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Erfiani
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Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Klasterisasi Desa di Provinsi Jawa Barat Berdasarkan Indeks Pembangunan Desa (IPD) Tahun 2021 Menggunakan Algoritma K-Prototypes Irsyifa Mayzela Afnan; Siti Hasanah; Anwar Fitrianto; Erfiani; Alfa Nugraha
Jurnal Statistika dan Aplikasinya Vol 7 No 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07206

Abstract

Cluster analysis is a method used to group data with similar characteristics. There are various clustering methods adapted to different types of data. K-Prototypes is a clustering method that can be applied to mixed numerical and categorical data. The data used in this study are mixed numerical and categorical data derived from the Village Potential data in 2021. The aim of this research is to group villages in West Java based on variables from the Indeks Pembangunan Desa (IPD). Clustering using three clusters adapted to village status according to IPD resulted in 931 villages in cluster-1, 1880 villages in cluster-2, and 2104 villages in cluster-3. The characteristics of cluster-1 villages are villages that have adequate health and education facilities and good infrastructure conditions. Cluster-2 has an average numeric variable lower than cluster-1 but higher than cluster-3.
Perbandingan Metode Complete Linkage, Average Linkage dan Ward’s untuk Pengelompokan Ketahanan Pangan di Provinsi Jawa Timur Amanda, Nabila; Yulianti, Riska; Fitrianto, Anwar; Erfiani; JUMANSYAH, L.M. RISMAN DWI
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v13n1.p227-235

Abstract

Provinsi Jawa Timur merupakan produsen utama padi nasional dengan total produksi mencapai 9,59 juta ton pada tahun 2023, yang memainkan peran penting dalam memenuhi kebutuhan pangan Indonesia. Namun, disparitas indikator ketahanan pangan di beberapa kabupaten/kota masih menjadi perhatian, di mana beberapa wilayah dikhawatirkan masuk dalam kategori rawan pangan. Penelitian ini berfokus pada analisis clustering untuk mengelompokkan ketahanan pangan kabupaten/kota di Jawa Timur dengan membandingkan tiga metode agglomerative hierarchical clustering, yakni complete linkage, average linkage, dan ward’s. Data yang digunakan terdiri dari 12 variabel terkait ketahanan pangan, seperti produksi padi, konsumsi kalori, akses listrik, umur harapan hidup, prevalensi stunting dan lain-lain. Ketiga metode dievaluasi menggunakan koefisien cophenetic yang menghasilkan bahwa metode average linkage memiliki performa terbaik dengan nilai cophenetic sebesar 0,859 yang mengindikasikan ketepatan representasi data yang tinggi. Metode ini mengelompokkan wilayah Jawa Timur menjadi tiga cluster dengan kategori rentan pangan, tahan pangan, rawan pangan yang mampu memberikan informasi penting bagi pengambilan kebijakan.
Pemodelan Tingkat Kecanduan Games Online Menggunakan Regresi Logistik Ordinal Hidayah, Nur; Indahwati; Fitrianto, Anwar; Erfiani; Aliu, Muftih Alwi
MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika Vol. 5 No. 1 (2024): MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika
Publisher : Universitas Tidar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31002/mathlocus.v5i1.4335

Abstract

Analisis regresi yang digunakan untuk memodelkan hubungan antara variabel prediktordan variabel respon yang berskala ordinal disebut regresi logistik ordinal. Data ini diperoleh darisurvei yang dilakukan oleh peneliti sebelumnya untuk mengukur tingkat kecanduan games onlinedengan menggunakan pemodelan matematika PEAR. Kecanduan games online menjadifenomena yang semakin mengkhawatirkan di era digital ini, dengan dampak negatif yangsignifikan pada aspek sosial, psikologis, dan akademik. Penelitian ini bertujuan untukmemodelkan model prediktif dengan mengukur tingkat kecanduan games online melalui aplikasiregresi logistik ordinal. Model ini mempertimbangkan beberapa variabel prediktor, yaitu umur,durasi bermain games, durasi bermain per hari, dan jenis games. Regresi logistik ordinaldigunakan karena variabel responnya, yaitu tingkat kecanduan bermain games, bersifat ordinaldan terdiri dari lebih dari dua kategori yang berurutan. Model ini menunjukkan akurasi sebesar92,5%, yang mengindikasikan kemampuan model dalam mengklasifikasikan tingkat kecanduanbermain games online dengan keandalan yang tinggi.
Analisis Regresi Logistik Biner untuk Mengidentifikasi Faktor-Faktor yang Mempengaruhi Keterdeteksian Kasus Perceraian di Indonesia Timur (Maluku, Maluku Utara, dan Papua Barat) Waliulu, Megawati Zein; Indahwati; Fitrianto, Anwar; Erfiani; Muftih Alwi Aliu
MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika Vol. 5 No. 1 (2024): MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika
Publisher : Universitas Tidar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31002/mathlocus.v5i1.4339

Abstract

Mayoritas kabupaten/kota di Provinsi Maluku, Maluku Utara, dan Papua Barat tidak melaporkan kasus perceraian dengan persentase sebesar 53,6%. Sementara itu, 46,4% kabupaten/kota di provinsi tersebut melaporkan adanya kasus perceraian pada tahun 2023. Penelitian ini menggunakan metode regresi logistik biner yang bertujuan untuk memodelkan serta mengidentifikasi faktor-faktor yang mempengaruhi kasus perceraian di Indonesia Timur. Penelitian ini penting dilakukan untuk memahami dinamika sosial dan ekonomi di Indonesia Timur. Hasil penelitian menunjukkan bahwa model regresi logistik biner memiliki ketepatan prediksi sebesar 77,27% dengan peubah jumlah pulau (X3), jarak ke ibu kota (X4), dan luas kabupaten/kota (X5) memberikan pengaruh yang signifikan terhadap kasus keterdeteksian perceraian pada taraf nyata 90%.
Comparison of Ordinal Logistic Regression and Geographically Weighted Ordinal Logistic Regression (GWOLR) in Predicting Stunting Prevalence among Indonesian Toddlers Setyowati, Silfiana Lis; Indahwati; Fitrianto, Anwar; Erfiani; Aliu, Muftih Alwi
Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Vol. 21 No. 2 (2024): Sainmatika : Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam
Publisher : Universitas PGRI Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31851/sainmatika.v21i2.15416

Abstract

Ordinal logistic regression is a type of logistic regression used for response variables with an ordinal scale, containing two or more categories with levels between them. This method is an extension of logistic regression where the observed response variable is ordinal with a clear order. It addresses spatial effects that can cause variance heterogeneity and improve parameter estimation accuracy compared to logistic regression. Geographically Weighted Regression (GWR) is a statistical analysis technique designed to account for spatial heterogeneity. GWOLR is an extension of OLS and GWR models that incorporates spatial elements into regression with categorical variables. This study compares the effectiveness of OLR and GWOLR in analyzing stunting prevalence in toddlers. Comparing OLR and GWOLR can help assess the spatial impact on stunting prevalence. This analysis could reveal that certain regions have a higher tendency for stunting prevalence, while others might have lower tendencies, thus helping in understanding regional disparities. Toddler height is a key indicator of health and nutrition in early growth. The prevalence of stunting for toddlers, according to WHO, is categorized into four levels: low, moderate, high, and very high. The Ordinal Logistic Regression model is better suited for modeling toddler stunting prevalence in Indonesia than the GWORL model. The Ordinal Logistic Regression model and the GWOLR both have a classification accuracy of 85.7%, but the OLR model has a lower AIC value. The GWOLR model is not suitable for analyzing stunting prevalence among Indonesian toddlers due to the lack of spatial variability in the data. The Breusch-Pagan test results indicate that there is no spatial heterogeneity in the data on stunting prevalence among Indonesian toddlers, as the p-value is less than the significance level of 0.05. The prevalence of undernourished toddlers is the main factor influencing stunting among Indonesian toddlers.
VISUALIZATION AND MAPPING OF HOUSEHOLD HOUSING CONDITIONS IN WEST JAVA USING MULTIDIMENSIONAL SCALING Hafsah, Siti; Rifda Nida’ul Labibah; Anwar Fitrianto; Erfiani; L.M. Risman Dwi Jumansyah
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08201

Abstract

This study aims to map household housing conditions in West Java using the Multidimensional Scaling (MDS) approach. West Java, as the most populous province in Indonesia, faces significant challenges regarding housing inequalities, infrastructure access, and socio-economic disparities between urban and rural areas. These disparities necessitate a comprehensive and systematic approach to identify vulnerable regions and inform targeted policy interventions. Using data from the 2023 National Socio-Economic Survey (Susenas), this study analyzes five main groups of variables: basic needs, housing facilities and ownership, socio-economic status, access to services and infrastructure, and household demographics and welfare. The Multidimensional Scaling (MDS) technique is employed due to its capability to reduce complex, high-dimensional data into a two-dimensional representation, allowing clearer visualization of regional disparities and interrelationships among variables. MDS also facilitates robust model evaluation, ensuring high-quality mapping results. The MDS results reveal significant variations in household conditions, with urban areas such as Bekasi and Depok City showing better infrastructure access and welfare outcomes compared to rural areas like Cirebon and Sukabumi District. Evaluation of the MDS model indicates excellent performance, with STRESS values ranging from 0.042 to 0.083 and RSQ values between 0.993 and 0.999, demonstrating high accuracy. This study addresses a research gap where few studies have comprehensively mapped housing inequalities in large, diverse regions like West Java using advanced multidimensional techniques. The findings emphasize the importance of policies focusing on infrastructure development and equitable distribution of social assistance in underdeveloped regions to reduce regional disparities.
LR-GLASSO Method for Solving Multiple Explanatory Variables of the Village Development Index Yunus, M.; Soleh, Agus M; Saefuddin, Asep; Erfiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5656

Abstract

Sustainable Development Goals (SDGs) are developments that maintain sustainable improvement in society’s economic, social, and environmental welfare. Kemendes PDTT RI has issued the Village Development Index (VDI) to provide information and the status of village progress to support village development to improve the National SDGS. Modeling with multiple explanatory variables causes a high correlation between explanatory variables, multicollinearity, and coefficient estimation results, which have a large variance and overfitting in the prediction results. The modeling solution uses LASSO and GLASSO. The binary categorical response data use binary logistic regression (LR), so LR-LASSO and LR-GLASSO are used. North Maluku Province has a VDI ranking that tends to fall in 2018-2022. On the basis of the mean and variance of the coefficient estimation results and misclassification errors, LR-GLASSO is better than LR-LASSO and LR. LR-GLASSO is recommended for analyzing VDI data because it has many explanatory variables and the correlation between them is relatively high. The Indonesian government recommendation, if it is to increase the status of VDI in Indonesia, especially in the north Maluku province, is to increase the number of electricity users, food and beverage stores, and other cooperatives. The Indonesian government also needs to pay attention to villages relatively far from the regent's office, between food and beverage stalls, and supporting health centers, because they still need to be developed compared to other villages, and more than 50% of the villages are underdeveloped. If the Village SDGs are formulated by increasing the VDI status, it will support the achievement of the SDGs goals nationally.
Comparative Performance of GLMM and GEE for Longitudinal Beta Regression in Economic Inequality Modelling Sihombing, Pardomuan Robinson; Erfiani; Khairil Anwar Notodiputro; Anang Kurnia
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2057

Abstract

Due to the shortcomings of conventional Gaussian methods, specialized models are frequently needed for longitudinal data analysis with bounded outcomes, such as the Gini ratio. In order to model economic inequality in Indonesia, this study compares the effectiveness of Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) for beta-distributed longitudinal data. Root Mean Square Error (RMSE) and pseudo R-squared values are used to assess model performance using panel data from 10 provinces between 2018 and 2024 as well as important socioeconomic indicators. With lower RMSE and higher explanatory power across all provincial subsets, the results consistently demonstrate that GLMM performs better than both GEE and generalized linear models (GLM). ANOVA tests verify that modeling methodologies, not data heterogeneity in GRDP or Gini values, are responsible for the differences in model performance. These results demonstrate how well GLMM handles complex data structures and within-subject correlations, providing more accurate and effective estimates in longitudinal beta regression scenarios. The study encourages the use of GLMM for more precise longitudinal analysis in economic and social research and offers insightful information for researchers modeling inequality indices.
IDENTIFICATION OF PRIMARY SCHOOL LITERACY ACHIEVEMENT FACTORS IN PROVINCE X USING ORDINAL STEPWISE LOGISTIC Azizah, Siti Nur; Gustiara, Dela; Fitrianto, Anwar; Erfiani; Silvianti, Pika
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09103

Abstract

Literacy is a foundational skill that underpins students’ academic success and lifelong opportunities. Low literacy skills can result in long-term disadvantages such as limited access to higher education, low productivity, and social inequality. Indonesia continues to face challenges in improving students' literacy achievement, particularly at the primary school level. According to the PISA 2022 results, Indonesia ranked 69th out of 81 countries, indicating that students’ literacy levels remain relatively low. This study aims to identify the factors that influence the literacy achievement of primary school students in Province X. The analytical method employed is ordinal logistic regression with a backward stepwise approach. The dependent variable is the level of literacy achievement (categorized as low, moderate, and good), while the independent variables include learning quality, teacher reflection and improvement, instructional leadership, school climate (including safety, diversity, and inclusiveness), and curriculum type. The results show that the final selected model follows the partial proportional odds assumption and includes only the significant predictors identified through backward stepwise elimination. Variables that positively influence literacy achievement include safety climate, diversity, inclusiveness, curriculum type, and teachers’ reflection and improvement of learning. Model evaluation using AIC, BIC, and accuracy measures indicates good predictive performance. These findings offer valuable insights for policymakers in designing strategies to enhance literacy through strengthening school climate and improving the quality of teaching and learning.
KOMPARASI TEKNIK UNDERSAMPLING DAN OVERSAMPLING PADA REGRESI LOGISTIK BINER DALAM MENDUGA FAKTOR DETERMINAN BERHENTI MEROKOK PENDUDUK LANJUT USIA Amelia, Reni; Indahwati; Erfiani; Fitrianto , Anwar; Rizki, Akbar
Jurnal TIMES Vol 10 No 2 (2021): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.97 KB) | DOI: 10.51351/jtm.10.2.2021652

Abstract

Teknik resampling adalah salah satu teknik pre-processing untuk menyeimbangkan distribusi data sehingga mengurangi efek distribusi kelas atau kategori yang tidak seimbang. Teknik resampling yang biasa digunakan adalah random oversampling dan random undersampling. Dalam penelitian ini, random oversampling digunakan untuk menyeimbangkan data dengan cara oversampling secara acak pada kelas minoritas (penduduk lansia yang berhenti merokok). Random undersampling digunakan untuk menyeimbangkan data dengan cara undersampling (mengeliminasi) secara acak kelas mayoritas (penduduk lansia yang masih merokok). Data yang telah diproses dengan resampling selanjutnya dilakukan pemodelan dengan model regresi logistik biner. Model regresi logistik biner dengan random undersampling merupakan model terbaik karena memiliki balanced accuracy terbesar. Peubah yang signifikan memengaruhi berhenti merokok adalah pendidikan, pekerjaan, akses internet, dan usia lansia.
Co-Authors Agus Mohamad Soleh Alfa Nugraha ALIU, MUFTIH ALWI Amanda, Nabila Amelia, Reni Anang Kurnia ASEP SAEFUDDIN Fitrianto, Anwar Gustiara, Dela I Made Sumertajaya Indahwati Irsyifa Mayzela Afnan Jumansyah, L.M. Risman Dwi L.M. Risman Dwi Jumansyah M. Yunus Merryanty Lestari P Muftih Alwi Aliu Nur Hidayah Pardomuan Robinson Sihombing Pika Silvianti Rifda Nida’ul Labibah Riska Yulianti, Riska Rizki, Akbar Setyowati, Silfiana Lis Siti Hafsah Siti Hasanah Siti Nur Azizah, Siti Nur Waliulu, Megawati Zein