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All Journal Journal of Embedded Systems, Security and Intelligent Systems Jurnal Testing dan Implementasi Sistem Informasi
I Gusti Agung Putu Mahendra
Politeknik Negeri Bengkalis
Computer Science & IT

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Improving FAQ Retrieval for Academic Regulations Using Semantic Embeddings and LLM Question Augmentation Fajri Profesio Putra; I Gusti Agung Putu Mahendra; Agus Tedyyana; Muhammad Noor
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2176

Abstract

Academic regulations in higher education are often documented in lengthy and formal handbooks, making it difficult for students to find relevant information using everyday language. This study developed a semantic FAQ retrieval system for academic regulations using IndoSBERT and question augmentation. The FAQ corpus was constructed from official academic and internship documents, resulting in 92 FAQ entries across 33 topical categories. Seed questions were generated from category–keyword pairs and expanded using simple rule-based augmentation and FLAN-T5-based paraphrasing. The dataset was evaluated using an 80:10:10 train–validation–test split. IndoSBERT was fine-tuned with Multiple Negatives Ranking Loss under three configurations: baseline, baseline with simple augmentation, and baseline with simple plus LLM-based augmentation. Retrieval performance was measured using Recall@1, Recall@3, Recall@5, and Mean Reciprocal Rank. The best result was achieved by the simple plus LLM augmentation configuration, with Recall@1 of 0.7848, Recall@5 of 0.8987, and MRR of 0.8396. These findings show that LLM-based question augmentation improves semantic retrieval robustness while keeping answers grounded in curated academic regulations.
Improving FAQ Retrieval for Academic Regulations Using Semantic Embeddings and LLM Question Augmentation Fajri Profesio Putra; I Gusti Agung Putu Mahendra; Agus Tedyyana; Muhammad Noor
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2176

Abstract

Academic regulations in higher education are often documented in lengthy and formal handbooks, making it difficult for students to find relevant information using everyday language. This study developed a semantic FAQ retrieval system for academic regulations using IndoSBERT and question augmentation. The FAQ corpus was constructed from official academic and internship documents, resulting in 92 FAQ entries across 33 topical categories. Seed questions were generated from category–keyword pairs and expanded using simple rule-based augmentation and FLAN-T5-based paraphrasing. The dataset was evaluated using an 80:10:10 train–validation–test split. IndoSBERT was fine-tuned with Multiple Negatives Ranking Loss under three configurations: baseline, baseline with simple augmentation, and baseline with simple plus LLM-based augmentation. Retrieval performance was measured using Recall@1, Recall@3, Recall@5, and Mean Reciprocal Rank. The best result was achieved by the simple plus LLM augmentation configuration, with Recall@1 of 0.7848, Recall@5 of 0.8987, and MRR of 0.8396. These findings show that LLM-based question augmentation improves semantic retrieval robustness while keeping answers grounded in curated academic regulations.
Edge-Based Early Warning for High-Speed Boat Stability Monitoring Muhammad Asep Subandri; Jamal; Fajar Ratnawati; I Gusti Agung Putu Mahendra; Agus Tedyyana; Budhi Santoso
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.11866

Abstract

Purpose - This study aims to develop and evaluate an edge-based early warning prototype for monitoring the stability of high-speed boats using real-time motion data. Design/methods/approach – The study employs an engineering prototype validation design consisting of system requirement analysis, architecture design, implementation, and validation. The system integrates an IMU (MPU-6050) for motion sensing, a Raspberry Pi-based edge computing unit for real-time processing, rule-based classification (Normal/Warning/Critical), and an MQTT-based communication framework connected to the SHISTAMO dashboard. Prototype validation includes functional testing, platform integration, and operational monitoring using controlled scenarios and expert-labeled events. Findings - The results show that the prototype successfully performs end-to-end integration from sensing to visualization. The system achieved 90.4% classification accuracy, with high recall in detecting critical conditions (96.7%), ensuring reliable identification of high-risk events. The local alarm response time was 182 ms, while the dashboard update delay averaged 1.24 s, indicating near-real-time performance. Communication reliability was also high, with 98.8% data delivery success and 97.2% offline synchronization. Research implications/limitations – The findings demonstrate prototype-level feasibility; however, validation is limited to controlled scenarios and does not yet represent diverse sea conditions. The rule-based thresholds and comfort proxy require further calibration and validation through extended sea trials and reference instrumentation. Originality/value – This study contributes an integrated edge-based maritime monitoring prototype that combines motion sensing, offline-capable alarming, real-time telemetry, and fleet-level logging in a single system, specifically tailored to the operational needs of high-speed boats.
Co-Authors Agus Tedyyana, Agus Fajar Ratnawati Fajri Profesio Putra Jamal Muhammad Asep Subandri Muhammad Noor Santoso, Budhi