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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 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 Cerdika: Jurnal Ilmiah Indonesia 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
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MODELING THE INCIDENCE OF MALNUTRITION IN BOGOR REGENCY USING ZERO-INFLATED NEGATIVE BINOMIAL MIXED EFFECT MODEL Sirodj, Dwi Agustin Nuriani; Sadik, Kusman; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0961-0972

Abstract

Modeling response variables in the form of count data generally uses a model based on the Poisson distribution. However, some conditions, such as the presence of excess zero, can be found in the data that result in overdispersion, which will have an impact on the resulting variance in the model. In this paper, three approaches, namely the Poisson Mixed Model, the Negative Binomial (NB) Mixed Model, and the Zero-Inflated Negative Binomial (ZINB) Mixed Model, are used to model the incidence of malnutrition in Bogor Regency. The data used in this study are secondary data sourced from the West Java open data website. Based on the results of data analysis, it appears that the ZINB Mixed Model method is a method capable of accommodating random effects, overdispersion, and excess zero in modeling malnutrition in Bogor Regency. Variables that significantly affect the occurrence of malnutrition cases in villages in Bogor Regency include the Number of Children Weighed Routinely Every Month, Number of Children Measured for Length and Height Twice a Year, Number of Children under 12 Months Old Who Received Complete Basic Immunization, Number of Posyandu (Integrated Health Post), and Number of Parents/Caregivers Participating in Monthly Parenting (PAUD).
TWOFOLD SUBAREA MODEL FOR ESTIMATING COMMUTER PROPORTION IN 10 METROPOLITAN AREAS Amin, Yudi Fathul; Indahwati, Indahwati; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1009-1022

Abstract

The metropolitan area is a major contributor to national GDP. The metropolitan area is a center of attraction for many people who come to earn income as commuters. Commuters are people who carry out work activities in the center of the metropolitan area, which are carried out by residents who live in suburban areas around the center of the metropolitan area and commute regularly every day. The availability of commuter statistics from surveys for presentation level down to the smallest administrative level, such as regencies/municipalities, is unreliable. This happens because this level of presentation has poor precision due to insufficient samples due to the Statistics Indonesia survey design for making estimates at the national and provincial levels. It can be done using small area estimation (SAE) to meet increasing data needs, but existing SAE models can often estimate only at one level. To meet data requests more effectively, a model is needed that can estimate several small areas simultaneously. In SAE, one of the SAE models that can do this is the twofold subarea model. The twofold subarea model produces estimates of the proportion of commuters with good precision at the subarea level (regencies/municipalities) and area level (metropolitan area), with the RRMSE percentage value of the estimated proportion of commuters being below 25% for all regions. The results of this research can be used to present commuter data at the regencies/municipalities level and metropolitan area level where there is a lack of samples and become a new opportunity for Statistics Indonesia to increase statistical production in small areas, which is more effective compared to other SAE methods which have so far been used only to estimate one area level.
MULTINOMIAL LOGISTIC REGRESSION MODEL USING MAXIMUM LIKELIHOOD APPROACH AND BAYES METHOD ON INDONESIA'S ECONOMIC GROWTH PRE TO POST COVID-19 PANDEMIC Purwanto, Arie; Suprayogi, Muhammad Aziz; Setiawan, Erwan; Loly, Joao Ferreira Rendes Bean; Rahman, Gusti Arviana; Kurnia, Anang
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/barekengvol19iss1pp51-62

Abstract

Economic growth in Indonesia has become a major concern in the global context, especially before and after the Covid-19 pandemic. Key sectors such as tourism, manufacturing, trade and transportation have been seriously affected by restrictions on travel and economic activity imposed to control the spread of the virus. Therefore, it is considered necessary to carry out modeling to describe existing conditions. In this research, two approaches were used, namely the Maximum Likelihood approach and the Bayes approach. The use of methods in general as research material for researchers to study these two methods further. So far the algorithm used for the Bayes concept method is Markov Chain Monte Carlo with Hasting's Metropolis method. The parameter estimation results obtained from both methods are considered quite identical. However, it is necessary to pay attention to the iteration procedure that will be carried out. The selection of factors used in the iteration process is very determining in obtaining estimated parameter values. Furthermore, the results obtained so far do not contain any fundamental differences regarding economic growth in Indonesia. In general, Indonesia can be said to be stable in terms of economic growth.
A COMPARISON OF LOGISTIC REGRESSION, MIXED LOGISTIC REGRESSION, AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION ON PUBLIC HEALTH DEVELOPMENT IN JAVA Setiawan, Erwan; Suprayogi, Muhammad Azis; Kurnia, Anang
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/barekengvol19iss1pp129-140

Abstract

The Public Health Development Index (Indeks Pembangunan Kesehatan Masyarakat - IPKM) is a combined parameter that reflects progress in health development and is useful for determining areas that need assistance in improving health development. Through IPKM modeling, factors that significantly influence regional public health development can be discovered. This research aims to find an appropriate model for modeling IPKM and determine the factors that significantly influence public health development. The data used is the 2018 IPKM data collected from 119 cities/regencies in Java. We propose three models namely logistic regression (LR), mixed logistic regression (MLR), and geographically weighted logistic regression (GWLR). The research results show that the MLR is the best model for modeling IPKM in Java based on the AIC value criteria. Based on the MLR model, the factors that have a significant influence on public health development are the egg and milk consumption level and the percentage of the number of doctors per thousand population.
FACTORS AFFECTING INDONESIAN PADDY HARVEST FAILURE: A COMPARISON OF BETA REGRESSION, QUASI-BINOMIAL REGRESSION, AND BETA MIXED MODELS Kusumaningrum, Dian; Hidayat, Agus Sofian Eka; Notodiputro, Khairil Anwar; Kurnia, Anang; Sartono, Bagus; Sumertajaya, I Made
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2611-2622

Abstract

The Paddy harvest failure rate is one of the key aspects in determining the total number of claims in a crop insurance policy. It is also an important factor indicating the fulfillment of targeted total production. Therefore, we proposed Beta Regression, Quasi Binomial Regression, and Beta Mixed Models which can be used to analyze significant variables affecting paddy harvest failure rates. Model selection and evaluations indicated that the Nested Beta Mixed Model is the best. Previous research has shown four significant fixed effect variables: drought, flood, pests, and disease risks. Pests and other types of risks also affect the variability of loss rate. All variables have positive effects, indicating higher values cause a higher possibility of a higher average harvest failure rate. High variability was shown for province, municipality, and farmers' random effects. Hence, to prevent a more significant loss rate, MoA should consider more intensive and innovative participatory activities in farmer groups to enhance good farming practices, especially for farmers who suffer from certain risks. These activities should also consider the local characteristics of each province or municipality. As for AUTP development and improvement, farmers with lower failure risks could be given a discounted premium to make it more appealing.
STRATEGY FOR ELIMINATING NEGLECTED TROPICAL DISEASES THROUGH INDIVIDUAL AND AREA ASPECTS USING THE HIERARCHICAL LOGISTIC REGRESSION METHOD Oktora, Siskarossa Ika; Matualage, Dariani; Amalia Pasaribu, Asysta; Fitriyani Sahamony, Nur; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2495-2506

Abstract

Filariasis is one of the Neglected Tropical Diseases (NTDs) that is often associated with poverty and marginalized community groups. Papua is the province with the highest number of chronic filariasis cases and has the largest number of endemic districts/municipalities compared to other provinces in Indonesia. Papua is also the province with the highest poverty rate in Indonesia. To support the government's filariasis elimination program, this study aims to determine variables that influence the incidence of filariasis in Papua at the individual and area levels. This study uses 2018 Indonesia Basic Health Research data from the Ministry of Health and regional data from BPS-Statistics Indonesia. The results using Hierarchical Binary Logistic Regression concluded that defecation behavior in latrines, prevention behavior against mosquito bites, participation in mass preventive drug administration, number of poor people, and number of health workers have a significant effect on the incidence of filariasis. In contrast, the variables age, gender, type of work, and level of education do not have a significant effect.
A Machine Learning Approach to Spatial Analysis of Paddy Field Conversion Using Multispectral Sentinel-2A Imagery Fauzan, Achmad; Kurnia, Anang
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3617

Abstract

The expanse of rice fields is a critical metric as it is intimately linked to agricultural productivity in a given locale. This study investigates the application of satellite imagery to quantify trice fields' acreage and temporal variations. The data utilized was acquired by the Sentinel-2A multispectral satellite. The variables employed are the image's baseband and spectral index. The research area encompasses the Sukamakmur sub-district in Bogor Regency, Indonesia. The types of machine learning models include Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and k-Nearest Neighbor (kNN). The simulation of class numbers is conducted to achieve the most stable and precise evaluation metric values. The XGBoost algorithm is used for the overall classification process of the region based on the optimal metric score. The model's accuracy, precision, recall, and F1-score are 92.37%, 92.3%, 92.38%, and 92.33%, respectively, indicating a very good performance. The model successfully captures a decline in rice field area between 2020 and 2023. Using the Modified Moran’s Index (MMI), the study reveals a positive spatial autocorrelation, indicating a clustered pattern in land-use change. Regions that experience either substantial or minor changes in land use are commonly situated near areas exhibiting similar characteristics. This study presents a spatially aware machine learning framework that enables the effective monitoring of agricultural land-use dynamics. In the future, this framework can be enhanced by integrating time-series forecasting and socio-economic data, supporting more informed decision-making in food security planning and agricultural policy development.
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.
Co-Authors . Hanniva . Marzuki . Sutriyati Abdullah Ilman Fahmi ACHMAD FAUZAN Achmad Fauzan, Achmad Adeline Vinda Septiani Agus Buono Agus M Soleh Agus Mohamad Soleh Agus Mohammad Soleh Agus Mohammad Soleh Ahmad Ansori Mattjik Ajeng Bita Alfira Aji Hamim Wigena Alkahfi, Cahya Alona Dwinata Amalia Pasaribu, Asysta Amin, Yudi Fathul Anik Djuraidah Ardiansyah, Muhlis Arie Anggreyani Arie Wahyu Wijayanto Arief Gusnanto Arien Citha Utami ASEP SAEFUDDIN Astri Fatimah Azka Ubaidillah Bagus Sartono Bambang Sumantri Beny Trianjaya Budi Susetyo Budi Waryanto Cici Suhaeni 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 Farit Mochamad Afendi Farit Mohamad Afendi Fauziah, Ghina Febryna Sembiring Fitri Dewi Shyntia Fitrianto, Anwar Fitriyani Sahamony, Nur Gerry Alfa Dito 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 Ikhlasul Amalia Rahmi Ina Widayanty Indah Herlawati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iwan Kurniawan Khairani, Fitri Khairil Anwar Notodiputro Khairil Anwar Notodiputro Kristuisno Martsuyanto Kapiluka Kusman Sadik Kusman Sadik Loly, Joao Ferreira Rendes Bean Matualage, Dariani Maulana Achiar, Anshari Luthfi Muhammad Nur Aidi Mulianto Raharjo Nashir, Husnun Newton Newton Nurul Hidayati Pardomuan Robinson Sihombing Pardomuan Robinson Sihombing Pasaribu, Asysta Amalia Pingkan Awalia Pramana, Setia Purba, Widyo Pura Purwanto, Arie Putri, Christiana Anggraeni Rachmat Bintang Yudhianto Yudhianto Rahardiantoro, Septian Rahayu, Melania Dwi Rahma Anisa 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 Sean Marshelle Septian Rahardiantoro Setyowati, Indah Rini Siregar, Jodi jhouranda Siskarossa Ika Oktora Siti Muchlisoh Sri Kustiara Suprayogi, Muhammad Azis Suprayogi, Muhammad Aziz Teguh Prasetyo Thooriq Ghaith Topan . Ruspayandi Triscowati, Dwi Wahyu Tyas, Maulida Fajrining Utami Dyah Syafitri Viarti Eminita Widoretno, Widoretno Yani Nurhadryani Yenni Angraini Yenni Kurniawati Yudistira Yudistira Yully Sofyah Waode Zafira Fakhriyah