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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

Klasifikasi Dehidrasi Tubuh Manusia Berdasarkan Citra RGB Pada Warna Urine Menggunakan Metode K-Nearest Neighbor Putri Nugraheni Utami; Veri Arinal; Dadang Iskandar Mulyana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3290

Abstract

Dehydration is a condition in the body where the amount of fluid that comes out is more than the amount of fluid that enters so that the body experiences a lack of fluids. The simplest way to find out if you are dehydrated is to check the color and amount of urine. If it is very dark and there is little water, then the body needs a lot of air. If the urine is clear, it means the body is in normal air balance. The level of dehydration between each person is different, so we need a system that can classify the level of dehydration objectively through urine, so that it can facilitate the process of early detection and diagnosis before being enforced. Based on the research that has been done, it is found that the Matrix Laboratory (Matlab) can classify dehydration in the human body, by utilizing urine images through several processes, namely preprocessing, segmentation, feature extraction of Red Green Blue and then the K-Nearest Neighbor method to classify a person's level of dehydration. From 30 urine image training data and 15 urine image test data with 3 classes of urine color levels, namely not dehydrated, mild dehydration and severe dehydration, the results obtained from the accuracy of the dehydration level classification using the K-Nearest Neighbor method of 93.3% obtained from 14 test data with accurate classification, and 1 test data with inaccurate classification.
Klasifikasi Kematangan Buah Pisang Ambon Menggunakan Metode KNN dan PCA Berdasarkan Citra RGB dan HSV Setya Putra Adenugraha; Veri Arinal; Dadang Iskandar Mulyana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3287

Abstract

Banana fruit or in scientific language is called Musa Paradisiaca. One type of banana that is easy to grow and develop in the tropics of Indonesia is the Cavendish Banana or commonly known as the Ambon Banana. The quality of Ambon bananas must be maintained because Ambon bananas grown in Indonesia also supply the needs of foreign markets. The quality of bananas is very influential from the time of harvesting, the level of maturity of bananas is related to marketing reach. Basically, farmers use manual methods in determining the maturity level of Ambon bananas, so there are several factors that can make the classification results less accurate. Based on these problems, a system was made to classify the maturity level of Ambon bananas by utilizing the RGB and HSV color features using the K-Nearest Neighbor (KNN) method. Classification uses image processing by utilizing matlab software for making a classification system with 3 classes, namely raw, ripe, and overcooked. The results of this study are expected to help Ambon banana farmers in classifying the maturity level of Ambon bananas. In this study using data obtained from the place of observation. The data used in this study were 41 data which were divided into 30 training data and 11 test data. The data is classified using the KNN method by measuring the distance to the nearest neighbor with a value of K=5. From this study, the results obtained accuracy of 90.9% with the results of the classification of test data as many as 10 data received accurate classification results and 1 data received inaccurate classification results.