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All Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Cut Dinda Rizki Amirillah
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mathematics

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Perancangan Aplikasi Document Management System Berbasis Web Universitas Nasional dengan Metode Waterfall Cut Dinda Rizki Amirillah; Septi Andriyana; Benrahman Benrahman
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 5, No 1 (2020)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (692.796 KB) | DOI: 10.30998/string.v5i1.6353

Abstract

One of the most important assets in supporting every work program implemented at the National University community is document management. In the current process, the organization will certainly produce data or documents that are not small with a variety of criteria. The number of physical documents stored in a place sometimes takes a long time to find and borrow the documents needed. The Document Management System (DMS) application is one of the problem solvers that is reliable enough to solve various problems regarding document archiving. In making a website-based DMS application using the Hypertext Preprocessor (PHP) programming language, with the Laravel and Postgres framewok as a database for storing data and using the Waterfall method. The application of DMS which is supported by a website-based application makes the application accessible wherever and whenever, documents are stored centrally, and easily access data between fields
Deteksi Transaksi Penipuan pada Sektor Perbankan Menggunakan Ruled-Based Model dan Pembelajaran Mesin Cut Dinda Rizki Amirillah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i2.17410

Abstract

This research aims to develop an effective fraud detection model in banking transactions using the rule-based model (RBM) approach and the isolation forest (IF) machine learning algorithm. Based on data from the Ministry of Communication and Information Technology, there were more than 405,000 online fraud cases during the 2019–2022 period, indicating the need for a reliable fraud detection system to protect customers. The research method involves collecting banking transaction data for four months through channels such as ATM, internet banking, and mobile banking. The RBM model was used as an initial approach, detecting suspicious transaction patterns based on defined rules. However, it has limitations in detecting transactions that are not defined in the rules. To complement this shortcoming, this research implemented IF, an effective unsupervised learning model for detecting anomalies using the isolation tree (iTree) technique to identify suspicious transactions. The results showed that the IF model could detect anomalous patterns not covered by RBM, thereby improving the accuracy of fraud transaction identification. The precision data of 99% indicates that the model’s predictions of anomalies are indeed anomalies, while a recall value of 1.0 shows that the model successfully identified all anomalies in the dataset. In conclusion, the combination of RBM and IF provides a comprehensive approach to fraud detection in the banking sector. IF’s ability to detect anomalies more dynamically and accurately can reduce fraud losses in the industry.
Co-Authors Ben Rahman Septi Andriyana