BILLAL, ACHMAD (2025) ANALISA FAKTOR RISIKO PENYAKIT DIABETES MELITUS MENGGUNAKAN MODEL ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES. S1 thesis, Universitas Mercu Buana.
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Abstract
Diabetes Mellitus (DM) is a chronic disease that continues to increase in prevalence worldwide. Early detection and identification of risk factors that contribute to DM are important in efforts to prevent and manage this disease. Along with the development of information technology, the use of machine learning has become an effective approach in analyzing health data. This study aims to analyze and compare the performance of two classification algorithms, namely Support Vector Machine (SVM) and Naïve Bayes, in predicting the risk of Diabetes Mellitus based on factors such as age, blood pressure, glucose levels, BMI, insulin, skin thickness, pregnancy, and family history. The dataset used was obtained from the Kaggle platform, consisting of 769 patient data. The research process includes data pre-processing stages, implementation of both algorithms, and evaluation of model performance using accuracy metrics. The results of the study showed that the SVM algorithm had a higher prediction accuracy compared to Naïve Bayes in classifying DM risk. These findings can be used as a reference in the development of decision support systems in the medical field and contribute to data-based health technology innovation. Keywords: Diabetes Melitus, Support Vector Machine, Naïve Bayes, Machine Learning, Prediksi Risiko. Penyakit Diabetes Melitus (DM) merupakan salah satu penyakit kronis yang terus meningkat prevalensinya di seluruh dunia. Deteksi dini dan identifikasi faktor risiko yang berkontribusi terhadap DM menjadi hal penting dalam upaya pencegahan dan pengelolaan penyakit ini. Seiring berkembangnya teknologi informasi, pemanfaatan machine learning menjadi pendekatan yang efektif dalam menganalisis data kesehatan. Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja dua algoritma klasifikasi, yaitu Support Vector Machine (SVM) dan Naïve Bayes, dalam memprediksi risiko Diabetes Melitus berdasarkan faktor-faktor seperti usia, tekanan darah, kadar glukosa, BMI, insulin, ketebalan kulit, kehamilan, dan riwayat keluarga. Dataset yang digunakan diperoleh dari platform Kaggle, terdiri dari 769 data pasien. Proses penelitian meliputi tahapan pra-pemrosesan data, implementasi kedua algoritma, dan evaluasi kinerja model menggunakan metrik akurasi. Hasil penelitian menunjukkan bahwa algoritma SVM memiliki akurasi prediksi yang lebih tinggi dibandingkan dengan Naïve Bayes dalam mengklasifikasikan risiko DM. Temuan ini dapat dijadikan sebagai referensi dalam pengembangan sistem pendukung keputusan di bidang medis serta memberikan kontribusi dalam inovasi teknologi kesehatan berbasis data. Kata kunci: Diabetes Melitus, Support Vector Machine, Naïve Bayes, Machine Learning, Prediksi Risiko.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 25 179 |
| NIM/NIDN Creators: | 41521010072 |
| Uncontrolled Keywords: | Diabetes Melitus, Support Vector Machine, Naïve Bayes, Machine Learning, Prediksi Risiko |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 600 Technology/Teknologi > 610 Medical, Medicine, and Health Sciences/Ilmu Kedokteran, Ilmu Pengobatan dan Ilmu Kesehatan |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Pandu Risdiyanto |
| Date Deposited: | 26 Sep 2025 01:17 |
| Last Modified: | 26 Sep 2025 01:17 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/98287 |
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