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Intan Berlianty
Industrial Engineering Departement Diponegoro University Jl. Prof. Sudarto, SH Tembalang Semarang 50239 Telp. 0247460052
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Pengembangan Model Fuzzy Tsukamoto untuk Penilaian Kondisi Lingkungan Kerja Fisik dalam Rangka Mencapai Standar K3 Berlianty, Intan; Titisariwati, Indun; Ashriyadi, Akmal
Impression : Jurnal Teknologi dan Informasi Vol. 4 No. 3 (2025): November 2025
Publisher : Lembaga Riset Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59086/jti.v4i3.1294

Abstract

Lingkungan kerja fisik yang tidak sesuai standar K3 menurunkan produktivitas dan membahayakan pekerja. Penelitian ini mengisi celah dengan mengintegrasikan logika fuzzy Tsukamoto ke dalam sistem pendukung keputusan (SPK) untuk evaluasi lingkungan kerja fisik—sebuah pendekatan inovatif yang belum banyak diterapkan dalam konteks K3 industri manufaktur. Tujuannya adalah mengembangkan model evaluasi yang memberikan rekomendasi perbaikan terukur sesuai standar K3. Studi kasus dilakukan di PT. ABC pada proses finishing pengecoran logam. Model fuzzy Tsukamoto dikembangkan dengan tiga variabel input (suhu, pencahayaan, kebisingan) dan satu output (waktu baku), lalu diimplementasikan dalam SPK. Kontribusi utama penelitian ini adalah model yang mampu menangani ketidakpastian data lingkungan dan menghasilkan rekomendasi perbaikan spesifik, berbeda dengan metode konvensional yang deterministik dan kurang adaptif. Hasil menunjukkan kondisi awal (suhu 33,8°C, kebisingan 92 dB) melebihi standar Permenaker No. 5/2018. Model merekomendasikan kondisi optimal: suhu 28,90°C, pencahayaan 131,9 Lux, dan kebisingan 85,02 dB, yang menurunkan waktu baku dari 22,89 menit menjadi 11,39 menit (peningkatan produktivitas 50,3%). Pengujian SPK memperoleh skor performa 9,605/10. Implikasi praktis penelitian adalah tersedianya alat bantu keputusan yang objektif bagi manajemen industri untuk mengevaluasi lingkungan kerja dan mendukung kepatuhan K3 secara sistematis. Model ini efektif dalam memberikan rekomendasi perbaikan berbasis data.   Physical work environments that do not comply with Occupational Health and Safety (OHS) standards can reduce productivity and endanger workers. This study addresses a research gap by integrating Tsukamoto fuzzy logic into a Decision Support System (DSS) for physical work environment assessment—an innovative approach that has not been widely applied in the context of OHS compliance in manufacturing industries. The objective is to develop an evaluation model that provides measurable improvement recommendations in line with OHS standards. A case study was conducted at PT. ABC in the metal casting finishing process. A Tsukamoto fuzzy model was developed with three input variables (temperature, lighting, noise) and one output variable (standard time), then implemented in a DSS. The main contribution of this research is a model capable of handling environmental data uncertainty and generating specific improvement recommendations, distinguishing it from conventional deterministic and less adaptive methods. Results indicate that the initial environmental conditions (temperature 33.8°C, noise 92 dB) exceeded the standards of Minister of Manpower Regulation No. 5 of 2018. The model recommends optimal conditions: temperature 28.90°C, lighting 131.9 Lux, and noise 85.02 dB, which reduces standard time from 22.89 minutes to 11.39 minutes (a 50.3% productivity increase). DSS testing achieved a performance score of 9.605 out of 10. The practical implication of this research is the provision of an objective decision-making tool for industrial management to systematically evaluate work environments and support OHS compliance. The model proves effective in providing data-driven improvement recommendations.  
Co-Authors Apriani Soepardi Ashriyadi, Akmal Chaeron, Mochamad Gunawan Madyono Putro Indun Titisariwati, Indun Insani, Mohamad Kamil Irwan Soejanto Laila Nafisah MIFTAHOL ARIFIN, MIFTAHOL Nursubiyantoro, Eko Praja, Siti Aqilla Sayyidha Winata Prangawayu, Nur Puryani . Putra, Favian Ersanta Andhika Raden Rara Arsia Permata Noor Rania Ayu Aziza Sadi Tri Wibawa Wahyu Tri Utami Yuli Dwi Astanti