MODEL RANTAI MARKOV UNTUK PREDIKSI TINGKAT PENGANGGURAN DI INDONESIA BERDASARKAN PENDIDIKAN TERAKHIR

Angelyca; Dina Olivia Nainggolan; Mutia Agustin Purba; Usnul Marisa Siregar; Roberto Karlos Sinaga; Sudianto Manulang; Alvi Sahrin Nasution

doi:10.24114/jkss.v23i1.64642

Authors

Angelyca Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Dina Olivia Nainggolan Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Mutia Agustin Purba Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Usnul Marisa Siregar Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Roberto Karlos Sinaga Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Sudianto Manulang Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Alvi Sahrin Nasution Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221

DOI:

https://doi.org/10.24114/jkss.v23i1.64642

Abstract

Unemployment is a significant economic issue that profoundly affects social stability and public welfare. One of the key factors influencing unemployment levels is the highest level of education attained by individuals. This study aims to predict unemployment levels in Indonesia based on educational attainment using the Markov Chain model. The data used are secondary data from the Central Bureau of Statistics (BPS) spanning the period from 2019 to 2024, covering six education levels: elementary school, junior high school, senior high school, vocational high school, diploma, and university. Each level is treated as a state in the Markov process. By constructing a transition probability matrix from historical data, this study projects the unemployment distribution through 2027. The results indicate that graduates of senior and vocational high schools consistently have the highest unemployment rates. The distribution of unemployment proportions tends to converge toward a stationary state. These findings are expected to serve as a reference for the formulation of future labor and education policies.