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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Gaussian IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Applied Statistics Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Zero : Jurnal Sains, Matematika, dan Terapan MIND (Multimedia Artificial Intelligent Networking Database) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Inferensi International Journal of Advances in Data and Information Systems InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Applied Data Sciences Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science Prosiding Seminar Nasional Official Statistics Jurnal Natural Eduvest - Journal of Universal Studies Xplore: Journal of Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Parameter: Jurnal Matematika, Statistika dan Terapannya Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education
Kusman Sadik
Program In Statistics And Data Science, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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On the Use of Zero-Inflated Mixed Models for Count Data: A Simulation and Empirical Evidence Anang Kurnia; Zafira Fakhriyah; Kusman Sadik; Dian Handayani
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1303

Abstract

This paper evaluates the performance of classical count regression models (Poisson, Negative Binomial, Generalized Poisson), zero-inflated models (Zero-Inflated Poisson/ZIP, Zero-Inflated Negative Binomial/ZINB, Zero-Inflated Generalized Poisson/ZIGP), and zero-inflated mixed models (ZIPMM, ZINBMM, ZIGPMM) for over-dispersed count data, particularly due to excess zeros and unobserved heterogeneity. Using simulation and empirical studies, we evaluated the performance of the models based on their predictive capability and their ability to yield valid inferences through hypothesis testing. The simulation, replicated 1000 times, involves 27 scenarios that combine various sample sizes, proportions of zero counts, and response variable distributions. Our findings indicate that ZIGPMM and ZINBMM provide the smallest root mean square error (RMSE) values. Although the Poisson model yields a relatively small RMSE, it does not adequately account for overdispersion, leading to underestimated standard errors and potentially misleading significance tests. The negative binomial model yields dispersion estimates closest to 1, indicating good performance, whereas ZIGP, ZINB, ZIGPMM, and ZINBMM perform better when zero counts are extremely high. Empirical analysis of data on under-five mortality due to pneumonia in Java Island, Indonesia, indicates that ZINB, ZINBMM, and ZIGPMM have the smallest Akaike Information Criterion (AIC), making them the most suitable models. These models show that exclusive breastfeeding and vitamin A have no significant effect on under-five child mortality due to pneumonia, while severe malnutrition has a statistically significant impact (α=0.05).
Evaluation of Imputation Methods for Clustering Categorical Time Series on Financial Sector Stock Data Rita Rahmawati; I Made Sumertajaya; Asep Saefuddin; Kusman Sadik
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/Jmathcos12460

Abstract

Missing values in financial time series data can affect the information structure of the data and impact the clustering results obtained. This research aims to evaluate the performance of several time series data imputation methods on the quality of categorical time series clustering on financial sector stock data on the Indonesia Stock Exchange. The imputation methods compared include linear interpolation, spline interpolation, and Kalman smoothing. The research data is in the form of daily closing prices of 92 financial sector stocks for the period 2 January 2023 to 31 October 2025. Numerical clustering was carried out using K-Means Time Series based on Dynamic Time Warping (DTW), while categorical clustering was carried out using K-Medoids with the Gower distance measure in two categorization schemes, namely five and seven categories. Evaluation of suitability between numerical and categorical clustering was carried out using the Rand Index (RI), Fowlkes–Mallows Index (FMI), and Jaccard Index. The research results show that the imputation method produces different clustering qualities. Linear interpolation provides the best and most consistent performance compared to other methods, especially in the seven-category scheme with an RI value of 0.6417, FMI of 0.4338, and Jaccard Index of 0.3256. These results show that linear interpolation is better able to maintain the information structure of the data in the categorical time series clustering process compared to spline interpolation or Kalman smoothing.
Robustness Evaluation of ANFIS, Hybrid GA-SVM, and SVM under Controlled Time Series Structures Sri Kustiara; Kusman Sadik; Hari Wijayanto
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.29313

Abstract

This study evaluates the robustness of ANFIS, hybrid GA-SVM, and SVM under synthetic time-series structures using a factorial simulation framework combined with empirical validation. From a practical perspective, robust coal price forecasting is essential for supporting energy planning, trade management, and policy decision-making under uncertain market conditions. Empirical analysis of Indonesian coal prices reveals nonstationary behaviour, high volatility, and nonlinear dynamics. Forecasting performance is assessed using walk-forward validation, where SVM and hybrid GA-SVM demonstrate comparable accuracy and outperform ANFIS on the empirical dataset. To systematically examine model sensitivity to structural variations, a  factorial simulation design is implemented by varying seasonality, volatility, and predictor–response structure across 12 scenarios with 100 replications each. The results indicate that volatility is the most dominant factor affecting forecasting error, with significant interaction effects among structural factors. ANOVA and post hoc analysis further confirm that model performance depends more on data characteristics than on algorithmic complexity. These findings demonstrate that factorial simulation provides a systematic and robust framework for evaluating forecasting models beyond conventional empirical comparisons, while offering deeper insight into the relationship between data structure and model performance. 
Optimizing Machine Learning for Daily Rainfall Prediction in Bogor: A Statistical Downscaling Approach Fradha Intan Arassah; Kusman Sadik; Bagus Sartono; Parwati Sofan
Eduvest - Journal of Universal Studies Vol. 5 No. 6 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i6.51307

Abstract

This study explores the use of machine learning models as a statistical downscaling technique to predict daily rainfall in Bogor, Indonesia. The general circulation model (GCM) is a leading tool for climate prediction, and this research applied a two-stage machine learning model to improve its predictions. The main objectives were to evaluate different GCM domains and handle missing data using two imputation approaches. The first stage involved constructing datasets with varying methods for addressing missing values, followed by the application of a support vector classification (SVC) model to classify rainy and non-rainy days. In the second stage, a recurrent neural network (RNN) model was developed to predict daily rainfall amounts. The results revealed that using random forest imputation for missing data enhanced model accuracy and reduced the root mean square error (RMSE). Among the different GCM domains, the 5 km resolution GCM data was the most accurate when compared to local station climatology. The SVC model, using a radial basis function kernel, achieved an impressive classification accuracy of 98.5%, while the RNN model achieved an RMSE of 16.19. These findings are valuable for improving rainfall predictions and can provide effective data-driven recommendations for disaster mitigation efforts in the region.
Analyzing Household Expenditures with Generalized Random Forests Eriski Isnanda; Khairil Anwar Notodiputro; Kusman Sadik
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.30104

Abstract

This study investigates the performance of Generalized Random Forest (GRF), which has been known to be useful in understanding heterogeneous treatment effects (HTE) and non-linear relationships in high-dimensional data. In this paper the performance of GRF was compared with Random Forest (RF), Generalized Linear Mixed Model (GLMM) as continuation of previous study conducted by Athey (2019). The data utilized in this study is from the National Socioeconomic Survey (SUSENAS) to predict household per capita expenditure in West Java, Indonesia. The models are evaluated based on their ability to handle outliers using Winsorization. The results show that RF performed the best, yielding the smallest MSE values, followed by GRF with reasonably good performance, and GLMM with the highest MSE, indicating its limitations in handling non-linear data patterns. These findings indicate that RF is the most accurate method for modeling per capita expenditure in West Java, with recommendations for further research to develop hybrid methods or use more specific random effects in GLMM
PEMODELAN DATA TERSENSOR KANAN MENGGUNAKAN ZERO INFLATED NEGATIVE BINOMIAL DAN HURDLE NEGATIVE BINOMIAL Kusni Rohani Rumahorbo; Budi Susetyo; Kusman Sadik
Indonesian Journal of Statistics and Applications Vol 3 No 2 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i2.247

Abstract

Health is a very important thing for humanity. One way to look at a person's health condition is through the number of unhealthy days which can also shows the productivity of the community in a region. Modeling the number of unhealthy days which are examples of count data can be done using Poisson regression. Problems that are often faced in data counts are overdispersion and excess zero. Poisson regression cannot be applied to data that experiences both of these. Zero Inflated Negative Binomial and Hurdle Negative Binomial modeling was performed on data with 2 conditions, uncensored and censored. The explanatory variables used are gender, age, marital status, education level, home ownership status and rural-urban status. According to the results of the AIC and RMSE calculation, Zero Inflated Negative Binomial on censored data showed the best performance for estimating the number of unhealthy days.
KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL Khusnul Khotimah; Kusman Sadik; Akbar Rizki
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.502

Abstract

Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.
Simulation Study of Robust Geographically Weighted Empirical Best Linear Unbiased Predictor on Small Area Estimation: Simulasi Metode Prediksi Tak Bias Linier Terbaik Empiris Terboboti Geografis Kekar pada Pendugaan Area Kecil Naima Rakhsyanda; Kusman Sadik; Indahwati Indahwati
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p50-60

Abstract

Small area estimation can be used to predict the population parameter with small sample sizes. For some cases, the population units that are close spatially may be more related than units that are further apart. The use of spatial information like geographic coordinates are studied in this research. Outlier contaminations can affect small area estimations. This study was conducted using simulation methods on generated data with six scenarios. The scenarios are the combination of spatial effects (spatial stationary and spatial non-stationary) with outlier contamination (no outlier, symmetric outliers, and non-symmetric outliers). The purpose of this study was to compare the geographically weighted empirical best linear unbiased predictor (GWEBLUP) and robust GWEBLUP (RGWEBLUP) with direct estimator, EBLUP, and REBLUP using simulation data. The performance of the predictors is evaluated using relative root mean squared error (RRMSE). The simulation results showed that geographically weighted predictors have the smallest RRMSE values for scenarios with spatial non-stationary, therefore offer a better prediction. For scenarios with outliers, robust predictors with smaller RRMSE values offer more efficiency than non-robust predictors.
Klasifikasi Halaman SEO Berbasis Machine Learning Melalui Mutual Information dan Random Forest Feature Importance SITI NURADILLA; KUSMAN SADIK; CICI SUHAENI; AGUS M SOLEH
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.114-129

Abstract

AbstrakProses optimasi SEO melibatkan banyak faktor yang saling terkait, sehingga sulit bagi tim SEO dalam menentukan halaman mana yang memerlukan perbaikan lebih lanjut. Penelitian ini bertujuan untuk mengembangkan model berbasis machine learning yang tidak hanya akurat dalam mengklasifikasikan halaman, tetapi juga efisien dalam memilih fitur yang paling informatif. Metode yang digunakan dalam penelitian ini melibatkan seleksi fitur menggunakan Mutual Information (MI) dan Random Forest Feature Importance (RFFI) untuk mengidentifikasi faktor-faktor yang paling penting untuk optimasi SEO, yang dimodelkan menggunakan Random Forest dan Weighted Voting Ensemble (WVE). Model dievaluasi berdasarkan Accuracy, Precision, Recall, dan ROC AUC. Hasil penelitian menunjukkan bahwa model Random Forest dengan 20 fitur berdasarkan RFFI, memberikan performa terbaik dengan ROC AUC sebesar 75.87%, Accuracy 77,74%, Precision 60,51%, dan Recall 71.29%. Model mampu membedakan secara efektif halaman yang membutuhkan optimasi SEO atau tidak.Kata kunci: Feature Importance, Random Forest, SEO, Seleksi Variabel, WVEAbstractThe SEO optimization process involves many interrelated factors, making it challenging to identify which pages need further improvement. This study proposes a machine learning-based model that is accurate in classifying web pages and efficient in selecting the most relevant features. Feature selection is performed using Mutual Information (MI) and Random Forest Feature Importance (RFFI) to identify key factors for SEO optimization, followed by modeling with Random Forest and Weighted Voting Ensemble (WVE). The model is evaluated using Accuracy, Precision, Recall, and ROC AUC. Results indicate that the Random Forest model with 20 features selected via RFFI delivers the best performance, achieving a ROC AUC of 75.87%, Accuracy of 77.74%, Precision of 60.51%, and Recall of 71.29%. The model effectively distinguishes between pages that require SEO optimization and those that do not.Keywords: Feature Importance, Random Forest, SEO, Variable Selection, WVE
Evaluating Ordinal Multivariate Models under Multicollinearity via Pairwise Likelihood: A Simulation Perspective Achmad Fauzan; Kusman Sadik; Anang Kurnia
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2282

Abstract

This study examines the effect of multicollinearity on ordinal regression through a two-stage Monte Carlo simulation. A synthetic population of 2,000,000 observations was generated with predictors drawn from a normal distribution, and responses simulated using an ordinal probit model. A Monte Carlo procedure was employed with 10 repetitions, each consisting of 100 random samples of 1,000 observations. Parameter estimation employed Maximum Likelihood Estimation (MLE) for univariate models and Pairwise Likelihood (PL) for multivariate models, with performance assessed using mean squared error (MSE), bias, and computation time. Results show that multicollinearity had negligible impact on estimator bias and MSE, confirming the robustness of both MLE and PL to correlated predictors. However, severe multicollinearity substantially increased computation time, indicating a trade-off between estimator stability and efficiency. These findings highlight PL as a viable approach for analyzing complex ordinal data, particularly in applications such as socio-economic surveys and health metrics where predictor correlation is unavoidable.
Co-Authors . Erfiani A.Tuti Rumiati Aam Alamudi ACHMAD FAUZAN Adib Roisilmi Abdullah Agus M Soleh Agus M Soleh Agus M. Soleh Agus Mohamad Soleh Agus Muhammad Saleh Ahmad Rifai Nasution Aji Hamim Wigena Aji Hamim Wigena Akbar Rizki Akbar Rizki Akbar Rizki Akmala Firdausi Amalia, Rahmatin Nur Anadra, Rahmi Ananda Shafira Anang Kurnia Andespa, Reyuli Andi Okta Fengki ASEP SAEFUDDIN Astari, Reka Agustia Astari, Reka Agustia Asysta Amalia Pasaribu Aulya Permatasari Azka Ubaidillah Bagus Sartono Baiq Dian Arianingsih Budi Susetyo Budi Susetyo Cici Suhaeni Cici Suhaeni Cici Suhaeni Dewi Zulyani Pomalingo Dhita Elsha Pangestika Dian Handayani Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Efriwati Efriwati Embay Rohaeti Eriski Isnanda Erwan Setiawan Evita Purnaningrum Fahira, Fani FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar Fradha Intan Arassah Freya, Wa Ode Rona Gede Awantara Gerry Alfa Dito Haikal, Husnul Aris Hari Wijayanto Hasnataeni, Yunia Hazan Azhari Zainuddin Hermawati, Neni I Gusti Ngurah Sentana Putra I Made Sumertajaya I Wayan Mangku Indahwati Iqbal, Teuku Achmad Jefita Resti Sari Khairi A N Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnul Khotimah Kusni Rohani Rumahorbo Latifah, Leli Lili Puspita Rahayu Logananta Puja Kusuma Logananta Puja Kusuma Lukmanul Hakim M Soleh, Agus Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Nur Aidi Muhammad Yusran Mulianto Raharjo Naima Rakhsyanda Naufalia Alfiryal Nisrina Az-Zahra, Putri Nur Khamidah Nurhidayah Nurhidayah Nusar Hajarisman Panji Lokajaya Arifa Parwati Sofan Purnama Sari Rifqi Aulya Rahman Rita Rahmawati Rizaldi Boer Rizki, Akbar Rizqi, Tasya Anisah ROCHYATI ROCHYATI Sabrina Adnin Kamila Sahamony, Nur Fitriyani Satriyo Wibowo Sentana Putra I Gusti Ngurah Siregar, Jodi jhouranda Siti Aisyah SITI NURADILLA Siti Raudlah Sitti Nurhaliza Soleh, Agus M Sri Kustiara Suhaeni, Cici Sundari, Marta Supriatin, Febriyani Eka Tendi Ferdian Diputra Titin Suhartini Titin Suhartini, Titin Tri Wahyuni Uswatun Hasanah Utami Dyah Syafitri Viarti Eminita Widhiyanti Nugraheni Yenni Angraini Yenni Kurniawati Yuli Eka Putri Zafira Fakhriyah