Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal Indonesian Journal of Applied Statistics International Journal of Advances in Data and Information Systems Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Enthusiastic : International Journal of Applied Statistics and Data Science
Anadra, Rahmi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mathematics

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
PENERAPAN K-MODES DALAM KLASTERISASI KABUPATEN/KOTA DI JAWA BARAT BERDASARKAN INDIKATOR INFRASTRUKTUR Rahman, Abd.; Anadra, Rahmi; Fitrianto , Anwar; Erfiani, Erfiani; Dwi Jumansyah, L.M. Risman
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.787

Abstract

Clustering is a statistical method used to group data based on certain similar characteristics, particularly in the context of complex and diverse data. This study aims to cluster districts/cities in West Java Province based on infrastructure indicators, namely access to clean water, sanitation, electricity, and energy, using the K-Modes clustering method. The data used is categorical data sourced from SUSENAS West Java 2023. The cluster analysis resulted in four distinct clusters, each representing significant differences in infrastructure characteristics across regions. The first cluster consists of 8 regions, the second cluster includes 7 regions, the third cluster consists of 1 region, and the fourth cluster contains 11 regions. These characteristic differences among clusters indicate infrastructure disparities that need to be addressed in planning more equitable development to improve the quality of life of people in West Java
Loan Approval Classification Using Ensemble Learning on Imbalanced Data Anadra, Rahmi; Sadik, Kusman; Soleh, Agus M; Astari, Reka Agustia
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 2, October 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss2.art1

Abstract

Loan processing is an important aspect of the financial industry, where the right decisions must be made to determine loan approval or rejection. However, the issue of default by loan applicants has become a significant concern for financial institutions. Hence, ensemble learning needs to be used with random forest and Extreme Gradient Boosting (XGBoost) algorithms. Unbalanced data are handled using the Synthetic Minority Over-sampling Technique (SMOTE). This research aimed to improve accuracy and precision in credit risk assessment to reduce human workload. Both algorithms used a dataset of 4,296 with 13 variables relevant to making loan approval decisions. The research process involved data exploration, data preprocessing, data sharing, model training, model evaluation with accuracy, sensitivity, specificity, and F1-score, model selection with 10-fold cross-validation, and important variables. The results showed that XGBoost with imbalanced data handling had the highest accuracy rate of 98.52% and a good balance between sensitivity of 98.83%, specificity of 98.01, and F1-score of 98.81%. The most important variables in determining loan approval are credit score, loan term, loan amount, and annual income.
Sentiment Analysis of Tokopedia Customer Reviews Using BiLSTM and IndoBERT with Comparative Analysis of Preprocessing and Labeling Methods Anadra, Rahmi; Wijayanto, Hari; Sadik, Kusman
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1458

Abstract

This study addresses key challenges in Indonesian sentiment analysis related to preprocessing, labeling strategies, and class imbalance. It compares the performance of BiLSTM and IndoBERT using user reviews collected from Tokopedia. The dataset was manually and automatically labeled, then processed under three preprocessing schemes. Both models were trained with tuned hyperparameters and imbalance-handling techniques and evaluated through twenty rounds of stratified five-fold cross-validation. Performance was assessed using balanced accuracy and F1-score. IndoBERT achieved the highest results, with balanced accuracy up to 0.85 and F1-scores up to 0.83, while BiLSTM reached balanced accuracy up to 0.78 and F1-scores up to 0.76. Applying class weight and focal loss improved model performance by approximately 2% to 11% over the baseline. BiLSTM demonstrated greater training efficiency, requiring only 1 to 2.5 minutes per epoch, compared with IndoBERT’s 2.6 to 3.6 minutes. Although manual labeling remained superior in capturing contextual nuance and emotional cues, GPT-based labeling showed strong agreement with the human annotations. A four-way ANOVA revealed that all main factors and several interactions significantly influenced classification outcomes. Overall, BiLSTM provides faster training efficiency, whereas IndoBERT delivers higher predictive accuracy.
Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Dampak Kerusakan Bencana Banjir Menggunakan K-Medoids Gustiara, Dela; Mulyaningsih, Anisa Dwi; Anadra, Rahmi; Widodo, Edy
Indonesian Journal of Applied Statistics Vol 5, No 2 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v5i2.65668

Abstract

The territory of Indonesia is located in geographical, geological, hydrological, and demographic conditions that allow Indonesia to be prone to disasters. The most common natural disaster in Indonesia is flooding. If accumulated, there have been 682 flood events in the country since the beginning of 2022. In Indonesia, especially West Java Province, flooding is the most common disaster, especially during the rainy season. So a study will be conducted that aims to determine the grouping of districts / cities in West Java Province based on the occurrence of flood disasters. The data used in this study were obtained from the publication of the National Disaster Management Agency. In this study, there are 4 variables of the impact of flood disasters, namely total deaths, total submerged houses, total damaged houses and total injured. The clustering method used in this research is K-Medoids. K-Medoids is one of the clustering methods that uses the partition clustering method in grouping a set of n objects into a number of k clusters. From the results of the K-Medoids analysis, three clusters were obtained. The first cluster consists of 3 districts/cities with high impact of flood disasters, the second cluster consists of 23 districts/cities with moderate impact of flood disasters, and the third cluster consists of 1 district/city with low impact of flood disasters. Based on the results of the analysis, efforts can be made by the government to focus more on designing steps that must be taken in preventing or overcoming the impact of flood disasters.Keywords: Cluster; K-Medoids; Floods West Java
Co-Authors Abd. Rahman Agus Mohamad Soleh Astari, Reka Agustia Dwi Jumansyah, L.M. Risman Edy Widodo Erfiani Erfiani Fitrianto, Anwar Gustiara, Dela Hari Wijayanto Kusman Sadik Mulyaningsih, Anisa Dwi