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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) SUBAKTYA, Unpar Community Service Journal
Kusnadi, Felivia
Center For Mathematics And Society, Department Of Mathematics, Faculty Of Information Technology And Science, Parahyangan Catholic University, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Prediction of Maternity Recovery Rate of Group Long-Term Disability Insurance Using XGBoost Kusnadi, Felivia; Wijaya, Andry; Lesmono, Julius Dharma
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16825

Abstract

To help insurers determine insurance rates incorporating maternity factors, it is crucial to understand the maternity recovery rate, which was a metric used by insurance companies to understand how much of the expenses associated with maternity care and related medical services are covered by their policies. This paper employed Extreme Gradient Boosting (XGBoost), a powerful method for handling complex data relationships and preventing overfitting, on North American Group Long-Term Disability dataset obtained from the Society of Actuaries, which listed maternity as one of its categories, to predict the maternity recovery rate. In comparison, other machine learning methods such as Gradient Boosting Machine (GBM) and Bayesian Additive Regression Tree (BART) were used, with Root Mean Squared Error (RMSE) values calculated the difference between predicted and observed maternity recovery rates. Four datasets, 3 imbalanced and 1 fairly-balanced, were created out of the original dataset to test each method’s predictive prowess. The study revealed that XGBoost performed exceptionally well on the imbalanced datasets, while BART showed slight superiority in fairly-balanced data. Furthermore, the model identified the duration, exposures, and age of participants in both predicting maternity recovery rates and the underwriting process. 
Pendampingan Pembelajaran Matematika Siswa SMA Santa Angela Bandung Kusnadi, Felivia; Kristiani, Farah; Sugiarto, Iwan; Fauzi, Rizky Reza
SUBAKTYA: UNPAR COMMUNITY SERVICE JOURNAL Vol. 1 No. 1 (2024): (JULI 2024) SUBAKTYA: UNPAR Community Service Journal
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/sucsj.v1i1.7930.20-33

Abstract

Kebanyakan siswa ingin hidup mereka bermakna, yakni dengan mengetahui tujuan hidup serta cara untuk mencapai tujuan tersebut dengan mengembangkan minat dan bakat mereka dalam suatu bidang. Untuk mengembangkan minat dan bakat yang dimiliki, para siswa memerlukan pembelajaran wawasan sejak dini. Salah satu cara yang telah dilakukan pihak sekolah ialah melalui tes minat dan bakat. Tes yang diberikan saat bangku kelas X atau XI tersebut belum tentu sesuai dengan keinginan mereka. Luaran dari hasil tes minat dan bakat mungkin membantu, namun belum tentu cocok ketika mereka sudah menempuh pembelajaran di bangku kuliah atau ketika mereka sudah bekerja nantinya. Hal ini diperparah dengan adanya pandemi COVID-19 yang memaksa semua proses belajar dan mengajar diselenggarakan secara daring. Minimnya interaksi antara siswa dan guru menyebabkan guru kurang dapat mengenal potensi yang sudah ada dalam diri setiap siswanya, sehingga para guru susah untuk memberikan saran dan masukan yang berguna untuk mengembangkan minat dan bakat anak tersebut. Kegiatan pendampingan pembelajaran matematika yang dikembangkan oleh beberapa dosen Program Studi Sarjana Matematika UNPAR mencoba membantu para siswa yang memiliki minat akan pelajaran matematika untuk mengenali potensi diri. Kegiatan ini diselenggarakan dengan memperkenalkan ilmu-ilmu dasar yang berhubungan dengan matematika. Tujuannya yakni untuk menarik minat para siswa dalam berkarir di bidang yang berhubungan dengan matematika.
Callable Bond's Value Analysis Using Binomial Interest Rate Tree Considering Early Redemption and Default Risks Kusnadi, Felivia; Tirtasaputra, Devina Gabriella
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 2 (2023): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i2.12125

Abstract

Bonds are known as one of low-risk investments and worth to be considered as a part of an investor's portfolio, however there are still underlying risks that could affect its price. This paper focuses on the effect of early redemption risk and default risk to a bond’s value. Using binomial interest rate tree method and its adjusted for default risk version, this paper wants to analyse how these risks affect Indonesian bonds’ values through simulations, while showing how these bonds can be used to construct the binomial interest rate trees. In the default risk simulation, more assumptions are made because of data limitations, which causes the first period recovery fraction to soar higher than the other periods. The analysis shows that, compared to present value of standard bonds, early redemption risk tends to cause the bond's present value to drop, while on the contrary, default risk tends to cause the bond's present value to rise. The cause of higher present value of bonds with default risk is explained by the high first period recovery fraction. 
Co-Authors Aryento, Jevilia Benny Yong Budiana, Stevanny Dharma Lesmono Farah Kristiani Fauzi, Rizky Reza Ferry Jaya Permana Hadiono, Vania Rosalie Irawan, Robyn Ivander Jeremy Iwan Sugiarto Iwan Sugiarto Johan, Jonathan Prasetyo Livia Owen Natasha Maria Setiady, Gabrielle Aretha Tirtasaputra, Devina Gabriella Wijaya, Andry