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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Panrita Abdi - Jurnal Pengabdian pada Masyarakat Sistemasi: Jurnal Sistem Informasi INOVTEK Polbeng - Seri Informatika JURNAL TEKNOLOGI DAN OPEN SOURCE Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-Dinamika: Jurnal Pengabdian Kepada Masyarakat REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Indonesian Journal of Electrical Engineering and Computer Science Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Journal of Informatics and Electronics Engineering SATIN - Sains dan Teknologi Informasi Journal of Embedded Systems, Security and Intelligent Systems Jurnal Teknik Informatika dan Teknologi Informasi
Fajar Ratnawati
Politeknik Negeri Bengkalis
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Other

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Multiclass IoT Intrusion Detection Based on Particle Swarm Optimization-Tuned Light Gradient Boosting Machine Fajar Ratnawati; Agus Tedyyana
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/jessi.v7i1.2612

Abstract

Purpose – This study aims to develop a robust multiclass intrusion detection system (IDS) for Internet of Things (IoT) environments by optimizing Light Gradient Boosting Machine (LightGBM) using Particle Swarm Optimization (PSO), with a focus on improving performance under severe class imbalance. Design/methods/approach – A PSO-based hyperparameter tuning framework is applied to LightGBM, where Macro F1-score is used as the fitness function to ensure balanced class performance. The model is evaluated on the RT-IoT2022 dataset using a leakage-safe stratified 70:15:15 split. Performance is assessed using Accuracy, Macro Precision, Macro Recall, Macro F1-score, Weighted F1-score, and Matthews Correlation Coefficient (MCC). Experiments are repeated across 10 runs, and statistical significance is validated using the Wilcoxon signed-rank test. Findings - The proposed PSO-LightGBM model significantly outperforms the baseline LightGBM. It achieves 99.75% accuracy, 97.10% macro F1-score, 99.75% weighted F1-score, and 99.37% MCC, compared to 85.36%, 25.44%, 84.23%, and 63.67%, respectively, for the baseline. The model demonstrates substantial improvement in minority-class detection, reducing misclassification and preventing class collapse observed in the baseline. Research implications/limitations – The findings highlight the effectiveness of Macro-F1-guided optimization for imbalanced multiclass IoT intrusion detection. However, the evaluation is limited to a single dataset and centralized experimental setting, which may affect generalizability. Originality/value – This study contributes a leakage-safe, Macro-F1-driven PSO-LightGBM framework with comprehensive evaluation, including class-wise analysis, repeated runs, and statistical testing, providing strong evidence for balanced multiclass IoT intrusion detection.
Development of a Certainty Factor-Based Expert System for Nutrition Consultation and Stunting Prevention in Coastal Areas: A Case Study of Bengkalis Regency Fajar Ratnawati; Niky Hardinata; Supendi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4858

Abstract

This study developed a Certainty Factor (CF)–based expert system to support nutrition consultation and stunting prevention in coastal settings using Bengkalis Regency as a case study. A design science approach was employed to analyze local service constraints, acquire expert knowledge, formalize a rule base, implement an offline-first prototype (mobile client and admin dashboard), and evaluate its performance. Knowledge was elicited from nutritionists, midwives, and community health workers and encoded as IF–THEN rules with expert confidence weights; Evidence (anthropometry, infection history, infant and young child feeding, sanitation, and socioeconomic factors) was mapped to CF values ​​and combined to yield risk scores and categories with explainable rule traces. Functional testing showed all user-story scenarios passed as expected, while initial expert validation and usability checks indicated the prototype provided rapid and standardized assessments suitable for first-line services. The results suggest the CF approach is feasible for coastal contexts with limited connectivity and can accelerate early screening and referrals. Future work will expand the local knowledge base, integrate electronic records, and conduct wider field trials to measure effectiveness at scale
Swarm intelligence for intrusion detection systems in internet of things environments Apri Siswanto; Akmar Efendi; Jaroji Jaroji; Fajar Ratnawati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.25857

Abstract

The rise of the internet of things (IoT) technology has brought new security challenges, necessitating robust intrusion detection systems (IDS). This research applies swarm intelligence (SI) principles, specifically the pigeon inspired optimization (PIO) algorithm, to enhance IDS effectiveness in IoT environments. Drawing on the behavior of social species, SI fosters decentralized control and emergent behavior from simple rules. These principles guide the PIO algorithm, making it apt for optimizing IDS. We utilize two comprehensive IoT datasets – the Canadian Institute for Cybersecurity (CIC) IoT dataset 2023 and the IoT dataset for IDS, aiming to boost the IDS’s capability to detect illicit attacks. By adapting the PIO algorithm, our IDS learns from the environment, adapts to evolving threats, and mitigates false-positive rates. Preliminary tests show that our SI-based IDS outperforms traditional systems’ accuracy, speed, and adaptability. This research advances SI applications in IoT security, contributing to developing more resilient IDS and ultimately enhancing IoT network security against a range of cyber threats.
PENERAPAN APLIKASI KONSULTASI GIZI UNTUK PENCEGAHAN DINI STUNTING DI POSKESDES PENAMPI Niky Hardinata; Fajar Ratnawati; Supendi Supendi
BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat) Vol. 5 No. 2 (2025): BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat) Desember 2025
Publisher : LPPM UNIKS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/bhakti_nagori.v5i2.4949

Abstract

Stunting masih menjadi permasalahan kesehatan serius di Indonesia, khususnya di wilayah kerja Poskesdes desa Penampi, kabupaten Bengkalis, provinsi Riau. Hal ini diakibatkan kurangnya pemahaman gizi masyarakat dan keterbatasan tenaga ahli di tingkat desa. Prevalensi stunting di Bengkalis berada pada tingkat 12,%. Hal ini mengalami penurunan dari sebelumnya yang berada pada tingkat 17,9%. Menjawab tantangan tersebut dan membantu pemerintah menurunkan tingkat stunting, maka dilaksanakan kegiatan pengabdian kepada masyarakat berupa penerapan aplikasi konsultasi gizi berbasis digital yang mampu membantu petugas kesehatan dan masyarakat dalam menilai status gizi, diagnosa awal stunting serta memberikan rekomendasi asupan yang tepat, khususnya bagi ibu hamil dan ibu balita. Melalui pelatihan dan pendampingan kader kesehatan serta tenaga medis, program ini tidak hanya meningkatkan kapasitas layanan gizi berbasis teknologi informasi tetapi juga mempercepat upaya pencegahan stunting sejak dini, sekaligus mendorong kesadaran masyarakat akan pentingnya pemenuhan gizi keluarga sebagai wujud nyata sinergi perguruan tinggi dan fasilitas kesehatan dalam penerapan IPTEK untuk solusi permasalahan lokal.
Sistem Rekomendasi Peran Karir Mahasiswa Rekayasa Perangkat Lunak Dengan Metode Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER) Nofia Zuriatin; Fajar Ratnawati
Journal of Informatics and Electronics Engineering Vol. 6 No. 01 (2026): Juni 2026
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v6i01.1583

Abstract

Mahasiswa Program Studi Rekayasa Perangkat Lunak (RPL) sering mengalami kesulitan dalam menentukan peran karir yang sesuai dengan minat dan kemampuan yang dimiliki. Permasalahan ini disebabkan oleh keterbatasan informasi serta belum tersedianya sistem pendukung keputusan yang mampu memberikan rekomendasi secara objektif dan terukur. Penelitian ini bertujuan untuk merancang dan membangun sistem rekomendasi peran karir bagi mahasiswa RPL menggunakan metode Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER) dengan pembobotan Rank Order Centroid (ROC). Sistem rekomendasi dikembangkan berdasarkan empat kriteria utama dengan total 33 subkriteria, enam alternatif peran karir, serta melibatkan 30 responden mahasiswa. Pengujian sistem dilakukan menggunakan metode black box testing dan pengujian hasil metode SMARTER berdasarkan preferensi mahasiswa. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu memberikan rekomendasi peran karir yang sesuai dengan minat mahasiswa dengan tingkat akurasi sebesar 83%. Sistem ini diharapkan dapat membantu mahasiswa RPL dalam menentukan peran karir secara objektif, sistematis, dan terarah.
Co-Authors Agus Tedyyana Agus Tedyyana, Agus Apri Siswanto Darni Kurniawati Tafanao Efendi, Akmar Elgamar Syam Fajri Profesio Putra Jaroji Jaroji Juniarto Muhamad Azren Nofia Zuriatin Rezki Kurniati, Rezki Rindi Devi Ruhaya Hayuni Supendi Supendi Supendi Tengku Musri