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Journal Innovations Computer Science
Published by Yayasan Kawanad
ISSN : 29619718     EISSN : 2961970X     DOI : https://doi.org/10.56347/jics
Core Subject : Science,
Computer Science & IT
Journal Innovations Computer Science (JICS) is a peer-reviewed, twice-annually published international journal that focuses on innovative, original, previously unpublished, experimental or theoretical research concepts. Journal Innovations Computer Science (JICS) covers all areas of computer & information science, applications & systems engineering in computer & information science. JICS core vision is to be an innovation platform in information technology and computer science. Articles of interdisciplinary nature are particularly welcome. All published article URLs will have a digital object identifier (DOI).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 137 Documents
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Analysis of Tokopedia Digital Security Strategy Against Cyber Threats Using the Risk Assessment Framework Approach Mirza Raziq Akbar, Muhammad; Yudhistiro, Kukuh; Rofiqul Muslikh, Ahmad
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.301

Abstract

The rapid digitalization of commerce in Indonesia has positioned Tokopedia as a central marketplace that facilitates large-scale transactions while managing vast amounts of sensitive user data. This reliance on digital infrastructures, however, exposes the platform to escalating cyber threats that jeopardize both operational continuity and consumer trust. This study evaluates Tokopedia’s cybersecurity strategies by applying the Risk Assessment Framework derived from ISO 27001 and ISO 31000. Using a qualitative descriptive design, the research draws exclusively on secondary sources such as peer-reviewed journals, industry reports, and case studies published between 2015 and 2025. The analysis identifies five dominant risks: large-scale data breaches, phishing and identity theft, ransomware attacks, insider threats, and system misconfigurations. Risk assessment results indicate that data breaches pose the most critical threat, with phishing and ransomware classified as medium but persistent risks. Tokopedia has implemented several protective measures, including encryption, multi-factor authentication, e-KYC verification, and privacy policies. Nevertheless, gaps remain in governance, routine audits, and employee awareness, leaving the platform vulnerable to recurring incidents. A comparative analysis with global platforms highlights the importance of proactive governance, systematic risk documentation, and continuous training, areas where Tokopedia is still underdeveloped. The findings underscore that cybersecurity should be recognized not merely as a technical safeguard or financial burden but as a strategic investment essential for resilience, consumer confidence, and sustainable growth in Indonesia’s competitive digital economy.
Optimization of SMOTE Application for Classification Accuracy of Heart Disease Risk Using Artificial Neural Network Ibnu Sarky, Fauzan; Poerwandono, Edhy
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.302

Abstract

Heart disease remains a leading cause of mortality worldwide, including in Indonesia, and is often difficult to detect at an early stage. One of the main challenges in the Indonesian healthcare system is the lack of fully digitalized data management and the issue of imbalanced patient datasets, which reduce classification accuracy. This study developed a web-based information system designed to manage patient records and automatically classify heart disease risk. The system was implemented using the CodeIgniter framework with a MySQL database, and applied an Artificial Neural Network (ANN) in combination with the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. A total of 60 secondary patient records were processed through preprocessing, data balancing, model training, and cross-validation. Experimental results demonstrated that the application of SMOTE improved model sensitivity, with performance metrics of 87.4% accuracy, 85.2% precision, 88.6% recall, and an AUC-ROC of 0.94. These findings confirm that integrating ANN and SMOTE into a web-based system enhances classification reliability and supports faster medical decision-making. However, the study also acknowledges certain limitations, including the restricted dataset size and the absence of validation in real clinical environments. Future work should expand the dataset, test the system in healthcare facilities, and compare performance with other algorithms such as Random Forest or SVM to identify the most optimal predictive model.
Clustering and Classification of Retail Sales Data: A Big Data and Data Mining Analysis Almagribi, Ahmad Bilal; Redjeki, Sri
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.303

Abstract

In the evolving retail landscape, data-driven decision-making has become essential for understanding customer behavior and predicting sales trends. This study integrates clustering and classification techniques to analyze retail sales data comprising 1,000 transactions obtained from Kaggle. Using the K-Means algorithm, three optimal customer clusters were identified through the Elbow Method, achieving an average within-centroid distance of 25,272.635 and a Davies–Bouldin Index of 0.443, indicating clear cluster separation. The subsequent classification phase compared the predictive performance of three algorithms—Naïve Bayes, Decision Tree, and Random Forest—on 70:30 training-to-testing data partitions. The Naïve Bayes algorithm attained 94.67% accuracy, while both Decision Tree and Random Forest achieved perfect classification accuracy of 100%. These findings highlight the robustness and adaptability of tree-based models for complex retail datasets, outperforming probabilistic methods in terms of accuracy and generalization. The results suggest that the integration of clustering and classification provides retailers with a powerful analytical framework for identifying high-value customer segments, optimizing marketing strategies, and enhancing inventory management. Despite achieving strong outcomes, the study acknowledges dataset limitations and recommends future research involving larger and more diverse datasets, as well as additional features, to expand model scalability and predictive precision.
Web-Based Thrifting Platform for Selling Second-Hand Goods (Your Favorite Shirt) Setya Putri, Heravi Atha; Ayu Rosanda, Pramaysella Ayu Rosanda; Tri Anggira, Tisa; Zakialdy, Vicky Vidiana
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.305

Abstract

This study examines the development and implementation of Your Favorite Shirt, a web-based platform designed to modernize thrift shop operations through digital transformation. Many thrift businesses in Indonesia still rely on manual processes or social media, leading to inefficiencies in inventory management, transaction tracking, and reporting. To address these challenges, a prototype-based development method was applied, involving requirement analysis, initial design, prototype construction, user testing, and iterative refinement. The system integrates key features such as multi-role login, product catalogues, transaction management, payment options, and order tracking. Evaluation through functional testing and the PIECES framework demonstrated positive results across performance, efficiency, control, and service dimensions, with an average system response time of under one second. User Acceptance Testing (UAT) with a Likert-scale questionnaire produced a mean score of 1.85, indicating strong user satisfaction. Beyond improving operational efficiency, the platform also supports sustainable fashion practices by curating quality secondhand products and reducing textile waste. However, risk assessment identified several areas requiring improvement, including the absence of a Business Continuity Plan, limited access control mechanisms, and lack of systematic documentation. Future development should focus on integrating social media APIs, implementing product recommendation systems, enhancing layered security, and deploying analytical dashboards to support data-driven decision-making.
AcaraKita: Integrated Digital Platform for Event Organizer Services in Indonesia Tegar Pangestu, Bukhori Debrillianda; Nurfattah, Fu’ad Na’im; Rahman, Haidar; Wido Prasojo, Nanda
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.306

Abstract

This study examines the development and implementation of AcaraKita, a web- and mobile-based digital platform designed to modernize event organizer (EO) services in Indonesia. The system integrates three primary actors—Admin, Admin EO, and Customer—each with distinct yet complementary roles involving vendor management, booking, verification, status tracking, and service reviews. The development process applied the Waterfall model, consisting of requirement analysis, design, implementation, testing, deployment, and maintenance, combined with IT governance evaluation using the COBIT 5 framework to ensure alignment with business objectives. Testing results indicated that all core features operated effectively, with an average response time of less than one second and a user satisfaction score of 4.139 on the Likert scale. The IT governance risk analysis highlighted the need for improvements in documentation, security, and business continuity planning. While the system demonstrates a solid foundation, further enhancements are necessary, including social media API integration, vendor recommendation systems, and analytics dashboards to support decision-making. Overall, AcaraKita strengthens EO digitalization, improves operational efficiency, and fosters service transparency in a sustainable manner.
Security Analysis of Midtrans Payment Gateway API against DDoS Attack and Rate Limiting Technique Using Node.js Widianto Putro, Faris; Matheos Sarimole, Frencis
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.308

Abstract

The development of digital transaction services has led to the widespread use of APIs in payment systems, including payment gateway services such as Midtrans. However, the open access to APIs also increases the risk of cyber attacks, one of which is Distributed Denial of Service (DDoS) which can destabilize the system and reduce user confidence. This research aims to analyze the potential DDoS threats to the Midtrans API and explore the application of rate limiting techniques using Node.js as one of the mitigation measures. The methodology used is a waterfall approach, which includes requirements analysis, system design, implementation, testing, and evaluation. The test design is done through simulating DDoS attacks on API endpoints, both before and after the application of rate limiting, by measuring parameters such as the number of requests, response time, and request success rate. It is hoped that this research can provide a clear picture of the importance of API protection in digital payment systems, and produce a technical approach that can be used as a reference in developing a secure and reliable system. This research is also expected to make practical and theoretical contributions in the field of API security and digital service traffic management.
Decision Tree-Based Predictive Model Development for RumahNet Customer Satisfaction Analysis in West Jakarta Yuliantoro, Dita Tri; Sarimole, Frencis Matheos
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.310

Abstract

The rapid growth of information technology has amplified the demand for fast and reliable internet services, particularly in urban centers such as West Jakarta. This study aims to design a predictive model of customer satisfaction for RumahNet’s Fiber to the Home (FTTH) services by applying the Decision Tree (C4.5) algorithm. A survey of 250 active subscribers was conducted using a Likert-scale questionnaire distributed through Google Forms, capturing perceptions of internet speed, connection stability, pricing, and technical support. The dataset was processed and analyzed using RapidMiner Studio within the Knowledge Discovery in Databases (KDD) framework. Results show that the model achieved an accuracy of 85.33%, precision of 91.93%, recall of 90.47%, and an F1-score of 91.18%. The decision tree revealed that internet speed and connection stability were the most critical determinants of satisfaction, followed by pricing and responsiveness of customer service. These findings suggest that prioritizing technical reliability while maintaining affordability and responsive support is essential for strengthening loyalty and reducing churn. The research demonstrates that Decision Tree modeling not only provides high predictive accuracy but also offers clear interpretability, making it a valuable tool for data-driven decision-making in the ISP sector.
Analysis of the JAKLITERA Website Information System Using the PIECES Method to Enhance Library Services at the Library and Archives Office of DKI Jakarta Province Anandita Putri, Syaharani; Tanti Rahayu, Agus; Aditiya, Arya
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.312

Abstract

The advancement of information technology has shifted libraries from traditional physical services to digital platforms. One example is JAKLITERA, a web-based information system developed by the Provincial Library and Archives Office of DKI Jakarta. This study aims to evaluate the performance and service quality of JAKLITERA using the PIECES framework (Performance, Information, Economics, Control, Efficiency, Service). A mixed approach was applied, combining quantitative data from 100 user questionnaires with qualitative insights from observation and administrator interviews. Validity and reliability testing confirmed that all research instruments were appropriate for analysis. Results showed that the Economics dimension achieved the highest mean score (3.95), while Control received the lowest (3.39). Overall, the average score of 3.62 categorized JAKLITERA as “satisfactory,” yet highlighted the need for improvements in security mechanisms and clarity of information. These findings suggest that JAKLITERA functions effectively as a digital literacy platform but requires continuous evaluation, technical optimization, and user-centered enhancements to ensure sustainable service delivery.
Implementation Analysis of Network Core Redundancy Using HSRP and VRRP Protocols for Enterprise Infrastructure Reliability Assurance Firdaus Syah, Tengku; Adriyanto, Sopan
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.314

Abstract

This study examines the role of redundancy protocols in enhancing the reliability of core networks within enterprise infrastructures. The research is motivated by the growing reliance of organizations on network-based services and the increasing risks of financial losses caused by downtime, which are projected to escalate alongside global traffic growth. A simulation-based approach was conducted using Cisco Packet Tracer to compare baseline networks without redundancy against implementations of Hot Standby Router Protocol (HSRP) and Virtual Router Redundancy Protocol (VRRP). The evaluation focused on key performance indicators, including failover time, uptime rate, packet loss, latency, and jitter. The results demonstrate that both protocols successfully provided automatic recovery mechanisms, achieving an average failover time of less than five seconds while maintaining stable connectivity. Further analysis revealed that HSRP excels in failover speed, whereas VRRP offers cross-vendor flexibility with consistent stability. These findings confirm that redundancy protocol selection should align with specific network requirements, yet both HSRP and VRRP have proven effective in minimizing downtime risks and ensuring service continuity in enterprise-scale environments.
Support Vector Machine and Histogram of Oriented Gradients-Based Classification System for Waste Type Identification Notonegoro, Danendra Satriyohadi; Mulyana, Dadang Iskandar
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.315

Abstract

This study examines the effectiveness of classical computer vision methods for modern waste classification by combining Histogram of Oriented Gradients (HOG) for feature extraction with Support Vector Machine (SVM) for classification. The TrashNet dataset, consisting of five categories—cardboard, glass, metal, paper, and plastic—was used as the primary benchmark. To address data limitations and improve generalization, augmentation techniques such as random rotations, horizontal flipping, and brightness adjustments were applied. Hyperparameter optimization was further conducted using GridSearchCV with the RBF kernel to determine the most effective configuration. The optimized model achieved an accuracy of 84.36%, representing a substantial improvement from the 60% baseline. These findings confirm that non-deep learning approaches remain relevant and can serve as computationally efficient alternatives to CNNs, which typically require GPUs and extensive training time. Challenges persist in classifying reflective materials such as glass and metal, where HOG descriptors are less effective. Future work should integrate complementary descriptors, including color and texture-based features, to enhance robustness and scalability. Overall, the study demonstrates that an optimized HOG-SVM pipeline offers a practical, resource-efficient solution for automated waste classification, with strong potential to support sustainable waste management in real-world applications.

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