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Journal of Computer Networks, Architecture and High Performance Computing
Published by Yayasan Cita Cendekiawan Al Khwarizmi
ISSN : 26559102     EISSN : 26559102     DOI : 10.47709
Core Subject : Science, Education,
Computer Science & IT Education
Journal of Computer Networks, Architecture and Performance Computing is a scientific journal that contains all the results of research by lecturers, researchers, especially in the fields of computer networks, computer architecture, computing. this journal is published by Information Technology and Science (ITScience) Research Institute, which is a joint research and lecturer organization and issued 2 (two) times a year in January and July. E-ISSN LIPI : 2655-9102 Aims and Scopes: Indonesia Cyber Defense Framework Next-Generation Networking Wireless Sensor Network Odor Source Localization, Swarm Robot Traffic Signal Control System Autonomous Telecommunication Networks Smart Cardio Device Smart Ultrasonography for Telehealth Monitoring System Swarm Quadcopter based on Semantic Ontology for Forest Surveillance Smart Home System based on Context Awareness Grid/High-Performance Computing to Support drug design processes involving Indonesian medical plants Cloud Computing for Distance Learning Internet of Thing (IoT) Cluster, Grid, peer-to-peer, GPU, multi/many-core, and cloud computing Quantum computing technologies and applications Large-scale workflow and virtualization technologies Blockchain Cybersecurity and cryptography Machine learning, deep learning, and artificial intelligence Autonomic computing; data management/distributed data systems Energy-efficient computing infrastructure Big data infrastructure, storage and computation management Advanced next-generation networking technologies Parallel and distributed computing, language, and algorithms Programming environments and tools, scheduling and load balancing Operation system support, I/O, memory issues Problem-solving, performance modeling/evaluation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 838 Documents
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The Effectiveness of Using Python-Based Interactive Games in Improving Student Motivation and Learning Outcomes Amril Samosir; Rizki Agung Wibowo; Tyan Tasa
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8987

Abstract

This study aims to analyse the effectiveness of using Python-based interactive games in improving student motivation and learning outcomes. The method used was Research and Development (R&D) based on the ADDIE model. The research design employed a One-Group Pretest-Posttest Design with 60 students as subjects. The research instruments consisted of a learning outcome test and a Likert-scale learning motivation questionnaire. Data analysis utilised descriptive statistics, N-Gain calculations, and a Paired Sample t-Test. The results showed that the average pre-test score of 67.01 increased to 82.40 on the post-test, representing a gain of 15.39 points. The learning motivation score rose from 70.85 to 82.99, an increase of 12.14 points. The N-Gain value of 0.47 falls into the moderate category. Statistical tests revealed a significant difference between pre- and post-intervention scores. These findings indicate that Python-based interactive games effectively enhance motivation and learning outcomes whilst supporting digital learning innovation
Development of an Android-Based Inventory Decision Support System Using Rapid Application Development Said Hambali Takhir
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9100

Abstract

Inventory management remains a significant challenge for many Micro, Small, and Medium Enterprises (MSMEs), particularly due to manual stock recording, delayed inventory updates, and inefficient purchasing decisions. Most existing mobile inventory applications focus only on inventory recording without providing analytical support for inventory replenishment. This study aims to develop an Android-based Inventory Decision Support System using the Rapid Application Development (RAD) method by integrating adaptive demand forecasting, Economic Order Quantity (EOQ), and Reorder Point (ROP) calculations into inventory management. The study employed a Research and Development (R&D) approach following the RAD phases of requirements planning, user design, construction, and cutover. Historical inventory transaction data were used to generate demand forecasts, calculate EOQ and ROP values, and automatically provide purchasing recommendations. Functional performance was evaluated using Black Box Testing, usability was assessed using the System Usability Scale (SUS), and forecasting accuracy was measured using Mean Absolute Percentage Error (MAPE). The developed application successfully integrated inventory management, forecasting, inventory optimization, and purchase recommendation features. Functional testing showed that all application modules operated correctly, while the system achieved an average SUS score of 82.5, indicating excellent usability. The forecasting model also demonstrated satisfactory prediction accuracy with a low MAPE value, enabling the application to generate timely reorder recommendations. The proposed system not only improves inventory recording accuracy but also supports intelligent inventory planning, helping MSMEs reduce stock shortages and optimize purchasing decisions.
Implementation of K-Medoids Clustering Method for B2B Service Price Segmentation at PT Telkom Tasikmalaya Elsa Amalinda; Andi Nur Rachman; Cecep Muhammad Sidik Ramdani
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9161

Abstract

PT Telkom Indonesia Witel Tasikmalaya faces strategic challenges in monitoring customer behavior in real-time and determining optimal Business-to-Business (B2B) service pricing strategies due to conventional data management. This study aims to design a Web-based Decision Support System to analyze B2B service pricing using the K-Medoids clustering algorithm with Manhattan Distance metrics. The research and system development are fully structured using the Agile methodology. The model evaluation is measured using the Davies-Bouldin Index. The data mining modeling indicates that the formation of three clusters is the most optimal partition with the smallest Davies-Bouldin Index value of 0.638. The clusters successfully map customer profiles from small enterprises to large corporations as a baseline price recommendation. The system is built using an implementation of vanilla JavaScript and Google Firebase serverless architecture, achieving success in functional black-box testing and a 90% score in user acceptance testing. This research provides a strategic contribution by accelerating the issuance of quotation documents and optimizing company revenue.
Implementation of K-Means Clustering for Grouping Post-Flood Disease Patterns in Affected Residential Settlements Hotler Manurung; Marto Sihombing; Ratih Puspadini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9210

Abstract

Flooding in Binjai City increases post-flood disease incidence in slum settlements due to inadequate sanitation and high environmental vulnerability, while disease records are often scattered, poorly organized, and late, slowing health interventions. This study groups post-flood disease data in affected residential areas using K-Means clustering based on disease type, village, and slum level. A total of 1,100 records were collected from five districts in Binjai City; after excluding incomplete records, 850 valid records were used in the 2-cluster scenario and 1,086 in the 3-cluster scenario, implemented in a Matlab-based application. In the 2-cluster scenario, Cluster 1 contained 536 records with centroid (3.64, 12.48, 2.41) and Cluster 2 contained 314 records with centroid (4.18, 16.25, 2.09). In the 3-cluster scenario, Cluster 1 contained 498 records with centroid (3.45, 11.62, 2.31), Cluster 2 contained 312 records with centroid (4.02, 16.47, 2.05), and Cluster 3 contained 276 records with centroid (2.91, 8.35, 2.76), all dominated by diarrhea in medium-to-high slum-level areas. Internal validity indices (Silhouette, Davies-Bouldin, Calinski-Harabasz), computed on a reference sample, support retaining the 3-cluster scheme for its finer, more actionable risk stratification. The results show that K-Means clustering groups affected areas by disease and slum characteristics, and a centroid-derived priority ranking of the clusters is proposed to support health priority setting, medical resource distribution, and data-driven post-flood disease mitigation.
Comparative Evaluation of Naïve Bayes and Support Vector Machine for Human Development Index Classification Dwi Asa Verano; Eka Putri Apriliani; Zahratul Aliah; Sarah Nur Arna Cholifah; Amanda Dwi Tio Agustin; Thomas; Shinta Puspasari
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9162

Abstract

The Human Development Index (HDI) is one of the most widely used indicators for evaluating the quality of human development through health, education, and living standards. Accurate HDI classification is essential for supporting evidence-based public policy and regional development planning. Recent advances in machine learning provide opportunities to improve classification performance compared with conventional statistical approaches. This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms for classifying the Human Development Index across Indonesian provinces and to identify the more effective classification model. The study employed a quantitative experimental approach using the 2024 Human Development Index dataset published by Statistics Indonesia (BPS), covering all 38 provinces. Four predictor variables were used: life expectancy at birth, expected years of schooling, mean years of schooling, and adjusted expenditure per capita. The dataset was preprocessed before being divided into training and testing sets using an 80:20 ratio. Both algorithms were implemented using Python's Scikit-learn library and evaluated through confusion matrix analysis, accuracy, precision, recall, and F1-score. Experimental results indicate that both algorithms successfully classified provincial HDI categories. However, SVM consistently outperformed Naïve Bayes, achieving an accuracy of 100% on the held-out test set (5-fold cross-validated mean accuracy of 86.4%), compared with 87.5% (cross-validated mean of 59.6%) obtained by Naïve Bayes. SVM also produced higher precision, recall, and F1-score, indicating stronger classification capability for multidimensional socioeconomic data. The findings demonstrate that Support Vector Machine provides superior performance for HDI classification compared with Naïve Bayes. This study contributes empirical evidence regarding the application of machine learning techniques in socioeconomic indicator classification and offers practical insights for decision support in regional development policy.
Metric Space Based Product Portfolio Optimization for MSMEs Using Genetic Algorithm Desi Vinsensia; Yulia Utami; Andika Pandu Ramadhan; Nabila Shayka
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9259

Abstract

Micro, Small, and Medium Enterprises (MSMEs) face capital allocation challenges across multi-category product portfolios in the digital era, while the classical Mean-Variance Optimization (MVO) model exhibits structural limitations due to normality assumptions, estimation instability on small samples, and its inability to capture more than two risk dimensions. This study developed and validated an MSME product portfolio optimization model based on metric space theory and genetic algorithm, capable of handling multidimensional operational risk and cardinality constraints. A weighted distance function was constructed on a three-dimensional feature space comprising contribution margin (?), sales coefficient of variation (?), and Margin of Safety (MOS). Metric space validity was proved deductively through verification of non-negativity, symmetry, and triangle inequality axioms. The genetic algorithm was designed with a three-step repair operator to satisfy allocation and cardinality constraints. The model was validated using secondary data from 10 product categories over a six-month period (April–September 2025) and benchmarked against Equal Weighting (EW) and MVO strategies. Formal proof and empirical verification on 1,000 data triplets yielded zero axiom violations. The genetic algorithm achieved the highest fitness value (0.71798), outperforming EW by 10.3% and MVO by 18.6%. The optimal portfolio selected four products: paper (28.2%), books (21.8%), measuring tools (29.0%), and electronics (20.9%), geometrically explained by inter-product distances in the distance matrix. The proposed model is mathematically rigorous and computationally superior in the validation case. Although constituting a proof-of-concept on a single secondary dataset, this framework offers potential for development into an MSME capital allocation decision support system
An Attribute-Based Explainable Recommendation System for Culinary Tourism Ni Wayan Priscila Yuni Praditya; Indah Pratiwi Putri; Hendra Di Kesuma
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9456

Abstract

The rapid growth of digital tourism platforms has increased the availability of culinary destination information, making it challenging for users to identify destinations that best match their preferences. Recommendation systems have become an effective solution for providing personalized recommendations; however, most conventional approaches only present recommendation results without explaining the underlying reasons, thereby reducing user trust and transparency. This study proposes an Explainable Content-Based Recommendation System for culinary tourism by integrating TF-IDF feature representation with the Cosine Similarity algorithm. Culinary destination data were collected and preprocessed through text normalization and TF-IDF vectorization to represent the characteristics of each culinary destination. Cosine Similarity was then applied to measure the similarity between user preferences and culinary destinations, while an explanation module was developed to provide understandable reasons for each recommendation based on shared culinary attributes, including category, main ingredients, flavor characteristics, and price range. The proposed system was implemented as a web-based application using Python, Flask, and MySQL. Experimental results show that the system achieved a Precision@5 of 0.92, a Recall@5 of 0.88, and an average response time of 0.73 seconds, indicating that the proposed approach is capable of generating relevant recommendations with efficient computational performance. Furthermore, the explanation module enhances recommendation transparency by enabling users to understand the factors contributing to each recommendation. These findings demonstrate that the proposed system provides a practical, lightweight, and explainable solution for culinary tourism recommendation and has the potential to improve user experience in selecting culinary destinations.
Analysis Of The Determinants Of Financial Resilience Among Retiree Customers Using An Explainable Ai (Xai) Approach Victor Saputra Ginting; Rahmatika Hizria; Said Hambali Takhir
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.9459

Abstract

Financial resilience among high-risk retiree customer segments is a crucial issue in credit risk management, particularly because traditional scoring models are often black-box in nature and fail to provide transparent insight into the economic and behavioral factors that influence a borrower's repayment capacity. This research pursues a dual objective: first, to develop a high-performing predictive model for financial resilience classification using ensemble Machine Learning methods, and second, to apply SHAP-based Explainable AI (XAI) techniques to identify and quantify the key determinants of resilience in an accountable manner. The research methodology involved processing data from the Kaggle platform, including stratified sampling and SMOTE oversampling to address class imbalance, along with a performance comparison among Logistic Regression, Random Forest, and XGBoost. The results show that Logistic Regression proved to be the best-performing model on the held-out test set, achieving an AUC of 0.9479 and an F1-Score of 0.7313 for the resilient class. Furthermore, SHAP analysis revealed that the strongest determinants driving financial resilience were the number of prior delinquencies, loan purpose (particularly business-purpose loans), and a low debt-to-income ratio, with credit score also contributing. In practical terms, these findings provide a transparent and humane credit assessment framework, enabling financial institutions to formulate more inclusive yet prudent lending policies by prioritizing customers' actual repayment capacity over age alone.

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