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All Journal Journal of Soft Computing Exploration Recursive Journal of Informatics
Zaaidatunni'mah, Untsa
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Computer Science & IT

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Implementation of Random Forest with Synthetic Minority Oversampling Technique and Particle Swarm Optimization for Predicting Survival of Heart Failure Patients Zaaidatunni'mah, Untsa; Sugiharti, Endang
Recursive Journal of Informatics Vol 2 No 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v2i2.76142

Abstract

Abstract. Heart failure is caused by a disruption in the heart’s muscle wall, which results in the heart’s inability to pump blood in sufficient quantities to meet the body’s demand for blood. The increasing prevalence and mortality rates of heart failure can be reduced through early disease detection using data mining processes. Data mining is believed to aid in discovering and interpreting specific patterns in decision-making based on processed information. Data mining has also been applied in various fields, one of which is the healthcare sector. One of the data mining techniques used to predict a decision is the classification technique. Purpose: This research aims to apply SMOTE and PSO to the Random Forest classification algorithm in predicting the survival of heart failure patients and to determine its accuracy results. Methods/Study design/approach: To predict the survival of heart failure patients, we utilize the Random Forest classification algorithm and incorporate data imbalance handling with SMOTE and feature selection techniques with PSO on the Heart Failure Clinical Records Dataset. The data mining process consists of three distinct phases. Result/Findings: The application of SMOTE and PSO on the Heart Failure Clinical Records Dataset in the Random Forest classification process resulted in an accuracy rate of 93.9%. In contrast, the Random Forest classification process without SMOTE and PSO resulted in an accuracy rate of only 88.33%. This indicates that the proposed method combination can optimize the performance of the classification algorithm, achieving a higher accuracy compared to previous research. Novelty/Originality/Value: Data imbalance and irrelevant features in the Heart Failure Clinical Records Dataset significantly impact the classification process. Therefore, this research utilizes SMOTE as a data balancing method and PSO as a feature selection technique in the Heart Failure Clinical Records Dataset before the classification process of the Random Forest algorithm.
Implementation of fuzzy tsukamoto in employee performance assessment Dewi, Meilina Taffana; Zaaidatunni'mah, Untsa; Al Hakim, M. Faris; Jumanto, Jumanto
Journal of Soft Computing Exploration Vol. 2 No. 2 (2021): September 2021
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v2i2.52

Abstract

Employees are one of the important things for the sustainability of a company, because employees are company assets. In addition, employee performance is also something that cannot be ignored because it determines the achievement of company goals. So it is important to monitor employee performance and conduct performance appraisals. With the addition of performance appraisal, the company can determine the provision of rewards, promotions, and punishments. It can be used as a work evaluation stage to improve the quality of work. Employee performance appraisal is based on several predetermined criteria, including responsibility, discipline, and attitude which in the end results in between two linguistic values, namely good or bad. One method for evaluating employee performance is the Tsukamoto fuzzy method. With the Tsukamoto fuzzy method, it is hoped that the assessment can be carried out fairly and measurably.
Co-Authors Al Hakim, M. Faris Dewi, Meilina Taffana Endang Sugiharti, Endang Jumanto Jumanto, Jumanto