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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) MATHunesa: Jurnal Ilmiah Matematika Hanif Journal of Information Systems Journal of Innovative and Creativity
Putri Maulidina Fadilah
Universitas Negeri Medan
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science 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.
PEMODELAN DAN PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK PREDIKSI BANJIR: STUDI KASUS KOTA MEDAN Putri Maulidina Fadilah; Putri Harliana; Imelda Wardani Br Rambe; Alvi Sahrin Nasution
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 01 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p235 - 242

Abstract

Banjir merupakan salah satu bencana hidrometeorologis yang sering terjadi di Indonesia, termasuk di Kota Medan, dan menimbulkan kerugian sosial ekonomi yang signifikan. Penelitian ini bertujuan untuk mengembangkan dan membandingkan model prediksi kejadian banjir menggunakan tiga algoritma machine learning, yaitu Random Forest (RF), Support Vector Machine (SVM), dan Decision Tree (CART). Data yang digunakan merupakan data hidrometeorologis Kota Medan periode 2021–2024 yang diperoleh dari BMKG dan Geoportal BNPB, meliputi variabel curah hujan (RR), suhu (TN, TX, TAVG), kelembapan (RH_AVG), kecepatan angin (FF_X), arah angin (DDD_X), serta jumlah kecamatan dan rumah terdampak. Tahapan penelitian meliputi pra-pemrosesan data, pemodelan, serta evaluasi menggunakan metrik akurasi, presisi, recall, F1-score, dan AUC. Hasil penelitian menunjukkan bahwa Random Forest memiliki kinerja terbaik dengan akurasi sebesar 86,24% dan AUC sebesar 0,851, yang menunjukkan kemampuan prediksi yang sangat baik. Analisis feature importance menunjukkan bahwa curah hujan (RR) merupakan faktor paling berpengaruh terhadap kejadian banjir, diikuti oleh temperatur minimum (TN) dan temperatur rata-rata (TAVG). Metode ROSE terbukti efektif dalam mengatasi ketidakseimbangan kelas dengan meningkatkan recall tanpa mengorbankan akurasi. Secara keseluruhan, hasil penelitian ini menunjukkan bahwa algoritma Random Forest lebih andal dan stabil dibandingkan SVM dan Decision Tree dalam konteks analisis data hidrometeorologis, serta berpotensi menjadi dasar pengembangan sistem peringatan dini banjir berbasis kecerdasan buatan di masa depan. Kata Kunci: Prediksi Banjir, Random Forest, Support Vector Machine, Decision Tree, ROSE.
Flood Prediction Model Using the Random Forest Algorithm in Padangsidimpuan City Alvi Nasution; Putri Maulidina Fadilah; Muhammad Hafiz
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v4i1.79

Abstract

Flooding is a complex environmental phenomenon influenced by meteorological, temporal, and urban conditions. This study aims to develop a flood risk prediction model in Padangsidimpuan City using the Random Forest algorithm by integrating meteorological and temporal variables. The data set consists of historical observations from Padangsidimpuan City, including rainfall, temperature, humidity, wind direction, and flood occurrence status. The data were processed and divided into training and testing sets to evaluate model performance. The results indicate that the Random Forest model achieves strong classification performance, with an accuracy of 0.98 and specificity of 0.99, demonstrating a high capability in correctly identifying non-flood conditions. The model also shows good discrimination ability, with a ROC-AUC value of approximately 0.87. However, the recall value of 0.50 suggests that only half of the actual flood events are correctly detected, primarily due to class imbalance between flood and non-flood data. Feature importance analysis reveals that short-term meteorological variables, particularly rainfall and temperature, along with temporal patterns, are the most influential factors in flood prediction. In addition, spatial interpretation shows that flood-prone areas in Padangsidimpuan City are concentrated in densely populated urban zones with limited drainage capacity, highlighting the influence of urban environmental conditions on flood risk.Overall, the Random Forest model provides a strong foundation for flood risk prediction in Padangsidimpuan City. However, further improvements in data balancing and model optimization are required to enhance sensitivity and support a more reliable flood early warning system.
Co-Authors Alvi Nasution Alvi Sahrin Nasution Dwi Heri Efrilia Feby Florensia Stepahni Sihotang Ichwanul Muslim Karo Karo Imelda Wardani Br Rambe Josafath Artur Situngkir Manullang, Sudianto Muhammad Hafiz Romia Romia Sri Dewi