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All Journal Elkom: Jurnal Elektronika dan Komputer Indonesian Journal of Innovation Multidisipliner Research
Ria Putri Sunaryo
Universitas Darma Persada
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering

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Perbandingan Metode Random Forest dan Convolutional Neural Network dalam Deteksi Website Phishing pada Lingkungan Universitas XYZ Elang Prasakti Ghani; Ria Putri Sunaryo
Elkom: Jurnal Elektronika dan Komputer Vol. 19 No. 1 (2026): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v19i1.3602

Abstract

Phishing attacks are a cybersecurity threat often used to steal sensitive user information through fake websites that resemble legitimate sites. Therefore, this study aims to analyze and compare the performance of the Random Forest and Convolutional Neural Network (CNN) algorithms in detecting phishing websites based on Uniform Resource Locator (URL) features. The dataset used was obtained from Web Application Firewall (WAF) security logs on the network infrastructure at XYZ University, which record URL access activities on the web system. The data was then processed and labeled into two categories: phishing and legitimate websites. The dataset used in this study consists of 549,346 URL records. The research stages include data exploration (Exploratory Data Analysis / EDA), URL text preprocessing, character-based feature extraction and URL tokenization, and model training using the Random Forest algorithm and a 1D Convolutional Neural Network (1D-CNN) architecture. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics as well as confusion matrix analysis. The results showed that the Random Forest model achieved an accuracy of 82.69%, while the 1D-CNN model achieved a higher accuracy of 95.94%. Furthermore, the CNN training process demonstrated a steady increase in accuracy and a decrease in loss values in each epoch. Based on these results, it can be concluded that the deep learning approach using CNN outperforms the Random Forest method in detecting URL-based phishing websites
Klasifikasi Gangguan Mental Menggunakan Algoritma K-Nearest Neighbors dan Support Vector Machine Berdasarkan Data Rekam Medis Ria Putri Sunaryo; Aning Fara Andina
Indonesian Journal of Innovation Multidisipliner Research Vol. 4 No. 3 (2026): Juli - September
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v4i3.1374

Abstract

Gangguan mental merupakan penyakit gangguan kesehatan yang disebabkan oleh ketidakmampuan dalam mengendalikan diri, halusinasi, delusi, ilusi, waham dan ketakutan yang berlebihan dalam berperilaku. Jenis-jenis gangguan mental umumnya terdiri dari Paranoid, Undifferentiated, Depresi, dan Shizoaffective. Penelitian bertujuan untuk membantu melakukan klasifikasi jenis gangguan mental menggunakan machine learning. Machine learning merupakan kecerdasan buatan, dimana suatu teknik dapat membuat komputer belajar dari sebuah data untuk membantu mengenali suatu pola klasifikasi. Model algoritma yang digunakan dalam penelitian ini menggunakan algoritma supervised learning yaitu suatu model algoritma dimana data label output sudah ditentukan berdasarkan jenis kategori. Data penelitian diperoleh melalui data rekam medis pasien di Rumah Sakit PQR dengan 94 fitur dan 4 kategori. Hasil akurasi pengujian dengan support vector machine menghasilkan akurasi 88%, sedangkan dengan k-nearest neighbors menghasilkan akurasi 81%. Pemilihan fitur yang signifikan dengan memanfaatkan metode recursive feature elimination dengan jumlah fitur menjadi 43 fitur menghasilkan akurasi lebih tinggi sebesar 90% pada support vector machine dan akurasi 94% pada k-nearest neighbors.  
Co-Authors Aning Fara Andina Elang Prasakti Ghani