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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Kolaborasi Jurnal Pengabdian Masyarakat Jurnal INFOTEL
Widiyanto, Agung
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Computer Science & IT Dentistry 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.
Gambling Comments Detection on Youtube: A Comparative Study of Tree-Based Boosting, LSTM and GRU Models Widiyanto, Agung; Prameswari, Mayesq; Abdul Latief, Muhammad
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1305

Abstract

The exponential growth of online gambling in Indonesia poses significant socio-economic challenges, particularly affecting vulnerable populations through sophisticated digital marketing strategies targeting social media platforms. This study addresses the critical need for automated detection systems to identify gambling-related content in YouTube comments. We scraped and manually labeled 11,673 comments from diverse YouTube videos, creating an extremely imbalanced dataset with gambling comments representing only 10% of the total data. Multiple machine learning approaches were developed and evaluated, comparing traditional gradient boosting methods (LightGBM, XGBoost, CatBoost) using TF-IDF features against deep learning models (LSTM & GRU) with Word2Vec embeddings. The experimental results demonstrate that gradient boosting methods significantly outperform deep learning approaches in generalization capability. LightGBM achieved the highest holdout F1-score with balanced precision (0.8912) and recall (0.8886), while XGBoost followed closely with comparable performance. In contrast, deep learning models exhibited severe overfitting, with GRU and LSTM showing excellent test performance but drastically reduced holdout recall (0.5022 and 0.4844, respectively). The findings indicate that the dataset size was insufficient for deep learning approaches to learn generalizable representations effectively. For practical deployment in YouTube gambling content detection, gradient boosting methods are recommended due to their superior performance with limited, imbalanced datasets.
TelUP Human Fall Dataset: A Motion Forecasting Study of Human Falls Widiyanto, Agung; Candraningtyas, Raphon Galuh; F.F, Andi Hisyam Helmi; Prameswari, Mayesq; Bashiran, Himam; Surahmat, Geugeut Nyarikawanti; Rahmah, Balqis Awaluna; Manika Dewi, Anak Agung Istri Candra; Yunus, Andi Prademon
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1420

Abstract

This study investigates multitask learning approaches for human motion forecasting and fall classification using pose data extracted from video sequences. A custom dataset, the TelUP HumanFall Forecasting Dataset, was developed, containing annotated video frames representing fall and non-fall scenarios captured from six participants. Pose information was extracted using YOLOv11, producing 17 keypoints per frame, which were normalized and segmented into temporal sequences for training. Three deep learning architectures, Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), were implemented and evaluated. The models were assessed in a subject-independent test set consisting of two participants to ensure generalization. Quantitative evaluation measured the forecast error using the mean per joint position error (MPJPE) and classification accuracy. The MLP achieved the lowest MPJPE of 0.2630 (131.5 pixels), while the LSTM obtained the highest classification accuracy of 92.89%. Qualitative analysis revealed limitations in the capture of complex joint dynamics. Despite fast training convergence, the results emphasize a trade-off between forecast precision and classification accuracy. Future work will explore more expressive architectures and improved pose extraction methods to enhance forecast realism.
Co-Authors Abdul Latief, Muhammad Andi Prademon Yunus Anjani, Meisy Dwi Bahtiar, Arvinanto Bashiran, Himam Candraningtyas, Raphon Galuh F.F, Andi Hisyam Helmi Henny Okta Piyani Hutomo, Muhammad Ihsan Prawira Jaya, Ekky Satria Manika Dewi, Anak Agung Istri Candra Pinem, Anwar Arifin Prameswari, Mayesq Rahmah, Balqis Awaluna Siti Khomsah, Siti Siti Rahmayuni Surahmat, Geugeut Nyarikawanti