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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT JTT (Jurnal Teknologi Terpadu) Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Bitnet: Jurnal Pendidikan Teknologi Informasi Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) JATI (Jurnal Mahasiswa Teknik Informatika) INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) MEANS (Media Informasi Analisa dan Sistem) Journal of Soft Computing Exploration Bima Abdi: Jurnal Pengabdian Masyarakat INCODING: Journal of Informatics and Computer Science Engineering Bulletin of Information Technology (BIT) Kode : Jurnal Bahasa Archive: Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Innovative: Journal Of Social Science Research Sasambo: Jurnal Abdimas (Journal of Community Service)
Insan Taufik
Universitas Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Classification of Purple Passion Fruit Ripeness Levels Using Convolutional Neural Network (CNN) Mochammad Gani Alfa Alkhoiri Siregar; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1787

Abstract

Passiflora edulis Sims (purple passion fruit) is a fruit that offers numerous health benefits and possesses high economic value. However, the manual assessment of ripeness by traders tends to be subjective and inconsistent, leading to post-harvest losses of up to 50%. This study developed a classification model for determining the ripeness level of purple passion fruit using a Convolutional Neural Network (CNN) and implemented it in a web-based application. The CNN model was designed to classify four ripeness stages (unripe, half-ripe, ripe, and rotten) with the addition of a non-passion-fruit class to enhance the system’s robustness. The dataset consisted of 2,000 images divided into five classes: four ripeness levels of purple passion fruit (unripe, half-ripe, ripe, and rotten) and one non-passion-fruit class as a comparator. All images were in JPG and PNG formats. The CNN architecture comprised four convolutional layers with 16, 32, 64, and 128 filters, respectively. Evaluation of various data-splitting ratios (80:20, 70:30, 60:40) and learning rates (0.001, 0.0001, 0.01) showed that the optimal configuration was achieved at a ratio of 80:20 with a learning rate of 0.001, resulting in a training accuracy of 96.72% and a testing accuracy of 95.76%, with a loss value of 0.1811. Validation using 5-Fold Cross Validation produced an average accuracy of 95.40%. The model was integrated into a web application developed using Flask and JavaScript, deployed on the PythonAnywhere cloud platform, enabling users to upload images and automatically obtain ripeness predictions to assist traders in sorting fruits more quickly and accurately.
Implementation of K-Means for Mapping Cleanliness Levels in Pancur Batu District Raisya Putri; AS Mansur; Hamidah Nasution; Insan Taufik; Adidtya Perdana
Bitnet: Jurnal Pendidikan Teknologi Informasi Vol. 11 No. 3 (2026): Bitnet: Jurnal Pendidikan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/bitnet.v11i3.13677

Abstract

This study aims to classify areas in Pancur Batu District based on environmental cleanliness levels using the K-Means Clustering method, analyze the characteristics of each cluster based on cleanliness parameters, and develop a web-based mapping system to visualize the clustering results spatially. The study utilized 578 image data analyzed using five parameters, namely visible waste density, drainage condition, percentage of clean roads, percentage of households with trash bins, and waste collection frequency. The results showed that the areas in Pancur Batu District were successfully grouped into three clusters: Cluster 0 (Clean) consisting of 6 villages, Cluster 1 (Dirty) consisting of 12 villages, and Cluster 2 (Very Clean) consisting of 7 villages. The clustering results were then visualized through a web-based mapping system to facilitate users in identifying the distribution of cleanliness levels across the study area. Expert validation conducted by a geography specialist obtained a total score of 42 with an average score of 4.2, which falls into the good category. Therefore, the implementation of the K-Means Clustering method was able to effectively classify areas based on environmental cleanliness levels and provide useful information to support environmental cleanliness management and decision-making processes in Pancur Batu District.
Penerapan Teknologi Mekanisasi Pakan Mandiri Guna Menekan Biaya Produksi Peternak Bebek Petelur di Sidodadi Batang Kuis Deli Serdang Yuni Warty; Insan Taufik; Dewi Wulandari; Muhammad Aswin Rangkuti; Tuti Hardianti; Silvia Dona Sari; Yanthy Leonita Perdana Simanjuntak
Bima Abdi: Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2026): Bima Abdi: Jurnal Pengabdian Masyarakat
Publisher : Yayasan Pendidikan Bima Berilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53299/ba-jpm.v6i3.5273

Abstract

Usaha peternakan bebek petelur di Desa Sidodadi, Kecamatan Batang Kuis, Kabupaten Deli Serdang menghadapi tingginya biaya pakan, ketergantungan pada pakan pabrikan, serta banyaknya pakan serbuk yang tercecer. Kegiatan pengabdian ini bertujuan menerapkan teknologi mekanisasi pakan mandiri untuk mengubah pakan serbuk menjadi pelet, meningkatkan efisiensi konsumsi, dan menekan biaya produksi. Pelaksanaan kegiatan meliputi sosialisasi, pengadaan dan instalasi mesin, pelatihan formulasi pakan, uji coba produksi, pendampingan, monitoring, dan evaluasi. Mitra memelihara sekitar 8.000 ekor bebek dengan kebutuhan awal 960 kg pakan per hari. Sebelum penerapan teknologi, biaya pakan mencapai Rp4.816.000 per hari dan masih menggunakan 80 kg pakan pabrikan. Setelah mesin digunakan, bahan pakan lokal dapat dicampur merata dan dibentuk menjadi pelet. Biaya pakan menurun menjadi Rp4.200.000 per hari, atau berkurang Rp616.000. Pakan pelet juga lebih seragam, tidak mudah tercecer, dan lebih optimal dikonsumsi ternak. Teknologi ini meningkatkan kemandirian peternak sekaligus mendukung keberlanjutan usaha melalui produksi pakan yang lebih efisien dan ekonomis. Rekomendasi untuk kegiatan pengabdian selanjutnya meliputi optimalisasi kapasitas mesin untuk komersialisasi pakan pelet ke peternak luar, serta hilirisasi produk pascapanen telur (seperti telur asin aneka rasa) untuk meningkatkan nilai tambah ekonomi kelompok.
Automatic Waste Type Detection Using YOLO for Waste Management Efficiency Alfattah Atalarais; Kana Saputra S; Hermawan Syahputra; Said Iskandar Al Idrus; Insan Taufik
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.770

Abstract

The management of waste in Indonesia is currently suboptimal, with only 66.24% being effectively managed, leaving 33.76% unmanaged. This highlights a significant challenge in waste management, primarily due to a lack of understanding in selecting appropriate waste types. Advances in deep learning and computer vision offer promising solutions to this issue. This study employs the YOLOv8l model, a well-regarded deep learning model for object detection, to develop an automated waste type detection system integrated with trash bins. The dataset comprises 2800 images across four classes, each containing 700 images, and is split with an 80:10:5 ratio for training, validation, and testing. Evaluation on test data yields a mean Average Precision (mAP) of 96.8%, indicating robust model performance in object detection. The model's accuracy is further validated with a score of 89.98%. Real-time testing conducted at Merdeka Park, Binjai, demonstrates the system's capability to detect waste with varying confidence levels, consistently above the 0.5 threshold. The highest confidence was observed in bottle detection at 0.94, and the lowest in cans at 0.64, underscoring the system's reliability across different detection scenarios within a 30cm range.
Implementation of MobileNet V3 In Classifying Butterfly Species with Android and Cloud Based Application Development Ihsan Zulfahmi; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.797

Abstract

This research aimed to develop an Android application capable of classifying butterfly species using cloud computing and deep learning technologies. MobileNetV3-Large, a Convolutional Neural Network (CNN) architecture, was employed to process and classify six butterfly species. The dataset was divided into two ratios, 70:30 and 80:20, for training and testing. Evaluation results indicated that the optimal model was achieved with an 80:20 ratio, yielding an accuracy of 94% and precision, recall, and F1-Score values exceeding 90% for each species class. Google Cloud Platform (GCP) was utilized to manage and run the model using the Cloud Run service, enabling the application to function efficiently even with limited resources on Android devices. The application incorporates an encyclopedia of species and a camera scanning feature, making it a valuable educational tool
Co-Authors Abdul Ghofur Abdul Ghofur Abdurrahman Adisaputera Adidtya Perdana, Adidtya Ahmad Landong Alfattah Atalarais Amran Sitohang Andriyani S, Rahayu Annisa Aulia Antonius Purba Arjon Samuel Sitio Arnita Arnita Arnita Arnita Ary Prandika Siregar AS Mansur Chairunisah Chairunisah, Chairunisah Dede Yusuf Dewi Wulandari Dinda Farahdilla Dharma Dinda Kartika Farhan Ramadhan, Haikal Fauzan Hafiz Harahap Fevi Rahmawati Suwanto Fitra, Awaludin Fristy Riandari Hamidah Nasution Hasugian , Paska Marto Hermawan Syahputra Ihsan Zulfahmi Irham Ramadhani Isda Pramuniati Izwita Dewi Kana Saputra S Kuraini, Atifa Nuzulul Lili Tansliova Lubis, Afiq Alghazali M. Revano Ananda Lubis MANSUR AS Mhd Hidayat Mhd Hidayat Mochammad Gani Alfa Alkhoiri Siregar Mohammed Hafizh Al-Areef Muhammad Noer Fadlan Muhammad, Fachrezzy Muthmainnah, Inna Najwa Latifah Hasibuan Nasution, Hamidah . Niska, Debi Yandra Nurliani Manurung Pane, M Iqbal Anata Pane, Yeremia Yosefan Parapak, R Putri Angela Pinem, Josua Purba, Boy Hendrawan Putri Sasalia S Putri, Rezkya Nadilla Raisya Putri Raiyan Fairozi Rangkuti, Muhammad Aswin Rezkya Nadilla Putri Said . Iskandar Samosir, Wahyu Ardiantito Saragih, Vinny Ramayani Silvia Dona Sari Simamora, Elmanani Sinaga, Marlina Setia Siregar, Angginy Akhirunnisa Sri Mulyana SUSIANA Syifa Cendikia, Yolanda Tarigan, Yosua Yosephine Tengku Ratna Soraya TONNI LIMBONG Trisnawati Hutagalung Tuti Hardianti Wahabi Hasibuan, Rahman Warjaya, Angga Wete Polili, Andi Wete Polili, Andi Yanthy Leonita Perdana Simanjuntak Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yuni Warty Yusfi Syawali Zulfahmi Indra, Zulfahmi