scholarly journals Perancangan Sistem Klasifikasi Penyakit Jantung Mengunakan Naïve Bayes

2020 ◽  
Vol 6 (1) ◽  
pp. 75
Author(s):  
Mufti Ari Bianto ◽  
Kusrini Kusrini ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Heart Disease ◽  
Heart Attack ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Average Accuracy ◽  
Classification Of Diseases ◽  
Fold Cross Validation ◽  
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

scholarly journals Comparison of Classification Data Mining C4.5 and Naïve Bayes Algorithms of EDM Dataset

TEM Journal ◽  
2021 ◽  
pp. 1738-1744
Author(s):  
Joseph Teguh Santoso ◽  
Ni Luh Wiwik Sri Rahayu Ginantra ◽  
Muhammad Arifin ◽  
R Riinawati ◽  
Dadang Sudrajat ◽  
...  
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
T Test ◽  
Bayes Method ◽  
Average Accuracy ◽  
Difference Test ◽  
Naive Bayes Method ◽  
Better Than

The purpose of this research is to choose the best method by comparing two classification methods of data mining C4.5 and Naïve Bayes on Educational Data Mining, in which the data used is student graduation data consisting of 79 records. Both methods are tested for validation with 10-ford X Validation and perform a T-Test difference test to produce a table that contains the best method ranking. Different results were obtained for each method. Based on the results of these two methods, it is very influential on the dataset and the value of the area under curve in the Naïve Bayes method is better than the C4.5 method in various datasets. Comparison of the method with the 10-Ford X Validation test and the T-Test difference test is that the Naïve Bayes method is better than C4.5 with an average accuracy value of 73.41% and an under-curve area of 0.664.

scholarly journals Discretization for Naive Bayes taking the specifics of heart data into account

2019 ◽  
Vol 43 (1) ◽  
pp. 1-14
Author(s):  
Jan Bohacik ◽  
Michal Zabovsky
Keyword(s):  
Heart Disease ◽  
Decision Support ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Patient Records ◽  
Equal Frequency ◽  
Discretization Algorithm ◽  
Fold Cross Validation

At the present time heart disease is a major cause of death. Factors such as physical inactiveness, obesity, diabetes, social isolation and aging are expected to make the situation worse. It is worsened even further with misdiagnosis of patients describing heart related issues. A probability decision support approach to diagnosis of heart disease based on Naive Bayes is discussed here as most hospitals collect patient records but these are rarely used for automatic decision support. The approach is analyzed on Statlog heart data with the focus on improving preprocessing methods. As the result, a discretization algorithm with Equal Frequency Discretization which considers the specifics of engaged heart disease patients is presented. Enhancements of achieved accuracy with the added discretization and in comparison with other machine learning algorithms are shown in experiments founded on 10-fold cross-validation.

scholarly journals User Loyalty Prediction Using Naive Bayes Method in "Udatari" an art Performance Marketplace

2021 ◽  
Vol 14 (1) ◽  
pp. 60
Author(s):  
Ngurah Agus Sanjaya ER ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
K Value ◽  
Customer Lifetime ◽  
Average Accuracy ◽  
Silhouette Index ◽  
The Right ◽  
Past Experiences ◽  
Naive Bayes Method

Udatari is the first traditional dance platform in Indonesia which provides information about traditional events such as, dance tutorials, group dancer and dance attributes. The tight competition in the startup world, requires Udatari as a new startup to manage application users optimally. Knowing loyal users will help startups determine the right marketing strategy. In this study, the method used for clustering is the K-Means method where this method seeks to classify existing data into several groups provided that the data in one group have the same characteristics as each other. The model used for the clustering process is RFM, namely recency, frequency and monetary. The purpose of this clustering is to get the segmentation of users who have different Customer Lifetime Value. The second method for conducting classification is the Naïve Bayes method, where this method predicts future opportunities based on past experiences. The purpose of this classification is to predict new users into the user segmentation obtained from the clustering results. From the results of this study, the optimum k value for K-Means are 3 clusters with the largest CLV value in the second cluster where testing on this method uses the Silhouette Index. Furthermore, for the test results of the Naïve Bayes method, the average accuracy value is 97.44% where the accuracy of each class is 92.31% for cluster 0 (first cluster), 100% for the second cluster and 100% for the third cluster. Keywords: K-Means, Naïve Bayes, Loyalty, Segmentation, RFM

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

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

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%.

scholarly journals Sistem Informasi Posyandu Ibu Hamil dengan Penerapan Klasifikasi Resiko Kehamilan Menggunakan Metode Naïve Bayes

2018 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Qomariyatul Hasanah ◽  
Anang Andrianto ◽  
Muhammad Arief Hidayat
Keyword(s):  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Set ◽  
Test Set ◽  
Fold Cross Validation

Sistem informasi posyandu ibu hamil dapat mengelola data kesehatan ibu hamil yang berkaitan dengan faktor resiko kehamilan. Faktor resiko kehamilan berdasarkan ketentuan Kartu Skor Poedji Rochyati (KSPR) digunakan bidan untuk menentukan resiko kehamilan dengan memberikan skor pada masing-masing parameter. KSPR memiliki kelemahan tidak dapat memberikan skor pada parameter yang belum pasti sehingga jika belum diketahui dengan pasti maka dianggap tidak terjadi. Konsep membaca pola data yang diadopsi dari teknik datamining menggunakan metode klasifikasi naive bayes dapat menjadi alternatif untuk kelemahan KSPR tersebut yaitu dengan mengklasifikasikan resiko kehamilan. Metode naïve bayes menghitung probabilitas parameter tertentu berdasarkan data pada periode sebelumnya yang telah ditentukan sebagai data training, berdasarkan hasil perhitungan tersebut dapat diketahui resiko kehamilan secara tepat sesuai parameter yang telah diketahui. Metode naïve bayes dipilih karena memiliki tingkat akurasi yang cukup tinggi daripada metode klasifikasi lainnya. Sistem informasi ini dibangun berbasis website agar dapat diakses secara mudah oleh beberapa posyandu yang berbeda tempat. Sistem dibangun mengadopsi dari model Waterfall. Sistem informasi posyandu ibu hamil dirancang dan dibangun dengan tiga (3) hak akses yaitu admin, bidan dan kader dengan masing-masing fitur yang dapat memudahkan penggunanya. Hasil dari penelitian ini adalah sistem informasi posyandu ibu hamil dengan penerapan klasifikasi resiko kehamilan menggunakan metode naïve bayes, dengan tingkat akurasi ketika menggunakan 17 atribut didapatkan 53.913%, 19 atribut didapatkan 54.348%, , 21 atribut didapatkan 54.783%, dan 22 atribut didapatkan 56.957%. Tingkat akurasi klasifikasi diperoleh menggunakan metode pengujian menggunakan Ten-Fold Cross Validation dimana training set dibagi menjadi 10 kelompok, jika kelompok 1 dijadikan test set maka kelompok 2 hingga 10 menjadi training set. Kata Kunci: Posyandu, Resiko Kehamilan, Waterfall, Datamining, Klasifikasi, Naïve bayes

scholarly journals Estratégias de Construções de Comitês de Classificadores Multirrótulos no Aprendizado Semissupervisionado Multidescrição

2017 ◽  
Vol 24 (2) ◽  
pp. 71
Author(s):  
Wilamis Kleiton Nunes Da Silva ◽  
Araken De Medeiros Santos
Keyword(s):  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Binary Relevance ◽  
Fold Cross Validation

Classificação multirrótulo é um problema de aprendizado supervisionado no qual um objeto pode estar associado a múltiplas classes. Dentre os diferentes métodos de classificação multirrótulo destacam-se os métodos BR (Binary Relevance), LP (Label Powerset) e RAkEL (RAndom k-labELsets). O trabalho realizou um estudo sobre as construções de comitês de classificadores multirrótulos construídos através da aplicação de técnicas de aprendizado semissupervisionado multidescrição. Os comitês de classificadores utilizados nos experimentos foram o Bagging, Boosting e Stacking; como métodos de transformação do problema utilizamos os métodos BR, LP e Rakel; na classificação multirrótulo semissupervisionada multidescrição foi utilizado o Co-Training; foram aplicados cinco diferentes algoritmos como classificadores base: k-NN (k Vizinhos Mais Próximos), J48 (Algoritmo de Indução de Árvores de Decisão), SVM (Máquinas de Vetores Suporte), NB (Naive Bayes) e o JRip (Extended Repeated Incremental Pruning). Todos os experimentos utilizaram a metodologia de validação cruzada com 10 grupos (10-fold Cross-Validation) e o framework MULAN, o qual é implementado utilizando o WEKA. Para os tamanhos dos comitês de classificadores adotamos os valores 3, 5, 7 e 9. Para a análise dos resultados foi utilizado o teste esta- tístico de Wilcoxon. Ao final das análises experimentais, verificou-se que a abordagem semissupervisionado apresentou resultados competitivos em relação ao aprendizado supervisionado, uma vez que as duas abordagens utilizadas apresentaram resultados estatisticamente semelhantes.   

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

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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