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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Ilmu Pertanian Indonesia Jurnal Penyuluhan MEDIA KONSERVASI Jurnal Manajemen dan Agribisnis FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Media Gizi dan Keluarga Buletin Peternakan Media Statistika Statistika Jurnal Manajemen Teknologi IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmu Komunikasi Sains Tanah Journal The Winners Journal of Economics, Business, & Accountancy Ventura Gadjah Mada International Journal of Business JAM : Jurnal Aplikasi Manajemen Journal of the Indonesian Mathematical Society Jurnal RISET Geologi dan Pertambangan Journal of Regional and City Planning JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Binus Business Review JURNAL HAMA DAN PENYAKIT TUMBUHAN TROPIKA Journal of Economic Education Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal SEPA (Social Economic and Agribusiness Journal) Informatika Pertanian BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Agrisocionomics: Jurnal Sosial Ekonomi Pertanian Jurnal Kebijakan Sosial Ekonomi Kelautan dan Perikanan KEK (Kajian Ekonomi dan Keuangan) STI Policy and Management Journal JURNAL PANGAN FIBONACCI: Jurnal Pendidikan Matematika dan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Matematika (Statmat) MEANS (Media Informasi Analisa dan Sistem) Jurnal Risalah Kebijakan Pertanian dan Lingkungan BISNIS & BIROKRASI: Jurnal Ilmu Administrasi dan Organisasi JURNAL ILMIAH GLOBAL EDUCATION Malcom: Indonesian Journal of Machine Learning and Computer Science Xplore: Journal of Statistics STATISTIKA Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Society Media Penelitian dan Pengembangan Kesehatan Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education eJEBA
ASEP SAEFUDDIN
Departemen Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Institut Pertanian Bogorm, Kampus IPB Darmaga, Bogor 16688
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N-Level Structural Equation Models (nSEM): The Effect of Sample Size on the Parameter Estimation in Latent Random-Intercept Model Eminita, Viarti; Saefuddin, Asep; Sadik, Kusman; Syafitri, Utami Dyah
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6 No. 1 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i1.38914

Abstract

Multilevel Structural Equation Modeling (MSEM) is claimed to address hierarchical data structures and latent response variables, but it becomes unstable with an increasing number of levels. N-Level SEM (nSEM) is an SEM framework designed to handle a growing number of levels in the model. The nSEM framework uses the Maximum Likelihood Estimation (MLE) method for parameter estimation, which requires a large sample size and correct model specification. Therefore, it is essential to consider the necessary minimal sample size to ensure accurate and efficient parameter estimation in the nSEM model. This study examined how sample size affects the performance of parameter estimators in nSEM models. We propose a method to evaluate the effect of many environments to estimate the results of factor loadings and environmental variance produced by the model. In addition, we also assess the impact of environment size on the estimation results of factor loadings and individual variance. The results were then applied to actual data on student mathematics learning motivation in Depok. The findings show that neither the number of environments nor the size of the environment affects the performance of fixed parameter estimation in the nSEM model. nSEM indicates excellent performance in estimating environmental variance at level 2 when the number of environments increases. Conversely, increasing the size of the environment worsens the performance of estimating individual variance parameters. Overall, the nSEM framework for the latent random-intercept (LatenRI) model performs well with increasing sample sizes. The application data on LatenRI models show almost similar estimation results.Keywords: hierarchical data; latent random intercept model; multilevel structural equation modeling; n-level structural equation modeling.AbstrakMultilevel Structural Equation Modeling (MSEM) diklaim dapat mengatasi struktur data hierarki dan variabel respons laten, namun menjadi tidak stabil dengan bertambahnya jumlah level. N-Level SEM (nSEM) adalah kerangka kerja SEM yang dirancang untuk menangani semakin banyak level dalam model. Kerangka kerja nSEM menggunakan metode Maximum Likelihood Estimation (MLE) untuk estimasi parameter, yang memerlukan ukuran sampel yang besar dan spesifikasi model yang benar. Oleh karena itu, penting untuk mempertimbangkan ukuran sampel minimal yang diperlukan untuk memastikan estimasi parameter yang akurat dan efisien dalam model nSEM. Studi ini menguji bagaimana ukuran sampel mempengaruhi kinerja penduga parameter dalam model nSEM. Kami mengusulkan metode untuk mengevaluasi pengaruh banyak lingkungan dalam memperkirakan hasil factor loadings  dan varians lingkungan yang dihasilkan oleh model. Selain itu, kami juga menilai dampak ukuran lingkungan terhadap hasil estimasi factor loadings dan varians individu. Hasilnya kemudian diterapkan pada data aktual motivasi belajar matematika siswa di Depok. Hasil menunjukkan bahwa baik jumlah lingkungan maupun ukuran lingkungan tidak mempengaruhi kinerja estimasi parameter tetap pada model nSEM. nSEM menunjukkan kinerja yang sangat baik dalam memperkirakan varians lingkungan pada level 2 ketika jumlah lingkungan meningkat. Sebaliknya, peningkatan ukuran lingkungan akan memperburuk kinerja pendugaan parameter varians individu. Secara keseluruhan, kerangka nSEM untuk model intersepsi acak laten (LatenRI) bekerja dengan baik dengan meningkatnya ukuran sampel. Data penerapan model LatenRI menunjukkan hasil estimasi yang hampir serupa.Kata Kunci: data hirarki; model intersep acak laten; model persamaan structural multilevel; model persamaan structural n-level. 2020MSC: 62D99
Support vector machine performance: simulation and rice phenology application Muradi, Hengki; Saefuddin, Asep; Sumertajaya, I Made; Soleh, Agus Mohamad; Domiri, Dede Dirgahayu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4878-4890

Abstract

In the case of classification, model accuracy is expected to result in correct predictions. This study aims to analyze the performance of two kinds of support vector machine (SVM) methods: the support vector machine one versus one (SVM OvO) method and the generalized multiclass support vector machine (GenSVM) method. This method will compare to the generalized linear model, namely the multinomial logistic regression (MLR) method. Simulations were conducted using SVM OvO and GenSVM methods to get an overview of the parameters affecting both methods' performance. Furthermore, the three classification methods are implemented in the case of modelling the rice phenology and tested for performance. Simulation results show that, however, the SVM OvO and GenSVM machine learning methods are sensitive to the choice of model parameters. The empirical study results show that the SVM OvO and GenSVM methods can produce satisfactory model accuracy and are comparable to the MLR method. The best rice phenology model accuracy was obtained from the SVM OvO model, where 79.20 ± 0.21 overall accuracy and 70.69 ± 0.29 kappa were obtained. This research can be continued by handling samples, especially when class members are a minority, and can also add random effects to the SVM model.
Performance Evaluation of ARIMA and LSTM Models to Handle Multi-Interventions in Automobile Production Forecasting Maghfiroh, Firda Aulia; Indahwati, Indahwati; Saefuddin, Asep
Jurnal Ilmiah Global Education Vol. 6 No. 4 (2025): JURNAL ILMIAH GLOBAL EDUCATION
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/jige.v6i4.4694

Abstract

Intervention refers to disturbances caused by internal or external variables, such as market changes, international events, or policy shifts. The dataset used in this study contains three intervention events, referred to as a multi-input intervention. The data consist of car production figures from PT Astra Daihatsu Motor obtained from the official GAIKINDO website. The forecasting task focuses on predicting PT Astra Daihatsu Motor’s production, which was influenced by three major interventions: policy changes in 2013, the impact of the COVID-19 pandemic in 2020, and the increase in SUV production in 2022. This study compares ARIMA and LSTM models for car production forecasting. The dataset covers monthly production data from January 2010 to June 2024, with a total of 174 observations. RMSE, MAPE, and SMAPE are employed as accuracy measures. Based on the testing data (May 2023–June 2024), the results show that the LSTM model outperforms ARIMA in capturing trend patterns, with lower error values of RMSE (4587.65), MAPE (10.37), and SMAPE (10.39), compared to ARIMA with RMSE (5059.48), MAPE (11.59), and SMAPE (10.50). Accordingly, LSTM represents a relevant and robust modeling alternative for production forecasting in operational decision-making, owing to its flexibility and strong capability in capturing complex data patterns.
BERTopic-Based Multi-Class Topic Classification on Indonesian Shopee E-commerce Reviews Using Ensemble Learning Alifviansyah, Kevin; Saefuddin, Asep; Rahardiantoro, Septian
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim 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.
Ekonomi Lokal dan Pembangunan Pedesaan di Dusun Berambai, Kalimantan Timur Arman Arman; Asep Saefuddin
Society Vol 8 No 2 (2020): Society
Publisher : Laboratorium Rekayasa Sosial, Jurusan Sosiologi, FISIP Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/society.v8i2.202

Abstract

The role of the local economy gets eroded due to the inclusion of capitalization in rural areas. This research examines the coal mining industry's influence on the local economy's existence in Berambai Hamlet, Bukit Pariaman Village, Tenggarong Seberang Sub-district, Kutai Kartanegara Regency, East Kalimantan Province, Indonesia. This research uses qualitative research methods; meanwhile, data collection methods use field observation and in-depth interviews. Interviews were conducted in stages through a snowball sampling to strengthen the observations' results. The results show that the local economy and livelihood in Berambai Hamlet are under pressure and eroded due to coal mining activities. Livelihood products shrank drastically, especially fish and rice, due to mining waste polluting rivers and agricultural land conversion to mining areas. Furthermore, other sources of income from farmworkers are not enough to fulfill the needs. The government needs to protect their livelihoods as a driving force for the local economy by integrating nature-based life. The government needs to develop local economic potentials, such as tourism areas, crafts, and artworks. The government also needs to strengthen village institutions. It must be carried out together with mining companies seriously. Furthermore, the government needs to maintain the unity of rural spatial and spatial planning.
AN APPLICATION OF GENETIC ALGORITHM FOR CLUSTERING OBSERVATIONS WITH INCOMPLETE DATA Ananda, Frisca Rizki; Saefuddin, Asep; Sartono, Bagus
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
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.v1i1.48

Abstract

Cluster analysis is a method to classify observations into several clusters. A common strategy for clustering the observations uses distance as a similarity index. However distance approach cannot be applied when data is not complete. Genetic Algorithm is applied by involving variance (GACV) in order to solve this problem. This study employed GACV on Iris data that was introduced by Sir Ronald Fisher. Clustering the incomplete data was implemented on data which was produced by deleting some values of Iris data. The algorithm was developed under R 3.0.2 software and got satisfying result for clustering complete data with 95.99% sensitivity and 98% consistency. GACV could be applied to cluster observations with missing value without filling in the missing value or excluding these observations. Performance on clustering incomplete observations is also satisfying but tends to decrease as the proportion of incomplete values increases. The proportion of incomplete values should be less than or equal to 40% to get sensitivity and consistency not less than 90. Keywords: Cluster Analysis, Genetic Algorithm, Incomplete Data.
THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL Nuramaliyah, Nuramaliyah; Saefuddin, Asep; Aidi, Muhammad Nur
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
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.v3i3.564

Abstract

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.
PEMODELAN STATISTICAL DOWNSCALING DENGAN LASSO DAN GROUP LASSO UNTUK PENDUGAAN CURAH HUJAN Yunus, M.; Saefuddin, Asep; Soleh, Agus M
Indonesian Journal of Statistics and Applications Vol 4 No 4 (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.v4i4.724

Abstract

One of the rainfall prediction techniques is the Statistical Downscaling Modeling (SDS). SDS modeling is one of the applications of modeling with covariates conditions that are generally large and not independent. The problems that will be encountered is the problem of ill-conditional data i.e multicollinearity and the high correlation between variables. The case of highly correlated data causes a linear regression coefficient estimators obtained to have a large variance. This research was conducted to make the statistical downscaling modeling using the lasso and group lasso for the prediction of rainfall. Group of the covariate scenario is applied based on the adjacent area, the high correlation between covariates and correlation between covariates and responses, and also the addition of dummy variables. Scenario six (grouping which is done by considering the covariates that have a positive correlation to the response is divided into 3 groups, 1 individual and the covariates that are negatively correlated with the response are divided into 2 groups, 1 individual) is better than the other scenarios in linear modeling without a dummy. Then, linear modeling with a dummy is better than without a dummy for both techniques. In linear modeling with a dummy, the Group lasso technique can be considered more in SDs modeling, because the difference in the RMSEP statistical value and the correlation coefficient value is significant.
Co-Authors . Marzuki . Sutriyati Achmad ACHMAD . Achmad Ramzy Tadjoedin adwendi, satria june Agus M Soleh Agus Mohamad Soleh Agustifa Zea Tazliqoh Ahmad A. Mattjik Ahmad Ansori Mattjik Aji H. Wigena Aji Hamim Wigena Aldi, Muhammad Nur Alif Supandi Alifviansyah, Kevin Alinda F. M. Zain Alkahfi, Cahya Ananda Shafira Ananda, Frisca Rizki Anang Kurnia Andres Purmalino Ani Suryani Anik Djuraidah Arief Daryanto Arista Marlince Tamonob Arman Arman Arman Arman Arman Arman Arman Arman Arnita Arnita Azagi, Ilham Alifa Bagus Sartono Bambang Indriyanto Basita Ginting Budhi Purwandaya, Budhi Budi Marwoto Budi Susetyo Bunasor Sanim Cece Sumantri Chalid Talib Citra Jaya Daowen Zhang Dede Dirgahayu Domiri Dede Dirgahayu Domiri, Dede Dirgahayu Dewi Juliah Ratnaningsih Diah Krisnatuti Dian Handayani Dian Kusumaningrum Dian Kusumaningrum, Doni Suhartono Dudung Darusman Eka Intan Kumala Putri Embay Rohaeti Eminita, Viarti Enny Kristiani Enny Kristiani Erfiani Erfiani Erfiani Eri Purnomohadi Etih Sudarnika Etty Riani Euis Sunarti Eva Z Yusuf Fatah Sulaiman Fitrah Ernawati Frisca Rizki Ananda Fulazzaky, Tahira H. R. Eddie Gurnadi HAJRIAL ASWIDINNOOR Hanny Nurlatifah Harapin Hafid H. Hardiansyah . Hardinsyah Hari Wijayanto Hartoyo, harry Hasnataeni, Yunia Hendra Prasetya Hengki Muradi Heny Suwarsinah Hermanto Siregar Hidayat Syarief Hilman Dwi Anggana Husaini . I Made Sumertajaya I Wayan Mangku Ida Mariati Hutabarat Indahwati Itasia Dina Sulvianti Jajang Jajang Jodi Vanden Eng Joko Affandi Joko Affandi Joko Sutrisno JOKO SUTRISNO Khairil Anwar Notodiputro Kristiani, Enny Kusman Sadik Lia Budimulyati Salman Lia Ratih Kusuma Dewi Lilik Noor Yuliati Lismayani Usman Lukmanul Hakim Lukmanul Hakim M. Yunus M. Yunus Maghfiroh, Firda Aulia Mangara Tambunan Margono Slamet Marimin , Marimin Marimin Marizsa Herlina Marliati . Marliati Marliati Mirnawati Sudarwanto Muggy David Cristian Ginzel Muhammad Nur Aidi Muradi, Hengki Musa Hubeis mutiah, siti Ni Nyoman Sawitri Nimmi Zulbainarni Ningsih, Wiwik Andriyani Lestari Ninuk Purnaningsih Nirawita Untari Nunung Nuryartono Nuramaliyah, Nuramaliyah Nurlatifah, Hanny Nurul Hidayati Nusar Hajarisman Pang S. Asngari Pien Budiyanto Prabowo Tjitropranoto Pradina, Fathia Anggriani Priyadi Kardono Purnomohadi, Eri R. Ruswandi Rahardiantoro, Septian Rahmadi Sunoko Rahmadi Sunoko Ratna Megawangi Rimun Wibowo Ristu Haiban Hirzi, Ristu Rita Kusriastuti Rizal Syarief Rizal Syarief Rizka Rahmaida Ronny Rachman Noor Rudy Priyanto S. Damanhur, Didin Santun R.P. Sitorus SANTUN R.P. SITORUS Sarah Putri Sarsidi Sastrosumarjo Sausan Nisrina Setiadi Djohar Setiawan Setiawan Siti Sundari Sitti Nurhaliza Sjafri Mangkuprawira Sjafri Mangkuprawira Soedijanto Padmowihardjo Soekirman Soekirman Soetrisno Hadi Sony Sunaryo Sri Yusnita Burhan Suhartono . Suhartono . Sumardjo Sumarjo Gatot Irianto Sumartono Sumartono Sutarman Sutarman . Suwarsinah, Heny Syafri Mangkuprawira Syafri Mangkuprawira Syarifah Iis Aisyah TADJOEDIN, ACHMAD RAMZY Tagor Alamsyah Harahap Talib, Chalid Tati Rajati Tati Suprapti Tiyas Yulita triguna, gunadi Ujang Sumarwan Umi Cahyaningsih Upik Kesumawati Hadi Utami Dyah Syafitri Wahida Ainun Mumtaza William A. Hawley Wiwik Andriyani Lestari Ningsih Yani Nurhadryani Yekti Widyaningsih Yenni Angraini Yudhistira Arie Wijaya Yuni Ros Bangun Yusuf, Eva Z Zinggara hidayat