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All Journal The IJICS (International Journal of Informatics and Computer Science) Journal of Embedded Systems, Security and Intelligent Systems Interaksi : Jurnal Pengabdian Kepada masyarakat
Sugi Hartono Sinambela
Universitas Budi Darma
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions

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Hybrid Fuzzy-Based Decision Modeling for Anomaly Prioritization in Smart Grid Systems Fadlina; Sugi Hartono Sinambela; Meng-Yun Chung
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/z5z1bk11

Abstract

Purpose – This study aims to develop an uncertainty-aware decision-support framework for prioritizing anomaly events in smart grid systems, addressing the limitation of existing approaches that focus primarily on binary anomaly detection rather than operational risk-based prioritization. Design/methods/approach – A hybrid framework is proposed by integrating lightweight statistical anomaly screening with a Mamdani-type fuzzy inference system (FIS) that models three risk dimensions: Impact, Likelihood, and Criticality. To enhance ranking performance while maintaining interpretability, fuzzy membership parameters and rule weights are optimized using a genetic algorithm (GA). The model is evaluated using the Open Power System Data dataset with 5-fold cross-validation. Findings - Experimental results show that the proposed model outperforms baseline methods, achieving NDCG@10 of 0.84, Recall@5 of 0.81, and F1-score of 0.86. The framework also reduces false alarm rates and maintains end-to-end latency below 300 ms on ARM-class hardware, demonstrating suitability for real-time edge deployment. Research implications/limitations – The study is limited to a single regional dataset and relies on offline optimization of fuzzy parameters. Future research may explore multi-regional validation, online adaptive learning, and integration with cyber-physical anomaly streams. Originality/value – This study introduces a novel hybrid framework that combines statistical screening, interpretable fuzzy risk modeling, and evolutionary optimization for anomaly prioritization. Unlike conventional detection-focused approaches, it emphasizes ranking quality, uncertainty handling, and real-time embedded feasibility, offering a practical and explainable solution for smart grid operations.
Implementation of the MARCOS Method in a Decision Support System for Foundation Scholarship Determination Sugi Hartono Sinambela; Denni M Rajagukguk; Pristiwanto; Muhammad Iqbal Batubara; R.L harmady Tamba
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9574

Abstract

The scholarship selection process often involves multiple criteria and is prone to subjectivity when conducted manually. This study aims to implement the Multi-Attributive Ideal-Real Comparative Analysis (MARCOS) method in a Decision Support System (DSS) to determine foundation scholarship recipients objectively and systematically. The research applies a quantitative approach by evaluating several student alternatives based on academic and non-academic criteria, including academic achievement, parents’ income, number of dependents, organizational activity, and social status. The MARCOS method is employed through decision matrix construction, normalization, weighting, utility value calculation, and ranking. The results indicate that the proposed system is able to generate clear and consistent rankings of scholarship candidates. Validation results show an accuracy of 80% when compared with the foundation’s manual decision process. These findings demonstrate that the MARCOS-based Decision Support System can improve accuracy, transparency, and efficiency in scholarship determination and can be adapted to other multi-criteria decision-making problems.
Decision Support System for Early Detection of Depression Risk Based on Lifestyle Patterns Using the ROC-WASPAS Method Fadlina; Sugi Hartono Sinambela
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9799

Abstract

This study develops a decision-support model for prioritizing lifestyle-based depression risk indicators using Rank Order Centroid (ROC) weighting and Weighted Aggregated Sum Product Assessment (WASPAS). The model evaluates five respondents against five criteria: stress level, sleep duration, social interaction, dietary pattern, and physical activity. Criterion priorities were converted into ROC weights, after which the decision matrix was normalized according to cost and benefit orientation. WASPAS then combined the weighted sum and weighted product components using an equal aggregation coefficient. Stress received the largest weight (0.456), followed by sleep duration (0.256), social interaction (0.156), dietary pattern (0.090), and physical activity (0.040). The final preference scores placed A1 first (0.569), followed by A3 (0.557), A2 (0.519), A5 (0.502), and A4 (0.434). These scores represent relative lifestyle profiles within the small study sample; they do not constitute a clinical diagnosis of depression. The model demonstrates a transparent calculation workflow that may support preliminary risk prioritization when its criteria, scales, and weights have been validated by mental-health professionals. Future work should use a larger sample, a validated depression instrument, sensitivity analysis, and external validation before practical screening is considered.
LOKAKARYA PROMPTING BERETIKA BERBASIS AI UNTUK MENDUKUNG BELAJAR MANDIRI SISWA SMAS PRAYATNA Murdani; Fadlina; Maringan Sianturi; Hery Sunandar; Sugi Hartono Sinambela; Ronda Deli Sianturi
Interaksi : Jurnal Pengabdian Kepada masyarakat Vol. 3 No. 02 (2026): Juli 2026
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/ijpkm.v3i02.118

Abstract

Pemanfaatan artificial intelligence (AI) generatif dalam pembelajaran perlu disertai kemampuan menyusun prompt yang tepat, memverifikasi informasi, menjaga data pribadi, dan menerapkan etika akademik. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pengetahuan dan keterampilan siswa dalam menggunakan AI secara kritis, aman, dan bertanggung jawab untuk mendukung belajar mandiri. Kegiatan dilaksanakan pada 24 Juli 2026 di SMAS Prayatna Medan dengan melibatkan 30 siswa kelas XI. Metode pelaksanaan meliputi pre-test, penyampaian materi, demonstrasi penggunaan AI, praktik penyusunan prompt, verifikasi keluaran AI, diskusi kasus etika, post-test, dan refleksi. Evaluasi dilakukan menggunakan tes pengetahuan dan rubrik penilaian praktik. Hasil menunjukkan bahwa nilai rata-rata peserta meningkat dari 70,0% pada pre-test menjadi 89,7% pada post-test, atau meningkat sebesar 19,7 poin persentase. Hasil praktik prompting beretika memperoleh nilai rata-rata 3,55 dalam kategori sangat baik. Aspek tertinggi adalah kepatuhan terhadap etika akademik sebesar 3,75, sedangkan aspek verifikasi informasi memperoleh nilai terendah sebesar 3,15 dalam kategori baik. Kegiatan ini menunjukkan bahwa lokakarya dapat memperkuat literasi AI dan keterampilan penggunaan AI secara etis untuk mendukung pembelajaran mandiri siswa.
Co-Authors Denni M Rajagukguk Fadlina Hery Sunandar Maringan Sianturi Meng-Yun Chung Muhammad Iqbal Batubara Murdani Pristiwanto R.L harmady Tamba Ronda Deli Sianturi