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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Jurnal Penelitian Pertanian Terapan Agriprima : Journal of Applied Agricultural Sciences Jurnal Agro Industri Perkebunan Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Applied Technology and Computing Science Journal JISA (Jurnal Informatika dan Sains) Journal of Community Development Jurnal Rekayasa elektrika Journal of Educational Engineering and Environment
Abdul Madjid
Politeknik Negeri Jember
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Nursing Public Health Social Sciences

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Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat Zilvanhisna Emka Fitri; Rizkiyah Rizkiyah; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i1.15535

Abstract

The decrease in quality and productivity of tomatoes is caused by high rainfall, bad weather and cultivation so that the tomatoes become rotten, cracked, and spotting occurs. The government is trying to provide training to improve the quality of tomatoes for farmers. However, the training was not effective so the researchers helped create a system that was able to educate farmers in the classification of damage to tomato quality. This system serves to facilitate farmers in recognizing tomato damage thereby reducing the risk of crop failure. In this study, the classification method used is backpropagation with 7 input parameters. The input consists of morphological and texture features. The output of this classification system consists of 3 classes are blossom end rot, fruit cracking and fruit spots caused by bacterial specks. The best accuracy level of the system in classifying tomato quality damage in the training process is 89.04% and testing is 81.11%.
Implementing K-Nearest Neighbor to Classify Wild Plant Leaf as a Medicinal Plants Zilvanhisna Emka Fitri; Lalitya Nindita Sahenda; Sulton Mubarok; Abdul Madjid; Arizal Mujibtamala Nanda Imron
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.2220

Abstract

in leaf shape. Therefore, this study aimed to create a system to help increase public knowledge about wild plant leaves that also function as medicinal plants by the KNN method. Leaves of wild plants, namely Rumput Minjangan, Sambung Rambat, Rambusa, Brotowali, and Zehneria japonica, are also medicinal plants in comparison. Image processing techniques used were preprocessing, image segmentation, and morphological feature extraction. Preprocessing consists of scaling and splitting the RGB components and using an RGB component decomposition process to find the color component that best describes the leaf shape and generate the blue component image. The segmentation process used a thresholding technique with a gray threshold value (T) of less than 150, which best separates objects and backgrounds. Some morphological feature extraction used are area, perimeter, metric, eccentricity, and aspect ratio. Based on the results of this research, the KNN method with variations in K values, namely 13, 15, and 17, obtained a system accuracy of 94.44% with a total of 90% training data and 10% test data. This comparison also affected the increase in system accuracy.
Application of Computer Vision for Digital Encyclopedia of Chili Varieties (Capsicum spp.) Muhammad Viqih Zamzami; Zilvanhisna Emka Fitri; Abdul Madjid; Arizal Mujibtamana Nanda Imron
Journal of Educational Engineering and Environment Vol. 3 No. 1 (2024): Journal of Educational Engineering and Environment
Publisher : Fakultas Teknik Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/jeee.v3i1.3831

Abstract

Chili is a vegetable commodity that has high economic value so that its production always increases every year. Several types of chilies are cultivated in Indonesia, namely cayenne pepper (Capsicum frutescens), curly red chili (Capsicum annuum L. var. longum), large red chili (Capsicum annuum L.) and paprika (Capsicum annuum var. grossum). Indonesia is a country that continues to develop innovation to produce many superior varieties, especially chilli plants. The problem arises that variations of other superior chili varieties can only be accessed through the official website of the ministry of agriculture, however, the data that can be accessed is limited (in the form of descriptions of chili varieties without physical appearance such as photos) so that it is quite difficult for the community and farmers to cultivate or utilize these varieties. This made researchers develop a digital encyclopedia of chili types using computer vision. This study uses a combination of digital image processing and intelligent systems. The image processing used is preprocessing such as cropping and splitting of RGB components, image segmentation and shape feature extraction. The features used are area, perimeter, major axis length, minor axis length and eccentricity. This feature is the input of the Naïve Bayes method which produces a system accuracy rate of 92%.
Co-Authors Abdurrahman Salim Ahmad Aris Ubaidillah Anni Nur Aisyah Ari Baskara Arizal Mujibtamana Nanda Imron Eka Renitasari Hasyim Asyari Imron, Arizal Mujibtamala Nanda Lalitya Nindita Sahenda Mega Silvia Muhammad Viqih Zamzami Rizkiyah Rizkiyah Sulton Mubarok Titien Fatimah Ujang Setoko Ujang Setyoko Virda Fauziah Wildan Bakti Nugroho Zilvanhisna Emka Fitri