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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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Feature selection in supervised machine learning: a case study of poverty dataset in West Java, Indonesia Sean Marshelle; Septian Rahardiantoro; Anang Kurnia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp524-535

Abstract

West Java, one of the largest provinces in Indonesia with a population exceeding 50 million, reported a poverty rate of 7.62% in 2023. Data from the national socio-economic survey or survei sosial ekonomi nasional (SUSENAS) show that poverty is multidimensional, encompassing aspects of employment, education, sanitation, housing, food security, technology, and government assistance. Addressing this complexity requires identifying the most influential factors that determine household welfare. This study applies and compares three feature selection approaches—filter, wrapper, and embedded—to the SUSENAS dataset to evaluate their effectiveness in identifying key poverty determinants. By prioritizing variables with the strongest predictive power, the study provides an evidence-based framework for more efficient and targeted poverty alleviation strategies. Results indicate that the information filter method combined with random forest (RF) and the least absolute shrinkage and selection operator (LASSO) embedded method combined with logistic regression (LR) deliver the best performance, improving model accuracy while reducing more than 65% of irrelevant features. The selected indicators highlight critical sectors such as food security, housing, and access to technology, which can serve as short-term policy priorities. In the long term, broader interventions in education, employment, sanitation, and government support are recommended. These findings demonstrate how data-driven feature selection can guide effective policy design for reducing poverty in West Java.
ALTERNATIF PENGGEROMBOLAN DATA DERET WAKTU DENGAN KONDISI TERDAPAT DATA KOSONG: Studi Kasus Penggerombolan Provinsi di Indonesia Berdasarkan Data Deret Waktu Rasio Gini tahun 2007 – 2017 Yusma Yanti; Septian Rahardiantoro
Indonesian Journal of Statistics and Applications Vol 2 No 1 (2018)
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.v2i1.55

Abstract

Panel data describes a condition in which there are many observations with each observation observed periodically over a period of time. The observation clustering context based on this data is known as Clustering of Time Series Data. Many methods are developed based on fluctuating time series data conditions. However, missing data causes problems in this analysis. Missing data is the unavailability of data value on an observation because there is no information related to it. This study attempts to provide an alternative method of clustering observations on data with time series containing missing data by utilizing correlation matrices converted into Euclid distance matrices which are subsequently applied by the hierarchical clustering method. The simulation process was done to see the goodness of alternative method with common method used in data with 0%, 10%, 20% and 40% missing data condition. The result was obtained that the accuracy of the observation bundling on the proposed alternative method is always better than the commonly used method. Furthermore, the implementation was done on the annual gini ratio data of each province in Indonesia in 2007 to 2017 which contained missing data in North Kalimantan Province. There were 2 clusters of province with different characteristics.
PENERAPAN ANALISIS LASSO DAN GROUP LASSO DALAM MENGIDENTIFIKASI FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN TUBERKULOSIS DI JAWA BARAT Stephan Chen; Khairil Anwar Notodiputro; Septian Rahardiantoro
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
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.v4i1.510

Abstract

Tuberculosis is the deadliest infectious disease in Indonesia, and West Java is a province with the largest number of tuberculosis cases in Indonesia. This research was conducted to identify variables and groups of variables that could explain the number of tuberculosis cases in West Java. The data used has many explanatory variables, and these variables form groups. LASSO and group LASSO analysis can be used for variables selection and handle data that has many explanatory variables, and group LASSO analysis can be used on data with grouped variables. The results of the LASSO analysis, variables that can explain the number of tuberculosis cases in West Java are the number of people with disabilities, the number of pharmacy staff, the number of malnourished people, the number of people working and the number of cities. According to the group LASSO analysis, the variables that can explain the number of tuberculosis cases in West Java are variables in the health and environmental groups. The government can focus on these factors if they want to reduce the number of tuberculosis cases in West Java.
OPEC Crude Oil Price Forecasting Using ARIMA with Ensemble Empirical Mode Decomposition Tiara Lutfiah Adisti; Agus M Soleh; Aam Alamudi; Septian Rahardiantoro; Akbar Rizki
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.v9i2p230-239

Abstract

World crude oil prices fluctuate every day. One source of crude oil traded is oil from crude oil exporting countries that are members of the Organization of the Petroleum Exporting Countries (OPEC). In the total of 40% of world crude oil is produced by OPEC. This makes forecasting the price of crude oil OPEC’s policy very necessary in order to maintain world oil market stability. Fluctuating oil price data is made simpler and easier to interpret by applying the Ensemble Empirical Mode Decomposition (EEMD) method. The EEMD method decomposes the data into a number of Intrinsic Mode Functions (IMF) and residual of the IMF. In this study, the ARIMA forecasting model is compared using the original data and the decomposition results in the form of IMF components and IMF residuals. The comparison of the two methods is seen based on the overall and average MAPE value of the forecasting results in five time ranges. The EEMD-ARIMA method has an average MAPE value of 9.09% and standard deviation MAPE value of 7.39%. OPEC crude oil price forecast in January-August 2021 ranges from $42.22 to $60.6 per barrel. The final result of the analysis in this study shows that the ARIMA method with decomposition data (EEMD-ARIMA) is better than the ARIMA method using original data
Spatio-temporal Clustering Analysis of Dengue Hemorrhagic Fever Cases in West Java 2016 – 2021: Analisis Penggerombolan Spasio-temporal Kasus DBD di Jawa Barat Tahun 2016 – 2021 Yusma Yanti; Septian Rahardiantoro; Gerry Alfa Dito
Indonesian Journal of Statistics and Applications Vol 7 No 1 (2023)
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.v7i1p56-63

Abstract

In 2020, WHO included dengue as a global health threat among 10 other diseases. This is also a problem in Indonesia, especially the province of West Java. Based on data from the Ministry of Health for 2022, West Java is the largest contributor to cases of Dengue Hemorrhagic Fever (DHF) in Indonesia. The spread of dengue fever is through mosquitoes, but climate also greatly influences the spread of this disease. The spread of West Java is quite wide, consisting of 27 city districts and a relatively high population density. This greatly influences the increase in the number of dengue fever cases. In this research, we will group years with the same dengue fever cases and identify groups of districts/cities in West Java with the same pattern of dengue fever cases for 2016 to 2021. The results obtained are that 2016 is the group with the highest number of cases. Meanwhile, from 27 city districts in West Java, three groups were obtained. Group 1 is the group with the highest number of cases consisting of Sukabumi City, Bandung City, Cimahi City, Depok City, Tasikmalaya City.
Missing Value Estimation Using Fuzzy C-Means in Classification of Chronic Kidney Disease: Pendugaan Missing Values Menggunakan Fuzzy C - Means Pada Pengklasifikasian Penyakit Ginjal Kronik Raisa Nida Eria; Aam Alamudi; Itasia Dina Sulvianti; Pika Silvianti; Septian Rahardiantoro
Indonesian Journal of Statistics and Applications Vol 9 No 1 (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.v9i1p21-32

Abstract

Based on World Health Organization (WHO) the cases of death due to Chronic Kidney Disease (CKD) ranked the 10th worldwide in 2020. CKD need to be done prevent early. History data to identify individuals predisposed to CKD in this research. In this research data contains missing values, therefore using Fuzzy C - Means (FCM) method to address it. The percentage of error in clustering CKD using FCM method is 20,25% and balanced accuracy of 84,80%. The result from classification using Classification and Regression Trees (CART) shows that accuracy value of 97,50%; sensitivity of 100,00%; and specificity of 92,86%. Individual suffer from CKD if having (1) hemoglobin more than or equal 13; spesific gravity 1,020 or 1,025; serum creatinine less than 1,3; albumin 1 or 2 or 3 or 4 or 5; and sugar 0 or 2 or 3 or 4 or 5, (2) hemoglobin more than or equal 13; spesific gravity 1,020 or 1,025; and serum creatinine more than or equal 1,3, (3) hemoglobin more than or equal 13 and spesific gravity 1,005 or 1,010 or 1,015, (4) hemoglobin less than 13 and red blood cell count less than 5,5.
Acne Severity Classification Study Using Convolutional Neural Network Algorithm with MobileNetV2 Architecture: Kajian Klasifikasi Tingkat Keparahan Jerawat Menggunakan Algoritma Convolutional Neural Network Faadiyah Ramadhani; Septian Rahardiantoro; Mohammad Masjkur
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.v8i2p112-128

Abstract

Data classification is a key technique in machine learning that maps patterns and features of input data into a target class. Significant developments in data classification occur in deep learning with neural networks and Convolutional Neural Networks (CNN) that are able to extract image features automatically. CNN can classify the level of a condition based on image data, one of which is the severity of acne. Acne (acne vulgaris) is a common skin disease with varying severity. This study aims to apply the CNN MobileNetV2 model to classify acne severity based on acne input images. The data consists of 1457 acne images at 4 severity levels divided into 80% training data and 20% test data. MobileNetV2 was used as a feature extractor through transfer learning. Fine-tuning and classification were performed using fully connected layers with ReLU and softmax activation functions. The model was evaluated with a confusion matrix and classification report. The model with a combination of hyperparameter batch size 16 and a learning rate of 0.00001 was the best model that achieved 87.29% accuracy with 89% precision, 84% recall, and 86% F1 score for classifying acne severity.
Headline-Based Indonesian Political Misinformation Classification: A Comparative Study of Naive Bayes, SVM, CNN, and IndoBERT Models Ade Ariyo Yudanto; Naila Nabiha Qonita; Nur Aulia Maknunah; Inria Purwaningsih; Septian Rahardiantoro; Agus Mohamad Soleh
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13325

Abstract

Political misinformation spreads rapidly through digital media, making early detection increasingly important. This study evaluates headline-based Indonesian political misinformation classification using Naive Bayes (NB), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and IndoBERT. Headlines were selected because they represent the first information encountered by readers and enable rapid screening, although they may not fully represent the article content. The dataset consists of 5,132 Indonesian political news headlines collected from TurnBackHoax and DetikNews Politics. Leakage-aware preprocessing was applied to reduce explicit source-related cues before model training. Experimental results show that IndoBERT achieved the best performance with an accuracy of 90.25% and a Macro F1-score of 90.24%, outperforming the other evaluated models. Additional analyses, including McNemar statistical significance testing, computational cost comparison, and error analysis, showed that although IndoBERT achieved the highest predictive performance, its improvement over the other evaluated models was not statistically significant. Error analysis further revealed that several misclassification cases involved stylistic overlaps between misinformation and factual headlines, suggesting the possibility of residual source-style dependency and shortcut learning. Therefore, the proposed models should be interpreted as learning linguistic patterns associated with the constructed headline dataset rather than performing direct factual verification of news claims.
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