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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Sinergi Scientific Journal of Informatics Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Jurnal SOLMA Logic : Jurnal Rancang Bangun dan Teknologi TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian kepada Masyarakat Nusantara Patria Artha Journal of Community Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Information System, Technology and Engineering Jurnal Informatika: Jurnal Pengembangan IT
Sidiq Syamsul Hidayat, Sidiq Syamsul
Politeknik Negeri Semarang
Aerospace Engineering Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Advanced Instance Segmentation of Aeroponics Tissue Culture-Based Seeds Potatoes Based on Improved YOLOv8l-small Avisyah, Gisnaya Faridatul; Kurnianingsih, Kurnianingsih; Hidayat, Sidiq Syamsul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3085

Abstract

To improve agricultural production, this study develops an advanced instance segmentation system for aeroponic tissue culture-based potato seedlings. We present an IoT system that integrates multiple sensors for humidity, temperature, pH, and turbidity to enable real-time monitoring. Additionally, we adapt the YOLOv8l-small computer vision model, an optimized version of YOLOv8, designed explicitly for efficient potato leaf disease detection and segmentation, even in resource-constrained IoT environments. YOLOv8 is a significant advancement in the YOLO series, for instance, segmentation, combining better accuracy, efficiency, and flexibility. YOLOv8 outperforms previous methods in generating precise segmentation masks while maintaining real-time performance. These innovations make YOLOv8 a robust choice for a variety of computer vision tasks, including instance segmentation, in both research and practical applications. When tested on a custom dataset of potato leaf pictures, the suggested model produced mask mAP50 of 0.842 and mAP50-95 of 0.566, with a model size of 36.1 MB and an inference duration of 9.3 ms. These outcomes are similar to those of the original YOLOv8l model, which had a slower inference time of 11.0 ms and a much larger model size of 92.3 MB, albeit at the expense of a somewhat higher mAP50 of 0.843. The study concludes that the proposed model provides similar accuracy with greater computational efficiency, making it ideal for IoT-based agricultural systems. Future research will explore additional aspects, while practical experiments aim to reduce labor costs.
Determining the Rice Seeds Quality Using Convolutional Neural Network Hidayat, Sidiq Syamsul; Rahmawati, Dwi; Prabowo, Muhamad Cahyo Ardi; Triyono, Liliek; Putri, Farika Tono
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1175

Abstract

Seed inspection is crucial for plant nurseries and farmers as it ensures seed quality when growing seedlings. It is traditionally accomplished by expert inspectors filtering samples manually, but there are some challenges, such as cost, accuracy, and large numbers. Speed and accuracy were the main conditions for increasing agricultural productivity. Machine learning is a sub-science of Artificial Intelligence that can be applied in research on the classification of rice seed quality. The pipeline of a machine learning system is dataset collection, training, validation, and testing. Model making begins with taking data on the characteristics of rice seeds based on physical parameters in the form of seed shape and color. The dataset used is two thousand images divided into two categories, namely superior seeds and non-superior seeds. Training and Validation was conducted using the Convolutional Neural Network (CNN) algorithm with the concept of cross-validation on Google Collaboratory notebooks. The ratio split of train data and validation data in modeling from a dataset is 80:20. The result of the model formed is a model with the development of a Deep Convolutional Neural Network (Deep CNN) that can classify the digital image data of rice seeds from the results of data calls uploaded into the system. The results of the experiment conducted on 30 test data can be analyzed so that the system can classify superior and non-superior seeds with a precision value of 93% and a recall of 95%.
Co-Authors Abdillah, Umar Fachri Aggie Brenda Vernandez Akhmad Jamaah, Akhmad Amin Suharjono Amin Syarifuddin Aprilia, Dita Ari Sriyanto Nugroho Arif Nursyahid, Arif Asyari, Rifa Atul Izza Avisyah, Gisnaya Faridatul Azhar, Rachael Al Budi Basuki, Budi Callista, Nada Vania Khansa DWI RAHMAWATI Eddy Triyono Endro Wasito, Endro Eni Dwi Wardihani, Eni Dwi Eriko Arvin Karuniawan Fatchun Hasim Firmansyah, Ardi Ghandie Kurnia Widi Hadwi, Sindung Hayuningtyas, Adika Dewi Helmy Helmy, Helmy Indra, Ragil Tri Iswanti, Iswanti Kabir, Noer Ni'mat Syamsu Karina Laras Novitasari Kurnianingsih Kurnianingsih, Kurnianingsih Lady, Rienisti Ellen Lestari, Melisa Yufit Lilik Eko Nuryanto, Lilik Eko Mara, Muhlasah Novitasari Muhammad Anif Muhammad Luthfi Mulyaman, Heri Bertus Parandy, La Mema Prabowo, M. Cahyo Adi Prabowo, M. Cahyo Ardi Prabowo, Muhamad Cahyo Ardi Prabowo, Muhammad Cahyo Ardi Putra, Ivandi Julatha Putri, Farika Tono Rakhman, Fikri Arif Rev. Ariawan Pratomo Riyadi, Aji Hari Robby, Muhammad Sarono Widodo, Sarono Setyoko Setyoko Shabiya, Kiara Izzatus Sindung Hadwi Widi Sasono, Sindung Hadwi Widi Siti Hasanah Sri Anggraeni Sri Anggraeni Kadiran, Sri Anggraeni sri hardiningsih H.S Sri Wahyuni Subuh Pramono syahid Syahid, syahid Taufiq Yulianto Triyono Triyono Triyono, Liliek Wasono, Adi Wilda Puspa Pratiwi