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All Journal Sinergi M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika
Leo, Joseph Andrew
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Optimization of preventive maintenance on critical machines at the Sabiz 1 plant using Reliability-Centered Maintenance method Cahyati, Sally; Puspa, Sofia Debi; Himawan, Riswanda; Agtirey, Novan Rojabil; Leo, Joseph Andrew
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.015

Abstract

Maintenance planning is the first step in an industry, especially when it comes to the trade-off between cost and reliability, which is the reason for this research's aim. The Reliability Centered Maintenance (RCM) method will be used in this research to optimize maintenance activity in critical machines at the Sabiz 1 plant to minimize downtime, costs incurred for machine repairs, and production losses. The tools of RCM that will be used such as FMEA to determine critical machines as the focus of analysis, a Fishbone Diagram to determine the causes of failure, an RCM worksheet to get preventive activities, and a statistical distribution approach to obtain appropriate preventive activity intervals. The result of data processing shows that all data has a lognormal distribution and can be continued using the lognormal distribution method. The results of this analysis are the preventive maintenance activities proposal and their intervals as a reference for Sabiz 1 plant maintenance planning. The preventive maintenance plan for three critical machines is the high-pressure pump is four days of inspection activities and two days for replacement activities; for the powder, base conveyor is four days of checking activities and 17 days for replacement activities; and for the extraction tower fan for inspection, activities is seven days. The prediction of the implementation impact of this maintenance planning will save maintenance costs around 70% compared to historical costs.
Customer Segmentation Analysis Using Random Forest & Naïve Bayes Method In The Case of Multi-Class Classification at PT. XYZ Puspa, Sofia Debi; Puspitasari, Fani; Riyono, Joko; Pujiastuti, Christina Eni; Bijlsma, David Leon; Leo, Joseph Andrew
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 4 (2023): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v8i4.532

Abstract

Cases of the COVID-19 pandemic are gradually decreasing every day in Indonesia, but the impact of the COVID-19 pandemic has greatly affected various sectors, especially the economy and business. Sales transactions have not yet reached the company's target due to weak public purchasing power. The accuracy of customer segmentation analysis and attractive promo voucher offers are needed to increase the opportunity for people's purchasing power for a product. This study aimed to predict the level of customer purchasing power using the random forest and naïve Bayes methods in the case of multi-class data classification at PT. XYZ. The classification is carried out to determine the type of promo voucher suitable to be offered to customers according to the level of customer purchasing power. The data used is historical daily transaction data from January 1, 2022, to December 31, 2022, which is the transition period for the COVID-19 pandemic. Evaluation using the random forest method produces an accuracy of 99.99%, while the naïve Bayes method produces an accuracy of 92.99%. The random forest and naïve Bayes methods can work very well on large data volumes. However, from the comparison results, it can be concluded that the performance of the random forest method is better than the naïve Bayes method in the multi-class classification case in predicting the level of customer purchasing power at PT. XYZ.
Co-Authors Agtirey, Novan Rojabil Bijlsma, David Leon Himawan, Riswanda Joko Riyono Pujiastuti, Christina Eni Puspa, Sofia Debi Puspitasari, Fani Sally Cahyati