Article Per Year (5 Year)
p-Index From 2021 - 2026
5.283
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Berkala Ilmu Perpustakaan dan Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Forum Geografi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science Informatika Pertanian BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) KEK (Kajian Ekonomi dan Keuangan) Jurnal Statistika dan Aplikasinya Journal of Soft Computing Exploration Enthusiastic : International Journal of Applied Statistics and Data Science Prosiding Seminar Nasional Official Statistics Jurnal Natural Xplore: Journal of Statistics Parameter: Jurnal Matematika, Statistika dan Terapannya Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Agus Mohamad Soleh
Department Of Statistics, IPB University, Jl. Lingkar Akademik Kampus IPB Dramaga, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
BINOMIAL REGRESSION IN SMALL AREA ESTIMATION METHOD FOR ESTIMATE PROPORTION OF CULTURAL INDICATOR Yudistira Yudistira; Anang Kurnia; Agus Mohamad Soleh
Indonesian Journal of Statistics and Applications Vol 2 No 2 (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.v2i2.63

Abstract

In sampling survey, it was necessary to have sufficient sample size in order to get accurate direct estimator about parameter, but there are many difficulties to fulfill them in practice. Small Area Estimation (SAE) is one of alternative methods to estimate parameter when sample size is not adequate. This method has been widely applied in such variation of model and many fields of research. Our research mainly focused on study how SAE method with binomial regression model is applied to obtained estimate proportion of cultural indicator, especially to estimate proportion of people who appreciate heritages and museums in each regency/city level in West Java Province. Data analysis approach used in our research with resurrected data and variables in order to be compared with previous research. The result later showed that binomial regression model could be used to estimate proportion of cultural indicator in Regency/City in Indonesia with better result than direct estimation method.
PENENTUAN NILAI AMBANG BATAS SEBARAN PARETO TERAMPAT DENGAN MEASURE OF SURPRISE Yumna Karimah; Aji Hamim Wigena; Agus Mohamad Soleh
Indonesian Journal of Statistics and Applications Vol 3 No 2 (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.v3i2.284

Abstract

Extreme rainfall can result in natural disasters such as floods and landslides. These natural disasters will cause damage and losses to the surrounding environment. Prevention of damage from natural disasters can be done by extreme rainfall estimation. Estimates of extreme rainfall are based on Generalized Pareto Distribution (GPD) which requires threshold value information. The threshold value can be determined by two methods, namely Mean Residual Life Plot (MRLP) and Measure of Surprise (MOS). The purpose of this study is to determine and compare the threshold values ​​of MRLP and MOS. The data used are 10-day and monthly rainfall data. The results of this study indicate that the procedure of MOS is shorter and easier than that of MRLP. Based on the cross validation result, the log-likelihood value of MOS is larger than that of MRLP, then MOS is better than MRLP.
Study of Spatial Autoregressive Regression With Heteroskedasticity Using the Generalized Method of Moments and Bayesian Approach : Kajian Regresi Spasial Autoregresif dengan Heteroskedastik Menggunakan Generalized Method of Moments dan Pendekatan Bayes Abialam Koesnandy H; Agus Mohamad Soleh; Farit Mochamad Afendi
Indonesian Journal of Statistics and Applications Vol 8 No 1 (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.v8i1p58-69

Abstract

Spatial dependence and spatial heteroskedasticity are problems in spatial regression. Spatial autoregressive regression (SAR) concerns only to the dependence on lag. The estimation of SAR parameters containing heteroskedasticity using the maximum likelihood estimation (MLE) method provides biased and inconsistent estimators. The alternative method that can be used are generalized method of moments (GMM) and Bayesian method. GMM uses a combination of linear and quadratic moment functions simultaneously so that the computation is easier than MLE. Bayesian method solves heteroskedasticity by modeling the structure of variance-covariance matrix. The bias are used to evaluate the GMM and Bayes in estimating parameters of SAR model with heteroskedasticity disturbances in simulation data. The results show that GMM and Bayes provides the bias of parameter estimates relatively consistent and smaller with larger number of observations. GMM and Bayes methods are applied to district/city GRDP data in Indonesia. The result show GMM method with Eksponential Distance Weights (EDW) matrix produces the minimum variance and the largest pseudo-R2
Siamese Model-Based Face Verification Using CNN and MobileNetV2 Abd Rahman; Agus Mohamad Soleh; Erfiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6996

Abstract

Face verification plays an important role in computer vision, especially in mobile and embedded systems with limited computational capacity. This study proposes a face verification system based on the Siamese Neural Network (SNN) architecture by integrating six embedding models. These models consist of a standard CNN, an L2-normalized CNN, a baseline MobileNetV2, a structurally adjusted MobileNetV2, a pre-trained MobileNetV2, and a fine-tuned MobileNetV2. The dataset includes facial images captured from three webcams and additional samples obtained from the Labeled Faces in the Wild and ImageNet datasets. The experimental procedure includes image preprocessing, construction of balanced positive and negative image pairs, model training, and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that the pre-trained MobileNetV2 and the standard CNN achieve the highest verification accuracy, reaching 100 percent and 99.998 percent, respectively. Among all models, the structurally adjusted MobileNetV2 presents the best trade-off by combining high accuracy, computational efficiency, and training stability while successfully avoiding overfitting. The real-time implementation involves only the structurally adjusted MobileNetV2 model due to its lightweight structure and consistent performance. This model produces low embedding distances, low latency, and high throughput during CPU-based inference. The performance outperforms GPU execution in one-by-one image processing. The proposed system offers a practical and efficient face verification solution for deployment in identity authentication applications on resource-constrained platforms. These findings support the development of scalable and adaptive biometric security systems that rely on deep learning.
Manifold Learning and Undersampling Approaches for Imbalanced Class Sentiment Classification Jumansyah, L.M. Risman Dwi; Soleh, Agus Mohamad; Syafitri, Utami Dyah
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a linear kernel without dimensionality reduction. RUS provided balanced but inconsistent results, while Review of Systems (ROS) combined with PCA (85% variance cumulative) improved predictions for negative reviews. Laplacian Eigenmaps were effective for negative reviews with 500 dimensions but less accurate for positive ones. This study highlights EasyEnsemble's superior performance in addressing the class imbalance, though optimization with manifold learning remains challenging.
DETECTION OF ADULTERATION IN COCONUT MILK USING CUCKOO SEARCH-OPTIMIZED XGBOOST ON HIGH-DIMENSIONAL FTIR SPECTRAL DATA I Gusti Ngurah Sentana Putra; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.8376

Abstract

Coconut milk adulteration is an important issue because it can reduce food quality and endanger consumers. This study aims to develop a rapid and accurate detection method for coconut milk adulteration using a combination of FTIR spectroscopy technology and the XGBoost machine learning algorithm optimized with the Cuckoo Search Algorithm (CSA). FTIR spectral data from traditional and instant coconut milk samples were analyzed using Standard Normal Variate (SNV) and Savitzky-Golay (SG) preprocessing to reduce noise and clarify spectral features. The XGBoost model was then optimized through CSA with hyperparameter tuning. The results showed that the combination of SNV+SG preprocessing increased the model accuracy by 84.44%, with a precision of 92.73% and an F1-score of 79.94%. In addition, CSA optimization provided a 19.7% increase in accuracy compared to the model without tuning. These findings prove the effectiveness of the CSA-XGBoost approach in analyzing high-dimensional spectral data and is a potential solution in efficiently detecting the authenticity of coconut milk. In conclusion, this approach has the potential to be widely applied to test the authenticity of other food products quickly, non-destructively and accurately.
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 Abd Rahman Abialam Koesnandy H Ade Ariyo Yudanto Afendi, Farit M Aji Hamim Wigena Alfa Nugraha Pradana Alfa Nugraha Pradana Anadra, Rahmi Anang Kurnia Andespa, Reyuli Andriansyah, . Anik Djuraidah Annisarahmi Nur Aini Aldania Ardhani, Rizky Arif Handoyo Marsuhandi Aris Yaman ASEP SAEFUDDIN Astari, Reka Agustia Baehera, Seta Bagus Sartono Belinda, Nadira Sri Budi Susetyo Cici Suhaeni Cici Suhaeni Daulay, Nurmai Syaroh Dede Dirgahayu Domiri Dede Dirgahayu Domiri Dede Dirgahayu Domiri, Dede Dirgahayu Deri Siswara Devi Andrian Dini Ramadhani Erfiani Erfiani Erfiani Erfiani Etis Sunandi Farit Mochamad Afendi Fitrianto, Anwar Fulazzaky, Tahira Hamim Wigena, Aji Hari Wijayanto Hari Wijayanto Hasnataeni, Yunia Hengki Muradi I Gusti Ngurah Sentana Putra I Gusti Ngurah, Sentana Putra I Made Sumertajaya Indahwati Inria Purwaningsih Jumansyah, L. M. Risman Dwi Jumansyah, L.M. Risman Dwi Karel Fauzan Hakim Khairil Anwar Notodiputro Kusman Sadik Kusnaeni Kusnaeni, Kusnaeni Latifah K. Darusman Leni Anggraini Susanti M. Yunus Mohamad Rafi Mubarak, Fadhlul Muhammad Nur Aidi Muhammad Nuruddin Prathama Muhammad Yusran Muradi, Hengki Naila Nabiha Qonita Nisrina Az-Zahra, Putri Nur Aulia Maknunah Nurhambali, M Rizky Nurizki, Anisa Pika Silvianti Rais Rizki Manaf, Silmi Anisa Rochman, Nur Septian Rahardiantoro Seran, Karlina Setyono Siti Arni Wulandya, Siti Arni Siti Hafsah Suhaeni, Cici Tarida, Arna Ristiyanti Tyas, Maulida Fajrining Uswatun Hasanah Utami Dyah Syafitri Yudistira Yudistira Yumna Karimah _ Aunuddin