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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) 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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Peningkatan Minat Pembelajaran Matematika Pada Siswa SMA Trinitas Bandung Selama Era Pandemi COVID-19 Felivia Kusnadi; Benny Yong; Farah Kristiani; Iwan Sugiarto; Livia Owen
Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Vol 6, No 1 (2022): Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM Universitas Respati Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52643/pamas.v6i1.1577

Abstract

Matematika merupakan ilmu yang tidak terlalu populer di kalangan siswa SMA. Hanya 23% siswa SMA yang tertarik akan mata pelajaran Matematika. Tuntutan kurikulum yang padat membuat para guru kesulitan untuk mengembangkan materi penerapan Matematika dalam kehidupan sehari-hari. Hal ini diperparah dengan keadaan COVID-19 yang membuat kegiatan belajar mengajar penuh secara daring. Untuk menumbuhkan minat para siswa di bidang Matematika, kami menyelenggarakan kegiatan pendampingan belajar secara daring yaitu pengenalan teori Matematika beserta aplikasinya kepada para siswa SMA Trinitas, terutama bagi mereka yang akan memilih jurusan saat nanti kuliah. Melalui komunitas pengabdian Siswa Belajar Matematika ini, para peserta dapat mengembangkan minat dalam mempelajari serta memberikan inspirasi dan penyegaran akan ilmu Matematika sehingga tercipta pola pikir kegunaan matematika serta implementasinya dengan menggunakan Excel.
Metode Excess-of-Loss Menggunakan Bayesian untuk Memodelkan Tingkat Keparahan Klaim Felivia Kusnadi; Benny Yong; Ferry Jaya Permana
MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Vol. 3 No. 2 (2020): Vol. 3 No. 2 September 2020
Publisher : Prodi Pendidikan matematika Universitas Islam Majapahit (UNIM), Mojokerto, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36815/majamath.v3i2.745

Abstract

Tingkat keparahan klaim merupakan variabel acak, terlebih pada data asuransi umum. Masalah utama dalam memodelkan data ini ialah kesenjangan yang besar antara nominal klaim. Penelitian ini dapat menyelesaikan masalah tersebut dengan mencocokkan distribusi ekor panjang terhadap data. Untuk menentukan distribusi yang digunakan, penulis menggunakan Quantile-Quantile plots beserta Akaike Information Criterion (AIC) untuk setiap model. Penulis menghitung peluang posterior untuk setiap model menggunakan Teorema Bayes dan menentukan besar premi murni untuk excess layer. Penelitian ini menunjukkan statistik dari premi murni per lapisan dengan tujuan agar dapat digunakan untuk memodelkan tingkat keparahan klaim dan menentukan harga premi murni pada excess layer.
Indonesian National Mortality Rates using the Whittaker-Henderson Graduation Method Setiady, Gabrielle Aretha; Kusnadi, Felivia
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.26316

Abstract

In this paper, we aim to present a graph depicting the quantified mortality rates for the entire population of Indonesia, derived from the 2019 World Health Organization (WHO) mortality data for Indonesia. First, the mortality rates which consisted of five-year age groups were interpolated to determine the rates for each individual age. Next, these rates were extrapolated to extend the data from age 85 up to age 110. The resulting crude rates were adjusted with the Whittaker-Henderson smoothing technique by utilizing Python and MS Excel. The refined results were then compared to the insured lives from the fourth Indonesian Mortality Rates Table (TMI IV). This assessment supplied the government with insights to help shape health policies and inform economic forecasts. The results indicated that male mortality rates were higher than those of females, although no significant difference was observed among the younger generation. On the contrary, mortality rates of old people were significantly greater compared to the insured lives which was due to WHO’s limited data availability and more comprehensive data collection process, compared to TMI IV’s insured lives through 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.
CALCULATION OF CREDITED INTEREST RATE WITH INVESTMENT YEAR METHOD AND PORTFOLIO METHOD Aryento, Jevilia; Kusnadi, Felivia; Lesmono, Dharma
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (560.648 KB) | DOI: 10.30598/barekengvol16iss3pp787-796

Abstract

The rate of return on investment for unit-linked insurance products in Indonesia is still volatile and depends on the investment instruments performance. However, the net return on investment that is given to policyholders is projected at the beginning of the year and is used as a benchmark for choosing the right investment instrument, referred to as the credited interest rate. Interest rates movements affect the yield of the credited interest rate. Therefore, the credited interest rate calculation requires appropriate methods to reduce the risk of loss, which are the Investment Year Method and the Portfolio Method. Research shows that the Investment Year Method is more appropriate in unstable interest rate condition, whereas the Portfolio Method is better utilized in a stabilized environment. In addition, this research also shows the strategy to manage investment instruments with asset rollover to suit the fluctuating credited interest rate.
BAYESIAN ADDITIVE REGRESSION TREE APPLICATION FOR PREDICTING MATERNITY RECOVERY RATE OF GROUP LONG-TERM DISABILITY INSURANCE Budiana, Stevanny; Kusnadi, Felivia; Irawan, Robyn
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.578 KB) | DOI: 10.30598/barekengvol17iss1pp0135-0146

Abstract

Bayesian Additive Regression Tree (BART) is a sum-of-trees model used to approximate classification or regression cases. The main idea of this method is to use a prior distribution to keep the tree size small and a likelihood from data to get the posterior. By fixing the tree size as small as possible, the approximation of each tree would have a little effect on the posterior, which is the sum of all output from all the trees used. Bayesian additive regression tree method will be used for predicting the maternity recovery rate of group long-term disability insurance data from the Society of Actuaries (SOA). The decision tree-based models such as Gradient Boosting Machine, Random Forest, Decision Tree, and Bayesian Additive Regression Tree model are compared to find the best model by comparing mean squared error and program runtime. After comparing some models, the Bayesian Additive Regression Tree model gives the best prediction based on smaller root mean squared error values and relatively short runtime.
ANALYSIS OF ROBUST CHAIN LADDER METHOD IN ESTIMATING AUSTRALIAN MOTOR INSURANCE RESERVES WITH OUTLYING DATASET Johan, Jonathan Prasetyo; Kusnadi, Felivia; Yong, Benny
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.104 KB) | DOI: 10.30598/barekengvol17iss1pp0225-0234

Abstract

Reserves are one of the most crucial components for an insurance company to make sure it has enough money to pay off all the incurred claims. The presence of outliers in the incurred claims data harbors risk on inaccurately predicting reserves to cover claim amounts, usually achieved by the standard chain ladder reserving method. To remedy the effect of the outliers, the robust chain ladder reserving method is used by setting the median value to predict estimated reserve. On this research, we utilized both methods on various datasets. The purpose of this paper is to determine the best method that can be utilized by insurance company in various scenario to obtain the most optimized reserved estimate that can minimize the risk of being unable to pay the insurance claim or even the risk of over allocating reserves that could pose profitability issue. The primary data used are the Australian domestic motor insurance claims from 2012 to 2017, obtained from Australian Prudential Regulation Authority (APRA). The dataset is then manipulated to have outliers. After calculating the estimation, the result is compared to assess the strength of the methods using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) calculation. In conclusion, we found that the robust chain ladder reserving method works better in an outlying dataset. We also identify cases in which robust chain ladder are not appropriately used.
PROFITABILITY CALCULATION AND ANALYSIS FOR INTEREST RATE SWAP USING THE HULL WHITE MODEL Hadiono, Vania Rosalie; Kusnadi, Felivia
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/barekengvol18iss2pp0863-0876

Abstract

The London Inter-Bank Offered Rate (LIBOR) volatility had resulted in higher interest rate risks faced by many big companies and financial institutions whose assets depend on the interest rate. Eventually, there was an appearance of the new financial product development that can be used for hedging, such as interest rate swap, one of the most popular methods, utilized by most financial institutions and big companies which use LIBOR as their benchmark. In fact, it is not uncommon for numerous companies to gain negative return from this swap transaction. Therefore, in this paper, we used the Hull-White model to predict the LIBOR rate, since this model has a decent level of accuracy, calculated using Root Mean Squared Error (RMSE) method. Furthermore, the estimation of LIBOR rate was used to calculate the net value of interest rate swap transaction, in three scenarios, using, respectively, the minimum value, the average value, and the maximum value of the LIBOR’s estimation results to provide an analysis of potential P/L (Profit & Loss) exposure due to the realization of interest rate swaps.
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.
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