Article Per Year (5 Year)
p-Index From 2021 - 2026
6.455
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 Indonesian Journal of Applied Statistics Informatika Pertanian BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MIND (Multimedia Artificial Intelligent Networking Database) Journal 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 Environmental Science Immunology & microbiology Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

Published : 63 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 63 Documents
Search

 3   4   5   6   7 
Nowcasting Indonesia's GDP Growth Using Machine Learning Algorithms Muchisha, Nadya Dwi; Tamara, Novian; Andriansyah, Andriansyah; Soleh, Agus M
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
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.v5i2p355-368

Abstract

GDP is very important to be monitored in real time because of its usefulness for policy making. We built and compared the ML models to forecast real-time Indonesia's GDP growth. We used 18 variables that consist a number of quarterly macroeconomic and financial market statistics. We have evaluated the performance of six popular ML algorithms, such as Random Forest, LASSO, Ridge, Elastic Net, Neural Networks, and Support Vector Machines, in doing real-time forecast on GDP growth from 2013:Q3 to 2019:Q4 period. We used the RMSE, MAD, and Pearson correlation coefficient as measurements of forecast accuracy. The results showed that the performance of all these models outperformed AR (1) benchmark. The individual model that showed the best performance is random forest. To gain more accurate forecast result, we run forecast combination using equal weighting and lasso regression. The best model was obtained from forecast combination using lasso regression with selected ML models, which are Random Forest, Ridge, Support Vector Machine, and Neural Network.
Bayesian Neural Network untuk Prediksi Diabetes: Uncertainty Quantification dalam Machine Learning Kamila, Sabrina Adnin; Sadik, Kusman; Suhaeni, Cici; Soleh, Agus Mohamad
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.103994

Abstract

Penelitian ini bertujuan mengevaluasi dan membandingkan kinerja tiga model machine learning, yaitu random forest (RF), feedforward neural network (FNN), dan bayesian neural network (BNN), dalam klasifikasi diabetes menggunakan Diabetes Health Indicators Dataset dari UCI Machine Learning Repository yang memiliki ketidakseimbangan kelas. Prapemrosesan data meliputi normalisasi fitur menggunakan StandardScaler dan penanganan ketidakseimbangan kelas dengan synthetic minority over-sampling technique (SMOTE). Evaluasi model dilakukan menggunakan metrik akurasi dan skor F1, yang didukung oleh classification report dan confusion matrix. Hasil evaluasi menunjukkan bahwa RF menghasilkan akurasi tinggi (0,8493) namun skor F1 yang rendah (0,3386), yang mengindikasikan rendahnya sensitivitas model terhadap kasus positif diabetes. FNN memberikan performa yang lebih seimbang dengan skor F1 sebesar 0,4490 setelah penyesuaian threshold optimal. Sementara itu, BNN mencapai akurasi 0,8498 dan skor F1 sebesar 0,4043, serta memiliki keunggulan tambahan berupa kemampuan mengukur ketidakpastian prediksi melalui pendekatan Monte Carlo Dropout. Dengan demikian, FNN lebih unggul dalam keseimbangan klasifikasi, sementara BNN lebih relevan untuk aplikasi medis yang membutuhkan informasi tingkat kepercayaan prediksi guna mendukung pengambilan keputusan klinis yang lebih andal.This study aims to evaluate and compare the performance of three machine learning models, namely random forest (RF), feedforward neural network (FNN), and bayesian neural network (BNN), for diabetes classification using the Diabetes Health Indicators Dataset from the UCI Machine Learning Repository, which exhibits significant class imbalance. Data preprocessing includes feature normalization using StandardScaler and class imbalance handling through synthetic minority over-sampling technique (SMOTE). Model performance is evaluated using accuracy and F1-score metrics, supported by classification report and confusion matrix analysis. The results show that RF achieves high accuracy (0.8493) but a low F1-score (0.3386), indicating poor sensitivity to positive diabetes cases. FNN provides more balanced performance with an F1-score of 0.4490 after optimal threshold adjustment. Meanwhile, BNN achieves an accuracy of 0.8498 and F1-score of 0.4043, while offering the additional advantage of uncertainty quantification through Monte Carlo Dropout. Therefore, FNN is more effective for balanced classification performance, while BNN is more suitable for medical applications that require prediction confidence information to support more reliable and informed clinical decision-making.Kata Kunci: Prediksi diabetes, kuantifikasi ketidakpastian, bayesian neural network, classification imbalance, machine learning.Keywords: Diabetes prediction, uncertainty quantification, bayesian neural network, classification imbalance, machine learning.
Deteksi Polycystic Ovary Syndrome (PCOS) Berbasis Machine Learning: Kombinasi SMOTE, Random Forest, Gradient Boosting, dan Bayesian Optimization Alfiryal, Naufalia; Sadik, Kusman; Suhaeni, Cici; Soleh, Agus Mohamad
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.109931

Abstract

Polycystic ovary syndrome (PCOS) merupakan gangguan endokrin yang umum terjadi pada wanita usia reproduktif. Kondisi ini dapat menyebabkan gangguan ovulasi, ketidakseimbangan hormon, resistensi insulin, serta meningkatkan risiko penyakit kardiovaskular, obesitas, dan gangguan psikologis. Meskipun prevalensinya cukup tinggi, sekitar 75% kasus PCOS masih belum terdiagnosis dalam praktik klinis akibat kompleksitas gejala dan keterbatasan metode diagnosis yang digunakan saat ini. Untuk mengatasi permasalahan tersebut, penelitian ini mengusulkan pendekatan berbasis machine learning guna meningkatkan akurasi dan efisiensi deteksi PCOS. Penelitian ini membandingkan performa dua algoritma pembelajaran terawasi, yaitu random forest dan gradient boosting, dalam melakukan prediksi PCOS. Dataset yang digunakan diperoleh dari repositori publik dan memuat berbagai fitur klinis yang berkaitan dengan PCOS. Untuk menangani permasalahan ketidakseimbangan kelas, metode synthetic minority over-sampling technique (SMOTE) diterapkan pada data pelatihan. Selain itu, bayesian optimization digunakan untuk melakukan penyetelan hiperparameter pada masing-masing model agar diperoleh performa yang optimal. Evaluasi performa model dilakukan menggunakan beberapa metrik, dengan area under the curve–receiver operating characteristic (AUC-ROC) sebagai metrik utama. Hasil penelitian menunjukkan bahwa model Gradient Boosting memberikan performa terbaik dengan nilai AUC sebesar 0,8983 dan nilai recall sebesar 0,95, yang mengindikasikan sensitivitas tinggi dalam mengidentifikasi kasus PCOS. Temuan ini menunjukkan bahwa kombinasi SMOTE dan bayesian optimization efektif dalam meningkatkan akurasi prediksi, khususnya pada dataset medis yang tidak seimbang. Pendekatan yang diusulkan memiliki potensi untuk diintegrasikan ke dalam sistem pendukung keputusan klinis guna mendukung proses skrining PCOS yang lebih dini dan andal.Polycystic ovary syndrome (PCOS) is a common endocrine disorder among reproductive-aged women. This condition can lead to ovulatory dysfunction, hormonal imbalance, insulin resistance, and an increased risk of cardiovascular disease, obesity, and psychological disorders. Despite its high prevalence, approximately 75% of PCOS cases remain undiagnosed in clinical settings due to the complexity of symptoms and limitations of current diagnostic methods. To address this issue, a machine learning-based approach is proposed to improve the accuracy and efficiency of PCOS detection. This study compares the performance of two supervised learning algorithms random forest and gradient boosting for PCOS prediction. The dataset used was obtained from a public repository and contains various clinical features associated with PCOS. To address the class imbalance problem, the synthetic minority over-sampling technique (SMOTE) was applied to the training data. Additionally, bayesian optimization was employed to fine-tune the hyperparameters of each model for optimal performance. Model performance was evaluated using several metrics, with the area under the curve–receiver operating characteristic (AUC-ROC) as the primary measure. The Gradient Boosting model achieved the best results, with an AUC of 0.8983 and a recall of 0.95, indicating high sensitivity in identifying positive PCOS cases. These findings demonstrate that the combination of SMOTE and Bayesian Optimization is effective in enhancing predictive accuracy, especially in imbalanced medical datasets. The proposed approach shows promise for integration into clinical decision-support systems to facilitate earlier and more reliable PCOS screening.Kata Kunci: Bayesian optimization; gradient boosting; PCOS; random forest; SMOTE.Keywords : Bayesian optimization; gradient boosting; PCOS; random forest; SMOTE.
Co-Authors Aam Alamudi Abd Rahman Afendi, Farit M Aji Hamim Wigena Alfa Nugraha Pradana Alfa Nugraha Pradana Alfiryal, Naufalia Anadra, Rahmi Anang Kurnia Andespa, Reyuli Andriansyah Andriansyah 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 Butar-butar, Victor Pandapotan Cici Suhaeni Dalimunthe, Amir Abduljabbar Daulay, Nurmai Syaroh Dede Dirgahayu Domiri Dede Dirgahayu Domiri Dede Dirgahayu Domiri, Dede Dirgahayu Deri Siswara Devi Andrian Dini Ramadhani Erfiani Erfiani Erfiani Etis Sunandi Farit Mochamad Afendi Fauzi, Asep Andri Fitrianto, Anwar Fulazzaky, Tahira Hamim Wigena, Aji Hari Wijayanto Hari Wijayanto Hasnataeni, Yunia Hengki Muradi Herlin Fransiska I Gusti Ngurah, Sentana Putra I Made Sumertajaya Indahwati Iqbal Hanif, Iqbal Jumansyah, L. M. Risman Dwi Kamila, Sabrina Adnin Karel Fauzan Hakim Karimah, Yumna Khairil Anwar Notodiputro Koesnandy H, Abialam Kusman Sadik Kusnaeni Kusnaeni, Kusnaeni Latifah K. Darusman Leni Anggraini Susanti Lutfiah Adisti, Tiara M. Yunus Mohamad Rafi Mubarak, Fadhlul Muchisha, Nadya Dwi Muhammad Nur Aidi Muhammad Nuruddin Prathama Muhammad Yusran Muradi, Hengki Nisrina Az-Zahra, Putri Nofrida Elly Zendrato NURADILLA, SITI Nurhambali, M Rizky Nurizki, Anisa Pika Silvianti Pusparani, Windyana Rahardiantoro, Septian Rais Rakhmalia, Riza Indriani Rizki Manaf, Silmi Anisa Rizki, Akbar Rochman, Nur Sentana Putra, I Gusti Ngurah Seran, Karlina Setyono Siregar, Indra Rivaldi Siti Arni Wulandya, Siti Arni Siti Hafsah Suhaeni, Cici Tamara, Novian Tarida, Arna Ristiyanti Trianjaya, Beny Tyas, Maulida Fajrining Ulfa, Yopi Ariesia Uswatun Hasanah Utami Dyah Syafitri Wigena, Aji H Yanke, Aldino Yudistira Yudistira Yudistira Yudistira Yumna Karimah _ Aunuddin