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All Journal International Journal of Electrical and Computer Engineering Jupiter Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal on Computing (Indo-JC) JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Terpadu STRING (Satuan Tulisan Riset dan Inovasi Teknologi) MULTINETICS Jurnal Mantik JATI (Jurnal Mahasiswa Teknik Informatika) Sains, Aplikasi, Komputasi dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Charity : Jurnal Pengabdian Masyarakat Journal of Software Engineering, Information and Communication Technology Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Asian Journal of Community Services (AJCS) eProceedings of Engineering Tekmulogi: Jurnal Pengabdian Masyarakat Journal of Law, Education and Business Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Ichwanul Muslim Karo Karo
Universitas Negeri Medan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Journal : JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)

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Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF Ichwanul Muslim Karo Karo; Romia Romia; Sri Dewi; Putri Maulidina Fadilah
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3317

Abstract

Twitter is one of the most popular social media platforms in the world nowadays. Twitter users in Indonesia are the fifth largest in the world and are always active in expressing themselves and getting information through tweets. A hoax is a lie created as if it were true. Hoaxes are also often spread via tweets. The spread of hoaxes is extremely dangerous because it can cause social discord and even misunderstanding. Therefore, hoaxes must be resisted. This study aims to build a system to detect hoaxes on Indonesian tweets. The objective of this research is to identify hoax Indonesian tweets by using the Naïve Bayes classifier with Term Frequency Inverse Document Frequency (TF-IDF). This study collects and annotates tweets from hoax tweets post which sent by a user account. This study also applied several text preprocessing techniques to provide datasets. To provide the best hoax prediction model, this work splits datasets into training and testing datasets. There are four experimental scenarios that refer to splitting the dataset. The experimental results showed that the hoax prediction model using Naïve Bayes with TF-IDF had 64% accuracy and recall, 69% and 67% precision, and a F1-score respectively. This result is also superior to the hoax prediction model when using the Naïve Bayes classifier without the TF-IDF. It means that TF-IDF has made a positive contribution to improving model performance. Finally, this research contributes by detecting news with a proclivity for hoaxes and filtering what is classified as hoaxes or not.
Analisis Sentimen Ulasan Aplikasi Info BMKG di Google Play Menggunakan TF-IDF dan Support Vector Machine Ichwanul Muslim Karo Karo; Justaman Arifin Karo Karo; Yunianto Yunianto; Hariyanto Hariyanto; Miftahul Falah; Manan Ginting
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3943

Abstract

Posting online reviews has become one of the most popular ways to express opinions and sentiments towards service applications. The Meteorology, Climatology and Geophysics Agency (BMKG) Info application is an Android and iOS-based mobile application that provides information on weather, climate, air quality, and earthquakes that occur in various regions in Indonesia. The information contained in this application is very important but has a worse value than other forecasting applications. Sentiment analysis is the process of classifying text into several classes such as positive sentiment, negative or not containing both. This research aims to analyze user reviews on the BMKG Info application from the Google Play website. The benefits obtained are as consideration for developers to improve the shortcomings of the application. The classification process uses Term Frequency-Inverse Document Frequency (TF-IDF) and the Support Vector Machine (SVM) algorithm. This research successfully collected 2500 reviews from users of the BMKG Info application on the Google PlayStore website using the web scraping method. Text preprocessing of the reviews used case folding, symbolic and stopword removal, tokenization, normalization, and stemming. User ratings help in identifying the sentiment label of a review, 66% of reviews are positive while the rest are negative. The most frequently reviewed topics with sentiment value are "application", "information", "update". This research conducted three experimental scenarios based on the composition of training data and test data. Based on the prediction model, the scenario with 75%:25% split data has the highest accuracy rate of 79%.
Co-Authors Abil Mansyur, Abil Adawiah Hasyani, Rabiahtul Ade Amelia, Tasya Adidtya Perdana, Adidtya Aditia Sanjaya Ahyar, Khoirul Ananda Khosuri Angelina Prima Kurniati Anggraini, Nisa Putri Aqila Aqila, Aqila Azizul Azhar Ramli Bachruddin Saleh Luturlean Bakti Dwi Waluyo Darari, Muhammad Badzlan Daulay, Leni Karmila Dedy Kiswanto Dian Septiana Dimas Pebrian Supandi Ester Berliana Ritonga, Yolanda Evelyn Keisha Silalahi Eviyona Laurenta Br Barus Fadillah, Wahyu Nur Falah, Miftahul Fitri Rahayu Fitria, Nur Anisa Gea, Kurnia Mildawati Ginting, Manan Gunawan, Rizky Habibi, Rizki Haraha, Melyana Hariyanto HARIYANTO HARIYANTO Hariyanto Hariyanto Hariyanto, Hariyanto Hendriyana Hendriyana Heru Nugroho Husna Batubara, Shabrina Ida Ayu Putu Sri Widnyani Jodi Kusuma Juan Steiven Imanuel Septory Justaman Arifin Karo Karo Karo karo, Justaman Arifin Karo Karo, Justaman Arifin Landong, Ahmad Lorinez S, Yohana Manan Ginting Mardiana Mardiana Maretha Br. Simbolon, Silvana Maulana Malik Fajri Maulidna, Maulidna Melania Justice Panggabean Miftahul Falah Miftahul Falah Mohd Farhan Md Fudzee Mohd Farhan MD Fudzee, Mohd Farhan Molliq Rangkuti, Yulita Mufida, Yasmin Muhammad Yusuf Mutiara Sihaloho, Laura Adelia Nasution, Aurela Khoiri Natasya, Amanda Nelza, Novia Nur Hafni Nurul Ain Farhana Nurul Ikhsan Panggabean, Suvriadi Permata Putri Pasaribu, Yohanna Purba, Desni Paramitha Putri Harliana Putri Maulidina Fadilah Ramadhani, Fanny Ramanti Dharayani Ramli, Azizul Azhar Rangkuti, Y. M Reinaldo Kenneth Darmawan Rennyta Yusiana Retno Setyorini Rizki Habibi Roby Dwi Hartanto Rohmat Saragih Romia Romia Said . Iskandar Salsabila, Aqila Shahreen Kasim Shahreen Kasim, Shahreen Simamora, Elmanani Sisti Nadia Amalia Sri Dewi Sri Dewi Sri Dewi Sri Suryani Supra Yogi Syahrin , Alvin Valentino, Bob Wahyu Nur Fadillah Wardhani Muhamad Warjaya, Angga Wibowo, Adinda Widi Astuti winsyahputra Ritonga Yahya Peranginangin Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yunianto Yunianto Yunianto Yunianto Yunianto Yunianto, Yunianto ZK Abdurahman Baizal