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All Journal agriTECH Indonesian Food and Nutrition Progress Jurnal Teknologi Pertanian (Agricultural Technology Journal) AGRITEKNO, Jurnal Teknologi Pertanian Jurnal Tekinkom (Teknik Informasi dan Komputer) Community Education Engagement Journal Journal of Applied Agricultural Science and Technology Jurnal Algoritma JUDIKAT: Jurnal Pengabdian Kepada Masyarakat The Indonesian Journal of Computer Science
Rini Yanti, Rini
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : Jurnal Algoritma

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Optimasi Algoritma Knn Menggunakan Smote Untuk Prediksi Stroke Khairi, Zuriatul; Yanti, Rini; Fitri, Triyani Arita; Fatdha, Eiva
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2474

Abstract

Stroke is a disease with a high mortality and disability rate, especially in Indonesia. Early detection of stroke risk is important to prevent serious consequences. This study examines the distribution of stroke cases based on age groups and evaluates the performance of the K-Nearest Neighbors (KNN) algorithm on imbalanced data and after applying the Synthetic Minority Oversampling Technique (SMOTE). The analysis uses two data division scenarios: 80:20 and 70:30 between training and test data. The results show that the risk of stroke increases with age. No cases were found in the 20–30 age group, cases began to appear in the 30–40 age group, and increased sharply above the age of 50. KNN without SMOTE had an accuracy of 95% (80:20) and 94% (70:30), but low recall, 0.04 and f1-score 0.07 (80:20), and recall 0.03 and f1-score 0.05 (70:30). After SMOTE, recall increased to 0.36 and f1-score 0.21 (80:20), and recall 0.28 and f1-score 0.17 (70:30). Accuracy decreased to 86% in both ratios, but recall and f1-score increased, indicating that the model was more sensitive to stroke cases. Overall, SMOTE effectively reduces majority class bias and helps the model recognize overlooked stroke patterns. However, sensitivity still needs to be improved through parameter tuning, selection of relevant features, or alternative algorithms to enhance prediction reliability.
Optimasi Klasifikasi Tingkat Obesitas Pada Remaja Berdasarkan Pola Hidup Menggunakan SVM Dengan Teknik Smote Setiawan, Andri; Yanti, Rini; Ali, Edwar; Yenni, Helda
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2509

Abstract

Obesity is a condition caused by an imbalance between energy intake and expenditure, characterized by excessive fat accumulation in the body. Obesity is influenced by four factors, namely genetics, economics, lack of activity, and diet. The purpose of this study is to analyze the effectiveness of the SMOTE method in improving the accuracy of classification in the Support Vector Machine method and to compare the accuracy of the Support Vector Machine method with the SMOTE and non-SMOTE techniques on adolescent obesity data. The dataset used was obtained from the Kaggle website, which contained 2,111 records. The model evaluation used a confusion matrix with accuracy, precision, recall, and F1-score measurements and used 80:20 data splitting. The results showed that the SVM model using Smote performed well with an accuracy of 88% for Linear SVM, 82% for RBF SVM, and 93% for Polynomial SVM, while the SVM model without Smote achieved an accuracy of 88% for Linear SVM, 79% for RBF SVM, and 91% for Polynomial SVM. The best classification model was then implemented into a Streamlit-based web application to facilitate the process of automatically predicting obesity levels, thereby helping to raise awareness of the potential risks of obesity.
Co-Authors Aini, Novia Nur Andri Setiawan Azhari, Zahra Basarah, Budiman Cahyanto, Muhammad N Chusnul Hidayat Dewita, Isma Dian Anggraini Suroto Dwi Haryono Edwar Ali Efrizoni, Lusiana Endang Sutriswati Rahayu Erlinda, Susi Fatdha, Eiva Fibri, Dwi Larasatie Nur Fidelima, Aviva Gunadi Hamdani Hamdani Hasan, Pratama Nur Helda Yenni, Helda Hidrotunnisa, Hidrotunnisa Khafsah, Fatya N Malya, Vivi Triani Manikharda, Manikharda Manurung, Nancy Eka Putri Mohammad Juffrie, Mohammad Muhammad Nur Cahyanto Muhammad Zulkifli, Muhammad Nurdiawati, Hermina Putri, Yuniar Wika Perdana Rahayu, Endang Safira, Tsaania Miftakhul Sakrora, Iqra Lugan Saputra, Wahyu Dwi T Sy Eiva Fatdha, T Sy Eiva Tanuwijaya, Shania Angeline Triwitono, Priyanto Triyani Arita Fitri Tyas Utami Wirta Agustin Wulandari, Pudji Yoyon Efendi Yudi Pranoto Zuriatul Khairi