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Erinda Anasia Agustin
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INDONESIA
Jurnal Riset Sistem Informasi
Published by CV. Denasya Smart Publisher
ISSN : 30479029     EISSN : 30479010     DOI : 10.69714
Core Subject : Science, Education,
Computer Science & IT Education Other
Jurnal Riset Sistem Informasi (JISSI) dengan 3047-9010, p-ISSN : 3047-9029 diterbitkan oleh Denasya Smart Publisher. Jurnal Riset Sistem Informasi(JISSI) memuat naskah hasil-hasil penelitian di bidang Sistem Informasi. Jurnal Riset Sistem Informasi (JISSI) berkomitmen untuk memuat artikel berbahasa Indonesia yang berkualitas dan dapat menjadi rujukan utama para peneliti dalam bidang ilmu Rekayasa Sistem, Teknologi Informasi, Sistem Informasi, Ilmu Komputer, Manajemen,Manajemen Informatika dan Bisnis. Jurnal ini terbit 1 tahun 4 kali (Januari, April, juli dan oktober).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 133 Documents
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RANCANG BANGUN SISTEM INFORMASI ABSENSI SISWA BERBASIS WEB MENGGUNAKAN QR CODE DENGAN FITUR LAPORAN OTOMATIS PADA SDN CIPAYUNG 01 Jaswa Ariansya; Titania Aulia Azahra
Jurnal Riset Sistem Informasi Vol. 3 No. 4 (2026): Oktober: Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/g30h7v28

Abstract

The development of information technology has significantly influenced various fields, including education. SDN Cipayung 01 still uses a manual student attendance system that relies on attendance books, requiring considerable time and risking recording errors. Based on these problems, a web-based student attendance information system using QR Code with automatic report features was developed. The system was built using the PHP programming language and MySQL database, accessible through a web browser. Students simply scan their QR Code to record attendance, which is then automatically saved to the database. Testing results show that the QR Code-based attendance system reduces attendance time from 10-15 minutes to 2-5 minutes per class, achieving approximately 66% time efficiency. All system features performed successfully in functional testing, including login, QR scanning, student data management, daily recaps, monthly reports, and CSV export.
ANALISIS KOMPARATIF MODEL KLASIFIKASI KEBUGARAN DAN EVALUASI REGRESI PREDIKSI KALORI DAN CHATBOT PADA EKOSISTEM FITTRACK AI Putra Hikmah Febryan; Eka Asa Setyaning Pratiwi; Asif Faroqi; Dhian Satria Yudha Kartika
Jurnal Riset Sistem Informasi Vol. 3 No. 3 (2026): Juli : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/w3de9498

Abstract

Interpreting physiological health data and self-reported nutrition records often poses a computational challenge for non-expert users. Therefore, this study aims to conduct a comparative analysis of fitness level classification modeling and predictive calorie regression evaluation. Both are integrated into a unified health tracking ecosystem called FitTrack AI. The performance of the Random Forest, XGBoost, and Support Vector Machine (SVM) algorithms was comprehensively compared for multi-class classification. Meanwhile, calorie burn estimates were evaluated using the Random Forest Regressor. As a holistic system, this ecosystem is also supported by body weight projection analysis (Linear Regression), dietary pattern mining (Apriori), and an automated logging interface based on a Large Language Model (Groq API). Test results show that XGBoost is the best classification model, with an accuracy rate of 76.37%, outperforming other algorithms. In the calorie prediction regression test, the model achieved highly accurate performance with a coefficient of determination (R²) of 0.996. In terms of ecosystem functionality, the interactive virtual assistant (FitBot) recorded a 90.0% success rate in executing tool calls for data entry and achieved a System Usability Scale (SUS) score of 90.1 (Very Good category). Overall, this multi-model analytical approach has proven to be robust and effective in translating the complexity of biological data into comprehensive and personalized digital health insights.
ANALISIS PERKEMBANGAN ARTIFICIAL INTELLIGENCE DALAM SISTEM KESELAMATAN AKTIF KENDARAAN OTOMOTIF DI ERA MODERN Nicholas Nicholas; Nico Saputra; Dorie P. Kesuma
Jurnal Riset Sistem Informasi Vol. 3 No. 3 (2026): Juli : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/0xhvtr51

Abstract

The rapid development of Artificial Intelligence (AI) has significantly transformed the automotive industry, particularly in active vehicle safety systems. AI enables vehicles to perceive their environment, make decisions in real time, predict risks, and adapt safety features according to driver characteristics. This study aims to analyze the development of AI in active automotive safety systems, identify its primary roles, evaluate its impact on accident prevention, and examine the implementation challenges from technical, regulatory, and ethical perspectives. This research employs a qualitative descriptive approach using a literature review method. Data were collected from scientific journals, conference proceedings, international standards, dissertations, and preprint repositories related to AI-based automotive safety technologies. The findings indicate that AI has evolved from simple object detection systems into advanced technologies capable of driver cognitive state inference, predictive safety analysis, and autonomous decision-making. AI-based systems such as Automatic Emergency Braking (AEB), Adaptive Cruise Control (ACC), and Driver Monitoring Systems (DMS) have demonstrated significant contributions to reducing accident risks and injury severity. However, challenges remain regarding adverse weather conditions, mixed traffic environments, model uncertainty, certification frameworks, and ethical accountability. The study concludes that AI has substantial potential to improve road safety, but successful implementation requires robust technical development, adaptive regulations, and comprehensive safety assurance mechanisms.

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