Analysis of Unemployment Rate in Indonesia Using Fuzzy Inference System

Penulis

  • Tiara Dwi Lestari Purba STIKOM Tunas Bangsa
  • Aklima Laduna Ramadya STIKOM Tunas Bangsa
  • Ega Wahyu Andani STIKOM Tunas Bangsa
  • Baginda Faustine Sinaga STIKOM Tunas Bangsa
  • Victor Asido Elyakim P STIKOM Tunas Bangsa

DOI:

https://doi.org/10.55123/jomlai.v4i1.5956

Kata Kunci:

Fuzzy Logic , Unemployment , BPS, Inference System, Classification

Abstrak

Unemployment is a complex problem that demands an analytical approach capable of handling data uncertainty. This study utilizes a fuzzy inference system to analyze unemployment rates in Indonesia, based on Central Statistics Agency (BPS) data for the 2023-2025 period. The fuzzy logic method was chosen due to its ability to handle linguistic variables and uncertainty in classifying unemployment levels. Input variables include education level, age group, and geographical area, while the output is a classification of unemployment risk (low, medium, high). The fuzzy inference process involves fuzzification, rule base formation, fuzzy logic inference, and defuzzification. BPS data indicates that the Open Unemployment Rate (TPT) experienced a consistent downward trend from 5.45% in February 2023 to 4.76% in February 2025. Nevertheless, the complexity of unemployment requires a flexible approach that can capture nuances of uncertainty, which conventional methods are unable to address. The research results show that the fuzzy inference system is capable of classifying unemployment levels with an accuracy of 87.3%. The highest unemployment rate is found in the 15-24 age group and among high school/vocational school graduates. This system can serve as a decision-making tool for the government in formulating more targeted employment policies.

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Unduhan

Diterbitkan

2025-03-20

Cara Mengutip

Tiara Dwi Lestari Purba, Aklima Laduna Ramadya, Ega Wahyu Andani, Baginda Faustine Sinaga, & Victor Asido Elyakim P. (2025). Analysis of Unemployment Rate in Indonesia Using Fuzzy Inference System . JOMLAI: Journal of Machine Learning and Artificial Intelligence, 4(1), 53–60. https://doi.org/10.55123/jomlai.v4i1.5956

Terbitan

Vol 4 No 1 (2025): Maret 2025

Bagian

Articles

Lisensi

Hak Cipta (c) 2025 Tiara Dwi Lestari Purba, Aklima Laduna Ramadya, Ega Wahyu Andani, Baginda Faustine Sinaga, Victor Asido Elyakim P

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