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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Jurnal Matematika Sains dan Teknologi Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JURNAL METEOROLOGI DAN GEOFISIKA Xplore: Journal of Statistics Jurnal Meteorologi dan Geofisika Indonesian Journal of Statistics and Its Applications
Rahardiantoro, Septian
2Department Of Statistics, Faculty Of Mathematics And Natural Science, IPB University, West Java, 16680, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Energy Engineering Mathematics Physics Other

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Acne Severity Classification Study Using Convolutional Neural Network Algorithm with MobileNetV2 Architecture: Kajian Klasifikasi Tingkat Keparahan Jerawat Menggunakan Algoritma Convolutional Neural Network Ramadhani, Faadiyah; Rahardiantoro, Septian; Masjkur, Mohammad
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.
OPEC Crude Oil Price Forecasting Using ARIMA with Ensemble Empirical Mode Decomposition Lutfiah Adisti, Tiara; Soleh, Agus M; Alamudi, Aam; Rahardiantoro, Septian; Rizki, Akbar
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
Feature selection in supervised machine learning: a case study of poverty dataset in West Java, Indonesia Marshelle, Sean; Rahardiantoro, Septian; Kurnia, Anang
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 Yanti, Yusma; Rahardiantoro, Septian
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 Chen, Stephan; Notodiputro, Khairil Anwar; Rahardiantoro, Septian
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.
Co-Authors . Hanniva Aam Alamudi Ade Gusalinda Agus M Soleh Agus Mohamad Soleh Agus Soleh Ahmad Ansori Mattjik Anang Kurnia Anik Djuraidah Astri Fatimah Azira Irawan Azizah Desiwari Bagus Sartono Chen, Stephan Cici Suhaeni Dito, Gerry Alfa Eria, Raisa Nida I Made Sumertajaya Itasia Dina Sulvianti Khairil Anwar Notodiputro Lutfiah Adisti, Tiara Mahdiyah Riaesnianda Marshelle, Sean Mohammad Masjkur Muhammad Shalih Mutaqin, Ahdan Darul Octavia Dwi Amelia Pika Silvianti Pika Silvianti Radinda Putri Maha Dewi Ramadhani, Faadiyah Resti Cahyati Rizki, Akbar Stephan Chen Totong Martono Yani Nurhadryani Yanti, Yusma Yanti, Yusma Yusma Yanti