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All Journal Journal of Mathematics Education and Application (JMEA) BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat MAJAMATH: Jurnal Matematika dan Pendidikan Matematika MES: Journal of Mathematics Education and Science Indonesian Journal of Education and Mathematical Science PHI: Jurnal Pendidikan Matematika Al'adzkiya International of Education and Sosial (AIoES) Journal Journal Mathematics Education Sigma [JMES] JUTECH : Journal Education and Technology Kognitif: Jurnal Riset HOTS Pendidikan Matematika Education Achievement: Journal of Science and Research Jurnal Abdimas Kartika Wijayakusuma Indo-MathEdu Intellectuals Journal MATH-EDU: Jurnal Ilmu Pendidikan Matematika Tsaqila Jurnal Pendidikan dan Teknologi ALSYS : Jurnal Keislaman dan Ilmu Pendidikan ARZUSIN: Jurnal Manajemen dan Pendidikan Dasar OMEGA: Jurnal Keilmuan Pendidikan Matematika Hanif Journal of Information Systems Jurnal Tadris Matematika (JTMT) Jurnal Pengabdiaan Masyarakat Larisma Tsabit
Azis, Zainal
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Hanif Journal of Information Systems

 1 
Predicting The Risk of Online Sales Fraud with The Naïve Bayes Approach on Facebook Social Media Pasha, Leony Ayu Diah; Azis, Zainal
Hanif Journal of Information Systems Vol. 2 No. 2 (2025): February Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v2i2.41

Abstract

The rapid development of digital shopping media is accompanied by increasing cases of online fraud, especially through social media platforms such as Facebook. This study aims to develop a prediction model for the risk of online sales fraud using the Naïve Bayes algorithm. The data used is the data of buying and selling transactions that occur through the Facebook marketplace. The data has been collected on the Kaggle platform so that it can be used directly. Data in the form of extracted features include seller characteristics, products sold, number of transactions, device usage and other fraud indicators. Important features that affect the potential for fraud are identified and used in the machine learning process. The results of the study show that the Naïve Bayes model is able to provide accurate predictions in identifying the risk of online sales fraud, with a satisfactory accuracy rate of 95%. The results of the study are expected to contribute to the development of a more effective fraud detection system and increase user confidence in making online transactions.
Comparison of Logistic Regression and K-Nearest Neighbor (KNN) Algorithms in a Heart Failure Prediction Dataset Nasution, Julia Namira; Azis, Zainal
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.53

Abstract

Heart failure is one of the leading causes of death worldwide. Early detection of heart failure risk is crucial to minimize its serious consequences. This study aims to compare the performance of two machine learning algorithms, namely Logistic Regression and K-Nearest Neighbor (KNN), in predicting heart failure using a dataset from the Kaggle platform. The research stages include data preprocessing, normalization, splitting into training and testing data, model implementation, and evaluation using a confusion matrix. Evaluation is based on accuracy, precision, recall, and F1-score metrics. The results show that Logistic Regression achieved an accuracy of 88.04% with an execution time of 0.022 seconds, while KNN achieved an accuracy of 85.51% with an execution time of 0.158 seconds. Logistic Regression outperformed in recall and F1-score, making it more effective for early detection of heart failure. Therefore, Logistic Regression is considered more optimal than KNN in the context of this study. However, Logistic Regression is not always superior to K-Nearest Neighbor, as prediction results highly depend on the characteristics of the specific case.
Co-Authors Afnaria, Afnaria Akbar, Rahmat Al Khowarizmi Ammy, Putri Maisyarah Annisa, Dewi Sry Ardiningtyas, Mesayu Azmi, Muhammad Bima Darmawati Darmawati Dedy Kiswanto Fajar, Maulana Haloho, Elysha Jefirayani Hasibuan, Ahmad Riady Ikhlas Hamzani Irvan Irvan Irvan, Irvan Ivan Ivan Khoiruddin Matondang Lubis, Tri Resti Andini Lubis, Ulfa Annisa Lucia Sri Istiyowati Marah Doly Nasution Muslihuddin, Rahmat Nasution, Asrindah Nasution, Julia Namira Nurhayati Nurhayati Nurliana, Maulidya Nurlinda, Epi Nurul Maulida Surbakti Panggabean, Suvriadi Pasha, Leony Ayu Diah Purba, Enniy purba, oktaviana nirmala Puspita, Ananda Putri, Linda Syah Rahim, Rani Rahman, Alrafiful rahmayani, sri Risna Mira Bella Saragih Rizka Fahruza Siregar RR. Ella Evrita Hestiandari Siambaton, Muhammad Zulfansyuri Siregar, Rosliana Siswadi Siswadi Surya Wisada Dachi Syahlan, Syahlan Tua Halomoan Harahap, Tua Halomoan Valentinus, Valentinus Yusma, Ira Zega, Lipur Ju Priman