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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa
Dyah Cita Irawati
Gunadarma University
Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Klasifikasi Jenis Daging Menggunakan Algoritma YOLOv8 Dyah Cita Irawati; Nur Fitriansyah Aji
Jurnal Ilmiah Informatika Komputer Vol. 30 No. 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2025.v30i2.243

Abstract

Dalam kehidupan sehari-hari tubuh manusia memerlukan konsumsi daging sapi sebagai salah satu sumber protein hewani karena memiliki kandungan zat besi, selenium, zinc, vitamin B kompleks dan omega 3. Dalam melakukan pembelian daging sapi merupakan persoalan tersendiri bagi masyarakat awam, karena secara kasat mata bentuk daging sapi dan daging lainnya, terutama daging babi, sangat tidak mudah untuk dibedakan. Kesulitan keterbatasan visual manusia yang timbul tersebut menyebabkan konsumen seringkali tertipu saat membeli daging sapi. Perbedaan secara umum kedua daging tersebut terletak pada warna dan tekstur daging. Untuk mengatasi hal tersebut diperlukan adanya peran teknologi yang bisa digunakan untuk membantu membedakan pengenalan jenis daging agar konsumen dapat mengenalinya secara lebih akurat. Penerapan model Deep Learning dengan menggunakan algoritma Convolutional Neural Network yaitu You Only Look Once v8 (YOLOv8) menjadi salah satu metode yang dapat diterapkan untuk mengenali daging sapi pada bidang informatika. Precision 0.974, Recall 1, mAP 0.955 menunjukkan hasil penelitian kinerja dan waktu komputasi menggunakan YOLOv8 pada daging babi, sedangkan pada daging sapi metode YOLOv8 menghasilkan Precision 1, Recall 0.994, mAP 0.995 dengan waktu komputasi kurang lebih 56.52 menit.
Optimalisasi Deteksi Tingkat Kematangan Tanda Buah Segar Kelapa Sawit Menggunakan YOLOV8 Dengan Platform Web Iffatul Mardhiyah; Dyan Prawita Sari; Zahwa Genoveva; Rifki Kosasih; Dyah Cita Irawati
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 30 No. 3 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i3.67

Abstract

Oil palm represents one of Indonesia’s principal commodities. Traditionally, farmers manually monitor the ripeness level of palm oil, but this method is neither effective nor efficient for large-scale harvests. Therefore, a system that can automatically detect the ripeness level of fresh fruit bunches (FFB) is needed. In this study, the YOLOv8 algorithm was used which was integrated into a web-based application. The system is designed to improve accuracy and efficiency in the grading process of oil palm fruits, which directly impacts the quality of processed products and palm oil production. The dataset used consists of 6.592 images obtained through the Roboflow platform, covering various ripeness categories. The system development follows the CRISP-DM approach, consisting of business understanding, data understanding, data preparation, modeling, evaluation and deployment. The model training process approximately 3,1 hours, with evaluation results showing a precision of 94,5%, recall of 94,7%, and a mean Average Precision (mAP) of 98%. The model’s performance is further supported by an F1-confidence curve of 95% and a precision-recall curve of 98%, indicating stable and accurate classification capabilities. The model is deployed through a Streamlit-based web interface, allowing users to perform real-time detection from images or videos without requiring additional installations.
Aplikasi Deteksi Website Phishing Berbasis Web Menggunakan Random Forest dan Ekstraksi Fitur URL Adytia Dwi Wulandari; Dyah Cita Irawati
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 30 No. 3 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i3.71

Abstract

Advancements in information technology have raised growing concerns among various stakeholders. Phishing attacks have become one of the most common cyber threats, targeting users by imitating legitimate websites to obtain sensitive information. This study aims to develop a web-based application by implementing a supervised learning approach using the Random Forest algorithm to automatically classify URLs as phishing or legitimate. The dataset used consists of 11,054 URL instances with 30 URL-based features. The research process includes data preprocessing, feature extraction, data splitting, and classification model development and evaluation using four data partition scenarios. Model performance was assessed using accuracy, precision, recall, and F1-score as evaluation metrics. The results of the experiments show that the model achieved optimal performance with an 80:20 data split, obtaining an accuracy of 97%, precision of 97%, recall of 98%, and an F1-score of 97%. Furthermore, the trained model was implemented in a web-based application, allowing users to automatically detect URLs.
Co-Authors Adytia Dwi Wulandari Dyan Prawita Sari Iffatul Mardhiyah Nur Fitriansyah Aji Rifki Kosasih Zahwa Genoveva