Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

Strategic plant maintenance planning in agriculture by integrating lean principles and optimization Simarmata, Gayus; Suwilo, Saib; Sitompul, Opim Salim; Sutarman, Sutarman
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6279-6286

Abstract

Operational planning within agricultural production systems plays a pivotal role in facilitating farmers' decision-making processes. This study introduces a novel mathematical model aimed at optimizing plant maintenance planning through the efficient allocation of labor, optimal utilization of machinery, and strategic scheduling. Utilizing mixed integer non-linear programming (MINLP), the model integrates lean principles to minimize waste and improve operational efficiency. The primary contributions of this study include the development of a comprehensive maintenance planning model, the application of advanced mathematical techniques in agriculture, and the enhancement of resource allocation strategies. The results demonstrate significant improvements in maintenance task scheduling, reduced downtime, and enhanced productivity, ultimately contributing to sustainable farming practices and food security. This model serves as a strategic decision-support tool for farmers, enabling data-driven planning and resource utilization to achieve both short-term efficiency and long-term agricultural viability.
The role of Louvain-coloring clustering in the detection of fraud transactions Mardiansyah, Heru; Suwilo, Saib; Nababan, Erna Budhiarti; Efendi, Syahril
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp608-616

Abstract

Clustering is a technique in data mining capable of grouping very large amounts of data to gain new knowledge based on unsupervised learning. Clustering is capable of grouping various types of data and fields. The process that requires this technique is in the business sector, especially banking. In the transaction business process in banking, fraud is often encountered in transactions. This raises interest in clustering data fraud in transactions. An algorithm is needed in the cluster, namely Louvain’s algorithm. Louvain’s algorithm is capable of clustering in large numbers, which represent them in a graph. So, the Louvain algorithm is optimized with colored graphs to facilitate research continuity in labeling. In this study, 33,491 non-fraud data were grouped, and 241 fraud transaction data were carried out. However, Louvain’s algorithm shows that clustering increases the amount of data fraud of 90% by accurate.