Implementation of Naïve Bayes Classification Method for Predicting Purchase

Harahap, Fitriana; Harahap, Ahir Yugo Nugroho; Ekadiansyah, Evri; Sari, Rita Novita; Adawiyah, Robiatul; Harahap, Charles Bronson

doi:10.1109/citsm.2018.8674324

Cited by 48 publications

(38 citation statements)

References 8 publications

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“…The naïve bayes algorithm, which assumes that each variable is independent, independent from each other, has no relationship or correlation that can affect the results [22]. Predictions based on the bayes theorem general formula in (1) [23]:…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

Measure the effectiveness of information systems with the naïve bayes classifier method

Triayudi

Sumiati

Dwiyatno

et al. 2021

IJ-AI

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Technological advances at this time are developing very fast, information systems became the frontline in technological advancements, the need for information systems to support jobs is increasingly high. However, its implementation for users does not have a significant impact, so that it needs to be reviewed and re-evaluated in the use of the information system built. The naive bayes classifier method can provide "effective" and "ineffective" conclusions and is used as material for evaluation and improvement. The purpose of this study is to contribute to measuring the effectiveness of the information system, to solve problems with the naïve bayes classifier method approach which has advantages in the process of classifying data and predicting data. From the test results three times, training has been conducted using 100 data, accuracy value of 84.82% and error 15.18%.

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Section: Naïve Bayes Classifiermentioning

confidence: 99%

Measure the effectiveness of information systems with the naïve bayes classifier method

Triayudi

Sumiati

Dwiyatno

et al. 2021

IJ-AI

View full text Add to dashboard Cite

show abstract

“…The Naive Bayes model is simple to construct and is particularly beneficial for huge data sets. Naive Bayes is recognized to outperform even the most sophisticated classification methods due to its simplicity [31][32][33].…”

Section: B-naïve Bayesmentioning

confidence: 99%

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

Hassan

Abdulazeez

2021

AJRCoS

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One of the main factors that assist to increase the growth of any country is Agriculture. The detection of diseases from plant leaf images is one of the most important fields of agricultural research. To identify disease factors, this field requires a reliable prediction approach. Data Mining (DM) is the process of analyzing data from different aspects and summarizing it into valuable information. It helps users to categorize and identify relationships between data from various dimensions. As there are many plants on the farm, detecting and classifying the diseases of each plant on the farm is extremely difficult for the human eye. And diagnosing each plant is very critical since these diseases may spread. DM classification is an important method that has a wide range of applications. It classifies each item in a set of data into one of a set of predefined classes. In this paper, a comparison of different DM classification methods such as Naive Bayes, Decision trees, SVM, and Random Forest algorithms has been illustrated by using of Weka Tool.

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“…The algorithm with the best performance can be used to build an article classification system that can make it easier to choose a theme that matches the articles it has. This system provides an opportunity for an article to be accepted so that it can get publication [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles

Winarti¹,

Indriyawati²,

Vydia³

et al. 2021

IJ-AI

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The match between the contents of the article and the article theme is the main factor whether or not an article is accepted. Many people are still confused to determine the theme of the article appropriate to the article they have. For that reason, we need a document classification algorithm that can group the articles automatically and accurately. Many classification algorithms can be used. The algorithm used in this study is naive bayes and the k-nearest neighbor algorithm is used as the baseline. The naive bayes algorithm was chosen because it can produce maximum accuracy with little training data. While the k-nearest neighbor algorithm was chosen because the algorithm is robust against data noise. The performance of the two algorithms will be compared, so it can be seen which algorithm is better in classifying documents. The comes about obtained show that the naive bayes algorithm has way better execution with an accuracy rate of 88%, while the k-nearest neighbor algorithm has a fairly low accuracy rate of 60%.

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Implementation of Naïve Bayes Classification Method for Predicting Purchase

Cited by 48 publications

References 8 publications

Measure the effectiveness of information systems with the naïve bayes classifier method

Measure the effectiveness of information systems with the naïve bayes classifier method

Plant Leaf Disease Detection by Using Different Classification Techniques: Comparative

Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles

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