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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi Jupiter Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics ICON-CSE Annual Research Seminar PROCEEDING IC-ITECHS 2014 Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPPPF (Jurnal Penelitian dan Pengembangan Pendidikan Fisika) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Linguistik Komputasional Sriwijaya Journal of Informatics and Applications Jurnal Generic JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pendidikan IPA Indonesia
Novi Yusliani
Department Informatics Engineering, Faculty Of Computer Science, Sriwijaya University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Physics Social Sciences Other

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Journal : Jupiter

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Klasifikasi Pertanyaan Berbahasa Indonesia Menggunakan Algoritma Support Vector Machine dan Seleksi Fitur Mutual Information syechky al qodrin aruda; Novi Yusliani; Alvi Syahrini
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 14 No 2-a (2022): Jupiter Edisi Oktober 2022
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./4796/5.jupiter.2022.10

Abstract

Text classification can be used to organize, arrange and categorize a text. Text classification can be used for all text documents even if a text has a large number of features. However, the large number of features can cause reduced accuracy in the performance results of the classification system because there are some features that have less relevance to a text category. The Mutual Information feature selection method combined with the Support Vector Machine (SVM) algorithm is used to improve performance results in the classification process for Indonesian question documents by eliminating features with weights below the threshold. The results showed that the use of the Mutual Information feature selection method on the SVM classification algorithm was able to produce the best performance with an accuracy value of 0.92, precision: 0.93, recall: 0.89, f-measure: 0.9, computation time: 7 s and number of features: 240. Keywords— Text Classification, Feature Selection, Support Vector Machine, Mutual Information
Analisis Sentimen di Twitter Menggunakan Algoritma Artificial Neural Network Novi Yusliani; Armenia Yuhafiz; Mastura Diana Marieska; Alvi Syahrini Utami
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6603/15.jupiter.2023.04

Abstract

Along with the development of social media, the amount of data in the form of opinions is increasing. The opinions in social media can be used to find out the assessments of social media users regarding something, one of which is the assessment of a candidate in politics. In general, the opinions in social media can be classified into two categories, namely positive and negative. Sentiment analysis is one of the research topics in the field of Natural Language Processing which aims to classify opinions into one of these categories. The opinions in social media that are often used as research objects are the opinions of Twitter users. This study uses an Artificial Neural Network (ANN) algorithm to be implemented in sentiment analysis system. The dataset used in this study is 1088 tweets consisting of 700 tweets labeled positive and 388 tweets labeled negative. The test results show that the best performance is produced when the data is divided into 80% for training and 20% for testing. The resulting percentages for each performance parameter used are accuracy is 61.3%, recall is 67.9%, precision is 75.1%, and f1-score is 71.3% using 0.01 for learning rate and 150 for epoch.
Co-Authors Abdiansah Abdiansah, Abdiansah Abdiansyah Ahmad Fali Oklilas Aini Nabilah Al Fatih, Zaky Alvi Syahrini Alvi Syahrini Utami Angelia, Nadya Anna Dwi Marjusalinah Annisa Darmawahyuni Ari Firdaus Ari Firdaus Ari Wedhasmara Ari Widodo Ariska, Meli Armansyah, Risky Armenia Yuhafiz Aruda, Syechky Al Qodrin Aspirani Utari Astero Nandito Ayu Purwarianti Az Zahra, Lutfiah Betharia Sri Fitrianti Danny Matthew Saputra Darmawahyuni, Annisa Darmawahyuni, Annisa Deris Stiawan Desty Rodiah Desty Roodiah Dhiya Fairuz Diah Kartika Sari Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Fadel Muhammad, Fadel Firdaus Firdaus Fitria Khoirunnisa Ghita Athalina Gilbert Christopher Jambak, Muhammad Ihsan Kanda Januar Miraswan Kartika, Diah Lidya Irfiyani Silaban M Fachrurrozi M. Fachrurrozi . Mastura Diana Marieska Melly Ariska Milka, Ikbal Adrian Muhammad Fachrurrozi Muhammad Fachurrozi Muhammad Naufal Rachmatullah Muhammad Omar Braddley Muhammad Raihan Habibullah Muhammad Rizqi Assabil Muharromi Maya Agustin Nur Hamidah Nurul Izzah Oktadini, Nabila Rizky Osvari Arsalan Plakasa, Gerald Rahma Haniffia Rahmannisa, Amanda Rahmat Fadli Isnanto Raisha Fatiya Reyhan Navind Shaquille Ridho Putra Sufa Rifka Widyastuti Rizki Kurniati Rizki Ramadandi Rusdi Efendi Saputra, Danny Mathew Saputra, Danny Matthew Sari, Tri Kurnia septi ana Siti Nurmaini Syechky Al Qodrin syechky al qodrin aruda Tiara Dewangga Tristi Dwi Rizki Wenty Octaviani Winda Kurnia Sari Yenny Anwar Yesi Novaria Kunang YUNITA Yunita Yunita Yunita Yunita Yunita Yunita