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All Journal G-Tech : Jurnal Teknologi Terapan
Rachmat Rachmat
Universitas Pejuang Republik Indonesia, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Silk Fabric Quality Prediction Using Random Forest and Simulated Physical Material Feature Data Moh. Zainuddin; Rachmat Rachmat; Muh. Rafli R.
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.9752

Abstract

This study develops an interpretable Random Forest workflow to predict silk fabric quality from simulated physical material feature data. The model uses 150 controlled observations with eight predictors: fabric thickness, fabric weight, thread density, tensile strength, elasticity, luster, softness, and surface defects. The simulated values were generated with class-specific truncated normal distributions based on measurable textile properties, standard testing variables, and domain assumptions. Data were evaluated using a stratified 80:20 holdout, stratified 5-fold cross-validation, weighted accuracy-related metrics, and confusion matrix analysis. Random Forest achieved 0.87 holdout accuracy, 0.88 precision, 0.87 recall, and 0.87 F1-score. It showed competitive performance against KNN, Decision Tree, SVM, ANN, and XGBoost while providing direct feature-importance interpretation. Surface defects, tensile strength, and thread density were the most influential predictors. The main contribution is a reproducible simulation and interpretation framework for early-stage silk fabric quality prediction. Because the dataset is simulated, the results demonstrate method feasibility and require validation with real textile production or laboratory data.
Comparing FAQ-Based and Retrieval-Augmented Generation Chatbots for Academic Service Question Answering Suherwin Suherwin; Moh. Zainuddin; Rachmat Rachmat
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10019

Abstract

Academic service units need question-answering systems that respond quickly while remaining aligned with institutional regulations. This study provides a controlled, deployment-oriented comparison of a lightweight FAQ chatbot and a Retrieval-Augmented Generation (RAG) chatbot for academic-service question answering. The novelty lies in evaluating both prototypes against the same version-controlled official-document scope, the same 60-question dataset, category-level service cases, paired statistical tests, inter-rater reliability, and RAGAS retrieval-quality metrics. The questions covered registration, KRS/KHS, schedules, tuition payment, thesis or final project, and administrative letters. Three validators assessed accuracy, relevance, response effectiveness, and response time using official documents as the reference standard. RAG achieved 88.33% accuracy, a mean relevance score of 4.48/5, and 86.67% response effectiveness; FAQ achieved 68.33%, 3.46/5, and 65.00%, respectively. Paired tests confirmed significant advantages for RAG in accuracy, relevance, and effectiveness, while FAQ was significantly faster (0.62 s vs 2.41 s). RAGAS evaluation showed context precision of 0.86, context recall of 0.82, faithfulness of 0.89, and answer relevancy of 0.91. The findings support a cautious hybrid strategy: FAQ for stable repetitive requests and RAG for contextual, document-dependent questions, subject to broader testing with live queries and stronger retrieval baselines.
Co-Authors Moh. Zainuddin Moh. Zainuddin Muh. Rafli R. Suherwin Suherwin