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INDONESIA
Journal of Applied Computer Science and Technology (JACOST)
Published by Indonesian Society of Applied Science
ISSN : -     EISSN : 27231453     DOI : https://doi.org/10.52158/jacost
Core Subject : Science,
Computer Science & IT
Fokus dan Ruang Lingkup Journal of Applied Computer Science and Technology (JACOST) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian bidang ilmu komputer dan teknologi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Ilmu Komputer dan Teknologi. Journal of Applied Computer Science and Technology (JACOST) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 102 Documents
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Pengembangan Website Harga Bapokting Real-time dengan Extreme Programming dan Integrasi API SILINDA Setiawan, Ridwan; Parlina, Rina; Gunadhi, Erwin
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/98c7mh73

Abstract

This study aims to implement the SILINDA API from the West Java Provincial Government on a prototype website to present real-time price data for Basic Necessities and Important Goods (Bapokting) in Garut Regency. This addresses the problem of reporting delays and potential data inaccuracies that arise from the manual process of reporting via WhatsApp and weekly recapitulation using Microsoft Excel. The system was developed using the Extreme Programming (XP) methodology, which includes the stages of planning, design, coding, and testing. System design utilizes Unified Modeling Language (UML), specifically use case and class diagrams. The implementation uses JavaScript with the React.js library for the frontend and Node.js with the Express.js framework for the backend. The result of this research is a website prototype that is synchronized with the SILINDA API to perform automatic price updates. System testing included unit testing with a black-box approach and acceptance testing using the System Usability Scale (SUS) method, which yielded an average score of 83, categorized as Grade A (Excellent) with an "Acceptable" level of acceptance. This research contributes a system that replaces the manual reporting process with a website synchronized with SILINDA, providing real-time data for the Disperindag ESDM, Garut Satu Data, and the general public. It also demonstrates the effectiveness of the XP method in building an adaptive system that is relevant to user needs.
Klasifikasi Pemohon Pinjaman dengan Hyperparameter Tuning dan Teknik Penyeimbangan Data Yulvida, Donata; Quinevera, Stefanie; Mardianto, Ricky; Joses, Steven
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/krjtrh05

Abstract

Loan classification is a critical component of credit risk management, as it categorizes loans based on risk levels and supports the financial stability of banks, where loan-related income represents a substantial share of assets. Effective classification aims to ensure secure asset allocation, minimize credit risk, and prevent potential repayment issues. This study enhances loan classification performance through two strategies: hyperparameter optimization of Decision Tree and Random Forest algorithms, and data balancing techniques to address class imbalance. Experimental results show that the Decision Tree achieves 89.21% accuracy with an F1-Score of 70.17%, while the Random Forest demonstrates higher performance, reaching 94.04% accuracy and an F1-Score of 79.75%. Random Oversampling reduces bias toward majority classes by improving model sensitivity, while hyperparameter tuning with GridSearchCV identifies optimal parameter settings, thereby strengthening predictive performance. The findings highlight that combining data balancing with hyperparameter optimization effectively improves accuracy and F1-Scores. These approaches are not limited to the algorithms tested but can also be applied to other classification methods, offering broader potential for enhancing credit risk prediction in banking.

Page 11 of 11 | Total Record : 102

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