Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JOIN (Jurnal Online Informatika) Industrial Research Workshop and National Seminar
Jonner Hutahaean
Unknown Affiliation
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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

Found 2 Documents
Search

 1 
Model Konseptual Sistem Pendukung Evaluasi Pembelajaran Berbasis Proyek dengan Integrasi Educational Data Mining dan Learning Analytics Derivansyah, Delvito Rahim; Mentari Ayu Alysia Sudrajat; Joe Lian Min; Jonner Hutahaean
Prosiding Industrial Research Workshop and National Seminar Vol. 16 No. 1 (2025): Vol. 16 No. 1 (2025): Prosiding 16th Industrial Research Workshop and National
Publisher : Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/irwns.v16i1.6642

Abstract

Evaluasi pembelajaran berbasis proyek (PjBL) pada pendidikan tinggi menghadapi tantangan objektivitas dan transparansi, terutama akibat kurangnya pemanfaatan data terstruktur untuk memantau progres individu mahasiswa. Penelitian ini merancang model konseptual sistem pendukung evaluasi pembelajaran yang mengintegrasikan Educational Data Mining (EDM) dan Learning Analytics (LA). Model konseptual ini difokuskan untuk memfasilitasi pemantauan komprehensif, deteksi dini mahasiswa berisiko, serta evaluasi objektif dan transparan. Penelitian ini menggunakan Design Science Research (DSR) dan menghasilkan dua artefak: (1) model konseptual sistem evaluasi yang mengintegrasikan setiap fase PjBL; dan (2) aplikasi Proof of Concept (PoC) berbasis analisis data logbook mahasiswa sebagai demonstrasi dan validasi fungsional terbatas dari model konseptual. Teknik analitik yang diterapkan meliputi text mining, analisis similaritas, klasifikasi, klasterisasi, analisis pola sekuensial, association rule mining, analisis sentimen, prediksi hasil proyek, feature engineering, relationship mining, analisis berbasis waktu, analisis deskriptif, dan deteksi anomali. Evaluasi artefak dilakukan secara sistematis berdasarkan tujuh pedoman DSR. Aplikasi PoC dievaluasi fungsionalitasnya menggunakan data logbook riil untuk menilai ketepatan implementasi teknik analitik. Hasil evaluasi menunjukkan model konseptual dan aplikasi PoC meningkatkan objektivitas evaluasi, memfasilitasi pemantauan dan intervensi dini, serta menyediakan dasar analitik untuk pengambilan keputusan.
Evaluating RAG Performance on Small Language Models for Low-Resource Devices through Chunking and Retrieval Methods Amelia Dewi Agustiani; Salsabila Maharani Putri; Jonner Hutahaean; Muhammad Rizqi Sholahuddin; Muhammad Riza Alifi; Ade Hodijah
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1733

Abstract

Retrieval-Augmented Generation (RAG) combines generative capabilities of language models with external document retrieval to answer questions grounded in reference texts. However, deploying RAG on low-resource devices like Android smartphones is challenging because SLMs have limited computational capacity and depend heavily on efficient chunking and retrieval. Although interest in on-device processing is growing, research on RAG configurations for SLMs under strict resource constraints especially for domain-specific tasks remains limited. This study therefore investigates which combinations of chunking technique, chunk size, overlap, and retrieval strategy best balance accuracy and speed on low-resource devices. The evaluation uses 148 Indonesian questions sourced from an official Hajj guidebook. The study consists of two phases retrieval and generation. Retrieval is evaluated using BLEU, ROUGE-L, MRR, MAP, and Hit@k, while answer quality is measured with BERTScore. The experiments compare different chunking methods (fixed-size or semantic), chunk sizes (128 or 256 tokens), overlaps (25, 50 and 100 tokens), and retrieval methods (dense, sparse, or hybrid). Results show that sparse retrieval with 256-token chunks and 100-token overlap yields the best answer quality (F1 = 0.726). However, 128-token chunks with the same overlap provide the fastest generation time (69.737 seconds). The main contribution of this study is a systematic evaluation of RAG configurations for fully on-device SLMs using a domain-specific Hajj and Umrah dataset not explored in prior research. The findings provide practical guidance for designing efficient and accurate RAG-based question-answering systems on low-resource devices.
Co-Authors Ade Hodijah Amelia Dewi Agustiani Derivansyah, Delvito Rahim Joe Lian Min Mentari Ayu Alysia Sudrajat Muhammad Riza Alifi Muhammad Rizqi Sholahuddin Salsabila Maharani Putri