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All Journal International Journal of Public Health Science (IJPHS) Jurnal Ilmu Pertanian Indonesia Jurnal Ekonomi Pembangunan EKSAKTA: Journal of Sciences and Data Analysis JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi JAM : Jurnal Aplikasi Manajemen Jurnal TIMES Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Kubik Journal of Accounting and Investment JURNAL KOLABORASI JIMKesmas (Jurnal Ilmiah Mahasiswa Kesehatan Masyarakat) Al-Jabar : Jurnal Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Desimal: Jurnal Matematika Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Socioeconomics and Development Jurnal Informatika Universitas Pamulang J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Teorema: Teori dan Riset Matematika Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Journal of Information System, Applied, Management, Accounting and Research Ecces: Economics, Social, and Development Studies Inferensi Journal of Data Science and Its Applications International Journal of Science, Engineering and Information Technology Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Statistika dan Aplikasinya KUBIK: Jurnal Publikasi Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika PROFETIK: Jurnal Mahasiswa Pendidikan Agama Islam SRIWIJAYA JOURNAL OF ENVIRONMENT MATHunesa: Jurnal Ilmiah Matematika VARIANSI: Journal of Statistics and Its Application on Teaching and Research Aceh International Journal of Science and Technology Jurnal Sains dan Informatika : Research of Science and Informatic STATISTIKA Scientific Journal of Informatics Jurnal Pendidikan Progresif Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan
Fitrianto, Anwar
Departemen Statistika, Fakultas MIPA, IPB University, Bogor 16680
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Comparison of SARIMA and BES for Forecasting Red Chili Production Agustina, Titin; Fitrianto, Anwar; Indahwati, Indahwati
Jurnal Ilmu Pertanian Indonesia Vol. 30 No. 2 (2025): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18343/jipi.30.2.333

Abstract

The goal of this study is to compare the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Bagging Exponential Smoothing (BES) models for forecasting red chili production. The secondary data used in this study came from BPS-Statistics Indonesia and the Ministry of Agriculture. The data include monthly national-level red chili production from January 2013 to December 2021. Data is analyzed using time series approaches such as SARIMA and BES. The performance of both systems was compared, and production forecasts were created using the best model. According to the research findings, for this dataset, the SARIMA (1,1,1)(0,1,1)12 technique outperforms the BES method since it has lower MAPE and RMSE values, 7.06 and 95,473, respectively. The best model was then applied to anticipate red chili production from January to December 2022, resulting in a highly accurate MAPE of 5.39. Keywords: Bagging Exponential Smoothing, red chili production, SARIMA
Perbandingan Metode K-Means dan OPTICS dalam Penggerombolan Kemiskinan Multidimensi di Indonesia Sari, Devi Permata; Rizqi, Tasya Anisah; Fitrianto, Anwar; M.S, Erfiani; Jumansyah, L.M. Risman Dwi
KUBIK Vol 9, No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v9i2.39877

Abstract

Kemiskinan multidimensi tetap menjadi tantangan serius di Indonesia meskipun telah mengalami penurunan dalam beberapa tahun terakhir. Penelitian ini bertujuan menganalisis dan membandingkan pola kemiskinan multidimensi di 34 provinsi Indonesia menggunakan metode K-Means dan OPTICS Clustering. Data kemiskinan multidimensi yang digunakan mencakup aspek ekonomi, pendidikan, ketenagakerjaan, dan standar hidup dari Badan Pusat Statistik. Analisis statistik deskriptif mengungkapkan kesenjangan signifikan antar provinsi dalam berbagai dimensi kemiskinan, dengan korelasi tertinggi sebesar 0,4 antara dimensi pendidikan dan status ketenagakerjaan. K-Means Clustering mengidentifikasi 5 cluster provinsi dengan karakteristik beragam, menunjukkan adanya trade-off antara akses fasilitas dan tingkat kemiskinan. Sementara itu, OPTICS Clustering menghasilkan 2 cluster utama, dengan cluster 1 terdiri dari 24 provinsi yang memiliki kondisi cenderung homogen dan cluster 2 terdiri dari 7 provinsi dengan karakteristik yang berbeda secara signifikan. Perbandingan performa menunjukkan OPTICS unggul dengan nilai Silhouette Index dan WCSS yang lebih baik dibandingkan K-Means. Temuan ini memberikan kontribusi penting dalam analisis kemiskinan multidimensi di Indonesia dan dapat dimanfaatkan untuk merancang program pengentasan kemiskinan yang lebih terlokalisasi sesuai karakteristik masing-masing cluster.
Analisis Pola Konvergensi Transpor Kelembapan Udara di Indonesia Bagian Barat Menggunakan K-Means dengan Pembobotan Statistik dan Hierarchical Shape-Based Clustering Pratiwi, Asri; Azis, Tukhfatur Rizmah; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
KUBIK Vol 9, No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v9i2.39753

Abstract

This study analyzes the convergence patterns of Vertically Integrated Moisture Transport (VIMT) in the western region of Indonesia using the K-Means method with statistical weighting and Hierarchical Shape-Based Clustering based on Dynamic Time Warping (DTW). Daily data on specific humidity, zonal wind speed, and meridional wind speed from 2020–2023 were used to calculate VIMT. Clustering methods were utilized to identify grouping patterns in moisture transport data. The results showed that moisture convergence significantly increased during the rainy season (November–February). Using the K-Means method, five clusters with clearer separations were obtained compared to the four clusters produced by the Hierarchical Clustering method. Performance evaluation using Silhouette and Calinski-Harabasz scores indicated that the K-Means method was superior, with scores of 0.37 and 104.88 compared to 0.13 and 96.34 for the Hierarchical method. This provides an understanding of the moisture transport patterns, serving as a reference for predicting weather and climate patterns, thereby supporting efforts to mitigate the impacts of extreme weather in Western Indonesia.
Classification Performance of Stacking Ensemble with Meta-Model of Categorical Principal Component Logistic Regression on Food Insecurity Data Pangestika, Dhita Elsha; Fitrianto, Anwar; Sadik, Kusman
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15315

Abstract

Purpose: Stacking is one type of ensemble whose base-models use different algorithms. The classification results from its base-models are categorical and tend to be associated with each other. They then become input for the stacking meta-model. However, there are no currently definite rules for determining the classifier that becomes the meta-model in stacking. On the other hand, recent research has found that CATPCA-LR can work well on categorical predictor variables associated with each other. Therefore, this study focuses on the classification performance of the stacking algorithm with the CATPCA-LR meta-model. Methods: The study compared the classification performance stacking with CATPCA-LR meta-model to stacking with other meta-models (random forest, gradient boost, and logistic regression) and its base-models (random forest, gradient boost, extreme gradient boost, extra trees, light gradient boost). This research used food insecurity data from March 2022. Result: The stacking algorithm with the CATPCA-LR meta-model performs better insecurity data regarding sensitivity, balanced accuracy, F1-Score, and G-Means values. This model offers a sensitivity of 46.28%, a balanced accuracy of 59.82%, an F1-Score of 37.82%, and a G-Means of 58.26%. Meanwhile, regarding specificity values, the light gradient boost (LGB) algorithm gives the highest value compared to other algorithms. This model provides a specificity value of 88.40%. Generally, the stacking with the CATPCA-LR meta-model algorithm provides the best performance compared with other algorithms on food insecurity data. Novelty: This research has explored a stacking classification performance with CATPCA-LR as meta-model.
The Comparison A-Optimal and I-Optimal Design in Non-Linear Models to Increase Purity Levels Silicon Dioxide Aliu, Muftih Alwi; Syafitri, Utami Dyah; Fitrianto, Anwar; Irzaman, Irzaman
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26253

Abstract

One of the obstacles that arise in optimal design is the non-linear model. The relationship between temperature factors and the temperature increase rates with the purity of silicon dioxide (SiO2) forms a non-linear pattern. Determining the optimal design for a non-linear model is relatively more complex than a linear model because it requires additional information in its information matrix. Therefore, this issue necessitates further research on optimal design in non-linear models. This study uses the polynomial Taylor approach to approximate the non-linear equation through a linear equation using the appropriate optimal design methods, namely A-Optimal and I-Optimal criterion. The point search algorithm used was variable neighborhood search, this algorithm searches for design points by exploring several different neighborhood structures. These two methods were chosen to compare the characteristics and performance of the designs produced, aiming to obtain an optimal design to improve SiO2 purity (non-linear case) using the same algorithm, VNS. The research results showed that the design pattern produced by the A-Optimal design formed three temperature groups, namely the minimum temperature of 800°C - 820°C, the middle temperature of 850°C, 860°C, and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The design pattern produced by the I-Optimal design formed a full quadratic pattern, namely the minimum temperature of 800°C and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The I-Optimal design demonstrated the best performance (most optimal) in the aspect of prediction variance compared to the A-Optimal design across all alternative points in this study to improve SiO2 purity.
Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification Indah, Yunna Mentari; Aristawidya, Rafika; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.28290

Abstract

Machine learning classification is an effective tool for categorizing data based on patterns, which is particularly useful in analyzing the Human Development Index (HDI) in Indonesia. HDI serves as a key indicator of regional development progress, making it crucial to classify HDI categories at the regency/city level to support targeted development planning. This study aims to compare the performance of three ensemble-based classification methods—Random Forest, XGBoost, and LightGBM—in classifying HDI categories in Indonesia. Data from the Central Bureau of Statistics (BPS) in 2023, comprising 514 observations across nine variables, was used for analysis. The study applied these algorithms to analyze the most influential variables affecting HDI. The results show that LightGBM outperformed both Random Forest and XGBoost, achieving an accuracy of 0.937 without outlier handling and 0.944 with outlier handling. Additionally, per capita expenditure was identified as the most influential factor in predicting HDI. These findings contribute to the field of statistical modeling by demonstrating how ensemble methods can improve classification accuracy and provide valuable insights for data-driven policymaking, thus enhancing regional development planning and supporting future HDI-related research.
PENINGKATAN AKURASI KLASIFIKASI INTERAKSI FARMAKODINAMIK OBAT BERBASIS SELEKSI PASANGAN OBAT TAKBERINTERAKSI Hilma Mutiara Winata; Farit Mochamad Afendi; Anwar Fitrianto
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.327

Abstract

Identifying the pharmacodynamics drug-drug interaction (PD DDI) is needed since it can cause side effects to patients. There are two measurements of drug interaction performance, namely the golden standard positive (GSP) which is the drug pairs that interact pharmacodynamics and golden standard negative (GSN), which is a drug pairs that do not interact. The selection of GSN in the previous which studies were only selected randomly from a list of drug pairs that do not interact. The random selection is feared to contain drug pairs that actually interact but have not been recorded. Therefore, in this study the determination of GSN was carried out by, first, grouping drug pairs included in the GSP using the DP-Clus algorithm with certain values of density and cluster properties. Then the drugs in different group would be paired and only the drug pairs in the GSN list are selected. It was found that our new proposed classification method increases the AUC value compared to the results obtained by random selection of GSN.
PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK Hasnita Hasnita; Farit Mochamad Afendi; Anwar Fitrianto
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.328

Abstract

One mechanism for Drug-Drug Interaction (DDI) is pharmacodynamic (PD) interactions. They are interactions by which the effects of a drug are changed by other drugs at the site of receptor. The interactions can be predicted based on Side Effects Similarity (SES), Chemical Similarity (CS) and Target Protein Connectedness (TPC). This study aims to find the best classification technique by first applying the scaling process, variable interaction, discretization and resampling technique. We used Random Forest, Support Vector Machines (SVM) and Binary Logistic Regression for the classification. Out the three classification methods, we found the SVM classification method produces the highest Area Under Cover (AUC) value compared to the other, which is 67.91%.
HUBUNGAN AKREDITASI DAN UJIAN NASIONAL PADA SEKOLAH NEGERI DENGAN GENERALIZED STRUCTURED COMPONENT ANALYSIS Rezi Wahyuni; Budi Susetyo; Anwar Fitrianto
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.342

Abstract

There are several views and tendencies that distinguish between schools and madrasas in several aspects, one of them is the curriculum. Madrasah as islamic educational institution contains more religious lessons compared to public schools. As a result, madrasah are considered less able to provide good result in educational achievement. Overall, the education system which is based on National Education Standards (SNP) is used for assessing the educational accreditation. SNP is the minimum criterion of education system in Indonesia can be evaluated from the National Examination (UN). As latent variable, SNP is measured through 124 items as variable indicators. One of methods which is used to measure the relationship among latent variables, and latent variables with their indicator variables is structural equation modeling (SEM). A component-based SEM is called Generalized Structured Component Analysis (GSCA). GSCA analysis based on measurement model, there were 9 indicators were not significant, in which 1 indicator of standard of education and staff (SPT), 5 indicators on standard of infrastructure (SSP), and 3 indicators on standard of cost (SB). Evaluation of the structural model, it was found that the path coefficient of standard of content (SI) to UN was not significant and standard of competency (SKL) given the biggest direct effect to UN. The overall goodness of fit model showed that the total variance that can be explained of all indicators and latent variables in evaluating model of accreditation and national examinations was 63.9%. The difference in the percentage of accreditation status between schools and madrasas shows different UN results. In the 2017-2018 period, MTsN had a higher percentage of accredited schools, in line with that the average MTsN UN obtained was better than that of SMP in all types of subjects.
PERBANDINGAN MODEL GARCH SIMETRIS DAN ASIMETRIS PADA DATA KURS HARIAN Isna Shofia Mubarokah; Anwar Fitrianto; Farit M Affendi
Indonesian Journal of Statistics and Applications Vol 4 No 4 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i4.709

Abstract

ARCH and GARCH models are widely used in financial data to describe its volatility pattern. The models assume the positive and negative return residual gives the same or symmetric influence on its volatility. However, in reality, this assumption is frequently violated, which is called heteroscedasticity. Therefore, to deal with heteroscedasticity and asymmetric data, the asymmetric GARCH models, which are EGARCH and GJR-GARCH models are used. This research aims to compare the models between symmetric and asymmetric GARCH to make financial data modeling. It uses daily data on three foreign exchange rates for IDR including IDR/CNY, IDR/JPY, and IDR/USD. The data series to be used here are from January 4, 2016, to January 20, 2020. This research method is started by selecting the best mean model for each data. Based on the best mean model, then modeling the mean and variance function are simultaneously conducted using the GARCH model. To test whether there was an asymmetric effect on the data, a Lagrange multiplier test was applied on the residuals of the GARCH model. The results show that the asymmetric effect was found in the IDR/CNY and IDR/JPY exchange rates. To overcome this asymmetric effect, EGARCH and GJR-GARCH model were applied to the two exchange rates. Then the two models are compared to find out which volatility model is better. Using AIC and BIC we find EGARCH as the best model for IDR/CNY exchange rates daily return and GJR-GARCH as the best model for IDR/JPY exchange rates daily return.
Co-Authors -, Salsabila A. A., Muftih Aam Alamudi Abd. Rahman Adeline Vinda Septiani Agung Tri Utomo Agus M Soleh Agus Mohamad Soleh Ahmad Syauqi Alfa Nugraha Alfa Nugraha Pradana Alfa Nugraha Pradana Alfa Nugraha Pradana Alfa Nugraha Pradana Alfi Indah Nurrizqi Alifviansyah, Kevin Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amalia Kholifatunnisa Amanda, Nabila Amatullah, Fida Fariha Amelia, Reni Amir Abduljabbar Dalimunthe Anadra, Rahmi Anang Kurnia Anang Kurnia Angelia, Riza Rahmah Anik Djuraidah Anisa Nurizki Annissa Nur Fitria Fathina Ardhani, Rizky Aristawidya, Rafika Askari, M. Aiman Asri Pratiwi, Asri Assyifa Lala Pratiwi Hamid Azis, Tukhfatur Rizmah Aziza, Vivin Nur Bagus Sartono Budi Susetyo Bukhari, Ari Shobri Cahya Alkahfi Choon, Lai Ming Daswati, Oktaviyani Defri Ramadhan Ismana Deri Siswara Dessy Rotua Natalina Siahaan Dessy Siahaan Devi Permata Sari Dian Handayani Dwi Jumansyah, L.M. Risman Erfiani Erfiani Erfiani Erfiani Erfiani Erfiani Fadilah, Anggita Rizky Fahira, Fani Farit M Affendi Farit M. Afendi Farit M. Afendi Farit Mochamad Afendi Fatimah Fatimah Fauziah, Monica Rahma Fulazzaky, Tahira Ghina Fauziah Gustiara, Dela Hari Wijayanto Harismahyanti A., Andi Hasnataeni, Yunia Hasnita Hasnita Heri Cahyono I Made Sumertajaya Ilham Azagi Ilmani, Erdanisa Aghnia Imam Hanafi Indah, Yunna Mentari Indahwati Indahwati Indahwati Indahwati, Indahwati Irsyifa Mayzela Afnan Irzaman, Irzaman Ismah, Ismah Isna Shofia Mubarokah Iswan Achlan Setiawan Iswati Ita Wulandari Jamaluddin Rabbani Harahap Jap Ee Jia Jia, Jap Ee Jumansyah, L. M. Risman Dwi Jumansyah, L.M. Risman Dwi Kapiluka, Kristuisno Martsuyanto Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnia N. K. Khusnia Nurul Khikmah Kriswan, Suliana Kusman Sadik L.M. Risman Dwi Jumansyah La Ode Abdul Rahman La Ode Abdul Rahman Linganathan, Punitha lmam Hanafi M. Aiman Askari M.S, Erfiani Manaf, Silmi Annisa Rizki Marshelle, Sean Megawati Megawati Muftih Alwi Aliu Muftih Alwi Aliu Muhadi, Rizqi Annafi Muhammad Irfan Hanifiandi Kurnia Muhammad Yusran mutiah, siti Nabila Ghoni Trisno Hidayatulloh Nadira Nisa Alwani Nashir, Husnun Nisa Nur Aisyah Novi Hidayat Pusponegoro Nugraha, Adhiyatma Nur Hidayah Nur Khamidah NURADILLA, SITI Nurizki, Anisa Pangestika, Dhita Elsha Pika Silvianti Pradnya Sri Rahayu Pratiwi, Nafisa Berliana Indah Punitha Linganathan Putri Auliana Rifqi Mukhlashin Putri, Mega Ramatika Putri, Oktaviani Aisyah Rafika Aufa Hasibuan Rahmatun Nisa, Rahmatun Rais Ramadhan, Syaifullah Yusuf Reka Agustia Astari Reni Amelia Reni Amelia Retna Nurwulan Riansyah, Boy Rifda Nida’ul Labibah Riska Yulianti, Riska Rizki Manaf, Silmi Anisa Rizki, Akbar Rizqi, Tasya Anisah Sachnaz Desta Oktarin salsa bila Sari, Jefita Resti Seta Baehera Setyowati, Silfiana Lis Siau Hui Mah Siau Man Mah Silmi Annisa Rizki Manaf Siregar, Indra Rivaldi Siti Hafsah Siti Hasanah Siti Nur Azizah, Siti Nur Sofia Octaviana Sony Hartono Wijaya Suantari, Ni Gusti Ayu Putu Puteri Suliana Kriswan Tangke, Nabillah Rahmatiah Titin Agustina Titin Yuniarty Yuniarty Uswatun Hasanah Utami Dyah Syafitri Utami, Annisa Putri Vitona, Desi Vivin Nur Aziza Waliulu, Megawati Zein Wan Muhamad, Wan Zuki Azman Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Waode, Yully Sofyah Winata, Hilma Mutiara Xin, Sim Hui Yenni Angraini Yudhianto, Rachmat Bintang Yuniarsyih R.A, Rizqi Dwi Yusuf, Fajar Athallah Zaenal, Mohamad Solehudin Zahid, Muhammad Farhan Zahra, Latifah Zein Rizky Santoso