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All Journal Majalah Ilmiah UPI YPTK Jurnal Komtekinfo
Hendra Azhar, Asbon
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Industrial & Manufacturing Engineering Social Sciences

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Understanding Digital Image Processing in Object Identification Against the Development of Information Technology Saptha Negoro, Wahyu; Hendra Azhar, Asbon; Adinda Destari, Ratih; Soeheri
Majalah Ilmiah UPI YPTK Vol. 32 (2025) No. 1
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jmi.v32i1.174

Abstract

Digital image processing is one of the important branches of computer science that plays a major role in supporting accurate and efficient object identification. This service aims to analyze the extent to which understanding the concepts and techniques of digital image processing can contribute to the progress of object identification, especially in the context of the increasingly rapid development of information technology. By using a descriptive-qualitative approach and literature study, this service uses various image processing methods such as segmentation, edge detection, and machine learning-based classification and others. The data used in this service is secondary data as an implementation of the object process in digital image processing for students' understanding of object detection. The results show that a deep understanding of image processing not only improves the accuracy of object identification, but also opens up opportunities for application development in various fields such as security, health, agriculture, and the manufacturing industry and this service can provide education for students to learn about the development of information technology in digital images. Thus, digital image processing is an important component in supporting digital transformation and information technology innovation in the future.  
Computer Vision Technology Innovation Education to Support Early Warning Systems for Rice Diseases Saptha Negoro, Wahyu; Adinda Destari, Ratih; Hendra Azhar, Asbon; Syahrian, Achmad
Majalah Ilmiah UPI YPTK Vol. 32 (2025) No. 2
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jmi.v32i2.199

Abstract

Rice plant diseases are one of the main factors causing decreased productivity and threatening national food security. Farmers' limited knowledge in recognizing early symptoms of disease often leads to delays in treatment. The results of this research are educated in community service with the aim of developing and implementing Computer Vision-based technological innovation education to support an early warning system for rice diseases. The methods used include collecting rice leaf images in the field, digital image processing, and applying Computer Vision models to recognize visual patterns of disease symptoms. Educational activities with students are carried out through training and mentoring for farmers and agricultural extension workers regarding the use of this technology as an early detection tool. The expected results of this service are increased understanding and ability of users or partners in identifying rice diseases more quickly and accurately, so that they can support appropriate decision-making in disease control and increase rice agricultural productivity in a sustainable manner.
Combination of Active Contour and CNN-based Segmentation Methods to Improve Accuracy in Detecting Rice Diseases Saptha Negoro, Wahyu; Adinda Destari, Ratih; Hendra Azhar, Asbon; Syahrian, Achmad
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.671

Abstract

Rice diseases are one of the main factors causing decreased productivity and threatening national food security. The main problem in controlling rice diseases is the delay and inaccuracy of symptom identification in the field. This study aims to develop an artificial intelligence-based rice disease detection system through a combination of Active Contour and Convolutional Neural Network (CNN) methods. The research object is rice leaf images taken from rice fields in Pulau Sejuk Village, Batubara Medan, with a dataset of 600 images consisting of healthy leaves and 3 types of rice diseases. The Active Contour method is used in the segmentation stage to extract leaf areas precisely, while CNN is applied for the disease classification process. The results show that this combination of methods can significantly improve the accuracy of rice disease detection. The developed system is expected to assist farmers and stakeholders in the early detection of rice diseases, thereby supporting food innovation and increasing sustainable agricultural productivity.
Co-Authors Adinda Destari, Ratih Saptha Negoro, Wahyu Soeheri Syahrian, Achmad