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All Journal Jurnal Sains dan Teknologi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan PROSIDING SEMINAR NASIONAL CENDEKIAWAN Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal Khatulistiwa Informatika Paradigma JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Informatika Kaputama (JIK) Building of Informatics, Technology and Science Abdimas Universal Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi JUSTIAN - Jurnal Sistem Informasi Akuntansi Reputasi: Jurnal Rekayasa Perangkat Lunak JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal AKAL: Abdimas dan Kearifan Lokal Jurnal Penelitian dan Pengabdian Masyarakat Jotika Journal of Entrepreneurship and Community Innovations Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Agung Sasongko
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Nursing Public Health Social Sciences Transportation Other

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Analisis Kinerja Logistic Regression dan Random Forest pada Deteksi Fraud E-Commerce Menggunakan SMOTE dan PCA Handayani, Kartika; Erni, Erni; Ardiyansyah, Ardiyansyah; Sasongko, Agung
Jurnal Sistem Informasi Akuntansi Vol. 6 No. 2 (2025): Periode September 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v6i2.11566

Abstract

The rapid growth of e-commerce platforms has increased the volume and complexity of digital transactions, which is accompanied by a rising risk of fraudulent activities. This study aims to apply and evaluate the performance of Logistic Regression and Random Forest algorithms for fraud detection in e-commerce transactions. To address the class imbalance problem, the Synthetic Minority Over-sampling Technique (SMOTE) is employed, while dimensionality reduction is performed using Principal Component Analysis (PCA). The dataset is divided into training and testing sets using an 80:20 ratio. Model evaluation is conducted under four scenarios: baseline without additional preprocessing, SMOTE only, PCA only, and a combination of SMOTE and PCA. The results indicate that Random Forest consistently outperforms Logistic Regression across most evaluation metrics, including Recall, F1-Score, and Area Under the Curve (AUC). The application of SMOTE significantly improves the model’s ability to identify fraudulent transactions, achieving the highest Recall of 80.79% using Random Forest. In contrast, the use of PCA, either alone or combined with SMOTE, tends to degrade model performance. This study concludes that Random Forest combined with SMOTE provides the most effective approach for fraud detection in highly imbalanced e-commerce transaction data.  
DETEKSI RETINOPATI DIABETIK ON-DEVICE MENGGUNAKAN MODEL MOBILENETV2 PADA APLIKASI MOBILE BERBASIS FLUTTER Mustopa, Ali; Sasongko, Agung; Hendini, Ade
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8641

Abstract

Retinopati Diabetik (RD) adalah penyebab utama kebutaan yang dapat dicegah, namun skrining manual seringkali sulit diakses dan mahal. Penelitian ini bertujuan membangun aplikasi mobile yang efisien untuk deteksi RD menggunakan Deep Learning. Model CNN berbasis MobileNetV2 dilatih dengan teknik transfer learning pada dataset APTOS 2019 yang dikelompokkan menjadi 2 kelas (RD dan Non-RD). Model terbaik dikonversi ke format TensorFlow Lite (TFLite) dengan optimasi kuantisasi untuk implementasi on-device pada aplikasi Flutter. Hasil penelitian menunjukkan model mencapai akurasi 97.3% pada data uji. Konversi TFLite berhasil mereduksi ukuran file sebesar 74% (menjadi 11.8 MB) dengan latensi inferensi rata-rata ~150 ms. Penelitian ini membuktikan kelayakan implementasi MobileNetV2 pada aplikasi mobile untuk skrining RD yang cepat, akurat, hemat biaya, dan menjaga privasi secara offline. Solusi ini berpotensi besar meningkatkan deteksi dini di fasilitas layanan kesehatan dengan sumber daya terbatas
Co-Authors Abdul Latif Ade Hendini Agustiani, Sarifah Ali Mustopa, Ali Annisa Dewi Akbari, Annisa Dewi Ardiyansyah Ardiyansyah Badariatul Lailiah Dadang Surjasa, Dadang Dewayana, Triwulandari Satitidjati Docki Saraswati Emelia Sari Erni Erni Eva Meilinda Ferdy Winanda firmansyah, Yoki Habyba, Anik Nur Hadikusumah, Prima Surya Indah Permata Sari Indra Prasetya Iqbal Rizantha, Muhamad Kartika Handayani Khultsum, Umi Lady Agustin Fitriana Latifah Latifah Latifah Latifah Lisnawanty Mardjono, Rully Marien, Yunita Miji Mohammad Kamal Reza Muhammad Sony Maulana Mustaqim, Daffa Akiel Nawawi, Hendri Mahmud Nurmalasari Nurmalasari Parwadi Moengin PUDJI ASTUTI Purwaningtias, Deasy Rada Claudya Verera Rada Claudya Verera Raja Sabaruddin Reza Maulana Risdiansyah, Deni Riski Annisa Safitri, Sri Dewi Ayu Sari Dewi Siti Khotimatul Wildah Soekarno, Didien Suhardini Suharyanto Suharyanto Tiena Gustina Amran Triwulandari SD, Triwulandari Wahyu Nugraha Wanty Eka Jayanti Wardiyanto Romadhon, Richo Dwi Wawan Kurniawan