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All Journal NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Informatika dan Komputer
Dafa Ikhwanu Shafa
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Computer Science & IT Social Sciences

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Pembuatan Rumah Burung Hantu sebagai Upaya Pengendalian Hama Tikus Ramah Lingkungan dan Mengurangi Ketergantungan Pestisida Kimia di Desa Pematang Kasih Ariqah Luthfiyah; Aprilia Putri Silaen; Cut Latifah Putri; Dafa Ikhwanu Shafa; Khairunnisa Khairunnisa
Nusantara: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 4 (2025): November: NUSANTARA Jurnal Pengabdian Kepada Masyarakat
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/nusantara.v5i4.6864

Abstract

The Community Service Program (KKN) in Pematang Kasih Village, Pantai Cermin District, focused on the construction of owl houses (RUBUHA) as an environmentally friendly effort to control rice field rats. The background to this activity was the high level of rat infestation that was detrimental to local farmers' rice harvests and the excessive use of chemical pesticides, which had the potential to negatively impact human health and ecosystem balance. The research method used was descriptive qualitative, with data collection techniques through direct observation in agricultural fields, interviews with farmer groups, and documentation of the owl house construction process. The results showed that the village community responded positively to this program because the use of owls as natural predators was proven to be more effective in controlling the rat pest population while reducing dependence on chemical pesticides. In addition, this program also increased public awareness of the importance of ecologically based pest control and preserving biodiversity. With the owl houses built around rice fields, it is hoped that a sustainable agricultural pattern can be created that supports the welfare of farmers while preserving the environment.
Classification of Organic and Non-Organic Waste Using Convolutional Neural Network (CNN) Muhammad Farhan; Mhd Farhan Aditiya; Dafa Ikhwanu Shafa; Supiyandi; Aidil Halim Lubis
Jurnal Ilmiah Informatika dan Komputer Vol. 2 No. 2 (2025): Desember 2025
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/xbpg4s54

Abstract

The increase in waste volume in Indonesia, which reached emergency levels in 2024, requires technological solutions that can assist in the sorting process quickly and accurately. Previous research on CNN-based waste classification generally focused on recyclable waste categories with many classes and used structured datasets, which did not adequately represent real-world waste conditions, especially organic waste, which has more varied shapes and conditions. Based on this gap, this study proposes a Convolutional Neural Network (CNN) model for classifying two main categories—organic and inorganic—using 25,077 images and direct testing on field samples. The model was trained using the Adam optimizer and categorical crossentropy loss. The results show high accuracy for inorganic waste (96%), but lower accuracy for organic waste (62%) due to the complexity of texture and natural damage. This study contributes to the field of informatics through the application of more applicable and realistic deep learning for automatic waste sorting systems, as well as opening up opportunities for the development of model architectures that are more adaptive to waste conditions in the actual environment.
Co-Authors Aidil Halim Lubis Aprilia Putri Silaen Ariqah Luthfiyah Cut Latifah Putri Khairunnisa Khairunnisa Mhd Farhan Aditiya Muhammad Farhan Supiyandi Supiyandi