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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Benefit: Jurnal Manajemen dan Bisnis
Bagus Jati Santoso
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Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Library & Information Science

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Peran Manajemen Risiko dalam Meningkatkan Kinerja Mesin Filling Emulsion dengan Pendekatan House of Risk (HOR) Muhammad Fauzan; Bagus Jati Santoso
Benefit: Jurnal Manajemen dan Bisnis Vol. 9 No. 2 (2024): Benefit : Volume 9 Desember No 2 tahun 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/benefit.vi.6720

Abstract

In the competitive paint industry, improving operational performance is crucial for achieving excellence. The filling process plays a key role in determining the quality and safety of the final product. Filling machines ensure consistency and accuracy in paint filling, as well as high productivity. To prevent downtime that could lead to breakdowns, optimal maintenance is necessary. Autonomous Maintenance (AM), a key pillar of TPM, aims to enhance machine effectiveness and minimize downtime, but it is often hindered by the lack of adequate risk management. This study aims to optimize the AM system on emulsion filling machines using the House of Risk (HOR) approach to identify and address priority risks. Risks are analyzed using Failure Mode and Effect Analysis (FMEA) to assess their impact and probability, leading to the formulation of effective prevention strategies. HOR helps reduce the likelihood of negative risks and provides better preventive recommendations, ultimately improving AM effectiveness and the company’s operational performance. Keywords: Autonomous Maintenance, House of Risk, Downtime, Filling machine.
DATA REFINEMENT APPROACH FOR ANSWERING WHY-NOT PROBLEM OVER K-MOST PROMISING PRODUCT (K-MPP) QUERIES Vynska Amalia Permadi; Tohari Ahmad; Bagus Jati Santoso
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 16, No. 2, Juli 2018
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v16i2.a754

Abstract

K-Most Promising (K-MPP) product is a strategy for selecting a product that used in the process of determining the most demanded products by consumers. The basic computations used to perform K-MPP are two types of skyline queries: dynamic skyline and reverse skyline. K-MPP selection is done on the application layer, which is the last layer of the OSI model. One of the application layer functions is providing services according to the user's preferences.In the K-MPP implementation, there exists the situation in which the manufacturer may be less satisfied with the query results generated by the database search process (why-not question), so they want to know why the database gives query results that do not match their expectations. For example, manufacturers want to know why a particular data point (unexpected data) appears in the query result set, and why the expected product does not appear as a query result. The next problem is that traditional database systems will not be able to provide data analysis and solution to answer why-not questions preferred by users.To improve the usability of the database system, this study is aiming to answer why-not K-MPP and providing data refinement solutions by considering user feedback, so users can also find out why the result set does not meet their expectations. Moreover, it may help users to understand the result by performing analysis information and data refinement suggestion.
Co-Authors Muhammad Fauzan Tohari Ahmad Vynska Amalia Permadi