Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan
Maharani, Natasha Clarissa
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Health Insurance Claim Classification using Support Vector Machine with Velocity Pausing Particle Swarm Optimization Jayanti, Luh Putu Dharma; Anam, Syaiful; Ardiyansa, Safrizal Ardana; Maharani, Natasha Clarissa
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.31914

Abstract

classification is a serious problem. Identifying claim classification is difficult. Machine Learning (ML) can predict potential claim decisions. Support Vector Machine (SVM) is a ML model that can generalize well to test data. SVM achieves an -score of 73.39% and 89.88% with a linear kernel, 73.34% and 73.34% with Radial Basis Function (RBF) kernel. Particle Swarm Optimization (PSO) improves the performance, because it can find the best parameters for SVM. However, the SVM parameters found by PSO are not guaranteed to be the global optimum. Velocity Pausing PSO (VPPSO) can address this problem. SVM-VPPSO performs better compared with SVM and SVM-PSO. SVM-VPPSO with linear kernel achieves -score of 90.17%, 90.16%, and 90.06% with 10, 20, and 30 particles respectively. The linear kernel also performs better than RBF kernel with a difference of 0.39% on the testing data. The best configuration is SVM-Linear-VPPSO with 10 particles. This configuration also achieves computation time of 46.938 seconds, which faster than SVM-Linear-VPPSO with 20 particles. The variance in computational time with 10 particles is 1.832 seconds, which better than with 20 particles with variance of 37.909 seconds.
OPTIMIZING HEART ATTACK DIAGNOSIS USING RANDOM FOREST WITH BAT ALGORITHM AND GREEDY CROSSOVER TECHNIQUE Ardiyansa, Safrizal Ardana; Maharani, Natasha Clarissa; Anam, Syaiful; Julianto, Eric
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1053-1066

Abstract

Cardiovascular disease stands as one of the primary contributors to global mortality, with the World Health Organization (WHO) reporting approximately 17.9 million deaths annually. Swift and accurate diagnosis of heart attacks is crucial to ensure timely and specialized intervention for patients afflicted by this ailment. A machine learning algorithm that can be employed for addressing such issues is the Random Forest algorithm. However, the efficacy of the model is significantly influenced by the features selected during the training phase. To mitigate this, the Binary Bat Algorithm (BBA) with greedy crossover has been utilized to enhance feature selection within the model. This approach is particularly adept at preventing convergence issues often associated with local minima. The optimal parameters for BBA with greedy crossover are determined to be , , , and . With these parameters, the proposed algorithm identifies the most relevant features, including age, gender, cp, chol, thalach, oldpeak, slope, and ca, achieving an accuracy of 94.19% on the training data and 91.8% on the test data. Furthermore, the precision and recall values for both classes range from 0.87 to 0.96, contributing to an approximate -score of 0.92. The proposed method has increased its -score by 0.05 if compared with the regular Random Forest model. These results underscore the effectiveness of the proposed algorithm in providing accurate and reliable predictions for heart disease diagnosis. As such, this model makes diagnosing heart attack more convenient and effective because it does not require too much medical features or patient data. Hopefully, the results of this research help medical practitioners make better and timely decisions in the diagnosis and treatment of heart attacks, as well as assist in planning more effective public health programs for heart attack prevention.
Co-Authors Ardiyansa, Safrizal Ardana Jayanti, Luh Putu Dharma Julianto, Eric Syaiful Anam