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All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal Statistika dan Komputasi (STATKOM)
Putrye Aufia Indah Lestari
Universitas Nahdlatul Ulama Sunan Giri
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences

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Simulasi Monte Carlo Untuk Prediksi Jumlah Klaim Asuransi Di BPJS Ketenagakerjaan Cabang Bojonegoro Putrye Aufia Indah Lestari
Jurnal Statistika dan Komputasi Vol. 1 No. 2 (2022): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (432.1 KB) | DOI: 10.32665/statkom.v1i2.1265

Abstract

Latar   Belakang: Simulasi adalah salah satu cara yang digunakan untuk memprediksi jumlah klaim di masa depan berdasarkan jumlah klaim bulanan pada periode sebelumnya. Simulasi data ini dapat dilakukan dengan simulasi Monte carlo, Tujuan: Untuk mengetahui  deskriptif  jumlah klaim  program asuransi dari Jaminan Pensiun (JP), Jaminan Hari Tua (JHT), Jaminan Kecelakaan Kerja (JKK), Jaminan Kematian (JKM), dan Jaminan Kehilangan Pekerjaan (JKP), serta mendapatkan hasil prediksi jumlah klaim asuransi tersebut dengan metode simulasi Monte Carlo. Metode: Digunakan metode kuantitaif dengan pendekatan perhitungan manual untuk simulasi Monte Carlo. Data yang digunakan adalah data sekunder yang diperoleh dari BPJS ketenagakerjaan Cabang Bojonegoro berupa Jumlah Klaim Tahun 2021. Hasil: Dari hasil simulasi Metode Monte Carlo didapatkan prediksi untuk 1 tahun ke depan dengan mean ekspektasi jumlah klaim program JKK adalah 54 perbulan, mean ekspektasi program JKM adalah 67 per bulan, mean ekspektasi jumlah klaim JHT adalah 525 per bulan dan mean ekspektasi program JP adalah 275 perbulan. Prediksi ini merupakan perkiraan untuk 12 bulan ke depan. Standar deviasi klaim JKK sebesar 1.54, standar deviasi klaim JKM sebesar 2.43, standar deviasi klaim JHT sebesar 33.71, dan standar deviasi klaim JP sebesar 9.10. Kesimpulan: Diperoleh prediksi jumlah klaim untuk masing-masing program asuransi yaitu JKK, JKM, JHT dan JP dengan kategori sesuai dengan mean ekspektasi.  
Application Of The Association Rule Method Based On Book Borrowing Patterns In Bojonegoro Regional Libraries Putrye Aufia Indah Lestari; Nita Cahyani
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2893

Abstract

The library is an institution that processes collections of written and printed works, to meet the educational, research, information, and recreation needs of its users. The Bojonegoro Library Service provides reading materials with a collection of around 24,130 book titles and around 24,130 book copies. The number of registered visitors was 1,424 people. From 2021-2022, there are 303 book lending transaction data. Knowing the results of the Association Rule with the Frequent Pattem-Growth algorithm in determining recommendations for book placement based on borrowing patterns in libraries in the Bojonegoro area. The method used is Association Rule Mining, to produce an efficient algorithm, the algorithm used is the Frequent Pattern Growth (FP-Growth) Algorithm. The characteristic of the FP-Growth algorithm is the data structure used in a tree called FP-Tree. By using FP-Tree the FP-Growth algorithm can directly extract frequent itemsets from FP-Tree. The results of the research carried out by applying the FP growth algorithm with a support value limit of 20% and a confidence value of 80% from a dataset of 144 book lending transactions which became frequent itemsets were a combination of itemsets, resulting in a strong rule of 5 association rules which met the requirements. Can help the Bojongoro Library and archives service to improve the quality of service and can provide recommendations for librarians and as a reference for placing classes of books that are more often borrowed together closer together.
Co-Authors Nita Cahyani