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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Kolaborasi Jurnal Pengabdian Masyarakat
Widiyanto, Agung
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Computer Science & IT Education Electrical & Electronics Engineering Health Professions Library & Information Science Medicine & Pharmacology Nursing Public Health

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Pelaksanaan Program SDGs Meningkatkan Kualitas dan Penjualan Umkm Amplang Berbasis Media Sosial di Kelurahan Sesumpu Siti Rahmayuni; Pinem, Anwar Arifin; Jaya, Ekky Satria; Piyani, Henny Okta; Widiyanto, Agung; Anjani, Meisy Dwi
Kolaborasi: Jurnal Pengabdian Masyarakat Vol 4 No 6 (2024): Kolaborasi: Jurnal Pengabdian Masyarakat
Publisher : Yayasan Inspirasi El Burhani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56359/kolaborasi.v4i6.454

Abstract

Introduction: This community service program is designed to improve the quality and sales of Amplang MSMEs in Sesumpu Village, Penajam District, by utilizing social media. Objective: Polaksar Bayah, Sesumpu Village, Penajam District Method: Training and Mentoring Result: increased income for entrepreneurs, strengthening production capacity, and improvements in business management practices carried out by participants. Thus, this program is expected to provide a significant positive impact on the development of MSMEs in the area Conclusion: The community service program implemented in Sesumpu Village has succeeded in achieving its goals in improving the quality and sales of Amplang MSMEs. Through a planned and participatory approach, this program is able to overcome various challenges faced by partners, including limited knowledge in business management and marketing
Class Weighting Approach for Handling Imbalanced Data on Forest Fire Classification Using EfficientNet-B1 Bahtiar, Arvinanto; Hutomo, Muhammad Ihsan Prawira; Widiyanto, Agung; Khomsah, Siti
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.63-73

Abstract

Wildfires pose a threat to ecosystems and human safety, necessitating the development of effective monitoring techniques. Detecting forest fires based on images of forest conditions could be a breakthrough. However, the model built from imbalanced data yields low accuracy. This research addresses the challenge of class imbalance in multiclass classification for forest fire detection using the EfficientNet-B1 model. This research examines the implementation of class weighting to improve model performance, with a particular focus on minority classes, specifically Fire and Smoke. A dataset of 7,331 training images was categorized into four classes. The results showed that employing the class weighting method achieved an accuracy of 90%. The training duration of 14 minutes and 45 seconds outperforms the data augmentation method in terms of time efficiency. This study contributes to the development of more effective methods for forest fire monitoring and provides insights for future research in machine learning applications in environmental contexts.
Co-Authors Anjani, Meisy Dwi Bahtiar, Arvinanto Henny Okta Piyani Hutomo, Muhammad Ihsan Prawira Jaya, Ekky Satria Pinem, Anwar Arifin Siti Khomsah, Siti Siti Rahmayuni