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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Guci, Abdi Negara
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA Astuti, Ani Budi; Guci, Abdi Negara; Alim, Viky Iqbal Azizul; Azizah, Laila Nur; Putri, Meirida Karisma; Ngabu, Wigbertus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1663-1672

Abstract

The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO >50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies.
DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM Anam, Syaiful; Guci, Abdi Negara; Widhiatmoko, Fery; Kurniawaty, Mila; Wijaya, Komang Agus Arta
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2281-2292

Abstract

Health insurance industry is very much needed by the community in handling the financial risks in the health sector. The number of claims greatly affects the achievement of profits and the sustainability of the health insurance industry. Therefore, filing claims by insurance users from year to year is important to be predicted in insurance firm. The Machine Learning (ML) method promises to be a good solution for predicting health insurance claims compared to conventional data analytics methods. Support Vector Machine (SVM) is one of the superior ML approaches. Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. Swarm intelligence-based algorithms can be used to select the best parameters from SVM. This method is capable of locating the global best solution, is simple to implemented, and doesn't involve derivatives. One of the best swarm intelligence algorithms is the Bat Algorithm (BA). BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. The metrics utilized for evaluation are accuracy, recall, precision, f1-score, and computing time. The experimental outcomes show that the proposed approach is superior to the conventional SVM and the hybrid of SVM and PSO in forecasting health insurance claims. In addition, the proposed method has a substantially shorter computing time than the hybrid of SVM and PSO. The outcomes of the experiments also indicate that the new classification model for predicting health insurance claim based on the SVM and BA can avoid over-fitting condition.
Co-Authors Alim, Viky Iqbal Azizul Ani Budi Astuti Azizah, Laila Nur Fery Widhiatmoko Mila Kurniawaty Putri, Meirida Karisma Syaiful Anam Wigbertus Ngabu Wijaya, Komang Agus Arta