Implementasi Algoritma C4.5 untuk Klasifikasi Status Tingkat Pengangguran di Indonesia Berdasarkan Jenjang Pendidikan: Penelitian

Fadel Tri Ismar; Muhammad Reza; Muhammad Faizal Afif; Muhammad Rayyan Saputra; Ammar Ammar

doi:10.31004/jerkin.v4i3.5076

Authors

Fadel Tri Ismar Universitas Bina Sarana Informatika
Muhammad Reza Universitas Bina Sarana Informatika
Muhammad Faizal Afif Universitas Bina Sarana Informatika
Muhammad Rayyan Saputra Universitas Bina Sarana Informatika
Ammar Ammar Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31004/jerkin.v4i3.5076

Keywords:

Unemployment, Education Level, C4.5 Algorithm, Data Mining, Decision Tree

Abstract

Unemployment is one of the main economic problems in Indonesia that fluctuates every year. Data from the Central Statistics Agency (BPS) shows that the level of education does not always guarantee the absorption of the workforce, where the phenomenon of educated unemployment (vocational high school/university graduates) often occurs. This study aims to classify the status of the national unemployment rate (category "High" or "Low") based on the variable of education level (elementary school, junior high school, senior high school, vocational high school, diploma, university). The method used is the C4.5 Data Mining Algorithm because of its ability to form a decision tree (Decision Tree) that is easy to interpret. Data processing is carried out using the RapidMiner tool by dividing the data into training data and test data. The results of this study are in the form of rules that can be used by the government or policy makers to determine which level of education contributes most to the high national unemployment rate.

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