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All Journal JOIV : International Journal on Informatics Visualization Jurnal INSYPRO (Information System and Processing) ILKOM Jurnal Ilmiah Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Jurnal Energi dan Lingkungan (Enerlink) Jurnal Bidan Cerdas Journal of Applied Data Sciences Jurnal Ilmiah Kesehatan
Ansar Suyuti
Universitas Hasanuddin, Gowa, South Sulawesi 92171, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Nursing Public Health

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Journal : JOIV : International Journal on Informatics Visualization

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Comparison Architecture of Convolutional Neural Network for Fertility Level of Paddy Soil Detection Natsir, Muh. Syahlan; Suyuti, Ansar; Nurtanio, Ingrid; Palantei, Elyas
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3342

Abstract

This study proposes to detect the fertility of paddy soil based on texture, the power of Hydrogen (pH), and the amount of production. Fertile paddy soil provides essential nutrients and supports optimal plant growth. Therefore, monitoring and analyzing soil fertility is crucial in agricultural land management, which significantly increases rice yields. Paddy soil is categorized into three parts: very fertile soil, fertile soil, and reasonably fertile soil. This research proposes a new approach to detecting soil fertility levels based on factors that influence soil fertility using the Convolutional Neural Network (CNN) algorithm. There are 558 paddy soil datasets of 178 very fertile datasets, 135 fertile datasets, and 245 quite fertile datasets. In this research, we conducted trials using the CNN, Resnet, Enet, and VGG19 models. According to the test results, the CNN model using the Adam optimizer and a learning rate of 0.001 achieves the highest training accuracy of 0.9687 and validation accuracy of 0.8333. This suggests that this model can accurately identify the fertility of paddy soil, making it easier to calculate the fertility of paddy soil through its use. Future research can expand this study by integrating additional soil parameters, such as nitrogen, phosphorus, potassium levels, and organic matter content, to improve classification accuracy further. Additionally, employing multimodal data sources, such as remote sensing and hyperspectral imaging, could enhance the model's robustness in various environmental conditions. Further optimization of deep learning architectures and Artificial Intelligence (AI) techniques can also provide better interpretability and usability for agricultural stakeholders.
Composition Model of Organic Waste Raw Materials Image-Based To Obtain Charcoal Briquette Energy Potential Saptadi, Norbertus Tri Suswanto; Suyuti, Ansar; Ilham, Amil Ahmad; Nurtanio, Ingrid
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1682

Abstract

Indonesia needs new renewable energy as an alternative to fuel oil. The existence of organic waste is an opportunity to replace oil because it is renewable and contains relatively less air-polluting sulfur. Previous research that has been widely carried out still utilizes coconut shell raw materials, which are increasingly limited in number, so other alternative raw materials are needed. A model is needed to make a formulation that can optimize the composition of organic waste raw materials as a basic ingredient for making briquettes. The research objective was to determine the best raw material composition based on digital image analysis in processing organic waste into briquettes. An artificial intelligence approach with a Convolutional Neural Network (CNN) architecture can predict an effective object detection model. The image analysis results have shown an effective model in the raw material composition of 60% coconut, 20% wood, and 20% adhesive to produce quality biomass briquettes. Briquettes with a higher percentage of coconut will perform better in composition tests than mixed briquettes. The energy obtained from burning briquettes is useful for meeting household fuel needs and meeting micro, small, and medium business industries.
Co-Authors ., Dewiani ., Yusran A. Ejah Umraeni Salam Adil, Andi Eri Andika Aditia Silvia Fazila Adnan - Akil, Yusri Syam Amil Ahmad Ilham Andani Achmad Andani Achmad Andi Ejah Umraeni Anshar, Muh Ardiaty Arief Ardiaty Arief . Arief, Ardiaty Azran Budi Arief B, Ian Adrian Baso, Muchlis Dewi, Dianti Utami Dewiani - Dewiani . Dewiani Dewiani Ejah Umraeni Salam Elyas Palantei Faisal Arya Samman Faizal A. Samman Faizal A. Samman . Faizal Arya Samman Fauzan, Arief Fighi S. Permadi Fighi S. Permadi . Fitriyanti Mayasari Gassing . Gassing Gassing Gunadin, Indar Chaerah Hasniaty Ida Rachmaniar S Ida Rachmaniar Sahali Ikhlas Kitta Ikhlas Kitta Imran Amin Ingrid Nurtanio Intan Sari Areni Irwan Setiawan K Kasmiati Merna Baharuddin Muh Anshar Muh. Anshar . Muh. Syahlan Natsir Nadjamuddin Harun Nappu, Muhammad Bachtiar Nappu, Muhammad Bahtiar Palantei, Elyas Palantei, Idris Rhiza S. Sadjad Rhiza S. Sadjad . S Sriwidyastuti Salam, Andi Ejah Umraeni Salama Manjang Sri Mawar Said Sri Ramadany Sri Ramadany Subir, Ade Nur Fatimah Sudirman Palaloi Sudirman Sudirman Syafruddin Syarif Wardi - Werna Nontji Wulan Wulan Yusran . Yusran Yusran Zaenab Muslimin Zahir Zainuddin