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Andri
Magister Komputer, STMIK Mikroskil
Computer Science & IT

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COMBINATION OF ACO AND PSO TO MINIMIZE MAKESPAN IN ORDERED FLOWSHOP SCHEDULING PROBLEMS Sastra Wandi Nduru; Ronsen Purba; Andri
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

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Abstract

The problem of scheduling flowshop production is one of the most versatile problems and is often encountered in many industries. Effective scheduling is important because it has a significant impact on reducing costs and increasing productivity. However, solving the ordered flowshop scheduling problem with the aim of minimizing makespan requires a difficult computation known as NP-hard. This research will contribute to the application of combination ACO and PSO to minimize makespan in the ordered flowshop scheduling problem. The performance of the proposed scheduling algorithm is evaluated by testing the data set of 600 ordered flowshop scheduling problems with various combinations of job and machine size combinations. The test results show that the ACO-PSO algorithm is able to provide a better scheduling solution for the scheduling group with small dimensions, namely 76 instances from a total of 600 inctances and is not good at obtaining makespan in the scheduling group with large dimensions. The ACO-PSO algorithm uses execution time which increases as the dimension size (multiple jobs and many machines) increases in a scheduled instance
COMBINATION OF LOGISTIC REGRESSION AND SVM ALGORITHM WITH HYBRID PSO AND GA BASED SELECTION FEATURE IN CORONARY HEART DISEASE CLASSIFICATION Sutrisno Situmorang; Pahala Sirait; Andri
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.968 KB)

Abstract

The world's high death rate from heart disease requires early prevention by medical doctors to diagnose heart disease early. The machine learning approach makes it possible to predict the risk of developing heart disease by examining certain values at a low cost. This study will contribute to the development of a combination of Logistic Regression and SVM models that integrate SVM and Logistic Regression algorithms by implementing selection features using hybrid PSO and GA methods. The combination concept of Logistic Regression SVM (LRSVM) applied to this study is to reduce the risk of SVM output errors by interpreting and modifying the output of SVM classifiers by the results of Logistic Regression analysis. The test results showed that LRSVM with pso-GA hybrid-based selection feature achieved better performance for coronary heart disease classification with 99.66% accuracy compared to classification accuracy with SVM algorithm without selection feature
Co-Authors Pahala Sirait Ronsen Purba Sastra Wandi Nduru Sutrisno Situmorang