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All Journal Building of Informatics, Technology and Science Muhammadiyah Journal of Geriatric
Syarifuddin, Faisal
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Health Professions Medicine & Pharmacology Neuroscience Public Health

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Comparison of Osteoarthritis Prevalence in Elderly Women with and Without Obesity Using Body Mass Index (BMI) Calculation at MH Thamrin Cileungsi Hospital for the Period of January – December 2022 Husna, Ihsanil; Setiawan, Dinda Aulia; Syarifuddin, Faisal; Farhan, Fanny Septiani
Muhammadiyah Journal of Geriatric Vol. 5 No. 2 (2024): Muhammadiyah Journal of Geriatric
Publisher : Faculty of Medicine and Health Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/mujg.5.2.68-73

Abstract

Background: Osteoarthritis (OA) is the most common form of arthritis in society throughout the world and is the main cause of disability in older adults. This disease has a slow development time with common main complaints such as pain, swelling and deformity in the affected area. This can result in limited movement and is a major cause of disability. Women are known to be more susceptible to developing OA, while advanced age is also a risk factor for developing OA. Purposes: This research is aiming to know the prevalence of OA in elderly women with and without obesity using the BMI calculation within the period of 2022. Methods: This research using a numerical analytic observation from secondary data of medical records with cross-sectional research design. Result: The study finds from the total of 196 patients, 133(67.9%) of them were obese whereas 63 (32.1%) weren't in obese condition. Conclusion: Therefore, that there was a significant difference between the average number of elderly women with and without obesity.
Analisis Perbandingan Algoritma Naive Bayes dan Support Vector Machine dengan Pendekatan TF-IDF Sebagai Klasifikasi Perintah Suara Syarifuddin, Faisal; Kusumaningsih, Dewi
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7160

Abstract

This study evaluates the performance of two classification algorithms, namely Naive Bayes and Support Vector Machine (SVM), in identifying voice commands in financial applications for the blind. The data used has gone through a preprocessing process including tokenization, stemming, and stopword removal, and was extracted using the TF-IDF method. The models were trained using a data sharing scheme of 80% for training and 20% for testing, then evaluated based on accuracy, precision, recall, and F1-score. The test results show that both models achieve a very high level of accuracy, with Naive Bayes achieving an accuracy of 98.6% and SVM reaching 98.4%. Both show high precision, recall, and F1-score in each voice command category, with the highest value in the "QRIS Payment" category which achieved a precision and recall of 1.00. Confusion matrix analysis shows that classification errors occur in minimal amounts. This study also shows that TF-IDF as a feature extraction technique is effective in improving speech recognition accuracy by giving more weight to relevant and rarely appearing words in the dataset, which helps the model to focus more on the most important information. With these results, both algorithms are proven to be effective in recognizing voice commands. However, Naive Bayes is slightly superior in accuracy, so it is more recommended for voice-based applications in digital financial systems. These findings support the development of more inclusive and accessible technology for the visually impaired.
Co-Authors Dewi Kusumaningsih Farhan, Fanny Septiani Ihsanil Husna Setiawan, Dinda Aulia