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All Journal Hemera Zoa JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Agro Ekonomi JTSL (Jurnal Tanah dan Sumberdaya Lahan) BAREKENG: Jurnal Ilmu Matematika dan Terapan Spatial : Wahana Komunikasi dan Informasi Geografi Jurnal Agro Ekonomi Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pusat Inovasi Masyarakat Xplore: Journal of Statistics Indonesian Journal of Statistics and Its Applications
Rahma Anisa
School Of Data Science, Mathematics, And Informatics - IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Pemodelan Pemodelan Angka Kematian Bayi di Jawa Barat Menggunakan Pendekatan Analisis Regresi Spline dan Kernel Riska Indah Puspita; Rahma Anisa; La Ode Abdul Rahman
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.332 KB) | DOI: 10.29244/xplore.v11i3.1026

Abstract

The Infant Mortality Rate (IMR) is a very sensitive indicator of health service efforts, especially those related to newborns. IMR is also one of the problems that need to solve and the target of the SDGs number 3 (Good health and well-being). Java Province consists of 27 regencies/cities with an IMR of 3,26/1000 live births in 2019. The pattern of IMR data in West Java province had a pattern that changes at certain points so that the modeling is carried out using nonparametric regression. The selected nonparametric regression approach was spline regression which able to adapt more effectively with the characteristics of the data and kernel regression is easy to implementation. The explanatory variables used are life expectancy, the percentage of poor people, the open unemployment rate and the average length of schooling. The best model given by spline regression at 3 knot and kernel regression with bandwidth 1.2; 1.2; 1.1; and 1. Based model evaluation, the spline regression model's performance is better than the kernel regression with MSE, RMSE, and MAPE values are 0.66; 0.81, and 18.54%
PENDUGAAN FAKTOR – FAKTOR YANG MEMENGARUHI KASUS STUNTING DI JAWA BARAT TAHUN 2021 MENGGUNAKAN REGRESI SPASIAL BINOMIAL NEGATIF Anik Djuraidah; Mely Amelia; Rahma Anisa
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.26984

Abstract

Stunting is a childhood growth and development disorder characterized by below-normal height.  West Java, with its stunting rate of 24.5 percent, is one of the provinces included in the top 12 priority provinces in implementing the National Action Plan to Accelerate Stunting. Stunting cases are count data and their occurrence is rare. The analysis for the count data is Poisson regression with the assumption that equidispersion must be met. One way to overcome overdispersion is to use negative binomial regression. This study aimed to determine predictors/factors affecting stunting cases in West Java province in 2021 using negative binomial spatial regression. The data in this study comes from the publication of the West Java Health Service and the West Java Central Statistics Agency in 2021 with districts/cities as the object of observation. There is a spatial effect in the stunting data, so the spatial regression model is suitable. The results show that there is an overdispersion in the Poisson regression. The spatial effect test shows that there is a spatial dependence on the response variable and some predictors. The negative spatial autoregressive binomial is the best model with the lowest AIC value. The factors that have a significant effect are the percentage of infants aged less than six months who are breastfed, the percentage of food processing establishments that meet the requirements, and the percentage of infants with low birth weight.
IMPLEMENTATION OF CUCKOO SEARCH-BASED ENSEMBLE VARIABLE IMPORTANCE IN THE CLASSIFICATION OF NON-CASH FOOD ASSISTANCE (BPNT) RECIPIENTS IN WEST JAVA Indra Mahib Zuhair Riyanto; Laras Suprapti; Salsabila Fayiza; Elke Frida Rahmawati; Farid Yafi Suwandi; Sachnaz Desta Oktarina; Rahma Anisa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2601-2612

Abstract

The BPNT program is a government initiative to efficiently distribute social assistance to poor households. However, the challenge of achieving accurate recipient identification remains a major obstacle. This research aims to build a classification model for BPNT recipients in West Java using machine learning methods (Random Forest, XGBoost, CatBoost, and LightGBM) and a Cuckoo Search-Based Ensemble Variable Importance (EVI) approach to identify which predictors most strongly affect classification. Class imbalance in the response data was addressed through weighting during model training, and performance was evaluated using balanced accuracy through 10-fold cross-validation. Although all models performed well, the variable importance results varied across models. Using the Random-Key Cuckoo Search algorithm, an EVI ranking was generated that integrated VI rankings from each model, achieving a minimum Spearman correlation of 0.6538. The results show that roof quality, living status, calorie consumption, and per capita expenditure are the main indicators for classifying BPNT recipients. This approach shows great potential to improve modeling interpretability and to provide stronger data-driven support for social policy-making.
PELATIHAN PEMBUATAN JAMU TERNAK UNTUK DOMBA DI AL-BAROQAH FARM KECAMATAN DRAMAGA, KABUPATEN BOGOR: Jamu Ternak Bayu Febram Prasetyo; Rini Madyastuti Purwono; Lina Noviyanti Sutardi; Anisa Rahma; Baharun Rasyid
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 4 (2025): Volume 6 No 4 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i4.48460

Abstract

Kesehatan ternak merupakan aspek krusial dalam mendukung keberhasilan usaha peternakan, khususnya pada sektor peternakan domba. Namun ketergantungan peternak terhadap obat-obatan sintetis masih tinggi, yang berdampak pada biaya produksi dan risiko resistensi antimikroba. Untuk mengatasi permasalahan ini, dilakukan kegiatan pengabdian kepada masyarakat berupa pelatihan pembuatan jamu ternak berbahan herbal lokal sebagai alternatif pengobatan alami. Kegiatan dilaksanakan secara partisipatif bersama peternak mitra Al-Baroqah Farm di Kecamatan Dramaga, Bogor. Materi pelatihan meliputi pengenalan bahan herbal (kunyit, kayu manis, lada, sambiloto, dan indigofera), teknik formulasi jamu fermentasi, serta praktik aplikasi pada domba. Hasil kegiatan menunjukkan bahwa peternak sangat antusias dan mampu mengikuti seluruh tahapan pelatihan dengan baik. Jamu yang diformulasikan terbukti dapat meningkatkan nafsu makan, imunitas, serta produktivitas domba, sekaligus mengurangi ketergantungan terhadap obat sintetis. Pelatihan ini berhasil memperkenalkan model pemeliharaan peternakan yang sehat, ekonomis, dan berkelanjutan berbasis kearifan lokal.
Comparative Study of Multiclass Models for Identifying Household Drinking Water Sources in West Java Defri Ramadhan Ismana; Bagus Sartono; Erfiani; Yuri Nurdiantami; Mohammad Masjkur; Aam Alamudi; Rahma Anisa
Indonesian Journal of Statistics and Applications Vol 10 No 1 (2026): Vol 10 Issue 1 June 2026
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.v10i1p119-128

Abstract

Data imbalance is a common challenge in classification modeling, typically driven by rare events such as fraud, credit default, student dropout, or infectious diseases. Multi-class imbalance is inherently more complex than binary classification because a class can be a minority relative to one class yet a majority to another. Access to safe drinking water is one of the key factors for public health. West Java, as the province with the largest population in Indonesia, has yet to achieve the target of 100% of households having access to safe drinking water. Identifying the types of drinking water sources can be approached using multi-class classification modeling. However, this case presents an issue of data imbalance. The CatBoost method is one of the recommended approaches for multi-class classification with imbalanced data. Additionally, the TabNet method is also considered to perform well in such cases. Therefore, this study aims to apply both methods and compare their performance in classifying household drinking water sources in West Java using 2023 SUSENAS data. The results show that CatBoost yielded an average MCC of 0.225, whereas TabNet exhibited an average of 0.209. In terms of computational efficiency, CatBoost recorded an average execution time of 25 seconds, compared to 134 seconds for TabNet. Therefore, the CatBoost method provides better multi-class classification performance and faster execution compared to TabNet. Although TabNet is superior in classifying minority classes, CatBoost is more accurate in predicting majority classes and identifying the most influential variables, such as building ownership status and regional classification
Determining Critical Yield Index of Area Yield Insurance based on Basis Risk Constraint Valantino Agus Sutomo; Dian Kusumaningrum; Aurellia Layvieda; Rahma Anisa
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
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.v5i1p205-219

Abstract

 Area yield index insurance at district level faces heterogeneous basis risk due to geographical conditions which implies to obtain unprecise critical index . Clustering and zone-based area yield scheme can reduce heterogeneous basis risk that leads to determine the suitable alternative for . On the previous research, we have obtained 7 clusters and 2 level of paddy productivity based on clustering assumption from primary data in Java. The suitable clustering assumption for calculating  is cluster based assumption, which gives the homogeneous paddy productivity under 7 clusters in Java. Therefore, our goal is to develop area yield index at district level (cluster based) with minimize basis risk at certain constraints for paddy farmer productivity in Java Indonesia. There are some methods for calculating  such as mean, median, winsor mean, one sigma, two sigma and  (first quartile) method on the basis risk constraints using confusion matrix. Furthermore, two basis risk constraints are the difference between overpayment and shortfall is not extremely far, and total basis risk does not exceed 20% of its total claim occurrence. Two sigma method has the lowest basis risk, overpayment, and shortfall, but it has lowest pure premium, small probability of claim, and low range of claim. Hence, we consider to use  (first quartile) method as alternative and suitable method to calculate  that satisfied two basis risk constraints. In conclusion, our research provides analytical calculation for area yield index at district level with pure premium as Rp 152,151 using  ( method), which is sufficient to cover the total claim and consistent with the simulation.
Nested Mixed Models with Repeated Measurements for Analyzing Gross Profit of Public Companies in West Java: Model Campuran Tersarang dengan Pengamatan Berulang untuk Analisis Data Laba Bruto Perusahaan Terbuka di Jawa Barat Alina Witri; Khairil Anwar Notodiputro; Rahma Anisa
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
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.v6i2p296-308

Abstract

The company's gross profit plays an important role in boosting the Gross Regional Domestic Product (PDRB) which will affect the revenue of local governments, known as Pendapatan Asli Daerah. Local governments often need information how gross profits of companies are different within each sector. It is not easy to investigate this matter especially if these companies are observed repeatedly and subsectors are nested within the sector. In this study, three factors were involved, i.e., sectors, subsectors which are nested in a particular sector, and time. It is assumed that the sectors and time of observation are fixed, whereas the subsectors are random. The response variable is the average gross profit per subsector of public companies in West Java. The objective of this study is to identify the variation of the subsectors, the effects of sectors as well as time on the average of the gross profit. Since the study involves fixed and random factors and the gross profit rate was observed more than one time, then a nested mixed model with repeated measurement is used. The results showed that there was no sector effect on the average gross profit, there is a variation in the average gross profit per subsector that is nested within the sector, and the time of observation did not influence the average gross profit.
Energy Sector Stock Price Forecasting with Time Series Clustering Approach: Peramalan Harga Saham Sektor Energi dengan Pendekatan Penggerombolan Data Deret Waktu Linda Sakinah; Rahma Anisa; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
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.v8i2p132-142

Abstract

Stock investment promises higher returns but carries high risks because unpredictable price fluctuations. Energy sector shows potential due to its highest sectoral index growth in 2022. However, this doesn’t indicate that stock price increases occur evenly among all issuers. Therefore, it’s necessary to analyze clustering of issuers based on similarity of their stock price movements and used for forecasting stock prices at cluster level. This study aims to evaluate performance of clustering energy sector issuers using autocorrelation-based distance and dynamic time warping(DTW), and to forecast stock prices at cluster level. The data used consists weekly closing stock prices. The clustering used hierarchical average linkage method. Stock price forecast for each cluster used ARIMA model and its performance was evaluated using rolling-cross validation. The results showed that DTW distance had the best clustering performance. Energy sector issuers were grouped into four clusters with strong cluster category, indicated by silhouette coefficient >0.71. ARIMA models for each cluster produced MAPE values between 10-20%, categorizing them as good forecasting models. Clusters A and D were recommended for investors because have highest potential for capital gain based on forecasted stock prices. That clusters also consisted of companies with strong fundamentals and dividend policies.
Cluster Level Time Series Forecasting on Indonesian Banking Stock Prices Using the Gated Recurrent Unit Method Faisal Arkan; Budi Susetyo; Rahma Anisa
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
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.v9i2p261-273

Abstract

In recent years, there has been a significant increase in the number of Single Investor Identification registrations in the Indonesian capital market, as reported by the Financial Services Authority. Many investors favor stocks for their potential for high returns and liquidity. However, stock investments come with high risks due to their fluctuating prices, which are influenced by multiple factors. With 47 listed banking companies in the Indonesia Stock Exchange, clustering can help identify investor patterns. Forecasting stock prices is essential for anticipating future fluctuations. The large number of issuers and the tendency of stock prices to fluctuate increase the potential for outliers, requiring an appropriate clustering method. A study using the k-medoid method and dynamic time warping distance revealed 41 banking companies clustered into 5 clusters with a silhouette coefficient of 0.524. The Gated Recurrent Unit modeling, based on prototypes from the formed clusters, showed an excellent forecasting performance with root mean squared error and mean absolute percentage error ranging from 1-10%. The forecast for the next 8 weeks indicated varying price increases for each cluster. The first and third clusters are recommended for investors looking to maximize capital gains, due to their price increases and diverse cluster member characteristics. Additionally, investors should consider dividends provided by certain banking companies in their investment decision-making process.
Optimization of Fuzzy C-Means Clustering with Particle Swarm Optimization on Socioeconomic Indicators of ASEAN Countries Cindy Indriyani; Siti Arbaynah; Ananda Putra Wijaya; Lusi Oktaviani; Fadhilah Yumna; Norashida Othman; Sachnaz Desta Oktarina; Rahma Anisa
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
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.v9i2p274-288

Abstract

Grouping data based on similarity in characteristics is commonly applied in various exploratory analyses. The Fuzzy C-Means algorithm offers flexibility through the degree of membership of data points in each cluster, but it is vulnerable to poor cluster center initialization, which increases the risk of getting trapped in local optima. To enhance the performance of Fuzzy C-Means, this study integrates the Particle Swarm Optimization method for determining cluster centers. The evaluation is conducted by comparing Fuzzy C-Means and Fuzzy C-Means-Particle Swarm Optimization across several cluster counts using three internal validation metrics, namely the silhouette coefficient, partition coefficient, and Xie-Beni Index. The results show that Fuzzy C-Means-Particle Swarm Optimization consistently yields higher silhouette coefficient and partition coefficient values, along with lower Xie-Beni Index values, compared to standard Fuzzy C-Means. This indicates that the integration of Particle Swarm Optimization can improve clustering quality in terms of cluster compactness and separation. This hybrid approach demonstrates significant potential in complex data clustering scenarios.
Co-Authors . Erfiani A.S. Satyaningtijas Aam Alamudi Abdullah Ilman Fahmi Afiq Dzakwan Anastia Aidi, Muhammad N Alina Witri Ananda Putra Wijaya Anang Kurnia Andhika Yudha Prawira Andhika Yudha Prawira Andhika Yudha Prawira Ani Safitri Anik Djuraidah Annisa Nur Hapsari Aurellia Layvieda Aurissa Putri Hawin Yusima Bagus Sartono Baharun Rasyid Bayu Febram Prasetyo Budi Susetyo Cindy Indriyani Danang Dwi Cahyadi Defri Ramadhan Ismana Desrayni Hanadhita Desrayni Hanadhita Desrayni Hanadhita Desrayni Hanadhita Dian Kusumaningrum Elke Frida Rahmawati Erfiani Fadhilah Yumna Fahrizal Hazra Faisal Arkan Fajar Agus Pradana Farah Annisa Mahmud Farid Yafi Suwandi Fazila Alya Dilta Hareera Haidar Muhammad Yasin Haryastuti, Rizqi Hera Maheshwari Herwin Pisestiyani I Made Sumertajaya Indonesian Journal of Statistics and Its Applications IJSA Indra Mahib Zuhair Riyanto La Ode Abdul Rahman Laras Suprapti Latief Mahir Rachman Lina Noviyanti Sutardi Linda Sakinah Lusi Oktaviani Madyastuti, Rini Mely Amelia Mohammad Masjkur Muhammad N Aidi Muhammad Nur Aidi Muhammad Rafif Danuja Nida Khairunnissa Norashida Othman Nur Indah Andini Nur Laela Fitriani Pasaribu, Sahat M. Pika Silvianti Pika Silvianti Putu Gita Karlina Jayanti Qomsatun Istiqomah Rama Adi Rianto Riska Indah Puspita Rizqi Haryastuti Ryska Putri Madyasari Sachnaz Desta Oktarina Sahat M. Pasaribu Salsabila Fayiza Sandra Dewi Hartanti SATRIYAS ILYAS Savitri Novelina Siti Arbaynah Srihadi Agungpriyono Supratikno, Supratikno Sutomo, Valantino A Valantino A Sutomo Valantino Agus Sutomo Wulan Andriyani Pangestu Yani Nurhadryani Yuri Nurdiantami Zamrah Mutmainah