Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian Masyarakat Media Elektrik
Martati Martati
Sekolah Tinggi Teknologi Bontang, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Penerapan Teknologi Tepat Guna Oven Pengering Ikan Asin Berbasis Pembangkit Listrik Tenaga Surya (PLTS) bagi Kelompok Nelayan Pa'Ristan di Kota Bontang Martati Martati; Akbar Akbar; Irfani Zuhrufillah
Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2026): Jurnal Pengabdian Masyarakat (Abdimas)
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/abdimas.v4i1.14096

Abstract

Proses pengeringan ikan asin pada Kelompok Nelayan Pa'Ristan di Kelurahan Tanjung Laut Indah, Kota Bontang, masih dilakukan secara terbuka sehingga sangat bergantung pada kondisi cuaca, berisiko terpapar debu dan serangga, serta menyebabkan proses pengeringan kurang optimal. Kegiatan pengabdian kepada masyarakat ini bertujuan menerapkan teknologi tepat guna berupa oven pengering ikan asin berbasis Pembangkit Listrik Tenaga Surya (PLTS) serta memberikan pelatihan, praktik penggunaan, dan pendampingan perawatan alat kepada mitra. Metode pelaksanaan menggunakan pendekatan partisipatif yang meliputi koordinasi dan identifikasi kebutuhan mitra, penerapan dan serah terima oven pengering berbasis PLTS, pelatihan penggunaan alat, praktik langsung oleh mitra, serta pendampingan perawatan dan pemeliharaan alat. Hasil kegiatan menunjukkan bahwa satu unit oven pengering ikan asin berbasis PLTS berhasil diterapkan dan diserahterimakan kepada Kelompok Nelayan Pa'Ristan. Selain itu, mitra mengikuti pelatihan, praktik pengoperasian oven, serta pendampingan perawatan dasar alat sebagai bagian dari penerapan teknologi. Dengan demikian, kegiatan pengabdian ini berhasil menerapkan teknologi tepat guna berupa oven pengering ikan asin berbasis PLTS yang disertai pelatihan, praktik, dan pendampingan sebagai upaya mendukung pemanfaatan teknologi pada Kelompok Nelayan Pa'Ristan.
Photovoltaic Power Output Prediction Using PCA–KNN: A Comparative Study with SVR and MLP on Historical Operational Data Ilham Maridi; Fiky Anggara; Martati Martati
Jurnal Media Elektrik Vol. 23 No. 3 (2026): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i3.14355

Abstract

The transition toward renewable energy integration in power systems presents challenges in managing photovoltaic (PV) power output owing to variations in operating conditions. This study evaluated the performance of K-Nearest Neighbor (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP), with and without Principal Component Analysis (PCA), for PV power output prediction. The dataset comprised 420 observations recorded at five-minute intervals over seven days of measurements from a PV system. The data were chronologically divided into 80% training and 20% testing sets, while normalization and PCA were exclusively fitted to the training data to prevent information leakage. The model performance was evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). The results show that KNN and PCA-KNN achieved the best and identical performance, with an MSE of 265.3385, RMSE of 16.2892, and R² of 0.8421. PCA did not improve KNN accuracy but maintained its predictive performance after the dimensional transformation. PCA-MLP showed a slight improvement over MLP, whereas SVR and PCA-SVR yielded lower performances. These findings indicate that the effect of PCA depends on both the dataset characteristics and the learning algorithm. Further studies with longer observation periods, more diverse datasets, and external validation are recommended to assess the generalizability of the models.
Co-Authors Akbar Akbar Fiky Anggara Ilham Maridi Irfani Zuhrufillah