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All Journal Jurnal TIMES Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jurnal Statistika dan Aplikasinya MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika
Erfiani
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Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Economics, Econometrics & Finance 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 : LPPM Universitas Negeri Jakarta

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.
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.
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.
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 Amelia, Reni ASEP SAEFUDDIN Fitrianto, Anwar Gustiara, Dela Indahwati Irsyifa Mayzela Afnan M. Yunus Muftih Alwi Aliu Nur Hidayah Pika Silvianti Rizki, Akbar Setyowati, Silfiana Lis Siti Hasanah Siti Nur Azizah, Siti Nur Waliulu, Megawati Zein