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Rafel Fernando
Universitas Duta Bangsa Surakarta
Computer Science & IT

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Implementasi NLP untuk Deteksi Teks Buatan AI (Chat-GPT) menggunakan Metode Naive Bayes Rafel Fernando; Yuliana Dewi Proboningrum; Septi Dwi Supriati; Nurmalitasari Nurmalitasari
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2026

Abstract

The development of artificial intelligence (AI) technology, especially large language models like ChatGPT, presents challenges related to the authenticity and validity of digital content. AI's ability to produce human-like text opens up opportunities for misuse, such as plagiarism and information manipulation. This study aims to develop an AI text detection system using the Multinomial Naive Bayes algorithm, due to its ease of use and high effectiveness algorithm has become a popular choice for text classification.. The dataset used is the Human ChatGPT Comparison Corpus (H3C), sourced from the ELI5 subreddit on Reddit, consisting of 800 entries of questions and answers from both humans and AI. The labeling process involves combining answers into a single column and assigning labels based on the source. Preprocessing steps include case folding, removal of digits and punctuation, tokenization, stopword removal, normalization, and text finalization. Text features are extracted using the TF-IDF method, limited to the top 1000 features. The model is trained on 80% of the data and tested on the remaining 20%. The evaluation shows an accuracy of 93%. These findings suggest that the Naive Bayes method is effective in distinguishing AI-generated from human-generated text and has potential as an automatic AI content detection tool.
Deteksi dan Klasifikasi Sampah Organik dan Anorganik Menggunakan Algoritma Yolo di Solo Technopark Rafel Fernando; Afu Ichsan Pradana; Sopingi
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2362

Abstract

The advancement of artificial intelligence (AI) technology, particularly in the area of computer vision, has encouraged the use of automatic object detection methods for various needs, including the classification of organic and inorganic waste. The problem of waste management in the Solo Technopark area which is still carried out manually causes the waste sorting process to not run optimally. This research focuses on developing and evaluating the performance of several YOLO models for detecting and classifying organic and inorganic waste types in real-time. The research dataset contains 6,758 waste images categorized into 10 object classes, obtained from Roboflow. The preprocessing stages include annotation, auto-orientation, and image resizing to 640×640 pixels. The dataset is then divided into 70% training data, 20% validation, and 10% testing. This study used three YOLO models, namely YOLOv11, YOLOv12, and YOLOv26 with epoch variations of 10, 30, 50, and 100. Model evaluation was carried out using precision, recall, mAP50, mAP50-95, and inference time metrics. The results showed that the best model was obtained on YOLOv26 epoch 100 with a precision value of 0.92, recall of 0.847, mAP50 of 0.892, mAP50-95 of 0.741, and inference time of 3.0 ms. These findings indicate that the YOLOv26 model has good capabilities in detecting and classifying organic and inorganic waste accurately and quickly, so it has the potential to be used as a basis for developing a real-time waste detection system.
Co-Authors Afu Ichsan Pradana Nurmalitasari Nurmalitasari Septi Dwi Supriati Sopingi Yuliana Dewi Proboningrum