Analysis of Slow Moving Goods Classification Technique:  Random Forest and Naïve Bayes

Jollyta, Deny; Gusrianty, Gusrianty; Sukrianto, Darmanta

doi:10.23917/khif.v5i2.8263

Cited by 5 publications

(5 citation statements)

References 12 publications

(12 reference statements)

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“…Naive Bayes is the basic concept of Bayes' Theorem, a data mining technique for classifying data. A dataset or classification process can be divided into her two phases: learning/training and testing/classification to obtain suitable classification parameters [26] [27]. The Naive Bayes Classifier is a method for classifying derivatives.…”

Section: Naïve Bayesmentioning

confidence: 99%

Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method

Firdaus

Yudhana

Riadi

2023

KINETIK

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Elections for president and vice president will take place in 2024. Heading into the election, promoted candidates were vying for public sympathy. People often discussed as presidential candidates are Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto. Therefore, we need a way to predict potential candidates and voter demographics from public opinion on Twitter using sentiment analysis. One of his methods commonly used to classify sentiment analysis is Naive Bayes. This study used the naive Bayes classifier and the TF-IDF extraction function to add weights to the text. Use the scikit-learn Python library to help determine the polarity of negative and positive sentiment classes in your dataset. The datasets used were Twitter datasets acquired from October to December 2022, for a total of 15,000 datasets. The best test scenario obtained by splitting the test and training data is 70% test data and 30% training data, with the highest accuracy generated from the 95% Ganjar dataset. Using the Anies, Ganjar, and Prabowo test data, the positive mood scores for each candidate were 833, 77, and 524, respectively, while the negative mood scores were 637, 1423, and 976, respectively. The test was performed using a confusion matrix and k-fold cross-validation, and the best results were obtained on the Ganjar data set. That is a confusion matrix of 94.93% and a k-fold cross-validation of 94.46%. The lowest f1-score for the positive class is 67% for the Anies dataset and 27% for the negative class for the Ganjar dataset.

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Section: Naïve Bayesmentioning

confidence: 99%

Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method

Firdaus

Yudhana

Riadi

2023

KINETIK

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“…Naïve bayes merupakan salah metoda pembelajaran mesin yang memanfaatkan perhitungan probabilitas dan statistik yang dikemukakan oleh ilmuwan Inggris Thomas Bayes, yakni memprediksi probabilitas pada masa depan berdasarkan pengalaman pada masa sebelumnya [15]. Naïve Bayes dapat dinyatakan dalam rumus sebagai berikut:…”

Section: B Naïve Bayesunclassified

Klasifikasi Mutu Pembelajaran Hybrid berdasarkan Algoritma C.45, Random Forest dan Naïve Bayes dengan Optimasi Bootsrap Areggating (Bagging) pada masa COVID-19

et al. 2022

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This study aims to find the best model from the 3 (three) models chosen, namely Random Forest, Naïve Bayes and C4.5 against the classification of hybrid learning quality held at SMK Cendekia Cirebon City during the COVID-19 period. The research method is carried out by referring to the approach or technique of machine learning with the stages of data collection, data preparation, data transformation, modeling and evaluation. The final results of this study are measured by the level of accuracy of hybrid learning quality aspects, namely teachers, subject matter, integrity, student motivation, and technology resources with bad, sufficient, good and very good categories.. Based on the experiments that have been carried out, the accuracy results for the Random Forest algorithm were obtained by 93.92%, Naïve Bayes by 93.22% and C4.5 by 77.16%. Thus this study recommends the best classification algorithm model based on dataset processing to measure this hybrid learning quality model is Random Forest

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“…In order to find unique information using machine learning, text mining involves turning unstructured text into a structured manner [5]. A classification algorithm from data mining can be used for classification [6], such as Support Vector Machine (SVM) algorithm. In this study, a fake news detection system for the Indonesian language based on news titles was developed using the SVM kernel and n-gram.…”

Section: Introductionmentioning

confidence: 99%

N-gram and Kernel Performance Using Support Vector Machine Algorithm for Fake News Detection System

Jollyta,

Gusrianty,

Prihandoko

et al. 2023

Ilk. J. Ilm.

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The modern technological advancements have made it simpler for fake news to circulate online. The researchers have developed several strategies to overcome this obstacle, including text classification, distribution network analysis, and human-machine hybrid methods. The most common method is text categorization, and many researchers offer deep learning and machine learning models as remedies. An Indonesian language fake news detection system based on news headlines was developed in this work using the Support Vector Machine (SVM) kernel and n-gram. The objective of this research is to identify the model that produces the best performance outcomes. The system deployment on the web will employ the model that produces the greatest outcomes. According to the research findings, the linear kernel SVM algorithm produces the best results, with an accuracy value of 0.974. Furthermore, the bigram feature used in the development of a classification model does not increase the precision of fake news identification in Indonesian. Utilizing the unigram function yields the most accurate results.

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Analysis of Slow Moving Goods Classification Technique: Random Forest and Naïve Bayes

Cited by 5 publications

References 12 publications

Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method

Public Opinion Analysis of Presidential Candidate Using Naïve Bayes Method

Klasifikasi Mutu Pembelajaran Hybrid berdasarkan Algoritma C.45, Random Forest dan Naïve Bayes dengan Optimasi Bootsrap Areggating (Bagging) pada masa COVID-19

N-gram and Kernel Performance Using Support Vector Machine Algorithm for Fake News Detection System

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