Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method

Serangan Jantung adalah salah satu penyakit yang paling mematikan tercatat di dunia, terdapat jumlah kasus baru Penyakit Jantung sebanyak 43,32% serta jumlah kematian sebanyak 12,91%. Pada tahun 2013 jumlah penderita Penyakit Jantung di Indonesaia sejumlah 61.682 orang, pada umumnya jumlah penderita penyakit ini terus meningkat dikarenakan kurangnya pengetahuan atau informasi tentang penyakit jantung tersebut, oleh karena itu dibutuhkan sebuah sistem yang dapat memberikan informasi serta klasifikasi penyakit secara dini yang dapat digunakan untuk klasifikasi apabila seseorang ingin mengetahui informasi ataupun gejala awal serangan jantung. Metode naïve bayes merupakan salah satu metode yang digunakan untuk melakukan klasifikasi berdasarkan probabilitas atau kemungkinan dari data sebelumnya, selain pendekatannya sederhana metode tersebut juga dapat melakukan klasifikasi secara baik. Mekanisme pengujiannya yaitu membagi 303 data kedalam 5 subset yang akan divalidasi dengan 5-fold cross validation. Hasil akhir dari penelitian ini adalah penerapan sistem klasifikasi dengan menggunakan metode naïve bayes yang akan menghasilkan nilai rata-rata akurasi sebesar 90,61%, presisi sebesar 87,44 %, dan recall sebesar 87,95%. Kata Kunci — klasifikasi, penyakit jantung, naïve bayesClassifier Heart attack is one of the most deadly diseases recorded in the world, there are a number of new cases of heart disease as much as 43.32% and the number of deaths as much as 12.91%. In 2013 the number of sufferers of heart disease in Indonesia amounted to 61,682 people, in general the number of sufferers of this disease continues to increase due to lack of knowledge or information about heart disease, therefore we need a system that can provide information and classification of diseases early that can be used for classification if someone wants to find out information or early symptoms of a heart attack. Naïve Bayes method is one of the methods used to classify based on the probability or likelihood of previous data, in addition to a simple approach the method can also do a good classification. The testing mechanism is to divide 303 data into 5 subsets that will be validated by 5-fold cross validation. The final result of this study is the application of the classification system using the Naïve Bayes method which will produce an average accuracy value of 90.61%, a precision of 87.44%, and a recall of 87.95%. Keywords — classification, heart disease, naïve bayes

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Forensic Malware Identification Using Naive Bayes Method

2020 International Conference on Information Technology Systems and Innovation (ICITSI) ◽

10.1109/icitsi50517.2020.9264959 ◽

2020 ◽

Author(s):

Beno Ramadhan ◽

Yudha Purwanto ◽

Muhammad Faris Ruriawan

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Naive Bayes Method

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PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)

SINTECH (Science and Information Technology) Journal ◽

10.31598/sintechjournal.v3i1.523 ◽

2020 ◽

Vol 3 (1) ◽

pp. 22-34

Author(s):

Komang Aditya Pratama ◽

Gede Aditra Pradnyana ◽

I Ketut Resika Arthana

Keyword(s):

Naive Bayes ◽

Intelligent System ◽

Naïve Bayes ◽

The State ◽

State University ◽

Bayes Method ◽

New Students ◽

Computer Based ◽

Entrance Test ◽

Naive Bayes Method

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.

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OPTIMASI METODE NAIVE BAYES DENGAN FEATURE SELECTION INFORMATION GAIN UNTUK PREDIKSI KETERLAMBATAN PEMBAYARAN SPP SEKOLAH

Jurnal Ilmiah SINUS ◽

10.30646/sinus.v17i1.378 ◽

2019 ◽

Vol 17 (1) ◽

pp. 1

Author(s):

Muqorobin Muqorobin ◽

Kusrini Kusrini ◽

Emha Taufiq Luthfi

Keyword(s):

Feature Selection ◽

Naive Bayes ◽

Information Gain ◽

Confusion Matrix ◽

Education Institution ◽

Naïve Bayes ◽

Bayes Method ◽

Education Development ◽

Main Requirement ◽

Naive Bayes Method

The cost of education is one component of input that is very important in implementing education. Because costs are the main requirement in an effort to achieve educational goals. SMK Al-Islam Surakarta is a private education institution that requires students to pay school fees in the form of Education Development Donations. Educational Development Donation is a routine school fee that is conducted every month. Based on last year's TU report, many students were late in paying Education Development Donations, around 60%. This is a big problem. The purpose of this study is that researchers will build a predictive system using the Naïve Bayes method. Because the method can classify the class right or late, in the payment of school fees. Data processing was taken from the dapodik data of schools in 2017/2018 with the test dataset taking 30 records. To find out the level of accuracy, this research was conducted with the Naive Bayes Method and the Information Gain Method for feature selection. Accuracy testing is done by the Confusion Matrix method. The results showed that the highest accuracy was obtained by combining the Naive Bayes Method with the Information Gain Method obtained by 90% accuracy.

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