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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Media Infotama Journal of the Indonesian Mathematical Society KEUNIS JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) SINTECH (Science and Information Technology) Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics International Journal of Remote Sensing and Earth Sciences (IJReSES) Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Building of Informatics, Technology and Science ESTIMASI: Journal of Statistics and Its Application Journal of Applied Data Sciences Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Natural Xplore: Journal of Statistics STATISTIKA 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 Limits: Journal of Mathematics and Its Applications The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Anang Kurnia
School Of Data Science, Mathematics And Informatics, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture 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 Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Transportation Other

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Determinants of Environmental Quality Index (EQI) in Indonesia in 2018-2022 Sihombing, Pardomuan Robinson; Erfiani, Erfiani; Notodiputro, Khairil Anwar; Kurnia, Anang
KEUNIS Vol. 13 No. 2 (2025): JULY 2025
Publisher : Finance and Banking Program, Accounting Department, Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/keunis.v13i2.6559

Abstract

The environment is a critical issue in sustainable development in Indonesia, with significant variations in environmental quality between regions. This study seeks to examine the influence of the Regional Government Budget, COVID-19 (as a dummy variable), Gross Regional Domestic Product (GRDP), and the Human Development Index (HDI) on the Environmental Quality Index (EQI) in Indonesia. The data for this study were obtained from BPS–Statistics Indonesia and the Ministry of Environment and Forestry, covering the period from 2018 to 2022. The analysis employs multiple linear regression using panel data. Panel model testing indicates that the fixed effects model with cross-sectional lag provides the best fit. The results show that, collectively, all variables have a significant influence on Indonesia's Environmental Quality Index (EQI). Individually, the Regional Government Budget for environmental purposes, the COVID-19 dummy variable, and the Human Development Index (HDI) have a significant positive impact on EQI. In contrast, Gross Regional Domestic Product (GRDP) has a significant negative effect. These findings highlight the need for comprehensive macro-socioeconomic policies to sustain and enhance environmental quality in Indonesia.
The Empirical Best Linear Unbiased Prediction and The Emperical Best Predictor Unit-Level Approaches in Estimating Per Capita Expenditure at the Subdistrict Level Fauziah, Ghina; Kurnia, Anang; Djuraidah, Anik
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.25037

Abstract

Purpose: This study aims to estimate and evaluate per capita expenditure at the subdistrict level in Garut Regency by employing unit-level Small Area Estimation (SAE) techniques, specifically utilizing the Empirical Best Linear Unbiased Predictor (EBLUP) and the Empirical Best Predictor (EBP) methods. Methods: The data used in this study are socio-economic data, specifically per capita household expenditure in Garut Regency. Socio-economic data generally skew positively rather than the normal distribution, so a method that can approximate or come close to the normal distribution is needed, for example, log-normal transformation. To improve the performance of EBLUP, which may lead to inefficient estimators because of violation of the assumption of normality, this study proposes the Empirical Best Predictor (EBP) method. It handles positively skewed data by applying log-normal transformation to sample data so that it more closely conforms to the desired distribution. Result: The EBP results are more stable than EBLUP since EBLUP is highly sensitive to outliers, and in cases where the normality assumption is violated, it produces a significant mean square error and inefficient estimators. Evaluating the estimates with both EBLUP and EBP shows Relative Root Mean Squared Error (RRMSE) values above 25%, especially in the subdistricts of Pamulihan, Sukaresmi, and Kersamanah. This is probably due to the household samples being taken in these three subdistricts being comparatively small compared to the other. Novelty: In this research, we use EBP to improve the performance of EBLUP, which produces inefficient estimators when the normality assumption is violated.
Evaluasi Performa Rmixmod dan KAMILA dalam Pengelompokan Perguruan Tinggi di Indonesia Berdasarkan Data Capaian Kinerja Bertipe Campuran Santoso, Andrianto; Kurnia, Anang; Hamim Wigena, Aji
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7376

Abstract

Clustering is a technique for grouping objects based on their similarities within clusters and their differences across clusters. In real-world, objects often have characteristics represented by a combination of numerical and categorical variables, requiring clustering techniques that can process mixed-type data. Model-based clustering is one of the approaches that can be utilized for such data. This study evaluates and compares two model-based clustering algorithms for mixed data type, Rmixmod, which employs a mixture model with maximum likelihood estimation and expectation-maximization, and KAMILA, which utilizes a semi-parametric approach. Both algorithms are implemented to cluster Indonesian higher education institutions based on their performance. The optimal number of clusters is determined using the Bayesian Information Criterion and the Silhouette Coefficient. Algorithms performance is evaluated using the Silhouette Coeeficient, the Calinski-Harabasz Index, and the Davies-Bouldin Index. The research results showed that the Rmixmod algorithm outperformed KAMILA in clustering Indonesian higher education institutions, with a Silhouette Coeeficient of 0.2878, a Calinski-Harabasz Index of 253.9433, and a Davies-Bouldin Index of 1.5321. The optimal number of clusters formed was five. Cluster interpretation is conducted by analyzing the mean values of PC and the distribution of categorical variables within each cluster. The clustering results are expected to serve as a foundation for the government in formulating strategic policies that are both effective and differentiated according to the characteristics of each group of higher education institutions.
Simultaneous inference for empirical best predictors in generalized linear mixed models: A poverty study in West Java SAHAMONY, NUR FITRIYANI; SADIK, KUSMAN; KURNIA, ANANG
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Accurate poverty mapping at the district and municipal levels remains challenging due to small sample sizes in household surveys, which often result in unstable direct estimates. To address this issue, this study employs microdata from the 2023 National Socioeconomic Survey (SUSENAS) to estimate household-level poverty proportions across 27 districts and municipalities in West Java Province using a binomial Generalized Linear Mixed Model (GLMM) combined with the Empirical Best Predictor (EBP) and Simultaneous Confidence Intervals (SCI). The GLMM framework captures household characteristics and random area effects to account for spatial heterogeneity. Three SCI approachesBonferroni correction, Bootstrap-t, and the Simes procedurewere implemented to evaluate EBP uncertainty while controlling the family-wise error rate. Results reveal substantial disparities, with Tasikmalaya (21.7%), Bandung Barat (15.5%), and Cianjur (12.8%) consistently above the provincial average of (6.8%), while urban areas such as Cimahi, Bekasi, and Depok report poverty rates below 2%. All methods achieved full empirical coverage (ECP = 100%), although interval widths differed: Bonferroni produced the widest intervals (AIW = 44.99), Bootstrap-t yielded the narrowest and most efficient (AIW = 29.16), and Simes provided intermediate but highly consistent results (AIW = 33.24). These findings underscore the methodological importance of integrating GLMM, EBP, and SCI for small area estimation while offering practical insights for evidence-based policy development and poverty reduction strategies in Indonesia.
CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA Dwi Wahyu Triscowati; Bagus Sartono; Anang Kurnia; Dede Dirgahayu; Arie Wahyu Wijayanto
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3217

Abstract

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.
POISSON MIXED MODELS WITH A BOOSTING APPROACH FOR THE ANALYSIS OF COUNT DATA Wulandari, Ita; Notodiputro, Khairil Anwar; Sartono, Bagus; Fitrianto, Anwar; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0815-0828

Abstract

Boosting is a powerful technique for enhancing predictive accuracy by iteratively reweighting observations, and is particularly effective in high-dimensional settings and for variable selection. While previous studies have demonstrated the advantages of integrating boosting with generalized linear mixed models (GLMMs) for binary outcomes, its application to count data within hierarchical frameworks remains limited. This study addresses that gap by extending boosting methods to count data through the development of a boosted Poisson mixed model (bPMM), a novel approach for small area estimation and variable selection in complex survey designs. The proposed model is applied to fertility data in the Indonesian provinces of Bali and East Nusa Tenggara, where the response variable is the number of live births and the predictors include twenty-eight socio-demographic covariates. Using the Akaike Information Criterion (AIC) for model selection, three significant variables were identified in Bali (Model 1), and one in East Nusa Tenggara (Model 2). The results demonstrate that bPMM not only improves variable selection in high-dimensional settings but also accommodates hierarchical structure in count data.
SURVIVAL ANALYSIS OF CHRONIC KIDNEY FAILURE PATIENTS USING THE COX STRATIFIED MODEL AND RANDOM SURVIVAL FOREST Hamid, Assyifa Lala Pratiwi; Susetyo, Budi; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1527-1540

Abstract

This study aims to analyze the factors influencing the survival of chronic kidney failure patients undergoing hemodialysis and to compare the performance of the Cox Stratified Model with the Random Survival Forest (RSF) using retrospective data from 741 patients at Asy-Syifa General Hospital, Indonesia. Data were analyzed using the Cox Stratified Model to address violations of the proportional hazards assumption and RSF to capture non-linear patterns and complex interactions among variables. The results showed that age, hypertension, diabetes, anemia, and hemodialysis frequency significantly affected survival, with a C-Index of 0.66 for the Cox Stratified Model and 0.6558 for RSF. The limitations of this study include its single-center retrospective design, which may limit generalizability, potential residual confounding from unmeasured variables, as well as the interpretability limitations and higher computational demands of RSF. The originality of this research lies in the direct comparison between advanced statistical models and machine learning methods in a cohort of chronic kidney failure patients in Indonesia, providing new insights for improving risk stratification and clinical prediction.
The Severe Stunting Cases of Children in Central Java Province Explained by Negative Binomial Regression Model Widiyanto, Rhendy K. P.; Fauzi, Fatkhurokhman; Fauzan, Achmad; Kurnia, Anang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.34846

Abstract

Severe stunting, or very short stature among children, remains a critical public health concern in Central Java Province. Robust statistical modelling is essential to identify the key factors associated with these cases and to guide targeted interventions. This study employs count regression models with an offset variable to analyze the factors influencing severe stunting cases across districts in Central Java. By using 2023 official data in districts level taken from the Ministry of Home Affairs and the Statistics Indonesia, we initially utilize a Poisson regression model in this study. However, due to evidence of overdispersion, a Negative Binomial regression model was adopted. Backward elimination was then applied to obtain the most parsimonious model. The Negative Binomial regression successfully addressed overdispersion. Five factors were identified as having a statistically significant influence on severe stunting cases: (1) the proportion of pregnant mothers with Chronic Energy Deficiency receiving nutritious food supplements, (2) the percentage of toddlers (6-23 months) receiving complementary nutritious food, (3) the proportion of households with access to good sanitation, (4) Gross Domestic Product per capita, and (5) the number of local healthcare facilities. These factors have negative relation to the stunting rates, meaning improving these factors will reduce the rates of severe stunting. The findings provide a validated statistical model for severe stunting and offer clear policy directions. To mitigate severe stunting, local governments should prioritize: enhancing nutritious food support for pregnant mothers and toddlers, improving household sanitation, stimulating local economic growth, and increasing accessibility to healthcare facilities.
K-Means Optimization Algorithm to Improve Cluster Quality on Sparse Data Yully Sofyah Waode; Anang Kurnia; Yenni Angraini
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3936

Abstract

The aim of this research is clustering sparse data using various K-Means optimization algorithms. Sparse data used in this research came from Citampi Stories game reviews on Google Play Store. This research method are Density Based Spatial Clustering of Applications with Noise-Kmeans (DB-Kmeans), Particle Swarm Optimization-Kmeans (PSO-Kmeans), and Robust Sparse Kmeans Clustering (RSKC) which are evaluated using the silhouette score. Clustering sparse data presented a challenge as it could complicate the analysis process, leading to suboptimal or non-representative results. To address this challenge, the research employed an approach that involved dividing the data based on the number of terms in three different scenarios to reduce sparsity. The results of this research showed that DB-Kmeans had the potential to enhance clustering quality across most data scenarios. Additionally, this research found that dividing data based on the number of terms could effectively mitigate sparsity, significantly influencing the optimization of topic formation within each cluster. The conclusion of this research is that this approach is effective in enhancing the quality of clustering for sparse data, providing more diverse and easily interpretable information. The results of this research could be valuable for developers seeking to understand user preferences and enhance game quality.
Feature selection in supervised machine learning: a case study of poverty dataset in West Java, Indonesia Marshelle, Sean; Rahardiantoro, Septian; Kurnia, Anang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp524-535

Abstract

West Java, one of the largest provinces in Indonesia with a population exceeding 50 million, reported a poverty rate of 7.62% in 2023. Data from the national socio-economic survey or survei sosial ekonomi nasional (SUSENAS) show that poverty is multidimensional, encompassing aspects of employment, education, sanitation, housing, food security, technology, and government assistance. Addressing this complexity requires identifying the most influential factors that determine household welfare. This study applies and compares three feature selection approaches—filter, wrapper, and embedded—to the SUSENAS dataset to evaluate their effectiveness in identifying key poverty determinants. By prioritizing variables with the strongest predictive power, the study provides an evidence-based framework for more efficient and targeted poverty alleviation strategies. Results indicate that the information filter method combined with random forest (RF) and the least absolute shrinkage and selection operator (LASSO) embedded method combined with logistic regression (LR) deliver the best performance, improving model accuracy while reducing more than 65% of irrelevant features. The selected indicators highlight critical sectors such as food security, housing, and access to technology, which can serve as short-term policy priorities. In the long term, broader interventions in education, employment, sanitation, and government support are recommended. These findings demonstrate how data-driven feature selection can guide effective policy design for reducing poverty in West Java.
Co-Authors . Hanniva . Marzuki . Sutriyati Abdullah Ilman Fahmi Achmad Fauzan Achmad Fauzan, Achmad Adeline Vinda Septiani Agus Buono Agus M Soleh Agus Mohamad Soleh Ahmad Ansori Mattjik Ajeng Bita Alfira Aji Hamim Wigena Alkahfi, Cahya Amalia Pasaribu, Asysta Amin, Yudi Fathul Anik Djuraidah Anisa, Rahma Ardiansyah, Muhlis Arie Anggreyani Arie Wahyu Wijayanto Arief Gusnanto ASEP SAEFUDDIN Astri Fatimah Azka Ubaidillah Bagus Sartono Bambang Sumantri Bambang Sumantri Beny Trianjaya Budi Susetyo Budi Susetyo Budi Waryanto Budi Waryanto Cici Suhaeni Citra Jaya Dede Dirgahayu Dede Dirgahayu Deiby T Salaki Dewi Juliah Ratnaningsih Dhea Dewanti Dian Handayani Dian Handayani Dian Kusumaningrum Dian Kusumaningrum Dian Kusumaningrum, Dwi Agustin Nuriani Sirodj Dwi Wahyu Triscowati Efriwati Efriwati Erfiani Erfiani Erfiani Erfiani Erfiani Erwan Setiawan Erwan Setiawan, Erwan Fahmi, Abdullah Ilman Farit Mochamad Afendi Farit Mohamad Afendi Fauzi, Fatkhurokhman Fauziah, Ghina Febryna Sembiring Fitri Dewi Shyntia Fitrianto, Anwar Fitriyani Sahamony, Nur Gerry Alfa Dito Hamid, Assyifa Lala Pratiwi Hamim Wigena, Aji Haq, Irvanal Hari Wijayanto Hari Wijayanto Hari Wijayanto Hestiani Wulandari Hidayat, Agus Sofian Eka Hidayat, Muhammad I Made Sumertajaya I Wayan Mangku IJSA, Indonesian Journal of Statistics and Its Applications Ikhlasul Amalia Rahmi Ina Widayanty Indah Herlawati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Ita Wulandari Iwan Kurniawan Khairani, Fitri Khairil Anwar Notodiputro Khairil Anwar Notodiputro Kristuisno Martsuyanto Kapiluka Kusman Sadik Kusman Sadik Loly, Joao Ferreira Rendes Bean Marshelle, Sean Matualage, Dariani Maulana Achiar, Anshari Luthfi Muhammad Nur Aidi Mulianto Raharjo Nashir, Husnun Newton Newton Newton, Newton Nurul Hidayati Pardomuan Robinson Sihombing Pasaribu, Asysta Amalia Pingkan Awalia Pramana, Setia Purba, Widyo Pura Purwanto, Arie Putri, Christiana Anggraeni Rahardiantoro, Septian Raharjo, Mulianto Rahayu, Melania Dwi Rahma Anisa Rahman, Gusti Arviana Retsi Firda Maulina Ristiyanti Ristiyanti Rysda Rysda Ryska Putri Madyasari Sahamony, Nur Fitriyani Santoso, Andrianto Santoso, Zein Rizky Sari Agustini Hafman Setyowati, Indah Rini Siregar, Jodi jhouranda Siskarossa Ika Oktora Siti Muchlisoh Suhaeni, Cici Suprayogi, Muhammad Azis Suprayogi, Muhammad Aziz Teguh Prasetyo Thooriq Ghaith Topan . Ruspayandi Trianjaya, Beny Triscowati, Dwi Wahyu Tyas, Maulida Fajrining Utami Dyah Syafitri Viarti Eminita Widiyanto, Rhendy K. P. Widoretno, Widoretno Wulandari, Hestiani Yani Nurhadryani Yenni Angraini Yenni Kurniawati Yudistira Yudistira Yudistira Yudistira Yully Sofyah Waode Zafira Fakhriyah