Search Results - (( data implementation bayes algorithm ) OR ( java implication based algorithm ))

Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour by Pebrianti, Dwi, Ariawan, Angga, Bayuaji, Luhur, Mahdiana, Deni, ,, Rusdah

“…The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. …”
Get full text
Get full text
Get full text
Get full text

Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

“…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
Get full text
Get full text
Get full text

Thematic textual hadith classification: an experiment in rapidminer using support vector machine (SVM) and naïve bayes algorithm by Yusof, Norzihani, Rosidi, Siti Aishah Rosidi, Ibrahim, Nuzulha Khilwani Ibrahim, Ahmed Ali, Ahmed El-Mogtaba Bannga

“…From the results, the different value of accuracy for both SVM and Naïve Bayes Algorithm was 2.4%. The Naïve Bayes Algorithm displayed better result comparing to SVM. …”
Get full text
Get full text
Get full text
Get full text

Sentiment Analysis of Airline Reviews Using Naive Bayes Algorithm / Ahmad Firdaus Maliki by Maliki, Ahmad Firdaus

Get full text
Get full text

Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…The support vector machines (SVM) algorithm obtained the overall best results of 94.5% accuracy, 91.8% precision, 91.7% recall, and 91.1% f-Measure while the naïve bayes (NB) algorithm obtained the best AUC score of 0.944 with the tweet data of Dato Seri Anwar. …”
Get full text
Get full text
Get full text

Comparison of hidden Markov Model and Naïve Bayes algorithms among events in smart home environment by Babakura, Abba, Sulaiman, Md Nasir, Mustapha, Norwati, Kasmiran, Khairul A.

“…In this paper, we propose Hidden Markov Model (HMM) and Naïve Bayes (NB) to test the accuracy and response time of the home data and to compare between the two algorithms. …”
Get full text
Get full text
Get full text

Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad by Ahmad, Nurul Atirah

“…Thus, this project aims to classify and generate a list of potential job applicants by analyzing various attributes of their LinkedIn accounts, such as title, location, skills, education, language, certification, and experience. This project implements the Naive Bayes algorithm as the classification algorithm. …”
Get full text
Get full text

Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak by Ishak, Najihah Salsabila

Get full text
Get full text

Analyzing customer reviews for ARBA Travel using sentiment analysis by Abdullah, Nurulain

“…Three machine learning algorithms which are Naive Bayes, Logistic Regression, and Support Vector Machine, were implemented and evaluated using cross-validation and performance metrics such as accuracy, precision, recall, and F1- score. …”
Get full text
Get full text

Bayesian Network Classifiers for Damage Detection in Engineering Material by Mohamed Addin, Addin Osman

“…The methodology used in the thesis to implement the Bayesian network for the damage detection provides a preliminary analysis used in proposing a novel fea- ture extraction algorithm (f-FFE: the f-folds feature extraction algorithm). …”
Get full text
Get full text

Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan by Md Roduan, Nur Syakinah

“…Future work on this subject should improve the findings by modifying the variables used and/or by using other methods in term of data collection or the algorithm itself.…”
Get full text
Get full text

Identification model for hearing loss symptoms using machine learning techniques by Nasiru Garba Noma

“…This research presents an efficient way to facilitate the hearing loss symptoms diagnosis process by designing a symptoms identification model that efficiently identify hearing loss symptoms based on air and bone conduction pure-tone audiometry data. The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
Get full text
Get full text
Get full text
Get full text

Analysis of Data Mining Tools for Android Malware Detection by Yusof, Robiah, Abdullah, Raihana Syahirah, Adnan, Nurul Syahirrah, Abd. Jalil, Nurlaily

“…However, the problem arises in deciding the most appropriate machine learning techniques or algorithm on particular tools to be implemented on particular data. …”
Get full text
Get full text
Get full text

Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

“…An analysis was done to see the effect of implementing different data reduction algorithms in classifying BSR disease. …”
Get full text
Get full text

Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

“…Since the 1960s, many algorithms for data classification have been proposed. …”
Get full text
Get full text

Predictive analytics for pacemaker medical instrument stock management of Transmedic Healthcare by Nawawi, Nafiz Danial

“…Power BI dashboards will be implemented to offer active insights into the stock level, demand forecast, and supplier performance in real-time, supporting decisions with data backing. …”
Get full text
Get full text

Emotion recognition and analysis of netizens based on micro-blog during covid-19 epidemic by Jiao, BianBian, Leelavathi, R., Lohgheswary, N., Nopiah, Z. M.

“…The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. …”
Get full text
Get full text
Get full text

Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…The machine learning algorithm, Support Vector Machine was chosen after comparing it with other algorithms such as Multinomial Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. …”
Get full text
Get full text

Author identification for under-resourced language Kadazandusun by Nursyahirah, Tarmizi, Suhaila, Saee, Dayang Hanani, Abang Ibrahim

“…Besides, this paper also examines the performance of two machine learning algorithms on the KadazanDusun data set by analyzing the stylometric features. …”
Get full text
Get full text
Get full text
Get full text

Travel Booking Analysis and Prediction for NZ Malaya using predictive analytics by Mohd Fauzi, Noor Fatin Natasha

“…For the three prediction experiments on trip package selection, marketing effectiveness, and sales forecasting three ML algorithms were applied: Decision Tree, Random Forest, and Naive Bayes. …”
Get full text
Get full text