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All Journal FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Jurnal Ilmu Dasar Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Disease Prevention and Public Health Journal JUITA : Jurnal Informatika Forum Geografi Jurnal Penelitian Pertanian Tanaman Pangan Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL METEOROLOGI DAN GEOFISIKA Jurnal Aplikasi Statistika & Komputasi Statistik Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Journal of Data Science and Its Applications ESTIMASI: Journal of Statistics and Its Application SRIWIJAYA JOURNAL OF ENVIRONMENT Xplore: Journal of Statistics Jurnal Meteorologi dan Geofisika Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Anik Djuraidah
Department Of Statistics, IPB University, Jl. Lingkar Akademik Kampus IPB Dramaga, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry 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 Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Transportation Other

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Spatial Clustering Regression in Identifying Local Factors in Stunting Cases in Indonesia Syam, Ummul Auliyah; Djuraidah, Anik; Syafitri, Utami Dyah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.29803

Abstract

Stunting is a significant health problem in Indonesia with high spatial disparities between regions. This study applies the Spatial Clustering Regression (SCR) method to analyze spatial patterns and identify local factors influencing stunting. SCR is a method that combines spatial regression and clustering analysis simultaneously using a k-means clustering-based formulation and a penalty likelihood function motivated by the Potts model to encourage similar clustering in adjacent locations with regression parameter estimation done locally in areas that have similar characteristics. This quantitative study uses secondary data from the Central Bureau of Statistics in 2022 covering 510 districts/cities, with one response variable (percentage of stunting) and seven explanatory variables reflecting socioeconomic, health, and infrastructure conditions. The results show that SCR divides the region into four spatial clusters characterized by different local factors. Cluster 1 has the lowest percentage of stunting that is influenced by access to clean water, sanitation, and education, Cluster 2 by poverty rate, number of public health centers, access to clean water, and education, Cluster 3 by poverty and nutrition of pregnant women, and Cluster 4 is the most vulnerable area with the highest stunting rate with a significant influential factor which is access to sanitation. The SCR approach allows for easier and more in-depth interpretation of results than other spatial methods such as GWR, as it can capture complex spatial patterns in the form of regional clusterings. These results provide a strong basis for formulating region-specific intervention policies, such as poverty alleviation and sanitation improvement in Cluster 4, strengthening health services in Cluster 2, developing education and nutrition programs in Cluster 3, and maintaining and improving nutrition consumption in Cluster 1.
Multilevel Semiparametric Modeling with Overdispersion and Excess Zeros on School Dropout Rates in Indonesia Tarida, Arna Ristiyanti; Djuraidah, Anik; Soleh, Agus Mohamad
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30102

Abstract

This study aims to identify key factors influencing high school dropout rates in Indonesia by applying advanced statistical modeling that accounts for complex data characteristics. Dropout data often display overdispersion (variability greater than expected) and excess zeros (many students not dropping out), which, if ignored, can bias conclusions.  To address this, we compare parametric models, Zero-Inflated Poisson Mixed Model (ZIPMM), Zero-Inflated Generalized Poisson Mixed Model (ZIGPMM), and Zero-Inflated Negative Binomial Mixed Model (ZINBMM), with their semiparametric counterparts (SZIPMM, SZIGPMM, SZINBMM). The semiparametric models use B-spline functions to capture nonlinear relationships between predictors and dropout rates, with flexibility. Model performance was evaluated using Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE) across 100 simulation repetitions to ensure robustness. Results show that the semiparametric ZIGPMM (SZIGPMM) outperformed other models, achieving the lowest average AIC (18969.62), suggesting the best trade-off between model fit and complexity. The optimal spline configuration used knot point 2 and order 3, with a Generalized Cross-Validation (GCV) score of 9.4107. Key predictors of dropout include school status (public or private), student-teacher ratio, distance from home to school, parental education level, parental employment status, and number of siblings. These findings provide actionable insights for education policymakers, emphasizing the need to address structural and socioeconomic barriers to reduce dropout rates effectively.
PENDEKATAN GEOGRAPHICALLY WEIGHTED ZERO INFLATED POISSON REGRESSION (GWZIPR) DENGAN PEMBOBOT FIXED BISQUARE KERNEL PADA KASUS DIFTERI DI INDONESIA Ismah, Ismah; Sumertajaya, I Made; Djuraidah, Anik; Fitrianto, Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 1 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (679.128 KB) | DOI: 10.30598/barekengvol14iss1pp039-046

Abstract

The number of deaths due to diphtheria is counts data and there is a considerable presence of zeros (excess zeros). Besides, data on the spread of disease are generally geographically oriented or observed in each particular region, which is a type of spatial data. Geographically Weighted Zero Inflated Poisson Regression (GWZIPR), as the development of Geographically Weighted Regression (GWR) and Zero Inflated Poisson (ZIP) models will be used as a model in processing provincial diphtheria data in Indonesia in 2018, with the independent variable percentage of diphtheria cases (X1), percentage of vaccinated numbers (X2) and percentage of the population (X3) in each province in Indonesia. Estimating model parameters uses the method of maximum likelihood estimation. While the weighting function used is fixed bisquare kernel. Data is processed using software R packages lctools. The results were obtained if the model involved all three independent variables, the effect of the three independent variables on the number of deaths due to diphtheria was not significant. This is because there is a strong and significant relationship between independent variables, so that if the model does not involve a variable percentage of the population (population density), the percentage of vaccinated people affects the number of deaths caused by diphtheria significantly in an area. So that the provision of immunization vaccines can reduce the number of deaths caused by diphtheria
PERFORMANCE OF LASSO AND ELASTIC-NET METHODS ON NON-INVASIVE BLOOD GLUCOSE MEASUREMENT CALIBRATION MODELING Abqorunnisa, Farah; Erfiani, Erfiani; Djuraidah, Anik
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 (446.747 KB) | DOI: 10.30598/barekengvol17iss1pp0037-0042

Abstract

Diabetes Mellitus (DM) is a disease that can occur in humans caused by conditions of high blood glucose levels (hyperglycemia). Detection of blood glucose levels can be done using invasive methods (injuring) and non- invasive methods (with infrared rays). Analytical methods are needed to model these results to obtain estimates of blood glucose levels. An alternative approach that can be used to analyze the relationship between invasive and non- invasive blood glucose levels is the calibration model. Problems that often occur in calibration modelling are multicollinearity and outliers. These problems can be overcome by adding new data, applying principal component analysis, and using LASSO and Elastic-Net regression to overcome calibration problems. The research data used was invasive and non-invasive blood glucose data in 2019, with as many as 74 respondents. The results of the study concluded that the summarization of the trapezoidal area in calibration modelling provides a good estimate. The performance of the Elastic Net method provides better prediction results than other models, with an RMSE value of 22.39. It has the most significant positive correlation value of 0.97, which means close to 1 so that the performance of the Elastic Net method can handle calibration modelling.
ROBUST STOCHASTIC PRODUCTION FRONTIER TO ESTIMATE TECHNICAL EFFICIENCY OF RICE FARMING IN SULAWESI SELATAN Pranata, Ismail; Djuraidah, Anik; Aidi, Muhammad Nur
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 (454.162 KB) | DOI: 10.30598/barekengvol17iss1pp0391-0400

Abstract

The stochastic production frontier (SPF) is the stochastic frontier analysis (SFA) method used to estimate the production frontier by accounting for the existence of inefficiency. The standard SPF assumes that the noise component follows a Normal distribution and the inefficiency component follows a half-Normal distribution. The presence of outliers in the data will affect the inaccuracy in estimating the parameters and leads to an exaggerated spread of efficiency predictions. This study uses two alternative models, the first with SPF Normal-Gamma and the second with SPF Student's t-half Normal, then the results are compared with standard SPF. This study uses data from statistics Indonesia on the cost structure of paddy cultivation household survey in 2014. This study aims to examine the effect of changes in distribution assumptions on the standard SPF model in estimating parameter value and the technical efficiency score in the presence of outliers. The parameter coefficient estimates similar results that apply to three SPF models. Only the standard error value in the alternative SPF model tends to be smaller than the standard SPF model. The Normal-Gamma model performs better in assessing residual with smaller root mean square error (RMSE) than the others, but the results of the estimated technical efficiency still contain outliers. The Student's t-half Normal model estimates technical efficiency no longer contains outliers, the range is shorter than the other models, and the results of estimating technical efficiency are not monotonous in the distribution of residual tails. The SPF Student's t-half Normal model is more robust in presence outliers than SPF Normal-half Normal and SPF Normal-Gamma.
APPLICATION OF PENALIZED SPLINE-SPATIAL AUTOREGRESSIVE MODEL TO HIV CASE DATA IN INDONESIA Pigitha, Nindi; Djuraidah, Anik; Wigena, Aji Hamim
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 (389.821 KB) | DOI: 10.30598/barekengvol17iss1pp0527-0534

Abstract

Spatial regression analysis is a statistical method used to perform modeling by considering spatial effects. Spatial models generally use a parametric approach by assuming a linear relationship between explanatory and response variables. The nonparametric regression method is better suited for data with a nonlinear connection because it does not need linear assumptions. One of the nonparametric regression methods is penalized spline regression (P-Spline). The P-spline has a simple mathematical relationship with mixed linear model. The use of a mixed linear model allows the P-Spline to be combined with other statistical models. PS-SAR is a combination of the P-Spline and the SAR spatial model so that it can analyze spatial data with a semiparametric approach. Based on data from monitoring the development of the HIV situation in 2018, the number of HIV cases in Indonesia shows a clustered pattern that indicate spatial dependence. In addition, the relationship between the number of positive cases and the factors tends to be nonlinear. Therefore, this study aims to apply the PS-SAR model to HIV case data in Indonesia. The resulting model is evaluated based on the estimates of autoregressive spatial coefficient, MSE, MAPE, and Pseudo R2. Based on the results, the PS-SAR model has an autoregressive spatial coefficient similar to the SAR model and has smaller MSE and MAPE than the SAR model.
Identifying Factors Affecting Waste Generation in West Java in 2021 Using Spatial Regression Djuraidah, Anik; Rizki, Akbar; Alfan, Tony
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.19664

Abstract

Responsible consumption and production is the 12th of the seventeen SDGs which is difficult for developing countries to achieve due to high waste production. Indonesia is the second largest producer of food waste in the world. Garbage is solid waste generated from community activities. Population density is an indicator to estimate the amount of waste generated in an area. The choice of West Java Province as the research area is based on the fact that this Province has the second highest population density in Indonesia. This study aimed to determine the predictors/factors that influence waste production in the districts/cities of West Java Province. The data used in this study are total waste as a response variable and GRDP (gross domestic product), total spending per capita, average length of schooling, literacy rate, number of MSMEs (micro, small, and medium enterprises), and several recreational and tourism places, the number of people's markets, and the number of restaurants as predictors. The methods used in this research are spatial autoregressive regression/SAR, spatial Lag-X/SLX, and spatial Durbin/SDM. The results of this study show that the SAR is the best model with the lowest BIC (74.442) and pseudo-R-squared (0.7934). Factors that significantly affect total waste production are literacy levels, the number of MSMEs, the number of traditional markets, and the number of recreational and tourist places. 
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.
Land Use Change Modelling Using Logistic Regression, Random Forest and Additive Logistic Regression in Kubu Raya Regency, West Kalimantan Pradana, Alfa Nugraha; Djuraidah, Anik; Soleh, Agus Mohamad
Forum Geografi Vol 37, No 2 (2023): December 2023
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v37i2.23270

Abstract

Kubu Raya Regency is a regency in the province of West Kalimantan which has a wetland ecosystem including a high-density swamp or peatland ecosystem along with an extensive area of mangroves. The function of wetland ecosystems is essential for fauna, as a source of livelihood for the surrounding community and as storage reservoir for carbon stocks. Most of the land in Kubu Raya Regency is peatland. As a consequence, peat has long been used for agriculture and as a source of livelihood for the community. Along with the vast area of peat, the regency also has a potential high risk of peat fires. This study aims to predict land use changes in Kubu Raya Regency using three statistical machine learning models, specifically Logistic Regression (LR), Random Forest (RF) and Additive Logistic Regression (ALR). Land cover map data were acquired from the Ministry of Environment and Forestry and subsequently reclassified into six types of land cover at a resolution of 100 m. The land cover data were employed to classify land use or land cover class for the Kubu Raya regency, for the years 2009, 2015 and 2020. Based on model performance, RF provides greater accuracy and F1 score as opposed to LR and ALR. The outcome of this study is expected to provide knowledge and recommendations that may aid in developing future sustainable development planning and management for Kubu Raya Regency.
A-Optimal Pada Mixture Amount Design Dengan Modifikasi Rancangan Petak Terbagi Menggunakan Algoritma Point-Exchange Sari, Mutia Dwi Permata; Syafitri, Utami Dyah; Djuraidah, Anik
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 12 No. 2 (2024): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v12i2.4072

Abstract

Abstract:A Mixture Amount Experiment (MAE) is a design that consists of a mixture variable and the total amount variable. In practice, the composition of the mixture is run on each total amount of mixture, which consequently cannot be completely randomized, so that a split-plot design approach is needed. This research aims to develop an algorithm to find a A-Optimal design for a mixture amount experiment with a modified split-plot design. A-Optimal design is seeking a design in which minimizing the covariance of the model parameter. The study case of this research involved three ingredients and two total amounts of mixtures with different constraints. In this research, the whole plot factor is the total amount of mixtures, while the subplot factor is the composition of the mixture. The A-Optimal design was generated based on the Second-Order Scheefe model. To Construct the A-optimal design, we used the point exchange algorithm. The result from this algorithm produced an optimum composition in each total amount of mixture. Abstrak:Rancangan Jumlah Campuran (MAE) terdiri dari komponen campuran dan jumlah total. Dalam prakteknya, komposisi campuran dijalankan pada setiap jumlah total campuran, akibatnya tidak dapat diacak sempurna, sehingga diperlukan pendekatan rancangan petak terbagi. Penelitian ini bertujuan untuk mengembangkan suatu algoritma untuk menemukan rancangan dengan kriteria A-Optimal untuk percobaan jumlah campuran dengan menggunakan modifikasi rancangan petak terbagi. Rancangan A-Optimal mencari rancangan yang meminimalkan kovarian parameter model. Studi kasus penelitian ini terdiri dari tiga bahan dan dua jumlah total campuran yang berbeda. Dalam penelitian ini, factor petak utama adalah jumlah total campuran, sedangkan faktor anak petak adalah komposisi campuran. rancangan A-Optimal dihasilkan berdasarkan model Second-Order Scheefe. Untuk Membangun rancangan A-optimal, menggunakan pendekatan algoritma point-exchange. Hasil dari algoritma ini menghasilkan komposisi optimum pada setiap jumlah total campuran.
Co-Authors . . Aunuddin Aam Alamudi Abqorunnisa, Farah Agus M. Soleh Agus Mohamad Soleh Agusta, Madania Tetiani Aji H Wigena Aji Hamim Wigena Aji Hamin Wigena Alfa Nugraha Pradana Alfa Nugraha Pradana Alfan, Tony Alwi Aliu, Muftih Anang Kurnia Anisa, Rahma Ardiansyah, Muhlis Aris Yaman Asep Andri Fauzi ASEP SAEFUDDIN Aunuddin Aunuddin Azizah Desiwari Bagus Sartono Banan Nabila Bimandra Djaafara Cici Suhaeni Cici Suheni Dani Al Mahkya Dewi Retno Sari Saputro, Dewi Retno Erfiani Erfiani Fauziah, Ghina Fitrianto, Anwar Hanifa Izzati Hardinsyah Haryanto, Sugi Herlina Hanum Herlina Hanum, Herlina I Made Sumertajaya I Wayan Mangku Ida Mariati Hutabarat Indahwati Intan Lukiswati Ira Yulita Ismah . Ismah, Ismah Itasia Dina Sulvianti Lismayani Usman Lusi Eka Afri Mely Amelia Miranti, Ita Miranti, Ita Mohamad Arif Pramarta Muhammad Nur Aidi Novi Hidayat Pusponegoro Oryza Sativa Pigitha, Nindi Pika Silvianti Pranata, Ismail Putri Astrini, Yufan Putri Astrini Rahardiantoro, Septian Rahma Anisa Resti Cahyati Resty Fanny Retna Nurwulan Retno Ariyanti Pratiwi Retsi Firda Maulina Ristiyanti Tarida, Arna Rita Rahmawati Rizki, Akbar Sarah Fadhlia Sari, Mutia Dwi Permata Sarimah Sarimah Septemberini, Cintia Setiawan Setiawan Sinaga, Enny Keristiana Siti Nur Laila Sony Sunaryo Sugi Haryanto Syam, Ummul Auliyah Tarida, Arna Ristiyanti Tasya Meilania, Gusti Titin Agustin Utami Dyah Syafitri Winda Chairani Mastuti Yoga Primanda Zulkarnain, Rizky _ Aunuddin