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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Tibuana : Journal of Applied Industrial Engineering
Rosyadi NR, Imron
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Computer Science & IT Control & Systems Engineering Education Library & Information Science Mathematics

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Penerapan Citra Berbasis K-Means Clustering untuk Mendeteksi Penyakit Bulai Pada Komoditas Jagung Madura Rosyadi NR, Imron; Prasetyowati, Erwin; Said, Badar
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Volume 13 No 3, Mei Tahun 2023
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/justit.13.3.206-211

Abstract

Pengembangan dan pembudidayaan jagung diperlukan seiring dengan meningkatnya konsumsi bahan makanan dan kebutuhan industri terutama produk makanan yang berbahan baku jagung. Dalam pengembangan jagung di Indonesia, kendala utamanya adalah gangguan Organisme Pengganggu Tanaman (OPT) utamanya penyakit, salah satunya adalah penyakit bulai. Penyakit ini dapat diketahui dengan terjadinya perubahan warna, sehingga diperlukan sebuah cara untuk mengetahui perbedaan antara warna daun sehat dan warna daun yang telah berubah akibat terserang penyakit bulai tersebut. Salah  satu  solusi  yang  bisa digunakan adalah  pengolahan citra. Oleh sebab itu tujuan penelitian ini untuk mendeteksi penyakit bulai berdasarkan warna daun pada tanaman jagung berbasis pengolahan citra digital, untuk menghasilkan hasil yang tepat dan objektif. Algoritma yang digunakan adalah algoritma K-Means Clustering. Penelitian ini menggunakan data latih sebanyak 50 citra dan data uji sebanyak 25 citra. Berdasarkan simulasi tingkat identifikasi penyakit bulai menggunakan K-Means Clustering mencapai tingkat akurasi 85%.Kata Kunci: pengolahan citra, segmentasi, K-Mean clustering
Application of K-Means Clustering for Detection Downy Mildew at Madura Corn Plant Using Digital Image Processing Rosyadi NR, Imron; Prasetyowati, Erwin; Said, Badar; Arifin, Syaiful; Ridoni, Mohammad Syafiir
Tibuana Vol 6 No 2 (2023): Tibuana
Publisher : UNIPA PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/tibuana.6.2.7845.147-152

Abstract

The development and cultivation of corn is necessary in line with the increasing consumption of food ingredients and industrial needs, especially food products made from corn. In the development of maize in Indonesia, the main obstacle is the disturbance of Plant Pest Organisms (OPT), especially diseases, one of which is downy mildew. This disease can be identified by a change in color, so we need a way to find out the difference between the color of healthy leaves and the color of leaves that have changed due to downy mildew. One solution that can be used is image processing. Therefore the aim of this study was to detect downy mildew based on leaf color in corn plants based on digital image processing, to produce precise and objective results. The algorithm used is the K-Means Clustering algorithm. This study uses 50 images of training data and 25 images of test data. Based on the simulation of downy mildew disease identification using K-Means Clustering it achieves an accuracy rate of 85%.  
The Application of Time Series Forecasting Method to Estimate National Salt Demands Rosyadi NR, Imron; Prasetyowati, Erwin; Susanti, Rina
Tibuana Vol 8 No 1 (2025): Tibuana
Publisher : UNIPA PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/tibuana.8.1.9942.1-8

Abstract

Salt is one of the most important commodities for domestic use and as a raw material for industry. It is essential to make an estimate salt requirement to meet them appropriately. The purpose of the study was to estimate salt needs using the time series forecasting method and to identify the most effective technique for salt needs forecasting. Forecasting analysis uses Naive, Moving Average, Weighted Moving Average, Exponential Smoothing, Exponential Smoothing with Trend, and Trend Projection methods. Forecasting accuracy is tested using MAD, MSE, and MAPE. Based on the results, the Trend Projection is the most effective time series forecasting technique for predicting salt requirements. This method was selected due to its lowest error rate value (MAD of 0.16, MSE of 0.04, and MAPE of 4.28%) compared to other methods. According to projected estimates, the amount of salt required in 2024 would be 4.86 million tons.
Co-Authors Arifin, Syaiful Erwin Prasetyowati Ridoni, Mohammad Syafiir Rina Susanti Said, Badar