Performance Comparison of CART And KNN Algorithms for Analyzing Early Predictions of Mental Health

Eling Sekar  Anggraeni; Lulu Amnah  Fitriya Maharani; Desi Riyanti; Ranggi Praharaningtyas  Aji; Pungkas Subarkah

doi:10.47709/cnahpc.v6i3.4232

Authors

Eling Sekar Anggraeni Universitas Amikom Purwokerto
Lulu Amnah Fitriya Maharani Universitas Amikom Purwokerto
Desi Riyanti Universitas Amikom Purwokerto
Ranggi Praharaningtyas Aji Universitas Amikom Purwokerto
Pungkas Subarkah Universitas Amikom Purwokerto

DOI:

10.47709/cnahpc.v6i3.4232

Keywords:

Early Prediction Analysis, Mental Health, CART Algorithm, CART Algorithm and KNN Algorithm

Dimension Badge Record

Abstract

Currently, mental health is an unresolved mental health problem both at the national and international levels. Mental health disorders are conditions where a person has difficulty in adjusting to the conditions around them. Mental Health is an important aspect of overall health. Efforts to maintain and improve it can help a person achieve better well-being in everyday life. This research aims to conduct Early Prediction Analysis related to mental health problems experienced by students by measuring the accuracy level of the analysis. This research was conducted using the CART (Classification and Regression Trees) and KNN (K-Nearest Neighbor) algorithms with a set of Mental Health Datasets consisting of 11 attributes and 101 data. The data is processed using the Weka Application and the accuracy results of each algorithm are obtained, amounting to 94.0594% for the CART Algorithm and 91.0891% for the KNN Algorithm. From this achievement, it can be concluded that the performance of the CART and KNN algorithms falls into the Excellent Classification category. Judging from the accuracy obtained, the CART algorithm has a higher accuracy value than the KNN algorithm, so the CART algorithm has a high performance for analyzing early prediction of mental health of students who do not take steps in seeking mental health support.

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