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All Journal Jurnal Ekonomi dan Teknik Informatika Bulletin of Intelligent Machines and Algorithms
Dhika Widiyanto
Politeknik Sawunggalih Aji
Computer Science & IT Economics, Econometrics & Finance

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SISTEM INFORMASI PENDAFTARAN MAHASISWA BARU BERBASIS WEB PADA POLITEKNIK SAWUNGGALIH AJI KUTOARJO Dhika Widiyanto
Jurnal Ekonomi dan Teknik Informatika Vol 11 No 2 (2023): JURNAL EKONOMI DAN TEKNIK INFORMATIKA
Publisher : Politeknik Sawunggalih Aji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37601/jneti.v11i2.237

Abstract

Politeknik Sawunggalih Aji Purworejo merupakan politeknik yang membuka tiga jurusan, yaitu D3 Akuntansi, D3 Teknik Informatika, D3 Administrasi Bisnis. Pengangkatan mahasiswa baru melalui seleksi saat calon mahasiswa mendaftar. Pada awal pendaftaran calon mahasiswa memilih jurusan mana yang akan diambil. Setiap jurusan memiliki bobot nilai tersendiri, dengan demikian proses seleksi membutuhkan sistem informasi yang tepat guna mengambil keputusan saat calon mahasiswa mendaftar. Dengan terbatasnya sistem informasi, maka pengambilan data calon mahasiswa membuat keputusan menjadi kurang akurat. Akibat proses minimnya sistem informasi pengelolaan data menjadi kurang rapi sehingga menyulikan guna mendapatkan analisa keputusan yang tepat. Pembuatan Sistem Informasi semakin diperlukan karena dalam hal pendaftaran harus tercatat dengan sedemikian mungkin dan jelas yang di buat dengan konsep sistem informasi.
High-Precision Credit Card Fraud Detection on Imbalanced Data Using Random Forest and 1D Convolutional Neural Networks Dhika Widiyanto
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 1 (2025): BIMA November 2025 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/k6hexq72

Abstract

Credit card fraud has become a significant challenge for the financial industry, resulting in substantial monetary losses and eroding consumer trust. Detecting fraudulent transactions is particularly challenging due to the severe class imbalance and high dimensionality of transaction data. This study proposes a systematic pipeline for fraud detection, integrating stratified sampling, Synthetic Minority Over-sampling Technique (SMOTE), and comparative evaluation of Random Forest (RF) and 1D Convolutional Neural Network (CNN) models. The performance of both models is assessed using standard metrics, including Accuracy, Precision, Recall, F1-Score, and Area Under the Receiver Operating Characteristic Curve (AUC). Experimental results demonstrate that RF achieves high precision (99.45%) on unseen test data, ensuring reliable detection of legitimate transactions. In comparison, CNN achieves near-perfect recall (99.95%) on training data, indicating a strong capacity to identify fraudulent patterns. Temporal analysis of transaction data further reveals distinct patterns between legitimate and fraudulent activities, highlighting the predictive importance of the Time feature. The findings provide practical guidance for deploying machine learning models in real-world financial settings: RF offers a reliable solution for immediate implementation, whereas CNN presents a promising approach for future enhancement after further validation.
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