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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Innovative and Creativity
Putri Maulidina Fadilah
Universitas Negeri Medan
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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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 Rata-Rata Pengeluaran Makan Siang Mahasiswa Prodi Statistika-24 Universitas Negeri Medan Menggunakan Teknik Simple Random Sampling Dwi Heri Efrilia; Feby Florensia Stepahni Sihotang; Josafath Artur Situngkir; Putri Maulidina Fadilah; Sudianto Manullang
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.1280

Abstract

Pengeluaran makan siang merupakan bagian penting dari kebutuhan harian mahasiswa, terutama bagi mahasiswa perantau yang harus mengatur keuangan secara mandiri. Penelitian ini bertujuan untuk menganalisis rata-rata pengeluaran makan siang mahasiswa Program Studi Statistika 2024 di Universitas Negeri Medan. Metode yang digunakan dalam pengambilan sampel adalah Simple Random Sampling (SRS), yaitu setiap anggota populasi memiliki peluang yang sama untuk terpilih menjadi sampel. Populasi sebanyak 83 orang dengan sampel sebanyak 69 responden. Data yang dikumpulkan didapatkan dari wawancara secara langsung dan dengan kuesioner online yang disebarkan kepada responden secara acak. Hasil penelitian menunjukkan bahwa terdapat variasi dalam pengeluaran makan siang mahasiswa yang dipengaruhi oleh beberapa faktor seperti pilihan tempat untuk makan, kebiasaan konsumsi, dan kondisi ekonomi masing-masing mahasiswa yang berbeda-bedan. Penelitian ini diharapkan dapat menjadi dasar dalam memahami pola konsumsi mahasiswa serta memberikan masukan bagi pihak kampus dalam merancang kebijakan atau program yang mendukung kesejahteraan mahasiswa.
Co-Authors Dwi Heri Efrilia Feby Florensia Stepahni Sihotang Ichwanul Muslim Karo Karo Josafath Artur Situngkir Manullang, Sudianto Romia Romia Sri Dewi