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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal Of Computer Science And Technology
Elfina Maulid
Universitas Media Nusantara Citra
Computer Science & IT Engineering

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Implementasi Algoritma Random Forest untuk Deteksi Phising pada Email Elfina Maulid; Eko Amri Jaya; Muhamad Pradana Hardi Wasesa
Journal of Computer Science and Technology (JOCSTEC) Vol 4 No 2 (2026): JOCSTEC - Mei
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/jocstec.v4i2.669

Abstract

Serangan phishing melalui email merupakan ancaman cybersecurity yang terus berkembang dan dapat menyebabkan kerugian finansial maupun kebocoran data yang serius. Untuk mengatasi permasalahan ini, dibutuhkan metode yang andal dan otomatis guna membedakan email phishing dari email yang sah. Algoritma Random Forest dipilih karena kemampuannya dalam menangani struktur data yang kompleks dan menghasilkan akurasi klasifikasi yang tinggi. Pada penelitian ini, algoritma Random Forest diimplementasikan untuk klasifikasi email phishing menggunakan dataset yang terdiri dari email phishing dan email yang sah. Dataset tersebut diproses melalui tahapan pra-pemrosesan, ekstraksi fitur, pelatihan model, dan evaluasi. Hasil penelitian menunjukkan bahwa sistem berhasil mengklasifikasikan email phishing dan email yang sah dengan tingkat keakuratan yang tinggi, sehingga efektif dalam mendukung proses penyaringan email secara otomatis dan aman.
Quantifying Target Leakage in SEER Breast Cancer Mortality Prediction Sestri Novia Rizki; Vani Maharani Nasution; Eko Amri Jaya; Verra Budhi Lestari; Elfina Maulid
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.8105

Abstract

Recent machine learning studies on the SEER Breast Cancer dataset report 95–98% accuracy (2022–2025), but their methods are rarely examined. Because the dataset is imbalanced (84.7% Alive), accuracy alone is a poor discrimination metric — a trivial always-Alive classifier scores near 85% — so this audit quantifies leakage inflation on ROC-AUC, computed identically across 12 models. We measure how much performance stems from target leakage via Survival Months, which encodes retrospective survival time unavailable at prediction. Undetected, such leakage could lead clinicians to overtrust a model's risk estimates, risking inappropriate care decisions. We train 12 models under three conditions: a) naïve, with Survival Months and a non-stratified split; b) leakage-free with stratified evaluation; and c) fixed-horizon (36- and 60-month) binary mortality, excluding censored cases. Under Condition A, the best ROC-AUC is 0.889 (accuracy 0.899). Removing leakage yields a consistent AUC ceiling of 0.74–0.77 across all 12 models — an inflation of +0.07 to +0.20 AUC points. SHAP shows Survival Months dominates feature importance by 3.0–4.3× under leakage; imbalance-handling methods (BorderlineSMOTE, class weighting) raise minority-class F1 but lower mean Brier Skill Score to −0.585 and −0.597. The contribution is the audit methodology itself — a reusable protocol for detecting leakage-driven inflation in registry-based clinical prediction.
Co-Authors Eko Amri Jaya Muhamad Pradana Hardi Wasesa Sestri Novia Rizki Vani Maharani Nasution Verra Budhi Lestari