formula from Slovin obtained 88 users then obtained by Webqual Index and Average Satisfaction. The results of research measuring the three dimensions of Webqual show that are in very good interpretation, The information quality dimension obtained 0.8392, the usability dimension obtained 0.8292, then the service interaction dimension obtained 0.8284. The findings from assessing the level of the satisfied user three variables of EUCS in particular content at a score of 4.27, ease of use at a score of 4.21, and timeliness at a score of 4.28 are at very satisfied level, then the other two variables are accuracy at a score of 4.15 and format at a score of 4.11 at a satisfied level.
Copyright (c) 2024 Muhammad Anshar Maulidi, Evi Dwi Wahyuni, Briansyah Setio Wiyono http://creativecommons.org/licenses/by-nc/4.0
COMPARISON LINEAR REGRESSION AND RANDOM FOREST MODELS FOR PREDICTION OF UNDERGROUND DROUGHT LEVELS IN FOREST FIRES
The increase in forest fires poses a significant risk due to its impact on underground dryness, which can cause long-term environmental damage and challenge fire suppression efforts. This research aims to develop a prediction model for underground drought levels in the context of forest fires using machine learning techniques. The methodology used in this research follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. This study analyzes a forest fire dataset, applies encoder labels to transform categorical variables, and uses linear regression and random forest models to predict underground drought levels. The goal is to create a predictive model that can help inform wildfire risk management strategies by anticipating underground drought levels. The results showed that the random forest model achieved higher prediction accuracy than the linear regression, with an R-squared value of 0.97. This suggests that the random forest model is a more robust tool for predicting underground drought levels, providing valuable insights for forest fire management. This research contributes to the understanding of underground drought levels, aiding the development of effective wildfire risk management strategies.
Copyright (c) 2024 Nur Alamsyah, Budiman Budiman, Titan Parama Yoga, R Yadi Rakhman Alamsyah http://creativecommons.org/licenses/by-nc/4.0
PREDICTING STUNTING IN TODDLERS IN WEST JAVA USING LINEAR REGRESSION BASED ON POVERTY LEVELS
Children's growth is disrupted by stunting, a chronic nutritional condition brought on by a prolonged shortage of nutrient intake. Under-five stunting is a major issue that affects many nations, particularly those with high rates of poverty. The aim of this research is to use the linear regression method based on the proportion of poverty to predict the risk of stunting in children under five in West Java. Growing children are particularly vulnerable to stunting, which can have long-term effects on their development and health. The research site was selected in West Java Province due to the region's high stunting rates and nofigur poverty rate. Precise forecasts are required to surmount the current issues. The research methodology employed is the descriptive quantitative technique. The data, which was projected using percentage values, covered the years 2014–2020. This study uses linear regression as its algorithm. According to the study's findings, there will be an 8.55% chance of toddler stunting in West Java in 2024. It is hoped that the government would be able to lower the risk of stunting by estimating the proportion of risk.
Copyright (c) 2024 Nabila Aulia Rahmah, Nabila Khairunisa, Naufal Ammar Hidayatulloh, Ultach Enri http://creativecommons.org/licenses/by-nc/4.0
LOGISTICS SERVICE INFORMATION SYSTEM AUDIT USING COBIT 5 FRAMEWORK
KBN Prima Logistik is a subsidiary affiliated with PT. Kawasan Berikat Nusantara which operates in the field of logistics services. In carrying out its business processes, it has implemented an Information System in the form of using desktop-based supporting business applications (Client Server). Some of the current problems are that the applications used are not functioning optimally, IT Governance procedures are not well defined by standards, there is no IT Division in the Organizational Structure that is responsible for IT management and there is a shortage of competent human resources in the IT field. Therefore, it is necessary to measure the Maturity Level and Capability Level of IT governance. So, research was carried out using the COBIT 5 framework as an Information System Audit model. The research method goes through the stages of problem identification, literature study, and domain determination which focuses on the DSS and MEA domains. The data collection method was through interviews and distributing questionnaires filled in by 20 respondents. The research results show that the Maturity Level value in the DSS and MEA domains shows an average value of 1.94 or 194%. Capability Level is still at Level 2 (Managed Process) from the expected Target Level 3 (Established Process). This indicates that although the IT processes has been run and implemented regularly with planning and monitoring according to business process objectives, but the management is not yet optimal and not well standardized. The results of the gap assessment show that the average gap value is 1.06.
Copyright (c) 2024 Husnul Hayat, Samudi Samudi http://creativecommons.org/licenses/by-nc/4.0