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All Journal Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Publikasi Ilmu Komputer dan Multimedia
Rusma Riansyah
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Computer Science & IT Industrial & Manufacturing Engineering

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Klasifikasi Jenis Bunga Iris Menggunakan Algoritma Klasifikasi Tradisional Alwi Syahputra; Rusma Riansyah; Dimas Aqila Aptanta; Muhammad Farhan; Mhd. Furqan
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1228

Abstract

This study aims to implement and compare the performance of two traditional classification algorithms, namely K-Nearest Neighbor (K-NN) and Naive Bayes to classify Iris flower types. The dataset used is the Iris Dataset which is a classic dataset in machine learning consisting of 150 samples with four features (sepal length, sepal width, petal length, and petal width) and three target classes (Iris Setosa, Iris Versicolor, and Iris Virginica). The research methodology includes data preprocessing, algorithm implementation, model evaluation using accuracy, precision, recall, and F1-score metrics, and comparative performance analysis. The results showed that the K-NN algorithm with k = 3 achieved an accuracy of 96.67%, while Naive Bayes achieved an accuracy of 93.33%. Both algorithms showed good performance in classifying Iris flower types, with K-NN slightly superior in terms of accuracy. This study proves that traditional classification algorithms are still relevant and effective for classification problems with less complex datasets.
Implementasi Website Deteksi Phishing Link Menggunakan SSL Validation dan URL Scoring Rusma Riansyah; Dimas Aqila Aptanta; Hafiz Aryanda; Muhammad Farhan; Ibnu Rusydi
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 4 No. 1 (2026): Februari: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v4i1.1439

Abstract

The rapid expansion of internet usage has led to a significant increase in cybersecurity threats, particularly phishing attacks delivered through malicious links. Phishing links are designed to imitate legitimate websites in order to deceive users and steal sensitive information. This study presents the implementation of a phishing link detection website based on SSL validation and URL scoring mechanisms. The proposed system integrates heuristic-based URL analysis with real-time SSL certificate validation obtained through the SSL handshake process. Digital certificates are verified using RSA-based digital signature verification issued by trusted Certificate Authorities (CAs). In addition, the SHA-256 hash algorithm is employed to generate certificate fingerprints and URL hashes to ensure data integrity and uniqueness. The system also evaluates HTTPS usage, domain and certificate consistency, certificate validity period, and RSA public key strength. All validation results are processed using a URL scoring system to generate a security score ranging from 0 to 100, which classifies links into safe, suspicious, or dangerous categories. Experimental results demonstrate that the proposed website is capable of effectively identifying phishing indicators and providing transparent cryptographic evidence in real time. This approach can assist users in making informed decisions and improving protection against phishing threats in web environments.
Implementasi Algoritma Convolutional Neural Network (CNN) untuk Pengenalan dan Klasifikasi Buah Berdasarkan Citra Digital Ahmad Fariz Fuady; Dwiky Oldi Amsyah; Muhammad Farhan; Rusma Riansyah; M. Dayyan Dhiyaul Haq
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol. 4 No. 2 (2025): Mei: Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupikom.v4i2.4116

Abstract

Object recognition, particularly fruit classification, plays a crucial role in various fields, ranging from agricultural automation to digital marketplaces. This study proposes a fruit classification system based on RGB images, developed using a Convolutional Neural Network (CNN) architecture consisting of convolutional layers, pooling layers, fully connected layers, and dropout for model stability. The model was trained using the Adam optimization algorithm on an augmented dataset to enhance data variation and reduce overfitting. The resulting model achieved an average accuracy of 98%, demonstrating the reliability of CNNs in pattern recognition tasks. To enhance usability, the model was integrated into a graphical user interface (GUI) built with MATLAB R2023b App Designer, allowing users to add datasets, train the model, and predict new images without writing any code. The findings highlight that while the model performs well, its accuracy remains dependent on consistent image backgrounds; therefore, expanding the variety of fruit types and background conditions in the dataset is essential to improve the system's robustness in real-world applications.
Co-Authors Ahmad Fariz Fuady Alwi Syahputra Dimas Aqila Aptanta Dwiky Oldi Amsyah Hafiz Aryanda Ibnu Rusydi M. Dayyan Dhiyaul Haq Mhd. Furqan Muhammad Farhan