KOMBINASI METODE K-NEAREST NEIGHBOR DAN NAÏVE BAYES UNTUK KLASIFIKASI DATA

Data mining banyak digunakan untuk membantu menentukan keputusan dengan memprediksi tren data masa depan. K-Nearest Neighbor dan Naïve Bayes merupakan metode-metode data mining untuk klasifikasi data yang cukup populer. Kedua metode tersebut masing-masing memiliki kelemahan. Proses pengolahan data dengan metode KNN lebih lama dibanding dengan Naïve Bayes. Berdasarkan penelitian yang dilakukan oleh beberapa peneliti, metode KNN dan Naïve Bayes memiliki nilai keakuratan yang cukup tinggi Pada penelitian ini, Naïve Bayes menghasilkan nilai keakuratan yang lebih kecil dibanding metode KNN. Dari permasalahan di atas, maka diusulkan metode kombinasi KNN dan Naïve Bayes untuk mengatasi kelemahan tersebut. Metode KNN, Naïve Bayes, dan metode kombinasi KNN-Naïve Bayes diujikan pada data yang sama untuk memperoleh hasil perbandingan persentase keakuratan dan lama waktu proses. Hasil pengujian ketiga metode dengan Nursery dataset, membuktikan bahwa, metode kombinasi KNN-Naïve Bayes berhasil mengatasi kelemahan pada Naïve Bayes ataupun KNN. Proses pengolahan data metode kombinasi KNN-Naïve Bayes lebih cepat dibanding KNN dan Naïve Bayes. Selain itu hasil persentase keakuratan yang diperoleh metode KNN-Naïve Bayes lebih tinggi dibanding dengan metode KNN dan Naïve Bayes

Forward Stagewise Naive Bayes

The naïve Bayes approach is a simple but often satisfactory method for supervised classification. In this paper, we focus on the naïve Bayes model and propose the application of regularization techniques to learn a naïve Bayes classifier. The main contribution of the paper is a stagewise version of the selective naïve Bayes, which can be considered a regularized version of the naïve Bayes model. We call it forward stagewise naïve Bayes. For comparison’s sake, we also introduce an explicitly regularized formulation of the naïve Bayes model, where conditional independence (absence of arcs) is promoted via an L 1/L 2-group penalty on the parameters that define the conditional probability distributions. Although already published in the literature, this idea has only been applied for continuous predictors. We extend this formulation to discrete predictors and propose a modification that yields an adaptive penalization. We show that, whereas the L 1/L 2 group penalty formulation only discards irrelevant predictors, the forward stagewise naïve Bayes can discard both irrelevant and redundant predictors, which are known to be harmful for the naïve Bayes classifier. Both approaches, however, usually improve the classical naïve Bayes model’s accuracy

Klasifikasi Berita Hoax Menggunakan Algoritma Naïve Bayes Berbasis PSO

Perkembangan teknologi informasi yang begitu cepat memicu penyebaran informasi hoax melalui internet menjadi tidak terkontrol. Sehingga diperlukan suatu sistem cerdas yang dapat melakukan klasifikasi konten berita hoax yang tersebar melalu media internet. Naïve Bayes merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi yang tinggi, akan tetapi Naïve Bayes memiliki kekurangan yaitu sangat sensitive dalam pemilihan fitur maka dari itu dibutuhkan metode Particle Swarm Optimization (PSO) untuk meningkatkan hasil akurasi. Proses klasifikasi hoax dapat dilakukan melalui tahap preprocessing kemudian pembobotan kata dan dilakukan klasifikasi menggunakan Naïve Bayes. Setelah dilakukan penelitian dengan metode Naïve Bayes dan metode Naïve Bayes berbasis PSO maka hasil yang didapat adalah Naïve Bayes menghasilkan akurasi sebesar 73.64% sedangkan Naïve Bayes berbasis PSO nilai akurasinya sebesar 91,82%. Tujuan dari penelitian ini adalah untuk melihat seberapa besar pengaruh PSO untuk meningkatkan akurasi pada klasifikasi berita hoax pada media sosial menggunakan pengklasifikasi Naïve Bayes. Setelah menggunakan PSO meningkat sebesar 18,18%

KLASIFIKASI MAMMOGRAM MENGGUNAKAN VECTOR FITUR DAN NAÏVE BAYES CLASSIFIER

KLASIFIKASI MAMMOGRAM MENGGUNAKAN VECTOR FITUR DAN NAÏVE BAYES CLASSIFIER - Fitur, Mammogram, Naïve Bayes classifier, WEK

DIAGNOSA KERUSAKAN BEARING MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS (PCA) DAN NAÏVE BAYES CLASSIFIER

Penelitian ini membahas tentang penggunaan data mining untuk mendiagnosa kerusakan yang terjadi pada bearing. Bearing merupakan salah satu komponen penting dalam mesin-mesin industri. Bearing berfungsi untuk mengurangi gesekan pada mesin atau komponen-komponen yang bergerak dan saling menekan antara satu dengan yang lainnya. Diagnosis kerusakan ini dapat menghindari terjadinya kerugian dan kerusakan komponen lain pada suatu mesin. Tahapan penelitian dimulai dengan prapemrosesan data menggunakan transformasi wavelet diskret, esktraksi fitur, reduksi fitur menggunakan PCA (Principal Component Analysis) dan proses klasifikasi menggunakan metode klasifikasi Naïve Bayes. Naïve Bayes adalah metode klasifikasi yang berdasarkan probabilitas dan Teorema Bayesian. Hasil penggunaan metode ini menunjukkan bahwa klasifikasi Naïve Bayes memiliki performa yang cukup bagus terlihat dari akurasi yang dihasilkan dari setiap data yang diuji. Kata kunci: Data mining, Diagnosa kerusakan, PCA, Klasifikasi Naïve Bayes This research was discussed about the usage of data mining which addressed for bearing fault diagnosis. Bearing was one of the essential parts in industry machinery. Bearing was used to reduce machines frictions or could be a moving component which oppressed each other. This fault diagnosis can avoid loss and damage of other machines components. This research was started with data preprocessing using wavelet discrete transformation, feature extraction, feature reduction using Principal Component Analysis (PCA), and classification process using Naïve Bayes classifier methods. Naïve Bayes Classifier is a classification method which based on probability and Bayesian theorem. Output of these method shows that Naïve Bayes classification have a good performance which shown by a good accuracy in each data test. Keyword: Data mining, Fault diagnosis, PCA, Naïve Bayes classificatio

Epileptic Seizure Detection in EEGs by Using Random Tree Forest, Naïve Bayes and KNN Classification

Epilepsy is a disease that attacks the nerves. To detect epilepsy, it is necessary to analyze the results of an EEG test. In this study, we compared the naive bayes, random tree forest and K-nearest neighbour (KNN) classification algorithms to detect epilepsy. The raw EEG data were pre-processed before doing feature extraction. Then, we have done the training in three algorithms: KNN Classification, naïve bayes classification and random tree forest. The last step was validation of the trained machine learning. Comparing those three classifiers, we calculated accuracy, sensitivity, specificity, and precision. The best trained classifier is KNN classifier (accuracy: 92.7%), rather than random tree forest (accuracy: 86.6%) and naïve bayes classifier (accuracy: 55.6%). Seen from precision performance, KNN Classification also gives the best precision (82.5%) rather than Naïve Bayes classification (25.3%) and random tree forest (68.2%). But, for the sensitivity, Naïve Bayes classification is the best with 80.3% sensitivity, compare to KNN 73.2% and random tree forest (42.2%). For specificity, KNN classification gives 96.7% specificity, then random tree forest 95.9% and Naïve bayes 50.4%. The training time of naïve bayes was 0.166030 sec, while training time of random tree forest was 2.4094sec and KNN was the slower in training that was 4.789 sec. Therefore, KNN Classification gives better performance than naïve bayes and random tree forest classification

Spam Classification on 2019 Indonesian President Election Youtube Comments Using Multinomial Naïve-Bayes

Text classification are used in many aspect of technologies such as spam classification, news categorization, Auto-correct texting. One of the most popular algorithm for text classification nowadays is Multinomial Naïve-Bayes. This paper explained how Naïve-Bayes assumption method works to classify 2019 Indonesian Election Youtube comments. The output prediction of this algorithm is spam or not spam. Spam messages are defined as racist comments, advertising comments, and unsolicited comments. The algorithms text representation method used bag-of-words method. Bag-of-words method defined a text as the multiset of its words. The algorithm then calculate the probability of a word given the class of spam or not spam. The main difference between normal Naïve-Bayes algorithm and Multinomial Naïve-Bayes is the way the algorithm treats the data itself. Multinomial Naïve-Bayes treats data as a frequency data hence it is suitable for text classification task

Komparasi Algoritma Data Mining Dalam Ketuntasan Belajar Daring Siswa Pada Masa Pandemi Covid 19

This research was conducted at SMA Negri 3 Selong and became the focus of students in class XI IPA and Social Studies. The sampling technique used purposive sampling method. This study aims to describe the extent to which the level of completeness of students during post-covid-19 pandemic learning with online media. This study uses a classification algorithm that functions to find a model that distinguishes data classes or data concepts, with the specific objective of determining the class of unknown object labels. The method used is the PSO-based Naïve Bayes and Naïve Bayes Comparison Algorithms. The results of this study indicate that the use of online media during online learning using the naïve Bayes algorithm is 83.91%, and the PSO-based naïve Bayes algorithm is 91.98%, from the experimental results and testing of the two algorithms, the results of the confusion matrix and AUC testing can be obtained which can be determined the best accuracy value is the PSO-based Naïve Bayes algorithm. As for the comparison of the results in the form of an accuracy value obtained by the Naïve Bayes Algorithm of 83.91% and the PSO-Based Naïve Bayes Algorithm of 91.98% and the difference in the level of accuracy of 8.07%, so it can be concluded that the algorithm that is suitable for classifying student learning completeness during the covid 19 pandemic is Naive Bayes based on particle swarm optimization

Backward Sequential Feature Elimination And Joining Algorithms In Machine Learning

The Naïve Bayes Model is a special case of Bayesian networks with strong independence assumptions. It is typically used for classification problems. The Naïve Bayes model is trained using the given data to estimate the parameters necessary for classification. This model of classification is very popular since it is simple yet efficient and accurate. While the Naïve Bayes model is considered accurate on most of the problem instances, there is a set of problems for which the Naïve Bayes does not give accurate results when compared to other classifiers such as the decision tree algorithms. One reason for it could be the strong independence assumption of the Naïve Bayes model. This project aims at searching for dependencies between the features and studying the consequences of applying these dependencies in classifying instances. We propose two different algorithms, the Backward Sequential Joining and the Backward Sequential Elimination that can be applied in order to improve the accuracy of the Naïve Bayes model. We then compare the accuracies of the different algorithms and derive conclusion based on the results