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Ferdiansyah Ferdiansyah
Universitas Bina Darma, Palembang
Computer Science & IT Control & Systems Engineering

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Analisis Data Mining Klasifikasi Berita Hoax COVID 19 Menggunakan Algoritma Naive Bayes Fani Prasetya; Ferdiansyah Ferdiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4852

Abstract

The rapid dissemination of information along with the rapid development of technology along with the massive speed of electronic media and the internet. But the rapid spread of news cannot guarantee that the information and news that we get can be validated from valid sources. Based on data released by Kominfo at the end of 2021, there were 1773 hoax news that were successfully clarified from the hoax news. Then during the Covid-19 pandemic itself, there were various hoaxes circulating in the community. Throughout 2021, the Ministry of Communications and Informatics discovered as many as 723 hoaxes about Covid-19. Based on the background above, the researchers and previous studies have discussed hoax detection in various fields. Such as, fraud detection in online writing style [1], classification of hoax news based on machine learning [3] and the application of nave Bayes and PSO algorithms for classification of hoax news on social media [4]. From here the researchers tried to carry out experiments on the nave Bayes classification algorithm to classify hoax covid 19 news. Based on the results of research that has been done, the nave Bayes model and cross validation can classify hoax news well, the resulting accuracy is 86.3% where 80-90% included in the good classification criteria. The data that is predicted to be incorrect is also not too much from a total of 300 datasets, only 41 are declared incorrect in labeling less than 2% of the total dataset, so it can be concluded that this model can be used as a reference if you want to proceed to a more complex prediction model, for example the model prediction using web-based machine learning.
Implementasi Algoritma Frequent Growth (FP-Growth) Menentukan Asosiasi Antar Produk Rangga Yogasuwara; Ferdiansyah Ferdiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4894

Abstract

Data accumulation is caused by the amount of transaction data stored. By utilizing the sales transaction data in the database, the data can be further processed into useful information for managers to make decisions. With the existence of data mining, it is hoped that it can help the Leaning Shop to find the information contained in the transaction data into new knowledge. Association Rule, which is a procedure in Market Basket Analysis to find relationships between items in a data set or it can be said that this association rule aims to find a collection of items that often appear at the same time and display them in the form of consumer habits in shopping. The FP-Growth algorithm is an algorithm that can be used to determine the data set that appears most often (frequent itemset) in a data, in the search for frequent itemset in a data set by generating a prefix-tree structure or often called the FP-Tree. From the test results it can be concluded that the application of data mining using the FP-Growth Algorithm can be used to analyze consumer spending patterns.
Co-Authors Fani Prasetya Rangga Yogasuwara