KLASIFIKASI KECENDERUNGAN TINGKAT DEPRESI MAHASISWA MENGGUNAKAN LEARNING VECTOR QUANTIZATION 3

PUTRI, DESRY KENCANA and Rini, Dian Palupi and Satria, Hadipurnawan (2022) KLASIFIKASI KECENDERUNGAN TINGKAT DEPRESI MAHASISWA MENGGUNAKAN LEARNING VECTOR QUANTIZATION 3. Undergraduate thesis, Sriwijaya University.

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Abstract

Depression is a serious health problem experienced by students and is often not realized. Depression needs to be known early on to reduce depression or prevent students from experiencing depression. This study aims to classify the tendency of the level of depression experienced by students which consists of three classification classes, namely, mild depression, moderate depression, and major depression. Classification in this study was carried out using the learning vector quantization 3 (LVQ 3) method. The LVQ 3 algorithm has two winning distances, namely the first closest distance (Dc) and the second closest distance (Dr). In this study, the input data used amounted to 120 data based on filling out The Patient Health Questionnaire (PHQ-9) questionnaire by the respondents. The input variables used are name, gender, semester, and symptoms of depression. Based on the results of testing on 120 data with the distribution of training data and test data of 90:10, 80:20, and 70:30 as well as a combination of specified learning parameters, the highest average accuracy reaches 99.35% at 90:10 data sharing. The best combination of learning parameters are learning rate 0.3, windows 0.3 and 0.4, epsilon 0.2, minimum learning rate 0.02 and learning reduction 0.1. Keywords: Depression, learning vector quantization 3, classification, college students, PHQ-9.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Depresi, learning vector quantization 3, klasifikasi, mahasiswa, PHQ-9
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) > T18 Modern
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Mrs. Desry Kencana Putri
Date Deposited:	15 Aug 2022 04:45
Last Modified:	15 Aug 2022 04:45
URI:	http://repository.unsri.ac.id/id/eprint/77213

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