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All Journal Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan
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Computer Science & IT

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Perancangan Sistem Monitoring Kualitas Udara Menggunakan IoT dengan Metode Prototype Revifal Anugerah; Tata Sutabri
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 3 No. 1 (2025): Januari : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v3i1.304

Abstract

In recent years, air quality has become an increasingly important issue in various cities around the world. Real-time air quality monitoring is essential for identifying pollution problems and taking appropriate actions. This article discusses the design of an Internet of Things (IoT)-based air quality monitoring system using the prototype method. The system is designed to monitor air quality parameters such as PM2.5, PM10, CO2, and temperature in real-time and present the data to users through a web-based application.
Analisis Penerapan Machine Learning dan Algoritma Anomali untuk Deteksi Penipuan pada Transaksi Digital Reyhand Ardhitha; Revifal Anugerah; Tata Sutabri
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 1 (2025): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i1.345

Abstract

Fraud in digital transactions has become a serious issue threatening the security and integrity of the fintech and e-commerce sectors. To address this problem, machine learning technology has emerged as an effective solution for automatically detecting anomalies and fraudulent transactions. This study aims to analyze the application of machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and Ensemble Learning, in detecting fraud in digital transactions. The research adopts a quantitative approach with experimentation, testing the effectiveness of the three algorithms using a digital transaction dataset consisting of both fraudulent and non-fraudulent transactions. The results show that the Random Forest algorithm performs the best in terms of accuracy and recall, followed by Ensemble Learning, which enhances fraud detection performance by combining multiple prediction models. Meanwhile, SVM demonstrates satisfactory performance but is prone to overfitting issues when handling large and complex datasets. The study also finds that the problem of imbalanced data can affect model accuracy, and data balancing techniques such as oversampling are required to improve fraud detection performance. Overall, the findings suggest that machine learning, particularly Random Forest and Ensemble Learning algorithms, can be relied upon to improve fraud detection in digital transactions. However, challenges such as model interpretability and the need for periodic algorithm updates still need to be addressed to enhance the effectiveness of fraud prevention systems in countering the ever-evolving nature of fraud.
Co-Authors Reyhand Ardhitha Tata Sutabri