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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Creative Research Journal
Irawan, Robyn
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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

Abstract

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.
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

Abstract

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

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.
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.
Co-Authors Benny Yong Budiana, Stevanny Elena, Felice Farah Kristiani Joan, Karin Kusnadi, Felivia