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All Journal JURIKOM (Jurnal Riset Komputer) Jurnal Pengabdian kepada Masyarakat Wahana Usada
Basir, Muhammad Ichsan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Health Professions Social Sciences Other

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Pendidikan Kesehatan Sebagai Upaya Peningkatan Pengetahuan Remaja tentang Kesehatan Mental Wenny Nugrahati Carsita; Kusumawati, Mira Wahyu; Basir, Muhammad Ichsan
Jurnal Pengabdian kepada Masyarakat Wahana Usada Vol. 7 No. 2 (2025): Desember: Jurnal Pengabdian kepada Masyarakat Wahana Usada
Publisher : Sekolah Tinggi Ilmu Kesehatan KESDAM IX/Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47859/wuj.v7i2.717

Abstract

Latar Belakang: Kesehatan mental merupakan komponen fundamental dalam menunjang kesejahteraan individu, khususnya pada masa remaja yang merupakan fase rentan terhadap berbagai tekanan kehidupan serta dinamika perkembangan emosional. Masalah kesehatan mental yang dialami pada masa remaja dapat memberikan dampak jangka panjang hingga masa dewasa. Kegagalan dalam menangani kondisi kesehatan mental berpotensi menimbulkan konsekuensi negatif terhadap kesehatan fisik maupun psikologis. Tujuan: Kegiatan ini bertujuan meningkatkan pengetahuan remaja tentang upaya pencegahan kesehatan mental. Metode: Kegiatan dilaksanakan selama satu hari dengan menggunakan metode ceramah dan sesi tanya jawab. Prosedur kegiatan diawali dengan pelaksanaan pre-test menggunakan kuesioner, dilanjutkan dengan pemberian pendidikan kesehatan dan diakhiri dengan post-test. Sasaran kegiatan ini adalah siswa kelas XI-1 berjumlah 30 orang. Hasil: Diketahui adanya peningkatan rata-rata skor dari 85 pada pre-test menjadi 93 pada post-test setelah pelaksanaan kegiatan. Simpulan: Terdapat peningkatan pengetahuan pada remaja setelah diberikan pendidikan kesehatan. Oleh karena itu, kegiatan serupa perlu terus dilaksanakan secara berkelanjutan sebagai upaya promotif dan preventif dalam menekan risiko gangguan kesehatan mental pada remaja.
Optimized Fault Prediction in Power Distribution Transformers Using Grey Wolf Optimizer-Based SVM and MLP Models Rosena Shintabella; Silaban, Meyer Mega Eklesia; Basir, Muhammad Ichsan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9435

Abstract

Distribution transformers are critical components of power distribution systems, and their reliability directly affects the continuity and quality of electrical energy supply. However, early-stage transformer faults are difficult to detect because their operational characteristics often closely resemble normal operating conditions, which can lead to undetected degradation and unexpected failures. This study aims to improve the accuracy and robustness of fault prediction in distribution transformers by proposing a hybrid approach that integrates the Grey Wolf Optimizer (GWO) with Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models. The main contribution of this research is a direct and systematic performance comparison between baseline machine learning models and their GWO-optimized counterparts, highlighting the effectiveness of metaheuristic optimization in enhancing classification performance. GWO is employed to optimize key model parameters, enabling improved convergence behavior, higher classification accuracy, and better generalization capability. The proposed models are evaluated under four transformer operating conditions, namely Light Load Imbalance, Light Overload, Normal, and Normal High Temperature, which represent practical scenarios in power distribution networks. Model performance is assessed using standard classification metrics, including Accuracy, Precision, Recall, and F1-Score. Experimental results show that the baseline SVM achieved an accuracy of 68%, while the baseline MLP reached 87% accuracy. After GWO-based optimization, the SVM–GWO model demonstrated a significant improvement, achieving 92% accuracy, whereas the MLP–GWO model produced the best overall performance, achieving 93% accuracy, precision, recall, and F1-score. These findings confirm that GWO-based optimization substantially enhances transformer fault prediction performance and demonstrates the strong potential of the proposed hybrid models for real-time monitoring and preventive maintenance of power distribution transformers.
Co-Authors Kusumawati, Mira wahyu Rosena Shintabella Silaban, Meyer Mega Eklesia Wenny Nugrahati Carsita