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Bakhtiyar Hadi Prakoso
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Perum. Griya Mangli Indah Blok AF-18 RT. 02 RW. 04, Kel. Mangli, Kec. Kaliwates, Kab. Jember, Jawa Timur, 68136
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INDONESIA
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer
Published by Sinergis Asa Professional
ISSN : -     EISSN : 27220850     DOI : https://doi.org/10.37148/bios
Core Subject : Science,
Computer Science & IT
BIOS: Jurnal Teknologi Informasi dan Rekayasa Komputer (e-ISSN. 2722-0850) is a scientific journal in the field of information technology and computer engineering managed by the Asa Professional Research & Development Center (PUSLITBANG), Jember, East Java, Indonesia. This journal is managed by lecturers and practitioners who come from various university backgrounds in Indonesia, especially Jember, East Java.The BIOS journal is published 2 (two) times a year, namely every March and September. The BIOS journal published in each edition consists of 5-10 articles per volume. The focus and scope of this journal are in the field of Information Technology and others that are still knowledge related, including: Databases System Data Mining / Web Mining Data Warehouse Artificial Intelligence Business Intelligence Cloud & Grid Computing Decision Support System Human-Computer Interaction Mobile Computing & Application E-System Machine Learning Deep Learning Information Retrieval (IR) Computer Network Multimedia System Information System Geographic Information System (GIS) Accounting information system Database Security System & Network Security Cryptography Fuzzy Logic Expert System Image Processing Computer Graphic Computer Vision Semantic Web e-Health and others related to Information Technology and Computer Engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 94 Documents
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Model HOT-FIT dalam Menilai Keberhasilan Implementasi Aplikasi Gemini serta Dampaknya pada Kepuasan Pengguna Dewi Lusiana; Aji Brahma Nugroho
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.218

Abstract

The rapid development of Artificial Intelligence (AI) has increased the use of the Gemini application to support information retrieval, task completion, and work productivity. This study aims to analyze the factors influencing the successful use of the Gemini application using the Human-Organization-Technology Fit (HOT-FIT) model. The variables examined include Service Quality, User Satisfaction, Organizational Structure, and Net Benefit. A quantitative approach with purposive sampling was employed. Data were collected from 100 respondents, consisting of students, lecturers, and employees, and analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results indicate that all constructs meet the validity and reliability requirements. The R-Square values of 0,588 for User Satisfaction and 0,676 for Net Benefit demonstrate satisfactory explanatory power. Hypothesis testing shows that Service Quality has a positive and significant effect on User Satisfaction (β=0.767; p<0.001), User Satisfaction has a positive and significant effect on Net Benefit (β=0.274; p=0.023), and Organizational Structure has a positive and significant effect on Net Benefit (β=0.599; p<0.001). These findings indicate that the HOT-FIT model effectively explains the success of the Gemini application, highlighting the importance of service quality and organizational support in maximizing its benefits.
Analisis Klasifikasi Stadium Kanker Payudara Menggunakan Algoritma Naïve Bayes Berdasarkan Data Rekam Medis Mudafiq Riyan Pratama; Sefia Ayu Maharani; Mochammad Choirur Roziqin; Dony Setiawan Hendyca Putra
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.243

Abstract

Breast cancer is one of the leading causes of cancer-related mortality among women, highlighting the need for faster and more accurate approaches to support disease staging. The utilisation of electronic medical records through data mining techniques provides an alternative approach for breast cancer stage classification. This study aimed to analyse breast cancer stage classification using the Naïve Bayes algorithm based on electronic medical record data from patients at Baladhika Husada Level III Hospital, Jember. A quantitative approach was employed using secondary data consisting of 476 breast cancer medical records selected from a total of 1,082 records. The research stages included data selection, data cleaning, categorical encoding, model development using the Naïve Bayes algorithm, and model evaluation using a Confusion Matrix based on accuracy, precision, and recall. Model performance was evaluated using nine training-testing split scenarios ranging from 10:90 to 90:10. The experimental results showed that the 90:10 split scenario achieved the best performance, with an accuracy of 87.50%, precision of 86.36%, and recall of 86.36%. These findings indicate that the Naïve Bayes algorithm is capable of classifying breast cancer stages effectively based on patients' clinical characteristics recorded in electronic medical records. The proposed approach demonstrates the potential of integrating electronic medical records and the Naïve Bayes algorithm to support the development of clinical decision support systems for breast cancer stage classification.
Klasifikasi Perilaku Keuangan UMKM Medan dengan Machine Learning dan SHAP Saliman; Rivaldi Lubis; Sunaryo Winardi; Mustika Ulina
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.224

Abstract

Good financial behavior is an important factor in sustaining micro, small, and medium enterprises (MSMEs), particularly in financial recording, debt management, and investment planning. Previous research using Structural Equation Modeling Partial Least Squares (SEM-PLS) identified financial attitude as the dominant factor influencing MSMEs’ financial behavior in Medan City. Based on these findings, this study develops a complementary machine learning-based approach to classify MSMEs’ financial behavior at the individual level and evaluate its consistency with SEM results through SHAP-based Explainable Artificial Intelligence (XAI). The dataset consists of 100 MSME respondents with seven main features, including three financial constructs and four demographic variables. Three ensemble algorithms, namely CatBoost, XGBoost, and Random Forest, were evaluated using hold-out and Stratified 5-Fold Cross-Validation. The results show that CatBoost achieved the best performance with 80.00% accuracy and 82.46% F1-score. SHAP analysis confirmed the dominance of attitude score and revealed the significant predictive contribution of demographic variables. Integrating machine learning and SHAP is an effective complementary approach to extend the understanding of MSMEs’ financial behavior comprehensively.
The Hybrid Cloud API Gateway Architecture with Zero Trust for Digital Banking B. Junedi Hutagaol; Riama Santy Sitorus; Nadya Allia Putri
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.226

Abstract

The rapid adoption of digital banking services has increased the reliance on Application Programming Interfaces (APIs) to support integration, service delivery, and collaboration with external partners. However, the growing exposure of APIs has also introduced significant security risks, while financial institutions must simultaneously address scalability demands and regulatory compliance requirements. This study aims to design a Hybrid Cloud API Gateway Architecture integrated with Zero Trust Security principles for digital banking systems. The research employs a qualitative approach using the Design Science Research (DSR) methodology, consisting of literature review, requirements analysis, architecture design, expert validation, and architecture evaluation. The proposed architecture integrates API Gateway capabilities, Hybrid Cloud Architecture, and Zero Trust mechanisms to provide secure, scalable, and resilient banking services. Critical workloads and sensitive data are maintained within private cloud environments, while scalable services are deployed in the public cloud to improve flexibility and resource utilization. Evaluation results indicate that the proposed architecture aligns with NIST SP 800-207 Zero Trust Architecture principles, addresses major risks identified in the OWASP API Security Top 10, and supports compliance with PCI DSS and ISO/IEC 27001 standards. The proposed framework provides a practical reference for developing secure, scalable, and compliant digital banking infrastructures.

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