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All Journal Semesta Teknika Sinkron : Jurnal dan Penelitian Teknik Informatika J-Intech (Journal of Information and Technology) Jurnal Ilmu Komputer SENTRI: Jurnal Riset Ilmiah Jurnal Komputer Antartika Kohesi: Jurnal Sains dan Teknologi
Fahlapi, Riza
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Aerospace Engineering Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Other

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Journal : J-Intech (Journal of Information and Technology)

 1 
Perbandingan Algoritma Klasifikasi Analisis Sentimen Pengguna Aplikasi Getcontact Dalam Pencegahan Penipuan Online Hermanto, Hermanto; Fahlapi, Riza; Kuntoro, Antonius Yadi; Asra, Taufik
J-INTECH (Journal of Information and Technology) Vol 12 No 1 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i1.1262

Abstract

Online fraud refers to various fraudulent acts carried out over the internet with the aim of fraudulently obtaining financial gain or personal information. We need to continue to spread awareness about the importance of security for ourselves and the people we know, where currently there are many different modes of online fraud. One application that is well known to the public is the GetContact application, which is an application designed to provide information about incoming calls, identify spam or fraudulent calls, and provide services related to a list of telephone contacts that have been registered by fellow users of the application. In this research, researchers will analyze the sentiment of comments from users of the Getcontact application by comparing the test results of classification algorithms, namely Naïve Bayes Classifier and SVM. This research process will begin with data sampling using the scrapping technique on Google Playstore and processing data from users of the Getcontact application using RapidMiner. After the preprocessing process and model testing with two textmining methods using algorithms, namely SVM and Naive Bayes, the evaluation and validation results show that Naïve Bayes has a higher level of accuracy than SVM. For Naïve Bayes, the accuracy value reached 82.97% with an AUC value of 0.500, while for SVM, the accuracy value was 78.00% with an AUC value of 0.926. These results show that Naïve Bayes is superior in classifying user comments on the Getcontact application on Google Play as positive and negative comments.
Analisis Sentimen Analisis Sentimen Terhadap Twitter Direktorat Jenderal Bea dan Cukai Menggunakan komparasi Algoritma Naïve Bayes dan Support Vector Machine Saputra, Dedi Dwi; Fahlapi, Riza; Kuntoro, Antonius Yadi; Hermanto, Hermanto; Asra, Taufik
J-INTECH (Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1274

Abstract

Direktorat Jenderal Bea & Cukai (DJBC) is a government agency in charge of guarding and serving export and import activities in Indonesia since its establishment in 1946 which is expected by the community as the front guard in protecting the community in this field, but in recent times there have been many cases involving the institution of the Directorate General of Customs & Excise which make this institution can affect the view of the performance of this institution. With the description of the problem above, it is very interesting to conduct research on public views using tweets from twitter @bravobeacukai and @beacukaiRI which are owned and processed by Direktorat Jenderal Bea & Cukai as a place to channel public opinions and views on this institution. This research uses the Smote method with Naïve Bayes and compared with Support vector machine methods for these results to compare the level of accuracy. The results of this study found that using the Smote method with Naïve Bayes obtained an Accuracy value of 78.95%, Precision 74.01%, Recall 89.41%, and AUC 0.650 while for the Smote method with Support vector machine is worth 73.35% Accuracy, Precision 67.88%, Recall 88.95%, and AUC 0.853. Based on the research results, the smote method with Naïve Bayes has the greatest results and is effective with the dataset studied.
Co-Authors Abdurrazaq Abdurrazaq, Abdurrazaq Abulkhoir, Moh. Azam Adiansyah, Riski Abimur Alfiansyah, Afif Alie, Jemmy Yosua Antonius Yadi Kuntoro Ardhiyansyah, Pramudhitya Argomasetyo, Firqi Ashari, Yayan Faqih, Muhammad Bayu Garamba, Yafianus Hafid, Danang Abu Hermanto Hermanto Kurniawan, Bebi Lasman Effendi Nitra, Ridatu Oca Rianto, Yan Riwanto, Muhammad Hilmy Saputra, Dedi Dwi siti nurlela Sukmawati, Alfiani Taufik Asra Waek, Gregorius