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All Journal EVOLUSI : Jurnal Sains dan Manajemen Indonesian Journal of Networking and Security - IJNS
Rizqi Agung Permana - STMIK Antar Bangsa Rizqi Agung Permana - STMIK Antar Bangsa
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Seleksi Atribut Pada Metode Support Vector Machine Untuk Menentukan Kelulusan Mahasiswa E-Learning Rizqi Agung Permana - STMIK Antar Bangsa
Evolusi : Jurnal Sains dan Manajemen Vol 4, No 1 (2016): Jurnal Evolusi 2016
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.674 KB) | DOI: 10.31294/evolusi.v4i1.647

Abstract

Abstract - Learning is a system of web-based communication platform that enables learners, without limitation of time and place, to access a variety of learning tools, such as discussion forums, ratings, content repositories, and document sharing systems. E-Learning can be just as effective as face-to-face in a conventional classroom teaching and learning, if proper teaching techniques and well-organized (Oztekin et al. 2013). Based on the data processing that has been done by comparing the Naive Bayes algorithm, Neural Network, Decision Tree and Machine Support Vector Machine using log data from students. Later in the tests to get the accuracy and AUC values of each algorithm so that the highest test results obtained by using support vector machine. Keywords: Data Mining, E-Learning, Support Vector Machine.   Abstrak - Learning adalah sistem platform komunikasi berbasis web yang memungkinkan peserta didik, tanpa batasan waktu dan tempat, untuk mengakses berbagai alat pembelajaran, seperti forum diskusi, penilaian, repositori konten, dan sistem sharing dokumen. E-Learning bisa sama efektifnya dengan tatap muka dalam pengajaran di kelas konvensional dan belajar, jika teknik mengajar yang tepat dan terorganisir dengan baik (Oztekin et al. 2013). Berdasarkan pengolahan data yang telah dilakukan dengan membandingkan algoritma Naive Bayes, Neural Network, Decision Tree dan Mesin Support Vector Machine menggunakan data log dari siswa. Kemudian di tes untuk mendapatkan akurasi dan AUC nilai masing-masing algoritma sehingga hasil tes tertinggi diperoleh dengan menggunakan mesin dukungan vektor. Kata kunci: Data Mining, E-Learning, Support Vector Machine.  
METODE KNN PADA SENTIMENT ANALISIS REVIEW PRODUK GAME ANDROID Sucitra Sahara - Universitas BSI; Rizqi Agung Permana - STMIK Antar Bangsa
Indonesian Journal of Networking and Security (IJNS) Vol 11, No 2 (2022): IJNS Juni 2022
Publisher : APMMI - Asosiasi Profesi Multimedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55181/ijns.v11i2.1769

Abstract

Abstract - Of the many comments that have been reviewed to obtain data set of positive and negative form of text that will be researchers for data classification by using the k-Nearest Neighbors (k-NN), k-NN is one of the most popular algorithms for pattern recognition. The rapid mushrooming android based application allows the vendor nor the parties competing businesses competing to create a variety of applications, ranging quality and high performance to quality is often questionable, so investigators held a screening of the application for android opinions or comments by people who have used the application and poured into the online media. Based on the data processing that has been done by comparing the Naive Bayes algorithm, Neural Network, Decision Tree and Machine Support Vector Machine using log data from students. Later in the tests to get the accuracy and AUC values of each algorithm so that the highest test results obtained by using support vector machine.
Co-Authors Sucitra Sahara