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All Journal Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter Jurnal Kesehatan Masyarakat Jurnal Manajemen Indonesia Jurnal Riset Informatika JURNAL SeMaRaK Jurnal Ilmiah Wahana Pendidikan Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal DIALEKTIKA : Jurnal Ilmu Sosial Jurnal Pengabdian Masyarakat Tjut Nyak Dhien Chimica Didactica Acta : Journal of Chemistry and Chemistry Education Cyber and Education Research Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) JPNM : Jurnal Pustaka Nusantara Multidisiplin OKTAL : Jurnal Ilmu Komputer dan Sains Triwikrama: Jurnal Ilmu Sosial Jurnal Sains Student Research ANDREW Law Journal Al-Zayn: Jurnal Ilmu Sosial & Hukum Jurnal Media Akademik (JMA) Jurnal Ekonomi dan Pembangunan Indonesia Jurnal Penelitian Progresif (JPP) Tamadduna: Jurnal Peradaban Jurnal Riset Multidisiplin Edukasi IPSSJ Sinergi : Jurnal Ilmiah Multidisiplin Cendekia: Jurnal Pengembangan Kurikulum dan Pendidikan Journal of Family and Sharia
Ilham Maulana
Unknown Affiliation
Religion Aerospace Engineering Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Public Health Social Sciences Other

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Journal : Jurnal Riset Informatika

 1 
Machine Learning for Stroke Prediction: Evaluating the Effectiveness of Data Balancing Approaches Muhamad Indra; Siti Ernawati; Ilham Maulana
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i4.344

Abstract

Stroke occurs due to disrupted blood flow to the brain, either from a blood clot (ischemic) or a ruptured blood vessel (hemorrhagic), leading to brain tissue damage and neurological dysfunction. It remains a leading cause of death and disability worldwide, making early prediction crucial for timely intervention. This study evaluates the impact of data balancing techniques on stroke prediction performance across different machine learning models. Random Forest (RF) consistently achieves the highest accuracy (98%) but struggles with precision and recall variations depending on the balancing method. Decision Tree (DT) and K-Nearest Neighbors (KNN) benefit most from SMOTE and SMOTETomek, improving their F1-scores (11.21% and 9.18%), indicating better balance between precision and recall. Random Under Sampling enhances recall across all models but reduces precision, leading to lower overall predictive reliability. SMOTE and SMOTETomek emerge as the most effective balancing techniques, particularly for DT and KNN, while RF remains the most accurate but requires further optimization to improve precision and recall balance.
Enhancing Obesity Risk Classification: Tackling Data Imbalance with SMOTE and Deep Learning Muhammad Syofian; Ilham Maulana
Jurnal Riset Informatika Vol. 6 No. 4 (2024): September 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3011.529 KB) | DOI: 10.34288/jri.v6i4.349

Abstract

Data imbalance is a significant challenge in classification models, often leading to suboptimal performance, especially for minority classes. This study explores the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in improving classification model performance by balancing data distribution. The evaluation was conducted using a confusion matrix to measure prediction accuracy for each class. The results indicate that SMOTE successfully enhances minority class representation and improves prediction balance, although some misclassifications remain. Therefore, in addition to oversampling, additional approaches such as class weighting or ensemble learning are required to further improve model accuracy. This study provides deeper insights into the role of SMOTE in addressing data imbalance and its impact on classification model performance.
Co-Authors . Elwina . Wusnah . Yunardi A Halim Aan Anwar Sihabudin Abda Abda Abdul Rohim, Abdul Adha Diva Anugrah Adityawarman Adityawarman Agus Nurulsyam Aldi Akbar Maulana Amalia Shalihat Amanda Amanda Andry Alamsyah Aqif Abi Aufa Ardo Nurbayana F Arie Budiawan Aryn Nabiel Budiono Asep Sobarna Atria Ghustira Ayu Rahmi Ayu Rahmi Azizah Tri Aulia Danang Nugroho Delviza Syari Destry Ardillah Dewi Herlina Sugiarti Dwi Nur Cahyani EKA SAFITRI Eka Safitri Fahroza Bintang Sumarno Fendi Setiawan Firman Floranta Adonara Gaol, Selamat Lumban Gilang Akbar M Ii Sujai Ika Dewi Nurmayani Imam Muhtarom Irfan Erfiana KHAIRI SUHUD Khairi Suhud Kurniasih Lasmauli Noverita Lelifajri Lelifajri M Labib Husein M.Afdal S Marlinda Marlinda Moh Isyraq Asyrafil A Muh Fanny Chamdani Muhamad Indra Muhammad Rajendra Muhammad Syofian Muhammad Umamuddin NAZARUDDIN NAZARUDDIN Nazaruddin Nazaruddin Novi Sylvia Octavia Octavia Okada Marscella Rahmadi Rahmadi Resa Aulia Ananda Rika Sri Endah Rinaldi Idroes Rio Saputra Sam Cay Samsudin Siswi Jayanti Siti Ernawati Supriatno Supriatno Suroto suroto Wahidullah Wahyu Ananta W Yazid Bindar Yono Cahyono Yusrizal Yusrizal