Comparison of C4.5 and Naïve Bayes Algorithm for Mustahik Classification

Suseno, Hendra Bayu; Wanhari, Awliya; Masruroh, Siti

doi:10.4108/eai.7-11-2019.2294560

Cited by 4 publications

(4 citation statements)

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“…The validation model [18] used is cross validation 10-fold stratified, which means dividing the training dataset into 10 equal parts and then do the learning process 10 times and use the rest of the dataset to perform the test. Several tests mention the use of this validation model stratification slightly increased yield [11].…”

Section: Model Validationmentioning

confidence: 99%

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Comparison of Classification Data Mining C4.5 and Naïve Bayes Algorithms of EDM Dataset

Santoso¹,

Ginantra²,

Arifin³

et al. 2021

TEM Journal

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The purpose of this research is to choose the best method by comparing two classification methods of data mining C4.5 and Naïve Bayes on Educational Data Mining, in which the data used is student graduation data consisting of 79 records. Both methods are tested for validation with 10-ford X Validation and perform a T-Test difference test to produce a table that contains the best method ranking. Different results were obtained for each method. Based on the results of these two methods, it is very influential on the dataset and the value of the area under curve in the Naïve Bayes method is better than the C4.5 method in various datasets. Comparison of the method with the 10-Ford X Validation test and the T-Test difference test is that the Naïve Bayes method is better than C4.5 with an average accuracy value of 73.41% and an under-curve area of 0.664.

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Section: Model Validationmentioning

confidence: 99%

“…Furthermore, research by Suseno [11] on the classification of people who receive zakat (mustahik). This paper proposes a comparison of the two classification methods in the case of people receiving zakat.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Classification Data Mining C4.5 and Naïve Bayes Algorithms of EDM Dataset

Santoso¹,

Ginantra²,

Arifin³

et al. 2021

TEM Journal

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“…Selain C4.5 metode naïve bayes classifier juga banyak digunakan untuk pendekatan pada sistem penerima zakat [9], [10]. Penelitian Suseno dkk [9] membandingkan metode Naïve Bayes Classifier dengan metode C4.5 menurut Suseno,dkk hasil akurasi C4.5 lebih baik dari pada metode Naïve Bayes Z Classifier. Namun menurut zhang dkk [5] naïve bayes classifier unggul diberbagai ukuran performa yang lain.…”

Section: Pendahuluanunclassified

Pendekatan Berbasis Kecerdasan Buatan Dengan Metode Naïve Bayes Untuk Website Baznas

Gunawan¹,

Wahyudi²,

Sari³

2021

JTekInfULM

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Badan amil zakat nasional (BAZNAS) adalah badan resmi nasional dalam menyalurkan zakat. Zakat adalah kewajiban umat Islam. Hal tersebut dijelaskan dalam kitab suci Alquran. Zakat perlu disalurkan dengan adil. Banyaknya data penerima zakat menyebabkan penyaluran sering terkendala. Dengan klasifikasi zakat antara fakir dan miskin akan menjadi solusi untuk kendala data yang banyak. Teknik kecerdasan buatan yang digunakan pada penelitian ini adalah Naïve Bayes Classifier. Ini adalah teknik klasifikasi berdasarkan Teorema Bayes dengan asumsi independensi antar prediktor. Secara sederhana, pengklasifikasian Naive Bayes mengasumsikan bahwa keberadaan fitur tertentu di kelas tidak terkait dengan keberadaan fitur lainnya. Label data penerima zakat ini ada dua, yaitu fakir dan miskin. Kecerdasan buatan sebagai metode dalam pembuatan prototype sistem pada website BAZNAS dalam penentuan penerima zakat. Pengukuran performa metode naïve bayes classifier menggunakan confusion matrix. Hasil dari metode naïve bayes classifier dalam mengklasifikasi penerima zakat cukup baik yaitu 96%.

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“…In addition to decision tree, support vector machines (SVM) [21], [22], Naive Bayes [23], [24], [25], neural networks [26], [27], [28], [29], Bayesian networks [30], and maximum entropy [31], [32], [33] are often used in sentiment analysis. You can choose one of these methods based on the problem you want to study [4].…”

Section: Introductionmentioning

confidence: 99%

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

et al. 2023

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The disparities in access to public assistances between rural and urban areas are analyzed in this study, corresponding to the intensity in which the constructs are related to expected quality, perceived quality and perceived value as influential factors in citizen satisfaction and loyalty. In Guayaquil, a survey is carried out at the household level, 428 valid questionnaires are obtained in the rural area of Tenguel and 521 Valid questionnaires in the urban area of Tarqui, applyingto the American Customer Satisfaction Index (ACSI). The research used a Structural Equation model (SEM) to evaluate the hypotheses raised, if they observe significant differences in citizen perception between inhabitants of urban and rural areas about the quality of community services, this as a determining cause in the level of citizen satisfaction and loyalty to choose their municipal authorities. The multigroup analysis allowed to identify inequalities in the observation of the quality of municipal or local communityassistances between rural and urban areas; the findings are considered to local public administrators for the design of public policy aimed at improving levels of citizen satisfaction and loyalty.Sentiment analysis is a way to automatically understand and process text data to figure out how someone feels about an opinion sentence. If there are too many reviews, it will take a lot of time and they will start to be biased. Sentiment classification tries to solve this problem by putting user reviews into groups based on whether they are positive, negative, or neutral. The dataset comes from Drone Emprit Academic. It is made up of tweets with the words "online learning method" in them, with as many as 4887 data crawled from them. Information Gain and adaboost on the C4.5 (FS+C4.5) method are used in the feature selection method. We use feature options to get rid of bias and improve accuracy. The results of the experiments will be compared to other algorithms like C4.5 and random forest. Based on the results, the accuracy of the two standard decision tree models (C4.5 and random forest) went up from 48.21% and 50.35% to 94.47 %. The value of how accurate it was went up by 44 percent. The FS+C4.5 model, on the other hand, has an RMSE of 0.204 and a correlation of 0.944. So, adding the feature selection technique to the sentiment analysis of bold learning education can make the C4.5 algorithm even more accurate.

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Comparison of C4.5 and Naïve Bayes Algorithm for Mustahik Classification

Cited by 4 publications

References 0 publications

Comparison of Classification Data Mining C4.5 and Naïve Bayes Algorithms of EDM Dataset

Comparison of Classification Data Mining C4.5 and Naïve Bayes Algorithms of EDM Dataset

Pendekatan Berbasis Kecerdasan Buatan Dengan Metode Naïve Bayes Untuk Website Baznas

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

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