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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 Desimal: Jurnal Matematika 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 Social Sciences Transportation Other

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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.
OPTIMIZING LANDSLIDE SUSCEPTIBILITY MAPPING IN CENTRAL SULAWESI WITH RECURSIVE FEATURE ELIMINATION AND RANDOM FOREST ALGORITHM Siregar, Indra Rivaldi; Djuraidah, Anik; Soleh, Agus Mohamad
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/barekengvol20iss2pp1019-1034

Abstract

Landslides are among the most destructive natural hazards, causing severe casualties, economic losses, and environmental degradation. Central Sulawesi, characterized by active tectonics such as the Palu-Koro fault, is highly susceptible to landslides, as tragically demonstrated in 2018. Therefore, developing accurate landslide susceptibility maps is essential to support comprehensive landslide mitigation efforts in this region. While machine learning, particularly Random Forest (RF), has proven highly effective for landslide modeling, previous studies around Palu have often overlooked model simplification through feature selection and hyperparameter optimization. This study proposes an integrated approach combining RF with Recursive Feature Elimination (RFE) to reduce model complexity and enhance predictive accuracy. This research utilizes 498 landslide events with fifteen conditions, including topography, environment, geology, and anthropogenic influences. The RFE-RF model achieves superior classification performance, with accuracy, balanced accuracy, and F1-scores exceeding 0.81, outperforming the RF without RFE and Logistic Regression baselines. These findings underscore the urgent need to integrate feature selection methods such as RFE into landslide modeling frameworks to improve predictive accuracy. High accuracy enables government authorities and stakeholders to develop more targeted and effective mitigation priorities. Spatial analysis indicates that Donggala, Palu, and Sigi are the most critical areas requiring prioritized mitigation, with over 9% of their territories classified as highly susceptible. Feature importance analysis reveals that elevation, slope, and land cover are the most influential factors. This study suggests that mitigation efforts should focus on the hills and mountainous areas on both sides of the Palu Valley, with recommended strategies emphasizing land cover management practices, such as reforestation, to enhance slope stability and reduce landslide risk.
Spatial Analysis of the Number of BPJS Ketenagakerjaan Participants using Geographically Weighted Panel Regression Sofia, Ayu; Zul’aina, Restu Apriani; Djuraidah, Anik; Pitri, Rizka
Desimal Vol. 9 No. 1 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v9i1.30408

Abstract

Uneven participation in employment-based social protection remains a major challenge in decentralized labor markets where regional economic structures, labor informality, and institutional capacity vary substantially across locations. Conventional panel regression and standard spatial econometric models generally assume homogeneous relationships across regions, potentially obscuring localized determinants of participation behavior. This study examines spatially varying determinants of BPJS Ketenagakerjaan participation in South Kalimantan Province, Indonesia, using Geographically Weighted Panel Regression (GWPR) applied to panel data from thirteen districts and cities during 2018–2022. The GWPR approach is employed because it allows regression coefficients to vary across space and time, enabling the identification of spatial nonstationarity that cannot be captured by global panel models. The results reveal clear spatial heterogeneity in participation dynamics. The number of registered companies emerges as the most consistent determinant, showing statistically significant positive effects across all districts with coefficients ranging from 0.099 to 0.143. In contrast, informal worker income demonstrates localized negative effects in several districts (−0.104 to −0.088), suggesting substitution between informal earnings and participation in formal protection schemes. Average years of schooling shows strong positive effects in selected regions (0.554–0.699), indicating the importance of human capital in increasing social insurance awareness and participation. Model adequacy testing further confirms that the GWPR specification provides a better representation of spatial variation than the global panel model.
BCBimax Biclustering Algorithm with Mixed-Type Data Hanifa Izzati; Indahwati Indahwati; Anik Djuraidah
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21519

Abstract

The application of biclustering analysis to mixed data is still relatively new. Initially, biclustering analysis was primarily used on gene expression data that has an interval scale. In this research, we will transform ordinal categorical variables into interval scales using the Method of Successive Interval (MSI). The BCBimax algorithm will be applied in this study with several binarization experiments that produce the smallest Mean Square Residual (MSR) at the predetermined column and row thresholds. Next, a row and column threshold test will be carried out to find the optimal bicluster threshold. The existence of different interests in the variables for international market potential and the number of Indonesian export destination countries is the reason for the need for identification regarding the mapping of destination countries based on international trade potential. The study's results with the median threshold of all data found that the optimal MSR is at the threshold of row 7 and column 2. The number of biclusters formed is 9 which covers 74.7% of countries. Most countries in the bicluster come from the European Continent and a few countries from the African Continent are included in the bicluster.
KAJIAN SIMULASI PENDUGAAN SELANG KEPERCAYAAN BOOTSTRAP BAGI ARAH MEDIAN DATA SIRKULAR Suhaeni, Cici; Sumertajaya, I Made; Djuraidah, Anik
Indonesian Journal of Statistics and Applications Vol 2 No 1 (2018)
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.v2i1.64

Abstract

The median direction is one of central tendency of circular data. The estimation process usually requires information about sampling distribution of statistic that want to be used as a parameter estimate. Theoretically, sampling distribution derived from population distribution. But, it is not easy to get sampling distribution of median although the population distribution is known. When the sampling distribution cannot be derived easily from population distribution, the bootstrap method can be an alternative to handle it. This study wants to evaluate the effect of increasing concentration parameter to the performance of bootstrap confidence interval estimation for median direction through simulation study. Three methods were used to estimate the interval which are equal-tailed arc (ETA), symmetric arc (SYMA), and likelihood-based arc (LBA). The most important criterion to evaluate them were true coverage and interval width. The simulation results that in general, the increasing of concentration parameter followed by more narrow interval. For small concentration parameter (k<1), all methods give unstable true coverage and interval width. The authors also identify that those three methods produce intervals with identical width when the parameter concentration is 20 or more. In terms of coverage and interval width, the best method was ETA.
ANALISIS REGRESI DATA PANEL PADA INDEKS PEMBANGUNAN GENDER (IPG) JAWA TENGAH TAHUN 2011-2015 Lukiswati, Intan; Djuraidah, Anik; Syafitri, Utami Dyah
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.v4i1.331

Abstract

The Gender Development Index (GDI) is a measure of the level of achievement of gender-based human development in Indonesia. Central Java Province is the largest province in Java with a GDI rate which tends to increase during the period of 2011 to 2015. Central Java's GDI, when compared to other provinces on Java Island, ranks third after DKI Jakarta and DI Yogyakarta. Central Java’s GDI consists of several observations for a certain period of time so that panel data regression analysis can be used. The purpose of this study was to model the GDI of women in Central Java with panel data regression and find out which explanatory variables significantly affected women's GDI in Central Java from 2011 to 2015. The results of this study indicate that explanatory variables that significantly influence women's GDI in Central Java are life expectancy, primary school enrollment rates, high school enrollment rates, and per capita expenditure.
PEMODELAN AUTOREGRESIF SPASIAL MENGGUNAKAN BAYESIAN MODEL AVERAGING UNTUK DATA PDRB JAWA Sarimah, Sarimah; Djuraidah, Anik; Wigena, Aji H
Indonesian Journal of Statistics and Applications Vol 3 No 3 (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.v3i3.376

Abstract

Economic data always contains spatial effects. Gross Regional Domestic Product (GRDP) in Java is one of economic data that describes spatial dependence between adjacent districts/cities. The method that is suitable for modeling GDRP is spatial regression with spatial dependence on lags that is spatial autoregressive. GDRP prediction used the Bayesian Model Averaging (BMA) method. The ten autoregressive spatial model that have highest posterior probability was chosen to determined the BMA model by posterior probability. The explanatory variables used in this study were (1) mean years of schooling (2) life expectancy (3) income per capita (4) local revenue (5) number of workers (6) district minimum salary. The results showed that the number of workers was chosen as a predictor for the ten models. The model that have highest posterior probability probability is 0.54 which contains five explanatory variables that are mean years of schooling, income per capita, local revenue, number of workers and district minimum salary and the pseudo R2 of the model is 0.696.
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 Ayu Sofia Azizah Desiwari Bagus Sartono Banan Nabila Bimandra Djaafara Cici Suhaeni Cici Suheni Dani Al Mahkya Dewi Retno Sari Saputro, Dewi Retno Erfiani Erfiani Fadhlia, Sarah Fauzi, Asep Andri Fauziah, Ghina Fitrianto, Anwar Hanifa Izzati 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 Lukiswati, Intan Lusi Eka Afri Mastuti, Winda Chairani Mely Amelia Miranti, Ita Miranti, Ita Mohamad Arif Pramarta Muhammad Nur Aidi Novi Hidayat Pusponegoro Oryza Sativa Pigitha, Nindi Pika Silvianti Pitri, Rizka Pranata, Ismail Putri Astrini, Yufan Putri Astrini Rahardiantoro, Septian Rahayu, Melania Dwi 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 Sarimah Sarimah, Sarimah Septemberini, Cintia Setiawan Setiawan Sinaga, Enny Keristiana Siregar, Indra Rivaldi Siti Nur Laila Sony Sunaryo Sugi Haryanto Suhaeni, Cici Syam, Ummul Auliyah Tarida, Arna Ristiyanti Tasya Meilania, Gusti Titin Agustin Utami Dyah Syafitri Wigena, Aji H Winda Chairani Mastuti Yoga Primanda Zulkarnain, Rizky Zul’aina, Restu Apriani _ Aunuddin