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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Matematika Sains dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Eksakta : Berkala Ilmiah Bidang MIPA JURNAL METEOROLOGI DAN GEOFISIKA Xplore: Journal of Statistics Jurnal Meteorologi dan Geofisika Indonesian Journal of Statistics and Its Applications
Rahardiantoro, Septian
Department Of Statistics And Data Science, School Of Data Science, Mathematics And Informatics, IPB University, Bogor
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Other

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Penerapan Metode DBSCAN dalam Memperbaiki Kinerja K-Means untuk Penggerombolan Data Tweet Astri Fatimah; Anang Kurnia; Septian Rahardiantoro; Yani Nurhadryani
Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v8i1.159

Abstract

Text Mining is collecting text data mining results from a computer to get information contained therein. Text data has a form of data that is not structured and difficult to analyze. The unstructured data can be used as structured data through pre-processing stages. Text data is represented as numerical data after going through the pre-processing stages using vector space model method and weighting method of inverse frequency document frequency so that it can be used for analysis. The K-Means cluster analysis is one method that can be used for unstructured data, but the K-Means method is not robust to noise. Outliers can be detected using Density Based Spatial Clustering of Application with Noise (DBSCAN) cluster analysis. Outliers obtained from DBSCAN results can be omitted in the data. Cluster analysis was carried out again after removal of outliers using the K-Means method with the same number of k clusters. Evaluation of the cluster that is used to see the goodness of the cluster results is Silhouette Coefficient (SC). The SC value of the K-Means method after removal of outliers has a significant increase of 0.21 for a small amount of data. Adding the amount of text data to cluster analysis also affects the number of clusters. This is influenced by the number of katas in a document that is given weight. The fewer katas that are given weight, the more number of clusters will be generated
Penggerombolan Data Panel Perusahaan Sektor Barang Konsumsi Radinda Putri Maha Dewi; Pika Silvianti; Septian Rahardiantoro
Xplore: Journal of Statistics Vol. 9 No. 1 (2020)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (240.38 KB) | DOI: 10.29244/xplore.v9i1.233

Abstract

The identification of the cluster of consumer goods sector companies is enough important study to examine the characteristics of the company based on its marketing management factors. This study seeks to cluster 23 consumer goods sector companies based on 4 marketing management factors, which are production costs, promotion costs, distribution costs, and sales value in 2012-2016. There are two parts of clustering that are carried out, the clustering of consumer goods sector companies based on the time series pattern for each marketing management factor with the ward method, and clustering of consumer goods sector companies using multivariate panel data using the k-means method. The results of the clustering for each marketing management factor using the ward method produced 2 groups in each factor, with cluster 2 having an average of each factor greater than group 1. The companies found in cluster 2 were PT Indofood CBP Sukses Makmur, PT Indofood Sukses Makmur, PT Mayora Indah, PT Unilever Indonesia Tbk, PT Handjaya Mandala Sampoerna Tbk, International Investama Tbk, PT Kalbe Farma Tbk, and PT Tempo Scan Pacific Tbk. On the other hand, clustering of multivariate panel data produced 6 groups where group 5  is the cluster with the highest average on each factor. Group 5 consists of PT Indofood Sukses Makmur and PT Handjaya Mandala Sampoerna Tbk. The company with the highest value in multivariate panel data is also found in the results of the cluster with the highest value for each marketing management factor.
Metode Alternatif dalam Pencarian Peringkat E-Commerce di Indonesia Berdasarkan Rating Pelanggan Azira Irawan; Aam Alamudi; Septian Rahardiantoro
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (641.744 KB) | DOI: 10.29244/xplore.v10i1.280

Abstract

The existence of the internet raises an online trading system using applications. The rise of online trading systems has triggered the emergence of various e-commerce in Indonesia that provide various kinds of customer needs. This also causes problems for customers, namely the difficulty in choosing quality e-commerce. The effort to overcome this problem is to rank e-commerce in Indonesia based on customer ratings. The method commonly used for ranking is the analytical hierarchy process (AHP) method, but in practice there are several variables that are not found in e-commerce so the AHP method cannot be used. The alternative method chosen is the ant colony optimization (ACO) method. The feasibility test of the ACO method in searching rankings for e-commerce data needs to be done because not all variables are in e-commerce. Simulations for ranking search are carried out using 2 generated data scenario with analytical hierarchy process (AHP) and ant colony optimization (ACO) method. The simulation results show that the ACO method is feasible to be used for ranking with blank data, then the application of the ACO method for e-commerce data in Indonesia. The best taboo results are based on the highest opportunity value and the highest correlation coefficient, namely in the second taboo, with three major ratings, namely JD, SP, and TP
Penggerombolan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan Metode K-Means dan Fuzzy C-Means . Hanniva; Anang Kurnia; Septian Rahardiantoro; Ahmad Ansori Mattjik
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (973.285 KB) | DOI: 10.29244/xplore.v11i1.855

Abstract

The achievement of the human development index in Indonesia differs between regions with striking gaps occurring in the western and eastern parts of Indonesia. This difference in achievement can be seen more clearly by grouping regencies/municipalities in Indonesia based on the four indicators of the human development index. With this aim, this study uses the k-means and fuzzy c-means methods to determine the optimal cluster size with two distance approaches, namely the Euclidean and Manhattan distances on the human development index indicators data in 2020. In addition, this study also seeks to identify the distribution of regencies/municipalities based on the characteristics of the human development index indicators in the clustering result. The result is that the best distance measure is Euclidean distance with optimal cluster size is four for k-means and six for fuzzy c-means. In addition, the clustering results obtained by the k-means method are more optimal than the fuzzy c-means because the evaluation value is better. In general, the four clusters formed were in accordance with the grouping carried out by BPS with the percentage of conformity reaching 66,54%. In summary, most regencies/municipalities on the Island of Sumatera, Java, Borneo and Sulawesi have higher life expectancy and percapita expenditure than many regencies/municipalities in the Nusa Tenggara Islands (besides Bali), Moluccas and Papua. Very high achievement for each HDI indicators is dominated by the capital city of each province with unfavorable conditions occurring in most regencies/municipalities in Papua Province.
Identifikasi Peubah yang Berpengaruh terhadap Ketidaklulusan Mahasiswa Program Sarjana BUD IPB dengan Regresi Logistik Biner Mahdiyah Riaesnianda; Aam Alamudi; Agus Soleh; Septian Rahardiantoro
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.727 KB) | DOI: 10.29244/xplore.v12i1.1055

Abstract

One of the entrances available at the Bogor Agricultural University (IPB) is the Regional Representatives Scholarship (BUD). Not all BUD IPB students were able to complete their studies because they dropped out (DO) or resigned. One of the efforts that IPB can do to reduce the dropout rate for BUD IPB students is to find out the variables that affect the failure of BUD IPB students. The variables that influence the failure of BUD IPB students are analyzed by binary logistic regression. There is an imbalance of data classes in the response variables so that the method that can be used to overcome this is the Synthetic Minority Over-Sampling Technique (SMOTE). The classification model with SMOTE resulted in a higher average sensitivity than the model without SMOTE from 10,66% to 61,91%. This confirms that the model with SMOTE is better at predicting the minority class (BUD IPB students who do not pass). The variables that affect the failure of BUD IPB students are gender, school status of origin, study program groups, the presence or absence of Pre-University Programs (PPU), type of sponsor, average report cards, and GPA in the Joint Preparation Stage (TPB) or General Competency Education Program (PPKU).
Analysis of Regency and City Pneumonia Clusters in West Java 2020 Yusma Yanti; Septian Rahardiantoro
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 1 (2023): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v20i1.6412

Abstract

Pneumonia is an infection of the respiratory tract caused by bacteria, viruses, or fungi. The number of pneumonia cases in West Java is relatively high, therefore, it is necessary to identify some group of regencies/cities in which have a common characteristic, to make it easier to handle. The data used is data on the number of pneumonia cases in 27 regencies/cities in West Java in 2020. In this study, chi-squared test was applied to determine the characteristics of pneumonia spread in West Java. Then, a regression-based analysis by using the Irregular Graph Fused LASSO method was used to provide the cluster of regencies/cities, by considering the adjacent locations of regencies/cities as a penalty matrix. The results obtained that the cases spread unevenly. The number of cases for every 1000 people in each regency/city was relatively high in the eastern part of West Java. There were 6 clusters obtained from 27 regencies/cities, with Pangandaran regency as the location with the highest cases occurred. Depok City and Bekasi City were locations with the lowest number of cases even though they have relatively high population numbers.
PENERAPAN METODE COKRIGING DENGAN VARIOGRAM ISOTROPI DAN ANISOTROPI DALAM MEMPREDIKSI CURAH HUJAN BULANAN JAWA BARAT Anik Djuraidah; Septian Rahardiantoro; Azizah Desiwari
Jurnal Meteorologi dan Geofisika Vol. 20 No. 1 (2019)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v20i1.594

Abstract

Curah hujan merupakan salah satu unsur iklim yang penting dalam pertanian. Informasi mengenai ukuran curah hujan dapat diketahui dari pos hujan pada suatu wilayah. Permasalahan yang dihadapi adalah tidak semua wilayah memiliki pos hujan, sehingga metode interpolasi spasial dapat digunakan dalam memprediksi besarnya curah hujan pada suatu wilayah. Metode cokriging merupakan salah satu metode interpolasi spasial yang bersifat Best Linear Unbiased Prediction (BLUP) dengan melibatkan minimum dua peubah. Peubah yang digunakan dalam penelitian ini dipilih berdasarkan keeratan hubungannya, yaitu peubah curah hujan dan elevasi pos hujan. Data yang digunakan dalam penelitian ini adalah curah hujan bulanan tahun 1981 hingga 2013 pada 38 pos hujan di wilayah Jawa Barat. Metode analisis diawali dengan menetukan variogram isotropi  yang ditentukan berdasarkan jarak spasial dan variogram anisotropi yang ditentukan berdasarkan jarak dan arah pada kedua peubah. Selanjutnya, variogram yang terbaik digunakan untuk prediksi curah hujan. Hasil penelitian menunjukkan variogram terbaik adalah variogram isotropi dengan hasil prediksi curah hujan bulanan yang mempunyai nilai reduced means square error berkisar antara 0.54 sampai dengan 1.46 dan nilai average error hampir 0.Rainfall is one of the important climatic elements in agriculture. The information on the amount of rainfall can be known from the weather station in a region. The problem faced is not all regions have its own weather station, so that spatial interpolation can be used to predict the amount of rainfall in a region. Cokriging is one of spatial interpolation that has properties BLUP (Best Linear Unbiased Prediction) that involved at least two variables. In this study, the variables used were the amount of rainfall and elevation of the weather station because these variables have a correlation. The data used in this study were monthly rainfall from 1981 to 2013 at 38 weather stations in West Java. The first step in analysis data was determined isotropy variogram determined based on spatial distance and anisotropic variogram determined based on distance and direction in the two variables. Furthermore, the best variogram was used for the rainfall prediction. The results showed the best variogram is isotropy with the results of monthly rainfall predictions with the cokriging method having reduced means square error values ranging from 0.54 to 1.46 and the average error value of almost 0. 
Spatial Clustering Using Generalized LASSO on the Gender and Human Development Index in Papua Island in 2022 Mutaqin, Ahdan Darul; Rahardiantoro, Septian; Masjkur, Mohammad
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9268

Abstract

Equitable development from a gender perspective needs attention. Based on data from the World Economic Forum (WEF), gender equality in Indonesia has increased. Even so, the island of Papua is still very low on gender equality. It can be seen from the Gender Development Index (IPG) from the Central Bureau of Statistics (BPS), there is a considerable gap between the Papua Island IPG and the National. IPG is a comparison between the Human Development Index (IPM) for Men and Women. Based on these conditions, this study aims to classify GPI, Male IPM, and Female IPM by region using the spatial clustering method in 2022. One of the analytical methods that can overcome these conditions is Generalized LASSO. Generalized LASSO can be used on data that only has a response variable (y) for clustering. Generalized LASSO clustering uses a penalty matrix D. The formation of the D matrix is formed by giving values -1 and 1 for areas that intersect or are adjacent and a value of 0 for other areas. The best clustering for IPG uses KNN with K = 3 and the number of clusters formed is 2 clusters. The best clustering for male HDI uses KNN with K = 2 and the number of clusters formed is 8. The best clustering for female HDI uses KNN with K = 2 and the number of clusters formed is 10 clusters.
BERTopic-Based Multi-Class Topic Classification on Indonesian Shopee E-commerce Reviews Using Ensemble Learning Kevin Alifviansyah; Asep Saefuddin; Septian Rahardiantoro
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37941

Abstract

The rapid growth of e-commerce platforms has resulted in a large volume of unstructured user reviews, creating challenges for scalable analysis. This study proposes a multi-class topic classification framework for Indonesian Shopee application reviews by integrating BERTopic-based embedding-driven topic modeling with ensemble learning. A total of 23,956 reviews are analyzed, with BERTopic applied exclusively to 19,167 training reviews to derive eight dominant topic labels, which serve as pseudo-labels for supervised classification using CatBoost and Extra Trees. Model performance is evaluated on a held-out test set under baseline and hybrid resampling settings to address severe class imbalance. The results show that hybrid resampling substantially improves balanced accuracy, particularly for CatBoost, while ROC–AUC remains consistently high, indicating robust class discrimination. Analysis of an unlabeled 2025 dataset, used solely in a deployment-style setting, reveals semantically consistent topic distributions on unseen data. Overall, the findings demonstrate that embedding-based topic modeling combined with ensemble learning provides an effective and scalable solution for multi-class topic classification in highly imbalanced e-commerce review data, with clear separation between training, evaluation, and post-deployment analysis.
Comparative Performance of Gradient Boosting Algorithms for Household Food Resilience Classification during The COVID-19 Nabila Syukri; Sachnaz Desta Oktarina; Septian Rahardiantoro
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 27 No. 04 (2026): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol27-iss04/667

Abstract

The need for data-driven analysis to understand socio-economic vulnerability, particularly in relation to food security, has intensified due to the pandemic. The growing volume of survey data demands analytical methods that can capture multidimensional relationships. Over time, food security evolves into the concept of food resilience, reflecting a household's capacity to withstand or recover from adverse conditions. This study uses machine learning techniques to categorize household food resilience, based on data from the World Bank's High Frequency Phone Survey (HFPS), covered 2,868 households in Indonesia. A comparative evaluation of gradient boosting algorithms (XGBoost, LightGBM and CatBoost) was conducted. Model performance was evaluated using accuracy, sensitivity, specificity, and AUC across ten repeated train-test splits, with statistical significance assessed using Friedman and Wilcoxon tests. The results show that CatBoost performed best and most consistently, achieving mean accuracy of 0.7592 and mean AUC of 0.8331, which is significantly higher than that of competing models. SHAP analysis further indicates that baseline vulnerability, financial concerns, income capacity, food price shocks and unmet healthcare needs are important features for identifying resilient households. These findings demonstrate that gradient boosting, particularly CatBoost, provides strong predictive power and interpretability to support data-driven decision-making in classifying food resilience.
Co-Authors . Hanniva Aam Alamudi Ade Ariyo Yudanto Ade Gusalinda Agus M Soleh Agus M Soleh Agus Mohamad Soleh Agus Soleh Ahmad Ansori Mattjik Akbar Rizki Anang Kurnia Anik Djuraidah ASEP SAEFUDDIN Astri Fatimah Azira Irawan Azizah Desiwari Bagus Sartono Cici Suhaeni Faadiyah Ramadhani Gerry Alfa Dito I Made Sumertajaya Inria Purwaningsih Itasia Dina Sulvianti Kevin Alifviansyah Khairil Anwar Notodiputro Mahdiyah Riaesnianda Mohammad Masjkur Muhammad Shalih Mutaqin, Ahdan Darul Nabila Syukri Naila Nabiha Qonita Nur Aulia Maknunah Octavia Dwi Amelia Pika Silvianti Pika Silvianti Radinda Putri Maha Dewi Raisa Nida Eria Resti Cahyati Sachnaz Desta Oktarina Sean Marshelle Stephan Chen Tiara Lutfiah Adisti Totong Martono Yani Nurhadryani Yanti, Yusma Yusma Yanti