SARI, ZENI WULAN (2023) PREDIKSI AWAL PENYAKIT DIABETES MENGGUNAKAN ALGORITMA NAÏVE BAYES. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
ABSTRAK Nama : Zeni Wulan Sari NIM : 41518310015 Program Studi : Teknik Informatika Judul Laporan Skripsi : Prediksi Awal Penyakit Diabetes Menggunakan Algoritma Naive Bayes Pembimbing : Afiyati, S.Si, MT Diabetes melitus merupakan penyakit kronis yang terjadi karena pankreas tidak menghasilkan cukup insulin (hormon yang mengatur gula darah atau glukosa), atau ketika tubuh tidak dapat secara efektif menggunakan insulin yang dihasilkan. Diabetes juga termasuk salah satu penyakit kronis yang mengancam jiwa dengan pertumbuhan tercepat yang telah mempengaruhi 422 juta orang di seluruh dunia menurut laporan Organisasi Kesehatan Dunia (WHO), pada tahun 2018. Untuk mengurangi kesalahan deteksi dan menghindari keterlambatan diagnosis penderita penyakit diabetes dapat dilakukan penerapan dan pemanfaatan teknik machine learning. Machine learning dianggap sebagai salah satu fitur kecerdasan buatan terpenting yang mendukung pengembangan sistem komputer yang memiliki kemampuan untuk memperoleh pengetahuan dari pengalaman masa lalu tanpa perlu pemrograman untuk setiap kasus. Pada penelitian ini, berfokus pada membangun model prediksi menggunakan algoritma machine learning dan teknik data mining untuk prediksi kemungkinan diabetes. Algoritma Naive Bayes diterapkan pada Pima Indians Diabetes Dataset. Setelah hasil penelitian diperoleh, terbukti algoritma Naive Bayes menghasilkan nilai akurasi menghasilkan akurasi sebesar 77%. Dengan akurasi precission sebesar 80% dan recall sebesar 85% pada saat memprediksi kategori ‘0’ (negatif diabetes), dan mendapatkan akurasi precission sebesar 70% dan recall sebesar 62% pada saat memprediksi kategori ‘1’ (positif diabetes). Kata Kunci : Machine Learning, Prediksi, Diabetes, Naive Bayes ABSTRACT Name : Zeni Wulan Sari NIM : 41518310015 Study Program : Informatics Engineering Title Thesis : Early Prediction of Diabetes Using the Naive Bayes Algorithm Counsellor : Afiyati, S.Si, MT Diabetes mellitus is a chronic disease that occurs when the pancreas does not produce enough insulin (a hormone that regulates blood sugar or glucose), or when the body cannot effectively use the insulin it produces. Diabetes is also one of the fastest growing life-threatening chronic diseases that has affected 422 million people worldwide according to a report by the World Health Organization (WHO), in 2018. To reduce detection errors and avoid delays in diagnosing diabetes, diabetes can be implemented and utilization of machine learning techniques. Machine learning is considered one of the most important features of artificial intelligence that supports the development of computer systems that have the ability to derive knowledge from past experiences without the need for programming for each case. In this study, it focuses on building predictive models using machine learning algorithms and data mining techniques to predict the likelihood of diabetes. The Naive Bayes algorithm is applied to Pima Indians Diabetes Dataset. After the research results were obtained, it was proven that the Naive Bayes algorithm produced an accuracy value of 77%. With an accuracy of 80% and a recall of 85% when predicting category '0' (negative diabetes), and obtaining an accuracy of 70% and a recall of 62% when predicting category '1' (positive diabetes). Keywords: Machine Learning, Prediction, Diabetes, Naive Bayes
| Item Type: | Thesis (S1) |
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| Call Number CD: | FIK/INFO 23 005 |
| NIM/NIDN Creators: | 41518310015 |
| Uncontrolled Keywords: | Machine Learning, Prediksi, Diabetes, Naive Bayes |
| 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 22 Sep 2023 05:33 |
| Last Modified: | 22 Sep 2023 05:33 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/81390 |
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