ANALISIS PROYEKSI KEBUTUHAN TENAGA KERJA BERDASARKAN SKILLS YANG DIBUTUHKAN MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER

Nur Azizah Firdausa; Ribka Rifanny Br Girsang; Dela Oktaviana; Astr Wahyuningsiam; Muhammad Habibi

doi:10.30989/ijds.v2i1.1346

Nur Azizah Firdausa Mahasiswa
Ribka Rifanny Br Girsang Universitas Jenderal Achmad Yani Yogyakarta
Dela Oktaviana Universitas Jenderal Achmad Yani Yogyakarta
Astr Wahyuningsiam Universitas Jenderal Achmad Yani Yogyakarta
Muhammad Habibi Universitas Jenderal Achmad Yani Yogyakarta

DOI: https://doi.org/10.30989/ijds.v2i1.1346

Keywords: Unemployment, Unemployment Reduction Strategy, Naive Bayes Classifier, Classification, Labor Demand Projection.

Abstract

In August 2023, Indonesia faced an unemployment rate of 7.86 million people, although there is no denying that the percentage of unemployment has decreased from the previous year. The data is categorized into four groups, namely unemployment involves those who are looking for work, trying to set up a business having trouble landing a job, and even those who have worked but have not started. The Covid-19 pandemic changed the paradigm of work to remote, but the need for job information remains key. Labor demand projections provide long-term insights into promising sectors and fields, guiding job seekers to develop skills according to labor market trends. This research was conducted using naive bayes classification, which is a text classification method that relies on the likelihood of keywords to compare training and testing documents. This classification method is expected to help reduce unemployment rates and align individual skills with industry needs, contributing to education and training policies to make smart career decisions in the digital era.