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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics MUQTASID InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Inovasi : Jurnal Ekonomi, Keuangan, dan Manajemen Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Indonesian Physical Review JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Sistem Komputer dan Informatika (JSON) Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Unri Conference Series: Community Engagement Journal of Applied Data Sciences Journal of English Language and Education International Journal of Trends in Mathematics Education Research (IJTMER)
Tulus Tulus
Departemen Matematika, FMIPA Universitas Sumatera Utara
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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Journal : journal of informatics and telecommunication engineering

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INDONESIAN TEXT DATASET FOR DETERMINING SENTIMENT CLASSIFICATION USING MECHINE LEARNING APPROACH Syahputra, Indra Edy; Tulus, Tulus; Efendi, Syahril
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (886.124 KB) | DOI: 10.31289/jite.v3i2.3153

Abstract

Advances in information technology encourage the emergence of unlimited textual information with the use of online media developing so rapidly that the emergence of the need for information presentation without reducing the value of the information presented. Basicaly the concept of the dataset is a general form of almost every discipline, where the dataset provides empirical basic information for research activities. Sentiment analysis is done to see opinions or feelings about a problem or identify and classify information trends from the problem. The dataset analysis in determining sentiment classification is a model of sentiment classification that has relevance to the dataset with the use of machine learning techniques with supervision that learns from experience to predict output from labeled input data and output from machine learning. The results of experiments and tests that have been carried out on machine learning techniques with supervision can classify sentiments in the tweet text properly and the level of accuracy can still be improved to a better direction with data namely baseline 100 (days) and 83 (weeks), naivebayes 100 (days) and 82 (weeks), maxent 100 (days) and 83 (weeks), and SVM 100 (days) and 83 (weeks).
Co-Authors Abdul Aziz Ahmad Agusnady, Alfian Ameilia Zuliyanti Siregar Amlys, Amlys Syahputra Silalahi Badawi, Afif Budhiarti Nababan, Erna Butar Butar, Kartika Dewi Christian Sinaga, Christian Christy, Claudia Dardanila, Dardanila Dewi, Rafiqa Efendi, Syahril Erna Budhiarti Nababan Eva, Eva Sartini Bayu Farida Ariani Hariadi , Hariadi Susilo Herman Mawengkang Indera Sakti Nasution Liana Liana Lubis, Rahmi Suliani Ma'ruf, Ma'ruf Tafsin Mahidin Mahidin Mardiningsih Mardiningsih Marpaung, Rony Genevent Melinda Melinda Meutia Nauly Muhammad Zarlis, Muhammad Muhibbuddin Muhibbuddin Nababan, Junerdi Nabiyah, Ismi Ratin Napitu, Daniel Halomoan Saragi Nasrul Arahman Panjaitan, Nismah Poltak Sihombing Putri, Mimmy Sari Syah Ramli, Ichwana Saib Suwilo Salim, Opim Sawaluddin Sawaluddin, Sawaluddin Siregar, Netti Herlina Sitompul, Opim Salim Sitompul Suharno Suharno Sulastri Sulastri Syahputra, Indra Edy Syamsul Arifin Tugiman Tugiman Zakarias Situmorang Zarlis, M Zulkifli Nasution