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INOVASI PEMBELAJARAN MATEMATIKA MELALUI PENULISAN MATEMATIS, PEMECAHAN MASALAH MATEMATIS, DAN SOAL-SOAL MATEMATIKA BERBASIS HOTS UNTUK PARA GURU SMP DAN SMA SANTA ANGELA BANDUNG Yong, Benny; Hoseana, Jonathan; Owen, Livia; Salim, Daniel; Wijaya, Andreas Parama
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol 30, No 1 (2024): JANUARI-MARET
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jpkm.v30i1.49105

Keterbatasan pengetahuan, kemampuan, dan keterampilan para guru di tingkat sekolah menengah merupakan salah satu kendala terbesar yang seringkali ditemui dalam pembelajaran matematika di Indonesia. Para guru terjebak dengan rutinitas harian tanpa dibekali dengan pelatihan, lokakarya, maupun pendampingan untuk memperbarui ilmu pengetahuan dalam rangka peningkatan kompetensi kognitif. Pada kegiatan pengabdian kepada masyarakat ini, dilakukan kegiatan pelatihan, workshop, dan pendampingan inovasi pembelajaran matematika dengan mitra SMP dan SMA Santa Angela Bandung. Kegiatan ini bertujuan untuk meningkatkan pengetahuan dan kemampuan guru akan tiga hal, yaitu penulisan matematis, strategi-strategi pemecahan masalah matematis, dan penyusunan soal-soal matematika berbasis keterampilan berpikir tingkat tinggi. Target pengabdian yang ingin dicapai adalah meningkatnya pemahaman konsep matematika para peserta yang tertuang dalam penulisan matematis yang baik dan benar, dimilikinya keterampilan dalam menyelesaikan masalah-masalah matematis dengan menggunakan strategi-strategi yang tersedia, dan perubahan jenis soal-soal yang digunakan dalam pembelajaran matematika dari soal-soal berbasis Lower Order Thinking Skills (LOTS) ke soal-soal berbasis Higher Order Thinking Skills (HOTS).

ANALISIS RISIKO RELATIF PENYEBARAN PENYAKIT DEMAM DENGUE DI KOTA BANDUNG MENGGUNAKAN MODEL POISSON: STUDI KASUS DATA RS SANTO BORROMEUS Yong, Benny; Kristiani, Farah; Irawan, Robyn
CREATIVE RESEARCH JOURNAL Vol 2 No 01 (2016): Creative Research Journal
Publisher : Badan Penelitian dan Pengembangan Daerah Provinsi Jawa Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34147/crj.v2i01.75

Kota Bandung merupakan kota dengan kasus penyakit Demam Dengue (DD) terbanyak diantara kota-kota lainnya di Jawa Barat pada tahun 2013. Penelitian ini menganalisis tingkat risiko relatif dari penyebaran penyakit DD di kota Bandung dengan menerapkan model Poisson. Data pasien penyakit DD diambil dari RS Santo Borromeus Bandung sebanyak 2.032 pasien. Hasil analisis dengan menggunakan model Poisson menunjukkan bahwa penduduk di kecamatan Coblong hampir selalu berada pada tingkat risiko yang sangat tinggi untuk terserang penyakit DD pada setiap bulan untuk masing-masing stadium, sebaliknya penduduk di kecamatan Cinambo hampir selalu berada pada tingkat risiko yang sangat rendah untuk terserang penyakit DD. Untuk stadium awal, stadium lanjut, dan seluruh stadium, banyak kecamatan di kota Bandung yang mengalami peningkatan kategori tingkat risiko dari bulan Maret ke April yang merupakan musim pancaroba. Sementara untuk stadium lanjut dan seluruh stadium, banyak kecamatan di kota Bandung yang mengalami penurunan kategori tingkat risiko dari bulan Agustus ke September yang merupakan musim kemarau. Hasil estimasi dari selang kepercayaan 95% menunjukkan bahwa rentang selang terbesar selalu berada di kecamatan Bandung Wetan dan terjadi pada bulan April. Kondisi ini berlaku untuk stadium awal, stadium lanjut, dan seluruh stadium.

Aplikasi Teori Bilangan dalam Permainan NIM Yong, Benny; Stefanus, Caesar; Hari, Vincent
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 1 No 2: September
Publisher : Prodi Pendidikan Matematika Universitas Pesantren Tinggi Darul Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/jmpm.v1i2.595

Di dunia ini terdapat banyak permainan yang berhubungan dengan Matematika, misalkan permainan kartu bridge, domino, catur, NIM, dan masih banyak lagi. Permainan NIM adalah suatu permainan strategi yang dimainkan oleh dua pemain dimana setiap pemain secara bergantian mengambil paling sedikit satu objek dengan aturan-aturan tertentu. Kemenangan permainan ini bergantung pada berapa banyak objek yang tersedia dan siapa yang bermain dahulu. Makalah ini akan menyajikan empat buah permainan NIM; NIM Maksima, NIM SatuEmpat, NIM Satu-Tiga-Empat, dan NIM Satu-Tiga-Lima-Tujuh. Pada permainan NIM ini, peranan Matematika dalam hal ini teori bilangan adalah menentukan suatu strategi untuk memenangkan permainan.

APPLICATION AND PERFORMANCE COMPARISON OF MULTI-OUTPUT MACHINE LEARNING FOR NUMERICAL-NUMERICAL AND NUMERICAL-CATEGORICAL OUTPUTS Joan, Karin; Irawan, Robyn; Yong, Benny
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1421-1432

Multi-Output Machine Learning is an advancement of traditional machine learning, designed to predict multiple output variables simultaneously while considering the relationships between these output variables. Multi-Output Machine Learning is essential as a decision support tool because decision-making in many problems generally considers multiple factors. The use of Multi-Output Machine Learning is more advantageous than conventional machine learning in terms of time efficiency, addressing data limitations, and ease of maintenance. These benefits will significantly impact cost savings for industries utilizing Big Data. The models used in this research include Multivariate Regression Tree, Multivariate Random Forest, and Multi-Output Neural Network. The Multivariate Regression Tree and Multivariate Random Forest are developed by modifying the splitting function using Mahalanobis distance. The topological changes introducing shared and private hidden layers are the key development of the Multi-Output Neural Network. The prediction results indicated a trade-off in error between two output variables when comparing the Multivariate Regression Tree and Multivariate Random Forest with their single output counterparts. Meanwhile, the Multi-Output Neural Network model successfully improved the prediction results for both output variables. This research also introduces Mixed Multi-Output Machine Learning, which can predict numerical and categorical output variables. The Mixed Multi-Output Machine Learning model utilizes the logit values from the Logistic Regression model to extend the range of prediction results beyond the 0 to 1 interval. Multi-Output Neural Network is the sole model that produces predictions with relatively small errors and high accuracy values.

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

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

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

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

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

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

INOVASI PEMBELAJARAN MATEMATIKA MELALUI PENULISAN MATEMATIS, PEMECAHAN MASALAH MATEMATIS, DAN SOAL-SOAL MATEMATIKA BERBASIS HOTS UNTUK PARA GURU SMP DAN SMA SANTA ANGELA BANDUNG Yong, Benny; Hoseana, Jonathan; Owen, Livia; Salim, Daniel; Wijaya, Andreas Parama
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 30 No. 1 (2024): JANUARI-MARET
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jpkm.v30i1.49105

Keterbatasan pengetahuan, kemampuan, dan keterampilan para guru di tingkat sekolah menengah merupakan salah satu kendala terbesar yang seringkali ditemui dalam pembelajaran matematika di Indonesia. Para guru terjebak dengan rutinitas harian tanpa dibekali dengan pelatihan, lokakarya, maupun pendampingan untuk memperbarui ilmu pengetahuan dalam rangka peningkatan kompetensi kognitif. Pada kegiatan pengabdian kepada masyarakat ini, dilakukan kegiatan pelatihan, workshop, dan pendampingan inovasi pembelajaran matematika dengan mitra SMP dan SMA Santa Angela Bandung. Kegiatan ini bertujuan untuk meningkatkan pengetahuan dan kemampuan guru akan tiga hal, yaitu penulisan matematis, strategi-strategi pemecahan masalah matematis, dan penyusunan soal-soal matematika berbasis keterampilan berpikir tingkat tinggi. Target pengabdian yang ingin dicapai adalah meningkatnya pemahaman konsep matematika para peserta yang tertuang dalam penulisan matematis yang baik dan benar, dimilikinya keterampilan dalam menyelesaikan masalah-masalah matematis dengan menggunakan strategi-strategi yang tersedia, dan perubahan jenis soal-soal yang digunakan dalam pembelajaran matematika dari soal-soal berbasis Lower Order Thinking Skills (LOTS) ke soal-soal berbasis Higher Order Thinking Skills (HOTS).

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