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Journal : Proceeding International Applied Business and Engineering Conference

RICE QUALITY DETECTION BASED ON DIGITAL IMAGE USING CLASSIFICATION METHOD Sellya Meizenty; Dadang Syarif Sihabudin Sahid; Juni Nurma Sari
International ABEC 2021: Proceeding International Applied Business and Engineering Conference 2021
Publisher : International ABEC

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Abstract

Rice is one of the staples that is included in the consistent list of staple food commodities (Bapok), currently some irresponsible people make the rice more durable, fragrant and whiter. Many assume that the rice is clean, odorless, and has a high price is rice with good quality and vice versa. From the existing problems the author wants to help the community to better determine good quality rice and good for consumption. This research will create a system that can recognize the type of rice based on the image of the rice. Rice data that has been collected will be sampled and trained using the K-Nearest Neighbors (k-NN) method where this method is used for the classification of the shortest distance calculation which will produce a class in the form of rice data classes, while to obtain parameter values from the rice image using the extraction feature. RGB color average (Red, Green, and Blue) and to get results with a good level of accuracy will use K-Fold Validation.
Early Detection Of Alzheimer Disease In Elderly Web-Based Using Support Vector Machine Classification Method Juni Nurma Sari; Syaparudin BS; Kartina Diah KW; Puja Hanifah
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

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Abstract

Alzheimer's disease is characterized by dimentia diseases that usually begin with a decrease in memory. The number of people in around the world with dimentia diseases is estimated to reach 47.5 million and is increased to quadruple by 2050. The risk factors that make someone exposed Alzheimer's disease are aging, alcohol consumption, anterosclerosis, diabetes mellitus, down syndrome, genetics, hypertension, depression, and smoking. Aging is the biggest risk factor for Alzheimer's disease. People with age 65 years and over have a higher risk. Therefore, it is important to early detect Alzheimer's disease in order to start planning adequate care and medical needs. This study aims to create a web-based system for early detection of Alzheimer's disease in the elderly using support vector machine classification. Detection of Alzheimer's disease using the metric Mini Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) obtained through questionnaires to find out about cognitive function, thinking ability and ability to perform daily tasks. Classification is carried out using the Support Vector Machine (SVM) algorithm. Alzheimer's classification testing uses a confusion matrix with an accuracy value of 85%. For system testing carried out User Acceptance Test with general practitioner, the results were obtained that all the features and functions of the system had run as expected.