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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat ILKOM Jurnal Ilmiah Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Nasional Cosphi Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK)
Kartika Chandra Pelangi
Universitas Ichsan Gorontalo
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering

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Journal : ILKOM Jurnal Ilmiah

 1 
PENGELOMPOKAN BUAH JERUK MENGGUNAKAN NAïVE BAYES DAN GRAY LEVEL CO-OCCURRENCE MATRIX Haba, Rahmat Karim; Pelangi, Kartika Chandra
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1951.348 KB) | DOI: 10.33096/ilkom.v12i1.494.17-24

Abstract

Tangerines are fruits that are rich in high vitamin C content. Every orchard owner always tries to improve the quality of their plantation. In the selection of tangerines to be classified as ripe or immature at harvest time, the garden planters are already accustomed, but sometimes the farmer grouping the ripe oranges has problems such as physical limitations of the farmer, which is caused by fatigue factor. because it is still grouping with conventional systems so it is not effective and efficient in classifying ripe oranges. So from that we need a computerized system that can help gardeners in classifying ripe oranges. One of the technologies currently developing in agriculture and plantations is digital image processing using a classification system based on the texture and naïve bayes method. Based on the results that have been made, that the classification system using the Naïve Bayes method on tangerine images can be classified and obtain effective and efficient performance based on testing of 82% so that it can be implemented.
Pengelompokan Buah Jeruk menggunakan Naïve Bayes dan Gray Level Co-occurrence Matrix Rahmat Karim Haba; Kartika Chandra Pelangi
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i1.494.17-24

Abstract

Tangerines are fruits that are rich in high vitamin C content. Every orchard owner always tries to improve the quality of their plantation. In the selection of tangerines to be classified as ripe or immature at harvest time, the garden planters are already accustomed, but sometimes the farmer grouping the ripe oranges has problems such as physical limitations of the farmer, which is caused by fatigue factor. because it is still grouping with conventional systems so it is not effective and efficient in classifying ripe oranges. So from that we need a computerized system that can help gardeners in classifying ripe oranges. One of the technologies currently developing in agriculture and plantations is digital image processing using a classification system based on the texture and naïve bayes method. Based on the results that have been made, that the classification system using the Naïve Bayes method on tangerine images can be classified and obtain effective and efficient performance based on testing of 82% so that it can be implemented.
The Implementation of GLCM and ANN Methods to Identify Dragon Fruit Maturity Level Muhammad Faisal; Maryam Hasan; Kartika Candra Pelangi
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1504.64-71

Abstract

The identification of the maturity level of dragon fruit in this study was divided into two groups of ripeness: the unripe and the ripe. This study aims to classify the maturity level based on dragon fruit images using the feature extraction method, the gray level co-occurrence matrix (GLCM). This research method consists of converting RGB data to grayscale, image normalization, detection of dragon fruit maturity, feature extraction, and identification. Data collection from real data totaled 60 images used in this study consisting of 40 training data and 20 testing data which are RGB image data in JPG format. Each data consists of 2 maturity categories. Training data consists of 20 images of 99% ripe dragon fruit and 20 images of 85%. Meanwhile, the testing data consisted of 10 of 99% ripe dragon fruit images and 10 of 85% ripe dragon fruit images. The image data is processed into a grayscale image which then detects the ripeness of the dragon fruit. After the maturity of the dragon fruit is obtained, segmentation is carried out on the location of the dragon fruit found. Then the feature calculation is performed using the Gray Level Co-Occurrence Matrix (GLCM). The Artificial Neural Network (ANN) algorithm is used for the identification process. The final test results show that the proposed method has been able to detect dragon fruit maturity level with an accuracy of = 9/10* 100% = 90%, calculated using the confusion matrix. Thus, implementing the Gray Level Co-Occurrence Matrix and Artificial Neural Network methods to the maturity level problem dragon fruit needs to be developed.
Co-Authors Abd Rahmat Karim Haba Aprianto Alhamad Iftinan Inayah Mohamad Irvan A. Salihi Maryam Hasan Muh Faisal Nurhayati Olii Rofiq Harun Sudirman S. Panna Tri Handayani Yulianty Lasena