scholarly journals Performance of Naïve Bayes, C4.5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets

2020 ◽  
Vol 9 (3) ◽  
pp. 2193-2197
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Specific Pattern ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Digital Format ◽  
Tree Classifier

Data mining usually specifies the discovery of specific pattern or analysis of data from a large dataset. Classification is one of an efficient data mining technique, in which class the data are classified are already predefined using the existing datasets. The classification of medical records in terms of its symptoms using computerized method and storing the predicted information in the digital format is of great importance in the diagnosis of various diseases in the medical field. In this paper, finding the algorithm with highest accuracy range is concentrated so that a cost-effective algorithm can be found. Here the data mining classification algorithms are compared with their accuracy of finding exact data according to the diagnosis report and their execution rate to identify how fast the records are classified. The classification technique based algorithms used in this study are the Naive Bayes Classifier, the C4.5 tree classifier and the K-Nearest Neighbor (KNN) to predict which algorithm is the best suited for classifying any kind of medical dataset. Here the datasets such as Breast Cancer, Iris and Hypothyroid are used to predict which of the three algorithms is suitable for classifying the datasets with highest accuracy of finding the records of patients with the particular health problems. The experimental results represented in the form of table and graph shows the performance and the importance of Naïve Bayes, C4.5 and K-Nearest Neighbor algorithms. From the performance outcome of the three algorithms the C4.5 algorithm is a lot better than the Naïve Bayes and the K-Nearest Neighbor algorithm.

scholarly journals Accuracy Analysis of K-Nearest Neighbor and Naïve Bayes Algorithm in the Diagnosis of Breast Cancer

2020 ◽  
Vol 12 (4) ◽  
pp. 151-159
Author(s):  
Irma Handayani ◽  
Ikrimach Ikrimach
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Type ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbor ◽  
Cancer Dataset ◽  
Bayes Algorithm

In the medical field, there are many records of disease sufferers, one of which is data on breast cancer. An extraction process to fine information in previously unknown data is known as data mining. Data mining uses pattern recognition techniques such as statistics and mathematics to find patterns from old data or cases. One of the main roles of data mining is classification. In the classification dataset, there is one objective attribute or it can be called the label attribute. This attribute will be searched from new data on the basis of other attributes in the past. The number of attributes can affect the performance of an algorithm. This results in if the classification process is inaccurate, the researcher needs to double-check at each previous stage to look for errors. The best algorithm for one data type is not necessarily good for another data type. For this reason, the K-Nearest Neighbor and Naïve Bayes algorithms will be used as a solution to this problem. The research method used was to prepare data from the breast cancer dataset, conduct training and test the data, then perform a comparative analysis. The research target is to produce the best algorithm in classifying breast cancer, so that patients with existing parameters can be predicted which ones are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that it can be detected earlier and is expected to reduce the mortality rate from breast cancer. By making comparisons, this method produces 95.79% for K-Nearest Neighbor and 93.39% for Naïve Bayes

scholarly journals PREDIKSI TINGKAT KELULUSAN TEPAT WAKTU DENGAN METODE NAÏVE BAYES DAN K-NEAREST NEIGHBOR

2019 ◽  
Vol 7 (1) ◽  
pp. 7-16
Author(s):  
Sidik Rahmatullah
Keyword(s):  
Data Mining ◽  
Human Capital ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Soft Skills ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Hard Skills

 Lulusan adalah status yang dicapai mahasiswa setelah menyelesaikan proses pendidikan sesuai dengan persyaratan kelulusan yang ditetapkan oleh program studi. Sebagai salah satu keluaran langsung dari proses pendidikan yang dilakukan oleh program studi, lulusan yang bermutu memiliki ciri penguasaan kompetensi akademik termasuk hard skills dan soft skills sebagaimana dinyatakan dalam sasaran mutu serta dibuktikan dengan kinerja lulusan di masyarakat sesuai dengan profesi dan bidang ilmu. Program studi yang bermutu memiliki sistem pengelolaan lulusan yang baik sehingga mampu menjadikannya sebagai human capital bagi progam studi yang bersangkutan.  Penelitian ini menggunakan metode data mining yang digunakan untuk memprediksi tingkat kelulusan mahasiswa menggunakan dua metode yaitu Naive Bayes dan K-Nearest Neighbor. Hasil dari penelitian ini dapat memprediksi mahasiswa tepat lulus atau terlambat. Uji coba dilakukan dengan menggunakan data lulusan mahasiswa S1 Sistem informasi STMIK Dian Cipta Cendikia Kotabumi  sebanyak 600 data untuk training dan 180 data untuk testing. Hasil uji coba menunjukkan bahwa dengan menggunakan Naive Bayes menghasilkan akurasi  sebesar 85%, sedangkan menggunakan algoritma K-nearest neighbor menghasilkan akurasi sebesar 68.89 %.

scholarly journals Comparative analysis on bayesian classification for breast cancer problem

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Wan Nor Liyana Wan Hassan Ibeni ◽  
Mohd Zaki Mohd Salikon ◽  
Aida Mustapha ◽  
Saiful Adli Daud ◽  
Mohd Najib Mohd Salleh
Keyword(s):  
Breast Cancer ◽  
Bayesian Networks ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Likelihood Estimation ◽  
Predictive Distribution ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor

The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.

scholarly journals Analisis Komparatif Evaluasi Performa Algoritma Klasifikasi pada Readmisi Pasien Diabetes

2016 ◽  
Vol 7 (4) ◽  
Author(s):  
Mochammad Yusa ◽  
Ema Utami ◽  
Emha T. Luthfi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Kappa Statistic ◽  
Naïve Bayes ◽  
Validation Dataset ◽  
K Nearest Neighbor ◽  
Fold Cross Validation

Abstract. Readmission is associated with quality measures on patients in hospitals. Different attributes related to diabetic patients such as medication, ethnicity, race, lifestyle, age, and others result in the calculation of quality care that tends to be complicated. Classification techniques of data mining can solve this problem. In this paper, the evaluation on three different classifiers, i.e. Decision Tree, k-Nearest Neighbor (k-NN), dan Naive Bayes with various settingparameter, is developed by using 10-Fold Cross Validation technique. The targets of parameter performance evaluated is based on term of Accuracy, Mean Absolute Error (MAE), dan Kappa Statistic. The selected dataset consists of 47 attributes and 49.735 records. The result shows that k-NN classifier with k=100 has a better performance in term of accuracy and Kappa Statistic, but Naive Bayes outperforms in term of MAE among other classifiers. Keywords: k-NN, naive bayes, diabetes, readmissionAbstrak. Proses Readmisi dikaitkan dengan perhitungan kualitas penanganan pasien di rumah sakit. Perbedaan atribut-atribut yang berhubungan dengan pasien diabetes proses medikasi, etnis, ras, gaya hidup, umur, dan lain-lain, mengakibatkan perhitungan kualitas cenderung rumit. Teknik klasifikasi data mining dapat menjadi solusi dalam perhitungan kualitas ini. Teknik klasifikasi merupakan salah satu teknik data mining yang perkembangannya cukup signifikan. Di dalam penelitian ini, model algoritma klasifikasi Decision Tree, k-Nearest Neighbor (k-NN), dan Naive Bayes dengan berbagai parameter setting akan dievaluasi performanya berdasarkan nilai performa Accuracy, Mean AbsoluteError (MAE), dan Kappa Statistik dengan metode 10-Fold Cross Validation. Dataset yang dievaluasi memiliki 47 atribut dengan 49.735 records. Hasil penelitian menunjukan bahwa performa accuracy, MAE, dan Kappa Statistik terbaik didapatkan dari Model Algoritma Naive Bayes.Kata Kunci: k-NN, naive bayes, diabetes, readmisi

scholarly journals Analisis Perbandingan Kinerja Algoritma Naïve Bayes, Decision Tree-J48 dan Lazy-IBK

2021 ◽  
Vol 5 (3) ◽  
pp. 1038
Author(s):  
Indra Rukmana ◽  
Arvin Rasheda ◽  
Faiz Fathulhuda ◽  
Muh Rizky Cahyadi ◽  
Fitriyani Fitriyani
Keyword(s):  
Breast Cancer ◽  
Decision Tree ◽  
Thoracic Surgery ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Breast Cancer Dataset ◽  
Decision Tree Algorithm ◽  
K Nearest Neighbor ◽  
Cancer Dataset

This research is focused on knowing the performance of the classification algorithms, namely Naïve Bayes, Decision Tree-J48 and K-Nearest Neighbor. The speed and the percentage of accuracy in this study are the benchmarks for the performance of the algorithm. This study uses the Breast Cancer and Thoracic Surgery dataset, which is downloaded on the UCI Machine Learning Repository website. Using the help of Weka software Version 3.8.5 to find out the classification algorithm testing. The results show that the J-48 Decision Tree algorithm has the best accuracy, namely 75.6% in the cross-validation test mode for the Breast Cancer dataset and 84.5% for the Thoracic Surgery dataset.

scholarly journals Sistem Prediksi Penyakit Kanker Serviks Menggunakan CART, Naive Bayes, dan k-NN

2018 ◽  
Vol 4 (2) ◽  
pp. 83
Author(s):  
Tutus Praningki ◽  
Indra Budi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Pap Smear ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Regression Trees ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Classification And Regression

Tersedianya data histori rekam medis pasien kanker serviks pada institusi pelayanan kesehatan, tidak disertai dengan proses ekstraksi menjadi sebuah pengetahuan atau informasi. Penggunaan teknik data mining sangat berpotensi untuk diimplementasikan kedalam sistem yang dapat melakukan prediksi penyakit kanker serviks. Pada penelitian ini berfokus pada dataset diagnosa medis pasien yang akan melakukan tes Pap Smear. Algoritma yang digunakan untuk melakukan klasifikasi penyakit kanker serviks adalah Classification And Regression Trees (CART), Naive Bayes, dan k-Nearest Neighbor (k-NN). Pengujian yang dilakukan terhadap algoritma CART Decision Tree, Naive Bayes, dan k-NN, menggunakan formula Confusion Matrix, dengan menggunakan teknik pemecahan dataset Holdout. Hasil pengujian terhadap algoritma yang digunakan, menunjukkan algoritma Naive Bayes memiliki akurasi terbaik sebesar 94,44%, sedangkan tingkat akurasi yang dihasilkan algoritma CART dan k-NN adalah 88,89%, 85,04%. Performa yang didapatkan oleh masing-masing algoritma yang digunakan, memungkinkan penggunaan sistem prediksi penyakit kanker serviks untuk mendukung keputusan klinis pada pasien baru. 

scholarly journals PENERAPAN DATA MINING TERHADAP DATA COVID-19 MENGGUNAKAN ALGORITMA KLASIFIKASI

2021 ◽  
Vol 21 (1) ◽  
pp. 44-52
Author(s):  
Rizka Dahlia ◽  
Nanik Wuryani ◽  
Sri Hadianti ◽  
Windu Gata ◽  
Arina Selawati
Keyword(s):  
Data Mining ◽  
South Korea ◽  
Respiratory System ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
The World ◽  
Bayes Algorithm

Coronavirus 2019 or more commonly referred to as COVID-19 is a type of virus that attacks the respiratory system. Until now the number of spread and the number of deaths caused by this virus continues to increase. As of April 21, 2020, based on data from the WHO, the total number of cases infected with this virus reached 2,397,217 with 162 deaths from all over the world. For South Korea itself, as of March 21, 2020, the total number of infected cases was 10,683 with a total of 237 deaths. In this study, researchers conducted data processing on the spread of COVID-19 in South Korea with Rapidminer using a classification algorithm, namely Naïve Bayes, C4.5, and K-Nearest Neighbor by performing the stages of selection, preprocessing, transfotmating, data mining and interpretation or evaluating the quality of the best accuracy of 80.79% with AUC of 0.881 achieved by the Naïve Bayes algorithm. The distribution of the data found that the influential attribute of the isolated class factor from the patient contained in the sex attribute where more women experienced isolation. Keywords— COVID-19, data mining, classification, C4.5, Naïve Bayes, K-NN

scholarly journals Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah

2021 ◽  
Vol 21 (1) ◽  
pp. 139-150
Author(s):  
M. Khairul Anam ◽  
Bunga Nanti Pikir ◽  
Muhammad Bambang Firdaus
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Naïve Bayes Classifier ◽  
Command Center

Pemerintah Pekanbaru saat ini sudah menerapkan teknologi dalam sistem pemerintahan, penerapannya saat ini masih mendapat keluhan dari masyarakat seperti layanan publik command center yang hanya sebagian masyarakat mengetahuinya dan penerapan cctv yang ada di Alat Pemberi Isyarat Lalu Lintas (APILL) yang belum berfungsi dengan baik. Penerapan teknologi lainnya oleh Pemerintah Pekanbaru dapat kita lihat dari keberadaan portal-portal web situs resmi Pemerintah. Sedangkan untuk melihat beragam komentar netizen dari twitter. Twitter menjadi tempat untuk mendapatkan data yang diungkapkan masyarakat melalui tweets yang diposting ke timeline. Analisa sentimen dilakukan untuk melihat pendapat atau kecenderungan opini netizen terhadap pemerintah Pekanbaru yang mengandung sentimen positif, negatif, dan netral. Data yang digunakan adalah tweet dengan jumlah dataset sebanyak 150 tweets. Data tersebut kemudian di analisa agar menjadi informasi. Analisa dilakukan menggunakan metode data mining yaitu Naïve Bayes Classifier, K-Nearest Neighbor (KNN), dan Decision tree. Penggunaan ketiga pendekatan ini berupaya untuk mengkategorikan hasil komentar netizen terkait penggunaan teknologi yang telah melalui proses analisis sentimen dan membandingkan keakuratan ketiga cara tersebut. Hasil akurasi yang didapatkan cukup beragam yaitu dari metode Naïve Bayes akurasi 100%, metode KKN akurasi 98,25%, dan metode decision tree akurasi 62,28%.

scholarly journals Penanganan Ketidakseimbangan Data pada Prediksi Customer Churn Menggunakan Kombinasi SMOTE dan Boosting

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Nana Suryana ◽  
Pratiwi Pratiwi ◽  
Rizki Tri Prasetio
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Customer Churn ◽  
Number Of Customers

Industri telekomunikasi menghadapi persaingan yang ketat antara penyedia layanan (service provider). Persaingan ini mengakibatkan customer churn atau berpindahnya pelanggan dari satu layanan ke layanan lain. Customer churn menjadi masalah utama karena dapat mempengaruhi pendapatan perusahaan, profitabilitas, serta kelangsungan hidup perusahaan. Oleh karena itu, mengetahui pelanggan yang akan melakukan churn secara dini menjadi salah satu cara yang cukup efektif dilakukan, karena dapat membantu perusahaan dalam membuat rencana yang efektif untuk tetap mempertahankan pelanggannya. Jumlah pelanggan yang mengundurkan diri dari layanannya saat ini biasanya dimiliki perusahaan dalam jumlah yang sedikit. Kondisi kekurangan data ini menyebabkan kesulitan dalam memprediksi customer churn. Tujuan umum dari penelitian ini adalah memprediksi pelanggan yang akan berpindah ke layanan lain atau mengundurkan diri dari layanannya saat ini. Sementara tujuan khusus penelitian Penelitian ini berusaha menangani ketidakseimbangan data dalam prediksi customer churn menggunakan optimasi pada level data melalui metode sampling yaitu Synthetic Minority Over Sampling. Kemudian dikombinasikan dengan optimasi level algoritma melalui pendekatan teknik Boosting. Pada penelitian beberapa algoritma prediksi seperti random forest, naïve bayes, decision tree, k-nearest neighbor dan deep learning yang akan diimplementasikan untuk mengetahui algoritma yang paling baik setelah dilakukan optimasi menggunakan SMOTE dan Boosting. Metode penelitian yang digunakan pada penelitian ini adalah CRISP-DM, yang merupakan kerangka penelitian data mining untuk penelitian lintas industri. Hasil penelitian ini menunjukan bahwa algoritma random forest merupakan algoritma yang menghasilkan akurasi paling optimal setelah dioptimasi menggunakan SMOTE dan Boosting dengan hasil akurasi 89,19%. The telecommunications industry faces stiff competition between service providers. This competition results in customer churn. Customer churn is a major problem because it can affect company revenue, profitability, survival, and service quality of the company. Therefore, knowing which customers will churn in the future early is one of the most effective ways to do it, because it can help companies make an effective plan to keep their customers. The number of customers who withdrew from its current services is usually owned by a small number. This lack of data causes difficulties in predicting customer churn. This problem then becomes a challenging issue in machine learning. The general purpose of this research is to predict customers who will churn. While the specific purpose of this research is to try to deal with data imbalances in predicting customer churn using optimization at the data level through the sampling method, namely Synthetic Minority Over Sampling (SMOTE). Then combined with algorithm level optimization through the Boosting technique approach. In this study, several prediction algorithms like the random forest, naïve Bayes, decision tree, k-nearest neighbor, and deep learning will be implemented to find out the best algorithm after optimization using SMOTE and Boosting. The method used in this study is CRISP-DM, which is a data mining research framework for cross-industry research. The results of this study indicate that the random forest algorithm is an algorithm that produces the most optimal accuracy after being optimized using SMOTE and Boosting with an accuracy of 89.19%.

scholarly journals Perbandingan Metode Klasifikasi Multiclass untuk Pemetaan Zona Risiko COVID-19 di Pulau Jawa

2021 ◽  
Vol 9 (1) ◽  
pp. 98-107
Author(s):  
Jesica Nauli Br. Siringo Ringo ◽  
Wahyu Joko Mursalin ◽  
Nisrina Citra Nurfadilah ◽  
Dwiky Rachmat Ramadhan ◽  
Wa Ode Zuhayeni Madjida
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Imbalanced Data ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Missing Value

Penambahan kasus COVID-19 yang besar di Indonesia, khususnya Pulau Jawa, membutuhkan berbagai upaya untuk mengendalikannya. Salah satu upaya efektif yang dapat dilakukan adalah tindakan preventif dengan memberi informasi mengenai kondisi suatu wilayah. Sebagai peringatan kepada masyarakat dan sebagai upaya pengambilan kebijakan daerah, Indonesia mengeluarkan zona risiko sampai pada tingkat kabupaten/kota melalui Satgas Penanganan COVID-19. Pembentukan level zona risiko tersebut menggunakan teknik konvensional yaitu pembobotan skor menggunakan informasi dari tiga jenis indikator. Dengan mempertimbangkan bahwa zona risiko merupakan hal yang penting dalam penentuan kebijakan terkait COVID-19, penelitian ini bertujuan untuk membangun model klasifikasi zona risiko kabupaten/kota di Pulau Jawa menggunakan beberapa teknik klasifikasi data mining dan menentukan model klasifikasi terbaik berdasarkan hasil evaluasi. Teknik klasifikasi yang digunakan sebagai perbandingan dalam penelitian ini adalah naive Bayes, decision tree, k-nearest-neighbor, dan neural network. Sebelum dilakukan pemodelan, data disesuaikan terlebih dahulu pada tahap preprocessing di mana pada tahap tersebut teridentifikasi terdapat permasalahan missing value dan imbalanced data. Permasalahan tersebut diatasi dengan imputasi data dan teknik oversampling. Hasil penelitian menunjukkan bahwa model k-nearest-neighbor merupakan model terbaik dibandingkan tiga model lainnya. Hasil tersebut didasarkan pada ukuran evaluasi keempat model di mana model k-NN memiliki nilai acccuracy, nilai rata-rata makro untuk sensitivitas, spesifisitas, dan ukuran F1 paling tinggi dibandingkan model lainnya.

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