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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Informatics and Data Science (J-IDS)
Aldiva Wibowo
Universitas Negeri Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Comparison of Support Vector Machine (SVM) and Random Forest Algorithm for Detection of Negative Content on Websites Hermawan Syahputra; Aldiva Wibowo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25861

Abstract

The amount of negative content circulating on the internet can damage people's morale so that social conflicts arise in society that threaten national sovereignty. Detecting negative content can help identify and prevent harmful events before they occur. This can lead to a safer and more positive online environment. Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm for Detection of Negative Content on Websites. The research contributions are 1) detect negative content on the internet with random forest and SVM, 2) comparing SVM and RF algorithms for detecting negative content on websites, 3) detection of negative content based on text focusing on the categories of fraud, gambling, pornography and Whitelist. The stages of this research are preparing a text content dataset on a website that has been labeled, preprocessing (duplicated data, text cleansing, case folding, stopward, tokenize, label encoding, data splitting, and determine the TF-IDF), finally performing the classification process with SVM and Random Forest. The dataset used in this study is a structured dataset in the form of text obtained from emails that have been registered on the TrustPositive website as negative content.  Negative content includes fraud, pornography and gambling. The results show the accuracy of the SVM is 97%, Precision 90% and Recall 91%, while for Accuracy in Random Forest is 92%, Precision 71%, and Recall 86%. The value obtained is the result of testing using 526 website URLs. The test results show that the Support Vector Machine is better than the Random Forest in this study.
Sistem Deteksi Konten Negatif pada Teks Website Menggunakan Metode Random Forest Aldiva Wibowo
Journal of Informatics and Data Science Vol 1, No 2 (2022): Vol 1, No 2 (2022)
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/j-ids.v1i2.42737

Abstract

Internet saat ini telah mengalami perkembangan yang signifikan sebagai sarana untuk mencari informasi yang memberikan dampak positif bagi kehidupan sehari-hari masyarakat. Namun, di samping itu, internet juga menjadi tempat yang efektif untuk menyebarkan informasi yang memberikan dampak negatif, seperti penyebaran konten negatif kepada masyarakat. Untuk mengatasi masalah ini, pemerintah telah menetapkan sistem laporan dan DNS (Domain Name System) yang dikenal sebagai Trust Positif. Namun, upaya tersebut masih tergolong tidak efektif karena hanya didasarkan pada DNS dan laporan masyarakat. Oleh karena itu, tujuan dari penelitian ini adalah untuk menggunakan metode Machine Learning untuk membuat model klasifikasi konten negatif website berdasarkan isi teks website. Metode yang digunakan dalam penelitian ini adalah Random Forest. Proses penelitian dimulai dengan melakukan preprocessing pada data teks dengan pendekatan NLP (Natural Language Processing) dan kemudian mengaplikasikan metode Machine Learning. Hasil dari pengujian menunjukkan bahwa Random Forest cukup efektif dalam penelitian ini, dengan nilai accuracy sebesar 92%, precision sebesar 77%, dan recall sebesar 86%. Nilai tersebut didapatkan melalui pengujian dengan menggunakan 526 url website.
Comparison of Support Vector Machine (SVM) and Random Forest Algorithm for Detection of Negative Content on Websites Hermawan Syahputra; Aldiva Wibowo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25861

Abstract

The amount of negative content circulating on the internet can damage people's morale so that social conflicts arise in society that threaten national sovereignty. Detecting negative content can help identify and prevent harmful events before they occur. This can lead to a safer and more positive online environment. Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm for Detection of Negative Content on Websites. The research contributions are 1) detect negative content on the internet with random forest and SVM, 2) comparing SVM and RF algorithms for detecting negative content on websites, 3) detection of negative content based on text focusing on the categories of fraud, gambling, pornography and Whitelist. The stages of this research are preparing a text content dataset on a website that has been labeled, preprocessing (duplicated data, text cleansing, case folding, stopward, tokenize, label encoding, data splitting, and determine the TF-IDF), finally performing the classification process with SVM and Random Forest. The dataset used in this study is a structured dataset in the form of text obtained from emails that have been registered on the TrustPositive website as negative content.  Negative content includes fraud, pornography and gambling. The results show the accuracy of the SVM is 97%, Precision 90% and Recall 91%, while for Accuracy in Random Forest is 92%, Precision 71%, and Recall 86%. The value obtained is the result of testing using 526 website URLs. The test results show that the Support Vector Machine is better than the Random Forest in this study.
Co-Authors Hermawan Syahputra