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Optimasi Kinerja Excavator Menggunakan Metode Overall Equipment Effectiveness (OEE) Abryandoko, Eko Wahyu; Wijiatin, Alfi; Rodhi, Nova Nevila
Journal of Industrial Engineering and Technology Vol. 6 No. 1 (2025): Desember 2025
Publisher : Universitas Muria Kudus

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Abstract

Excavator is the main tool in the mining process. If the excavator is damaged, it will affect the mining process, reduce production targets, the cost of repairing damage will be high, and in the end the company will suffer losses. At PT. United Tractors Semen Gresik, based on the company's historical data from January to December 2022, the effectiveness of the Excavator unit in Mine Operation Area 1 (Tuban site) averaged 57%, of the minimum OEE standard set by the company of 75%. So it is necessary to improve to meet company standards. The purpose of this research is to optimize the performance of excavators for limestone and clay mining at PT. United Tractors Semen Gresik. The method approach in this study is carried out by calculating availability, utilization and productivity idex, then the value of overall equipment effectiveness (OEE) can be identified. After obtaining the factors that affect excavator performance, optimization is carried out using fishbone diagram analysis. The results of the research conducted show that the average total value of Overall Equipment Effectiveness (OEE) is 70%, which is still below the OEE standard set by the company, which is 75%. The factors that have the most influence on the low effectiveness of the Excavator unit using the Overall Equipment Effectiveness (OEE) method analysis are the loss time, downtime, and actual productivity factors.
Mathematical Modeling for Climate-Based Optimization of Rice Planting Schedules Moh Yusuf Dawud; Masahid Masahid; Eko Wahyu Abryandoko
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 6 (2025): December 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i6.2285-2296

Abstract

The stability of rice production is greatly influenced by the dynamics of climate variability that changes rapidly and is unpredictable. This study developed a climate-based planting scheduling model that utilizes daily climate data and annual production data for the period 2016–2024. The predictive model was built through multiple linear regression to examine the effects of temperature, rainfall, humidity, and wind speed on crop yields and ARIMA to project climate and rice production until 2029. Data were obtained from BMKG, BPS, and related regional agencies, then processed to produce an adaptive planting schedule. The regression results showed high accuracy with R² = 0.99, Adjusted R² = 0.961, MAE = 5.980, and RMSE = 6.770. Rainfall showed a negative effect (p = 0.025) on rice production. The optimization model produced the two most profitable planting months each year and provided more stable yields than conventional planting patterns. Five-year production projections show fluctuations influenced by climate conditions, including a sharp decline in 2027 and a rebound in 2029. The development of an adaptive schedule model allows for alternative decision-making in areas vulnerable to climate change.