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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Media Infotama Journal of the Indonesian Mathematical Society KEUNIS JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan SINTECH (Science and Information Technology) Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics International Journal of Remote Sensing and Earth Sciences (IJReSES) Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Building of Informatics, Technology and Science ESTIMASI: Journal of Statistics and Its Application Journal of Applied Data Sciences Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Natural Cerdika: Jurnal Ilmiah Indonesia Xplore: Journal of Statistics STATISTIKA Parameter: Jurnal Matematika, Statistika dan Terapannya Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications The International Journal of Remote Sensing and Earth Sciences (IJReSES)

Anang Kurnia

School Of Data Science, Mathematics And Informatics, IPB University, Indonesia

Author-ID : 2479473

Agriculture, Biological Sciences & Forestry Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

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Perbandingan Kinerja Model Berbasis RNN pada Peramalan Data Ekonomi dan Keuangan Indonesia: Performance Comparison of RNN-Based Models in Forecasting Indonesian Economic and Financial Data Alkahfi, Cahya; Kurnia, Anang; Saefuddin, Asep
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1415

Peramalan deret waktu merupakan salah satu elemen kunci dalam analisis ekonomi dan keuangan. memungkinkan pemangku kepentingan untuk membuat perkiraan terhadap berbagai indikator ekonomi sebelum data resmi dirilis. Dalam konteks ini, model pembelajaran mesin seperti Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) menunjukkan potensi yang menjanjikan dalam memprediksi data deret waktu. Sejumlah penelitian juga menegaskan bahwa LSTM dan GRU mampu mengungguli kinerja RNN. Kedua model tersebut memiliki mekanisme untuk mengatasi masalah vanishing gradient yang sering ditemui pada model RNN konvensional. Penelitian ini menitikberatkan untuk menguji kinerja ketiga model tersebut pada data-data yang ada di Indonesia. Agar hasil lebih komprehensif, penelitian ini akan menguji model pada tiga jenis data yang berbeda meliputi IHSG, nilai ekspor dan PDB. Hasil penelitian ini mengindikasikan bahwa secara keseluruhan, model GRU menunjukkan kinerja terbaik, diikuti oleh model LSTM yang juga kompetitif dibandingkan RNN. Selain akurasi, model GRU dan LSTM juga menonjol dalam hal stabilitas kinerja, ditandai dengan simpangan baku yang relatif kecil jika dibandingkan dengan RNN. Keunggulan ini menjadi semakin signifikan terutama saat diterapkan pada model PDB dimana hanya tersedia untuk periode waktu yang pendek.

Improving the risk profile of Indonesian enterprise taxpayers using multilabel classification Prasetyo, Teguh; Susetyo, Budi; Kurnia, Anang
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4323-4333

Optimizing tax revenues is difficult in Indonesia due to obstacles such as tax evasion and tax avoidance. It is closely related to an organization's compliance with tax regulations, known as the taxpayers risk profile. However, this mechanism does not accurately detect tax avoidance and tax evasion risks. To overcome this limitation, we use a multilabel classification machine learning method in this study, which classifies a single observation into one or more labels at once. The approach involves problem transformation (binary relevance and label powerset), algorithm adaptation (multilabel k-nearest neighbor (ML-kNN) and multilabel-adaptive resonance associative map (ML ARAM)), and ensemble (label space partitioning and random k-label sets with disjoint (RAkELd)). Based on the model performance comparisons, we discovered that the ML-ARAM method based on deep learning is the best, with an average F1-score of 95.5% and a hamming loss of 7.4%. We also examine the feature importance of the best model to reduce the dimensions of features so that we can identify the dominant factors that encourage a taxpayer entity to engage in tax avoidance or tax evasion. The findings of this study improve the accuracy of tax avoidance risk detection and tax evasion risk profiles using machine learning methods, ensuring maximum tax revenues in Indonesia.

Identification of Atherosclerosis Based on The Differences in Cholesterol and Creatinine in Indonesia with Multivariate Analysis of Variance Maulana Achiar, Anshari Luthfi; Aidi, Muhammad Nur; Kurnia, Anang; Widoretno, Widoretno
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 24 No. 03 (2023): 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/vol23-iss03/417

Atherosclerosis is a chronic inflammatory disease indicated by plaque build-up in the arteries due to increased total cholesterol, low-density lipoproteins (LDL), triglycerides, and decreased high-density lipoproteins (HDL). It is also associated with disruption of renal function high creatinine blood level. This study aims to identify atherosclerosis based on differences in total cholesterol, HDL, LDL, triglycerides, and creatinine levels in 35.509 residents from 33 provinces and rural-urban areas in Indonesia. This study uses two-factor MANOVA where the province and rural-urban are the factors, followed by ANOVA and Tukey's test. Results show differences between total cholesterol, HDL, LDL, triglyceride, and creatinine levels of the residents among provinces and rural-urban areas. The Residents from Bangka Belitung and North Sulawesi provinces have the highest risk of atherosclerosis, and Jambi province has the most balanced condition. Urban residents tend to be at risk for atherosclerosis due to high levels of LDL, while rural residents are at risk by low HDL or high creatinine levels

MODEL APPROACH OF AGGREGATE RETURN VOLATILITY: GARCH(1,1)-COPULA VS GARCH(1,1)-BIVARIATE NORMAL Pasaribu, Asysta Amalia; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2069-2082

Aggregate risk is an aggregation of single risks that are both independent and interdependent. In this study, aggregate risk is constructed from two interdependent random risk variables. The dependence between two random variables can be determined through the size of dependence and joint distribution properties. However, not all distributions have joint distribution properties; the joint distributions may be unknown, so motivating the use of the Copulas in this study is needed. Sometimes, the Copula model is introduced to construct joint distribution properties. The Copula model in this research is used in financial policies such as investment. In the investment sector, the aggregate risk comes from the sum of the single risks and returns. The model used in aggregate return is the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model. The data used in this study is the closing price data for Apple and Microsoft stocks from January 01, 2010, to January 01, 2024. The best model selection is the model with the GARCH-Bivariate Normal approach with the smallest MSE value. Model GARCH(1,1)-Bivariate Normal is the best model for the volatility model of aggregate return.

Bibliometric Mapping and Trend Analysis of Beta Regression Modeling: A Decade of Development (2015–2024) Sihombing, Pardomuan Robinson; Erfiani, Erfiani; Notodiputro, Khairil Anwar; Kurnia, Anang
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14949

Beta regression is a statistical model designed to handle dependent variables that assume values within the open interval (0, 1), such as rates, proportions, or percentages. The study aimed to determine the development of beta regression over the last 10 years with a bibliometric approach. The source of the article database used comes from the Scopus website. The tool used for analysis is R software with a bibliometrix package. The results of this study show that there are 293 articles published in the Scopus Journal. Research develops in various research fields. The author with the most articles is Cribari-Neto, F., with the most significant number of documents, i.e., 12. According to the author's country of origin related to the beta regression method, Brazil has the most countries, while Indonesia is in 12th place. Therefore, research on beta regression still has excellent potential to continue to be developed.

Mendeteksi Unsur Depresi pada Unggahan Media Sosial Menggunakan Metode Machine Learning dengan Optimasi Berbasis Inspirasi Alam Santoso, Zein Rizky; Wigena, Aji Hamim; Kurnia, Anang
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.45516

Social media has now become an inseparable part of everyday life, including in expressing emotions and mental states. One popular platform is X (formerly Twitter), where many users indirectly share signs of depression. This study develops a classification model to detect indications of depression in social media posts, using machine learning algorithms and feature selection techniques based on nature-inspired algorithms. The classification algorithms used include Naïve Bayes, k-Nearest Neighbors (k-NN), Decision Tree, Random Forest, and XGBoost. Each algorithm is combined with feature selection techniques using Particle Swarm Optimization (PSO), Bat Algorithm (BA), and Flamingo Search Algorithm (FSA). The models are evaluated based on accuracy, precision, recall, F1-score, and the number of features used. The results show that the combination of the Random Forest method with FSA-based feature selection (RF-FSA) delivers the best performance, with an accuracy of 82.2%, balanced precision and recall, and efficient feature usage. Another strong alternative is XGBoost with FSA (XGB-FSA), although it requires more features and longer computational time. This study demonstrates that selecting the right feature selection algorithm, particularly FSA, can significantly improve both the accuracy and efficiency of depression text classification models. The resulting model is expected to serve as a useful tool for early detection of depression symptoms from social media posts, allowing for quicker and more targeted interventions.

TEXT CLUSTERING ONLINE LEARNING OPINION DURING COVID-19 PANDEMIC IN INDONESIA USING TWEETS Tyas, Maulida Fajrining; Kurnia, Anang; Soleh, Agus Mohamad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

To prevent the spread of corona virus, restriction of social activities are implemented including school activities which reaps the pros and cons in community. Opinions about online learning are widely conveyed mainly on Twitter. Tweets obtained can be used to extract information using text clustering to group topics about online learning during pandemic in Indonesia. K-Means is often used and has good performance in text clustering area. However, the problem of high dimensionality in textual data can result in difficult computations so that a sampling method is proposed. This paper aims to examine whether a sampling method to cluster tweets can result to an efficient clustering than using the whole dataset. After pre-processing, five sample sizes are selected from 28300 tweets which are 250, 500, 2500, 10000 and 20000 to conduct K-Means clustering. Results showed that from 10 iterations, three main cluster topics appeared 90%-100% in sample size of 2500, 10000 and 20000. Meanwhile sample size of 250 and 500 tend to produced 20%-60% appearance of the three main cluster topics. This means that around 8% to 35% of tweets used can yield representative clusters and efficient computation which is four times faster than using entire dataset.

SMALL AREA ESTIMATION OF MEAN YEARS SCHOOL IN KABUPATEN BOGOR USING SEMIPARAMETRIC P-SPLINE Putri, Christiana Anggraeni; Indahwati, Indahwati; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

The Fay-Herriot model, generally uses the EBLUP (Empirical Best Linear Unbiased Prediction) method, is less flexible due to the assumption of linearity. The P-Spline semiparametric model is a modification of the Fay-Herriot model which can accommodate the presence of two components, linear and nonlinear predictors. This paper also deals spatial dependence among the random area effects so that a model with spatially autocorrelated errors will be implemented, known as the SEBLUP (Spatial Empirical Best Linear Unbiased Prediction) method. Using data from SUSENAS, PODES, and some publication from BPS, the main objective of this study is to estimate the mean years school at kecamatan level in Kabupaten Bogor using EBLUP, Semiparametric P-Spline approach and SEBLUP method. The results show that based on the RRMSE value, the cubic P-Spline model with three knots predicts the mean years school better than EBLUP. Meanwhile, the addition of spatial effects into the small area estimation has not been able to improve the estimated value of the P-Spline semiparametric approach.

SUBDISTRICT CLUSTERING IN WEST JAVA PROVINCE BASED ON DISEASE INCIDENCE OF JKN PARTICIPANTS PRIMARY SERVICES Nashir, Husnun; Kurnia, Anang; Fitrianto, Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

One of the efforts that can be done to optimize health services and the distribution of facilities and infrastructure efficiently in a wide scope is by profiling and clustering areas in the province of West Java to the scope of sub-districts that have similar characteristics of disease category. The methods that will be compared to get the best clustering are hierarchical clustering and ensemble clustering. The data used as the object of research is the BPJS Kesehatan capitation primary service sample data for the 2017-2018 period. Some of the important variables used include: primary disease diagnosis data (ICD-10) of patients at the puskesmas, service time, type of visit, and location of service sub-district. This study uses several evaluation metrics Silhouette coefficient, Dunn index, Davies-Bouldin index, and C-index to determine the optimal number of clusters formed. In addition, descriptive analysis and visualization of the clustering results are also used as considerations in selecting the optimal cluster. Based on the evaluation results, the optimal method is hierarchical clustering with complete linkage. This method produces three clusters: cluster 1 consists of 5 sub-districts that have a high/dominant mean value in almost all disease categories, cluster 2 consists of 26 sub-districts that have a medium mean value, and cluster 3 consists of 589 sub-districts that have a low mean value. Most of the members of clusters 1 and 2 are sub-districts located in the districts/cities around the national capital (DKI Jakarta) and the provincial capital (Bandung) while the members of cluster 3 are mostly sub-districts located in suburban districts/cities or far from the central government.

A COMPARISON OF LOGISTIC REGRESSION AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) ON COVID-19 DATA IN WEST SUMATRA Haq, Irvanal; Aidi, Muhammad Nur; Kurnia, Anang; Efriwati, Efriwati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1749-1760

An understanding of factors that affect the recovery time from a disease is important for the community, medical staff, and also the government. This research analyzed factors that affect the recovery time of Covid-19 sufferers in West Sumatra. In addition, the consumption of a herbal made from Sungkai leaves, which is believed by some people in West Sumatra to accelerate the healing from Covid-19, was also included in the analysis. The recovery time here was categorized into two classes (binary): 1 for within 2 weeks, and 0 for more than 2 weeks. The methods used were logistic regression and geographically weighted logistic regression (GWLR). GWLR provides estimates of parameters for each location. The data used in this study is Covid-19 data of 2021 taken from the Regional Research and Development Agency (Litbangda) of West Sumatra with a total of 764 observations collected from 19 regencies/cities in West Sumatra. The results showed that there was no difference between the logistic regression model and the GWLR models based on the values of AIC and the ratio of deviance and degrees of freedom (df). The addition of spatial factors through GWLR models did not provide additional information regarding the recovery of Covid-19 sufferers within 2 weeks or more than 2 weeks. The logistic regression model gives the result that, at significance level α = 10%, residence, vaccination status, and symptoms significantly affect the recovery time within 2 weeks or more for Covid-19 sufferers, while other variables, namely sex, age, Sungkai leaves consumption status, and ginger consumption status have no significant effects.

Co-Authors . Hanniva . Marzuki . Sutriyati Abdullah Ilman Fahmi ACHMAD FAUZAN Achmad Fauzan, Achmad Adeline Vinda Septiani Agus Buono Agus M Soleh Agus Mohamad Soleh Agus Mohammad Soleh Agus Mohammad Soleh Ahmad Ansori Mattjik Ajeng Bita Alfira Aji Hamim Wigena Alkahfi, Cahya Alona Dwinata Amalia Pasaribu, Asysta Amin, Yudi Fathul Anik Djuraidah Ardiansyah, Muhlis Arie Anggreyani Arie Wahyu Wijayanto Arief Gusnanto Arien Citha Utami ASEP SAEFUDDIN Astri Fatimah Azka Ubaidillah Bagus Sartono Bambang Sumantri Beny Trianjaya Budi Susetyo Budi Waryanto Cici Suhaeni Cici Suhaeni Citra Jaya Dede Dirgahayu Dede Dirgahayu Deiby T Salaki Dewi Juliah Ratnaningsih Dhea Dewanti Dian Handayani Dian Handayani Dian Kusumaningrum Dian Kusumaningrum Dian Kusumaningrum, Dwi Agustin Nuriani Sirodj Dwi Wahyu Triscowati Efriwati Efriwati Erfiani Erfiani Erfiani Erfiani Erfiani Erwan Setiawan Erwan Setiawan, Erwan Farit Mochamad Afendi Farit Mohamad Afendi Fauziah, Ghina Febryna Sembiring Fitri Dewi Shyntia Fitrianto, Anwar Fitriyani Sahamony, Nur Gerry Alfa Dito Hamim Wigena, Aji Haq, Irvanal Hari Wijayanto Hari Wijayanto Hari Wijayanto Hestiani Wulandari Hidayat, Agus Sofian Eka Hidayat, Muhammad I Made Sumertajaya I Wayan Mangku Ikhlasul Amalia Rahmi Ina Widayanty Indah Herlawati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iwan Kurniawan Khairani, Fitri Khairil Anwar Notodiputro Khairil Anwar Notodiputro Kristuisno Martsuyanto Kapiluka Kusman Sadik Kusman Sadik Loly, Joao Ferreira Rendes Bean Matualage, Dariani Maulana Achiar, Anshari Luthfi Muhammad Nur Aidi Mulianto Raharjo Nashir, Husnun Newton Newton Nurul Hidayati Pardomuan Robinson Sihombing Pardomuan Robinson Sihombing Pasaribu, Asysta Amalia Pingkan Awalia Pramana, Setia Purba, Widyo Pura Purwanto, Arie Putri, Christiana Anggraeni Rachmat Bintang Yudhianto Yudhianto Rahardiantoro, Septian Rahayu, Melania Dwi Rahma Anisa Rahma Anisa Rahman, Gusti Arviana Retsi Firda Maulina Ristiyanti Ristiyanti Rysda Rysda Ryska Putri Madyasari Sahamony, Nur Fitriyani Santoso, Andrianto Santoso, Zein Rizky Sari Agustini Hafman Sean Marshelle Septian Rahardiantoro Setyowati, Indah Rini Siregar, Jodi jhouranda Siskarossa Ika Oktora Siti Muchlisoh Sri Kustiara Suprayogi, Muhammad Azis Suprayogi, Muhammad Aziz Teguh Prasetyo Thooriq Ghaith Topan . Ruspayandi Triscowati, Dwi Wahyu Tyas, Maulida Fajrining Utami Dyah Syafitri Viarti Eminita Widoretno, Widoretno Yani Nurhadryani Yenni Angraini Yenni Kurniawati Yudistira Yudistira Yully Sofyah Waode Zafira Fakhriyah

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