Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal on Informatics Visualization and Social Computing
Teguh Rijanandi
Telkom University

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Design and Development of An Android-Based Server Monitoring Application Using The aaPanel API with The Waterfall Method Teguh Rijanandi; Eko Risdianto; Mohammad Qais Rezvani
Journal on Informatics Visualization and Social Computing Vol. 1 No. 1 (2025): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v1i1.44

Abstract

Background of study: The evolution of cloud computing has transformed server management, yet many management interfaces remain web-based and are not optimized for mobile devices. aaPanel, a popular server control panel, also faces this challenge of providing a mobile-friendly monitoring experience. Aims and scope of paper: This research aims to design and build a mobile application named "aaPanel Mobile" to efficiently monitor vital server statistics on Android devices, addressing the gap in mobile accessibility for aaPanel users. Methods: The software development follows the Waterfall model, covering requirements, design, development, testing, deployment, and maintenance. The application was built using Expo React Native, integrates with the aaPanel API for data retrieval, and its functionality was verified through black-box testing. Result: Testing results confirmed that all primary functionalities were successfully implemented. The application correctly displayed resource usage statistics, provided real-time data updates, and listed websites as expected. Conclusion: The "aaPanel Mobile" application has met its design and functional objectives, proving to be a viable tool ready for use in a production environment to help administrators monitor servers from anywhere.
Development of an Eye Care Chatbot Based on Llama 3.2 1B Using 4-bit QLoRA Technique Teguh Rijanandi; Eko Risdianto
Journal on Informatics Visualization and Social Computing Vol. 2 No. 1 (2026): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v2i1.220

Abstract

Background: The ratio of ophthalmologists to the population in Indonesia is extremely low, leading to unequal access to eye care and making it difficult for the public to obtain early information regarding eye symptoms.Aims: This study aims to develop and validate an efficient, domain-specific eye care chatbot using the Llama 3.2 1B Instruct model, focusing on an optimal fine-tuning pipeline for limited hardware constraints.Methods: Utilizing an Experimental Research (Model Development) design, a QLoRA 4-bit quantization technique was applied on a Google Colab T4 GPU (15.64GB VRAM). The training dataset comprised 16,742 samples from Kaggle Eye Care and MedQuad Indonesian Translation, with a subset of 5,000 samples used for experiments.Result: After training for 3 epochs (750 steps), the Training Loss decreased from 1.3394 to 0.7188 (46.3% improvement), while Perplexity reached 2.3620, categorized as excellent. The model maintained stability with a final gap of 0.1407 between training and validation loss.Conclusion: The Meta-Llama 3.2 1B Instruct model, fine-tuned with 4-bit QLoRA, is highly effective for building a domain-specific healthcare chatbot, successfully operating within an 8GB VRAM limit while maintaining high generalization capabilities.
Co-Authors Eko Risdianto Mohammad Qais Rezvani