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All Journal JURIKOM (Jurnal Riset Komputer) JPEMAS: Jurnal Pengabdian Kepada Masyarakat
Nugroho, Aswin Mulyo
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Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pemberdayaan Industri Rumahan Melalui Pembuatan Pichi-Pichi Sebagai Alternatif Penghasilan Tambahan Mujahidah, Luai Al; Rahma, Nandita; Nugroho, Aswin Mulyo; Aulia, Dewi Nisa; Sangbani, Syeh; Zannuba, Whina Antonia; A’malina, Idam Zummy; Lokananta, Aidya Shafa; Shohehudin, Diyan
JPEMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 1 (2024): JPEMAS : Jurnal Pengabdian Kepada Masyarakat
Publisher : Yayasan Pendidikan Tanggui Baimbaian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71456/adc.v3i1.1036

Abstract

Tujuan dari kegiatan ini adalah memberdayakan industri rumah tangga dengan memanfaatkan kekayaan alam yang ada di sekitar sebagai alternatif penghasilan tambahan. Pemberdayaan industri rumahan menjadi salah satu solusi penting dalam meningkatkan kesejahteraan ekonomi masyarakat, khususnya di kalangan ibu rumah tangga dan pelaku usaha kecil. Salah satu bentuk industri rumahan yang dapat dikembangkan adalah produksi makanan tradisional seperti pichi-pichi. Pichi-pichi adalah makanan berbahan dasar singkong yang memiliki rasa manis, tekstur kenyal, dan biasanya disajikan dengan taburan kelapa parut. Pichi-pichi memiliki potensi sebagai alternatif penghasilan tambahan bagi masyarakat karena bahan bakunya yang mudah didapat dan biaya produksinya yang relatif rendah. Selain itu, produk ini memiliki daya tarik tersendiri di pasar, terutama di kalangan masyarakat yang gemar mengonsumsi camilan tradisional.
Perbandingan MobileNetV2, DenseNet121, InceptionV3, dan Xception pada Klasifikasi Citra Panel Surya Bersih dan Berdebu Nugroho, Aswin Mulyo; Mustafidah, Hindayati; Fitriani, Maulida Ayu; Supriyono, Supriyono
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8688

Abstract

The buildup of dust on solar panels can greatly diminish energy output, lower system efficiency, and raise operational expenses. A productive way to tackle this problem is to utilize image classification through Convolutional Neural Network (CNN) techniques. This study examines the classification capabilities of four CNN models, namely MobileNetV2, DenseNet121, InceptionV3, and Xception, using transfer learning. These models leverage pre-trained weights from large datasets such as ImageNet to accelerate convergence and improve generalization. The dataset of images utilized in this research is obtained from Kaggle and includes pictures of both clean and dusty solar panels. The dataset was divided into training, validation, and testing subsets using a stratified approach to ensure balanced class distribution across all subsets. During training, class weighting was used to address potential class imbalance. The models were developed using TensorFlow with multi-GPU support, optimized using the AdamW optimizer, and fine-tuned to enhance performance. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. Among all the architectures evaluated, the Xception model achieved the best performance with an accuracy of 90.52%, outperforming MobileNetV2 with an accuracy of 87.92%, DenseNet121 with 89.78%, and InceptionV3 which achieved 87.73%. These results indicate that modern CNN-based models can effectively recognize relevant visual patterns to detect dust on solar panels.
Perbandingan MobileNetV2, DenseNet121, InceptionV3, dan Xception pada Klasifikasi Citra Panel Surya Bersih dan Berdebu Nugroho, Aswin Mulyo; Mustafidah, Hindayati; Fitriani, Maulida Ayu; Supriyono, Supriyono
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8688

Abstract

The buildup of dust on solar panels can greatly diminish energy output, lower system efficiency, and raise operational expenses. A productive way to tackle this problem is to utilize image classification through Convolutional Neural Network (CNN) techniques. This study examines the classification capabilities of four CNN models, namely MobileNetV2, DenseNet121, InceptionV3, and Xception, using transfer learning. These models leverage pre-trained weights from large datasets such as ImageNet to accelerate convergence and improve generalization. The dataset of images utilized in this research is obtained from Kaggle and includes pictures of both clean and dusty solar panels. The dataset was divided into training, validation, and testing subsets using a stratified approach to ensure balanced class distribution across all subsets. During training, class weighting was used to address potential class imbalance. The models were developed using TensorFlow with multi-GPU support, optimized using the AdamW optimizer, and fine-tuned to enhance performance. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. Among all the architectures evaluated, the Xception model achieved the best performance with an accuracy of 90.52%, outperforming MobileNetV2 with an accuracy of 87.92%, DenseNet121 with 89.78%, and InceptionV3 which achieved 87.73%. These results indicate that modern CNN-based models can effectively recognize relevant visual patterns to detect dust on solar panels.
Co-Authors Aulia, Dewi Nisa A’malina, Idam Zummy Fitriani, Maulida Ayu Hindayati Mustafidah Lokananta, Aidya Shafa Mujahidah, Luai Al Rahma, Nandita Sangbani, Syeh Shohehudin, Diyan Supriyono Supriyono Zannuba, Whina Antonia