Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Journal of Artificial Intelligence and Digital Business International Journal of Mechanical, Industrial and Control Systems Engineering
Resti Yuliana
Universitas Bina Bangsa
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Pengendalian Kualitas Produk Sol Sepatu Menggunakan Metode Failure Mode and Effects Analysis (FMEA) di CV Kitos Jaya Dede Khaerul Ilmi; Eka Lailita Eti Varina; Resti Yuliana
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.13178

Abstract

CV Kitos Jaya merupakan perusahaan manufaktur alas kaki di Kabupaten Tangerang yang menghadapi tingkat kecacatan sol sepatu sebesar 9,47% dari total produksi selama periode Mei 2025 hingga April 2026, dengan jenis cacat dominan berupa Short Shot, Pitting, dan Kontaminasi Warna yang berdampak pada pemborosan material serta tidak efesienya biaya produksi. Penelitian ini bertujuan mengidentifikasi faktor penyebab kecacatan, menentukan prioritas risiko kegagalan, serta menyusun rekomendasi perbaikan melalui metode Failure Mode and Effects Analysis (FMEA). Pendekatan yang digunakan adalah kuantitatif deskriptif dengan alat bantu pengendalian kualitas berupa check sheet, histogram, diagram Pareto, fishbone diagram, dan FMEA. Data diperoleh melalui observasi lapangan serta kuesioner FMEA yang diisi oleh empat operator dan satu manajer produksi, kemudian dianalisis menggunakan nilai Severity, Occurrence, dan Detection untuk memperoleh Risk Priority Number (RPN = S × O × D). Hasil penelitian menunjukkan bahwa Short Shot menempati prioritas pertama dengan total RPN 4.224, disusul Pitting dengan RPN 3.450, dan Kontaminasi Warna dengan RPN 2.490, di mana penyebab dengan RPN tertinggi secara konsisten bersumber dari belum adanya SOP baku pada proses produksi. Rekomendasi perbaikan disusun dengan pendekatan 5W1H dan diharapkan mampu menekan tingkat kecacatan serta meningkatkan konsistensi kualitas produk sol sepatu di CV Kitos Jaya.
Analysis of Physical and Mental Load on Wheat Flour Production Operators Using %Cvl and Nasa-Tlx Methods Fayyadh Annas; Eka Lailita Eti Varina; Resti Yuliana
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 3 No. 3 (2026): September :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v3i3.486

Abstract

The wheat flour processing industry involves production activities that require operators to maintain physical and mental conditions throughout the work process. Operators in the milling and mixing sections perform prolonged standing, machine monitoring, material handling, production target achievement, and work in hot, noisy, and dusty environments. This study aimed to analyze physical and mental workload, compare both workloads, and formulate improvement recommendations for wheat flour production operators. A descriptive quantitative approach was applied using the Cardiovascular Load (%CVL) and NASA-Task Load Index (NASA-TLX) methods. The sample consisted of 30 milling and mixing operators selected using total sampling. %CVL data were obtained by measuring heart rate using a pulse oximeter, while mental workload was assessed using the NASA-TLX questionnaire. The results showed an average %CVL of 21%, with 24 operators (80%) classified as having a low workload and 6 operators (20%) requiring improvement. Meanwhile, the average NASA-TLX score was 79.7, with 14 operators (46.7%) classified as having a high workload and 15 operators (50%) as very high. Mental workload was primarily influenced by Physical Demand, Effort, and Temporal Demand. The findings indicate a discrepancy between physiological responses and perceived workload. Improvements can be achieved through periodic evaluations, task allocation optimization, effective rest periods, and training.
Co-Authors Dede Khaerul Ilmi Eti Varina, Eka Lailita Fayyadh Annas