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All Journal Research Report - Engineering Science JURNAL PENGABDIAN KEPADA MASYARAKAT Suska Journal of Mathematics Education Jurnal Matematika Sains dan Teknologi JMPM: Jurnal Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Communication in Biomathematical Sciences Limits: Journal of Mathematics and Its Applications Creative Research Journal JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Majalah Ilmiah Matematika dan Statistika (MIMS) Bina Ekonomi: Majalah Ilmiah Fakultas Ekonomi Universitas Katolik Parahyangan Pakem : Jurnal Pengabdian Kepada Masyarakat Limits: Journal of Mathematics and Its Applications
Benny Yong
Jurusan Matematika, Fakultas Teknologi Informasi Dan Sains Universitas Katolik Parahyangan, Bandung
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA Elena, Felice; Irawan, Robyn; Yong, Benny
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1957-1972

Abstract

A service provider is a business that provides services or the expertise of an individual in a certain sector. A service provider’s customer flow could be very dynamic, with both new and churning customers. For the purpose of minimizing the number of churning customers, the company should perform a customer churn analysis. Customer churn analysis is the process of identifying a pattern or trend in churning customers. In order to classify and predict churning customers, machine learning techniques are required to build the classifier model. This paper will use the Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and hybrid Adaptive Boosting-SVM (AdaBoost-SVM) model. The hybrid AdaBoost-SVM model is a boosting model which uses SVM as its basis classifier instead of a decision tree. The models will be implemented using airlines and telecommunication customers churn data. The usage of oversampling technique is required to balance the number of observations in both classes of training data. Furthermore, a model comparison will be conducted using the F1-Score and the AUC score as the evaluation metric. The analysis shows that LightGBM performs the best result in both dataset with the highest F1-Score and the shortest computational time. In addition, the boosting model AdaBoost-SVM has a better performance than the SVM model due to the boosting algorithm which always minimizes the model error in each iteration. Despite having a better result, AdaBoost-SVM performs in the longest computational time, making it computationally expensive for large datasets. Additionally, the imbalanced nature of the datasets presents challenges in model performance, requiring the application of oversampling techniques to mitigate bias towards the majority class. In conclusion, LightGBM is the best model to classify churning customers based on the higher F1-Score, AUC score, and the shortest computational time.
IMPLEMENTASI PROYEK PEMODELAN MATEMATIKA PADA KURIKULUM MERDEKA DI SEKOLAH KRISTEN IMMANUEL PONTIANAK Yong, Benny; Wijaya, Andreas Parama; Salim, Daniel; Owen, Livia
PAKEM : Jurnal Pengabdian Kepada Masyarakat Vol 5 No 2 (2025): Pakem : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pakem.5.2.87-96

Abstract

To prepare students for the rapidly evolving demands of the modern era, the Indonesian Ministry of Education, Culture, Research, and Technology introduced the Merdeka Curriculum. This curriculum provides schools and teachers with greater autonomy to design learning experiences tailored to local contexts, student potential, and individual characteristics. It emphasizes competency-based learning, integrating relevant skills and knowledge applicable to daily life and the labor market. The curriculum aims to foster creativity, critical thinking, and adaptability among students. Despite its progressive orientation, a significant challenge in its implementation is the readiness of educators. Addressing this issue requires collaborative efforts among schools, teachers, parents, communities, and the government. This community service program was designed to support teachers in implementing the Merdeka Curriculum, particularly in mathematics education. The program involved training and workshops on mathematical modeling projects aligned with the Pancasila Student Profile Strengthening Project (P5). The primary objective was to provide participants with a comprehensive understanding of curriculum implementation in mathematics and to assist them in designing student-centered project-based learning activities. The program was conducted in partnership with Immanuel Christian School, under the Kampung Bali Protestant Church Foundation in Pontianak, West Kalimantan. Overall, the mentoring activities have been highly positive, although certain materials require further refinement to ensure participants attain a more comprehensive understanding. The three strongest aspects of this program are the instructor’s deep mastery of the subject matter, the quality of the content presentation, and the organization of materials along with effective time allocation. Conversely, the aspects needing improvement are participants’ comprehension of the data analytics series, the overall relevance of the content to their needs, and their understanding of the matrix and Bayesian series
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.
PARAMETER ESTIMATION OF LOGNORMAL AND PARETO TYPE I DISTRIBUTIONS USING FREQUENTIST AND BAYESIAN INFERENCES Then, Jenisha; Permana, Ferry Jaya; Yong, Benny
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp141-152

Abstract

Extreme events are events that rarely occur but they cause substantial losses. Insurance companies need to take extreme events into account in risk management because extreme events can have a negative impact on the company's financial health. As a result, insurance companies need an appropriate loss model that matches the empirical data from these extreme events. A distribution that is heavy-tailed and skewed to the right is a good distribution for modeling the magnitude of losses from extreme events. In this paper, two distributions with heavy tails and skew to the right will be used to model the magnitude of losses from extreme events, namely the lognormal distribution and the Pareto distribution type I. The parameters of these distributions are estimated using two inferences, namely the frequentist and Bayesian inferences. In the frequentist inference, two methods are applied, namely the moment method and maximum likelihood. On Bayesian inference, two prior distributions are used, namely uniform and Jeffrey. Test model suitability is carried out by visually comparing the model distribution function with the empirical distribution function, as well as by comparing the Root Mean Square Error (RMSE) value. The visualization results of the distribution function and RMSE values ​​show that in general, the Bayesian inference is better at estimating parameters than the frequentist inference. In the frequentist inference, the maximum likelihood method can provide better estimated values ​​than the moment method. In the Bayesian inference, the two prior distributions show a relatively similar fit to the data and tend to be better than the frequentist inference.
Pemodelan banyaknya kematian berdasarkan kasus konfirmasi COVID-19 di Indonesia, Malaysia, Thailand, dan Filipina menggunakan model linear tergeneralisasi Ha, Marlyn; Permana, Ferry Jaya; Yong, Benny
Majalah Ilmiah Matematika dan Statistika Vol. 25 No. 2 (2025): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v25i2.53694

Abstract

In early 2020, the COVID-19 disease, caused by the SARS-CoV-2 virus infection, became a global pandemic impacting the entire world, including Indonesia. To monitor the spread of COVID-19 and determine appropriate strategies to mitigate its impact, the World Health Organization (WHO) routinely reported confirmed case data and death case data due to COVID-19. Mathematical modeling can help understanding the relationship between the number of deaths based on daily confirmed cases. One simple mathematical model is the linear regression model. The linear regression model requires the assumption of homoscedasticity, and when this assumption fails, linear regression cannot be used. In this research, a generalized linear model (GLM) is used to address the shortcomings of the linear regression model. This research will predict the number of daily deaths based on daily confirmed case data using GLM based on historical data from Indonesia, Malaysia, Thailand, and Philippines. The functions used to describe the relationship between predictor and response variables include normal or Gaussian, Poisson, gamma, and negative binomial distributions. To evaluate whether the model fits the data, we used Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC). Additionally, the goodness of fit of the model in predicting the number of deaths is measured by finding the mean squared error (MSE). The best model is determined by considering the smallest AIC, BIC, and MSE values. The simulation results show that the GLM using the normal distribution is the best model in Indonesia, Malaysia, and Philippines, while the GLM using the negative binomial distribution is the best model in Thailand. Using the GLM, it was found that deaths occurred 14 days after a patient was confirmed with COVID-19 in Indonesia, 11 days in Malaysia, 12 days in Thailand, and 13 days in Philippines. Keywords: COVID-19, GLM, AIC, BIC, MSEMSC2020: 92C60, 62P10, 62J02, 62F10
Aplikasi Metode Proses Hirarki Analitik dan Pemrograman Integer 0-1 Dalam Menentukan Komposisi Pemain Sepak Bola pada Football Manager 2019 Christoper Aryo Pambudi; Benny Yong; Taufik Limansyah
Limits: Journal of Mathematics and Its Applications Vol. 19 No. 1 (2022): Limits: Journal of Mathematics and Its Applications Volume 19 Nomor 1 Edisi Me
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Football has became a favorite sport of the world community. Every supporter of a football team would want their team to win the competition they participated in. Formation, strategy, and composition of teams are factors that influence the team's victory in a match. These three factors are the responsibility of a football coach in concocting his team in winning. This paper will discuss the application of the Analytical Hierarchy Process and the Integer 0-1 Program to assist football coaches in composing the composition of football players in a match. The AHP is used in this case to calculate the priority weights of each soccer player criteria while the Integer 0-1 Program is used to get eleven players to be deployed in a match. The results of both methods are simulated using the game Football Manager 2019 with team Manchester United in the English Premier League. Based on simulations conducted during the two season matches, Manchester United was able to finish in a fairly stable ranking in the English Premier League standings for two seasons.
Pemodelan dan Perhitungan Premi Asuransi Keamanan Siber dengan Model Non-Markov Ivander Jeremy; Felivia Kusnadi; Benny Yong
Limits: Journal of Mathematics and Its Applications Vol. 19 No. 2 (2022): Limits: Journal of Mathematics and Its Applications Volume 19 Nomor 2 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

The development of information and communication technology not only has positive impacts, but also negative impacts, especially in the cybersecurity sector. Insurance companies need to create a relatively new insurance product, namely cybersecurity insurance. However, development of cybersecurity insurance still needs further investigation because there is no standard actuarial table like mortality table in life insurance. This article will discuss the modeling of infection and recovery process of a node and various other connected nodes in a computer network of the company using non-Markov model in the case of absence of dependence between cybersecurity risks, applying the Monte Carlo simulation method to obtain experimental data with various distributions – Weibull, Lognormal, and Inverse Gaussian – for the calculation of premium charged by insurance companies to insured companies interested in purchasing cybersecurity insurance products. Standard deviation premium principle and exponential utility premium principle are used to calculate premium. We concluded that the infection and recovery time with a long-tailed distribution has a lower premium price compared to those with a short-tailed distribution.
Analisis Perbandingan Bilangan Reproduksi Dasar pada Model Penyebaran Penyakit Dengue dengan Pengaruh Faktor Usia di Kota Bandung Vania Junisha; Farah Kristiani; Benny Yong
Limits: Journal of Mathematics and Its Applications Vol. 16 No. 2 (2019): Limits: Journal of Mathematics and Its Applications Volume 16 Nomor 2 Edisi De
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Penyakit Dengue merupakan salah satu masalah kesehatan yang utama di masyarakat Indonesia pada umumnya dan di kota Bandung pada khususnya. Pada penyebarannya, ternyata terdapat perbedaan tingkat risiko transmisi antara kelompok usia anak dan orang dewasa pada penyakit Dengue. Sebagai salah satu strategi pencegahan penyebaran penyakit ini, dapat dengan melalui pemodelan dari sistem dinamika penyebarannya. Penelitian ini akan menganalisa model penyebaran penyakit Dengue di kota Bandung dengan memperhitungkan faktor individu anak dengan kasus simtomatik dan asimtomatik. Bilangan Reproduksi Dasar (BRD) sebagai nilai ambang batas penyebaran penyakit ini akan dicari dan dianalisis dengan menggunakan metode Matriks Generasi dan Laju Pertumbuhan Intrinsik dan dengan menerapkan nilai parameter-parameter dan data banyaknya kasus dengue di kota Bandung pada tahun 2016-2018. Titik kesetimbangan dari kondisi bebas penyakit dan endemik juga akan ditentukan untuk memverifikasi keakuratan model yang dibuat. Dari hasil analisisnya, disimpulkan bahwa kedua metode menghasilkan bentuk BRD yang memiliki karakter yang berbeda dan diterapkan pada kondisi yang berbeda pula. Jika data real tersedia, maka lebih baik menerapkan metode Laju Pertumbuhan Intrinsik. Sebaliknya, jika data real tidak lengkap tersedia, maka disarankan menggunakan metode Matriks Generasi
Kontrol Penyebaran Penyakit SARS dengan Menggunakan Analisis Sensitivitas pada Bilangan Reproduksi Dasar Benny Yong; Putri Efelin
Limits: Journal of Mathematics and Its Applications Vol. 17 No. 2 (2020): Limits: Journal of Mathematics and Its Applications Volume 17 Nomor 2 Edisi De
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Makalah ini membahas analisis sensitivitas pada bilangan reproduksi dasar pada model penyebaran penyakit SARS dengan pengaruh vaksinasi. Model melibatkan individu rentan, individu terinfeksi tapi belum dapat menularkan, individu yang diisolasi, individu terinfeksi yang dapat menularkan dan belum terdiagnosa SARS, individu pulih, dan individu meninggal karena penyakit SARS, dan individu rentan yang telah divaksin. Karena ketidakpastian dalam penaksiran nilai parameter yang mengakibatkan bervariasinya nilai bilangan reproduksi dasar, akan dilakukan simulasi Monte Carlo pada bilangan reproduksi dasar dengan menggunakan berbagai distribusi untuk setiap parameternya. Hasil analisis sensitivitas pada model penyebaran penyakit SARS dengan pengaruh vaksinasi menunjukkan bahwa parameter proporsi individu isolasi yang berpotensi menginfeksi individu rentan mempunyai pengaruh positif terbesar dalam penyebaran penyakit SARS untuk semua kondisi nilai bilangan reproduksi dasar. Parameter proporsi individu rentan yang berhasil divaksin sebelum terjadinya SARS dalam suatu populasi mempunyai pengaruh negatif terbesar dalam penyebaran penyakit SARS ketika kondisi bilangan reproduksi dasar bernilai kurang dari satu, sedangkan parameter laju pemulihan dari individu isolasi mempunyai pengaruh negatif terbesar dalam penyebaran penyakit SARS untuk kondisi bilangan reproduksi dasar bernilai lebih dari satu.
Simulasi Perhitungan Premi Asuransi Kesehatan dan Jiwa pada Penderita Covid-19 yang Dipengaruhi Model Penyebaran Penyakit Menular SIDRS Patrick Louis Lucin; Farah Kristiani; Benny Yong
Limits: Journal of Mathematics and Its Applications Vol. 20 No. 1 (2023): Limits: Journal of Mathematics and Its Applications Volume 20 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Determination of health and death insurance benefits according to the needs of policyholders is very important to determine from the beginning of making an insurance policy, especially for insurance that takes over the risk of being infected with the COVID-19 virus. Several factors that must be taken into account in determining the amount of benefits and premiums due to COVID-19 are the human population factor that is susceptible, infected and death in the SIDRS infectious disease spread model. In this study, the influence of these three factors on actuarial calculations is examined in more depth to produce an appropriate premium determination formula by taking into account two payment schemes in lump sum and annuity. From the simulation results by applying data on COVID-19 cases in Indonesia to determine the parameters of the SIDRS model, it is concluded that the premium with an annuity benefit payment scheme is smaller than the premium with a lump sum benefit scheme. Furthermore, it is also concluded that if the population of policyholders increases, the premium price will also be lower.
Co-Authors Agus Sukmana Ananda Shafira Ananda Shafira Anggriawan, Vanessa Caesar Stefanus Caesar Stefanus, Caesar Christoper Aryo Pambudi Christopher Aryo Pambudi Daniel Salim Dharma Lesmono Elena, Felice Erwinna Chendra Farah Kristian Farah Kristiani Farah Kristiani Felivia Kusnadi Ferry Jaya Permana Ferry Jaya Permana Ha, Marlyn Hari, Vincent Hari, Vincent Irawan, Robyn Irawan, Robyn Ivander Jeremy Ivander Jeremy Ivonne Martin Iwan Sugiarto Joan, Karin Johan, Jonathan Prasetyo Jonathan Hoseana Junisha, Vania Kusnadi, Felivia Liem Chin Livia Owen Maria Anestasia Patrick Louis Lucin Patrick Louis Lucin Putri Efelin Putri Efelin Taufik Limansyah Taufik Limansyah Then, Jenisha Vania Junisha Vincent Hari Wijaya, Andreas Parama