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All Journal Jurnal Computer Science and Information Technology (CoSciTech)
Fauzan Salim
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Computer Science & IT

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Klasifikasi Berita Palsu Menggunakan Pendekatan Hybrid CNN-LSTM Fauzan Salim; Wahyudhy, Adhe Indra
Computer Science and Information Technology Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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Abstract

Fake news detection has become a major challenge in the rapidly evolving digital era. This study proposes a hybrid approach combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to enhance the accuracy of fake news classification. CNN is utilized to extract local features from text, while LSTM captures temporal relationships in sequential data. The dataset used is sourced from Kaggle, consisting of 44,919 fake and real news articles. The classification process includes several stages, such as data preprocessing, tokenization, and transforming text into numerical representations before being processed by the hybrid CNN-LSTM model. Evaluation results indicate that the hybrid CNN-LSTM model achieves an accuracy of 99%, outperforming individual CNN and LSTM models. With high precision and recall rates, this method proves to be effective in classifying fake news, significantly contributing to the development of a more accurate and reliable fake news detection system.
Klasifikasi Penyakit Daun Kentang dengan Transfer Learning Menggunakan CNN optimalisasi Arsitektur MobileNetV2 Gunawan, Rahmad; Fauzan Salim; Wahyudhy, Adhe Indra; Wibowo, Angga Yudha; Yordan, Gibril; Filamori, Refly Fauzan
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.8599

Abstract

Potatoes are a major food crop with high economic value, but they are susceptible to various Diseases impacting potato leaves can significantly influence their quality and productivity. This research focuses on identifying diseases in potato leaves through the Convolutional Neural Network (CNN) approach, leveraging transfer learning with the MobileNetV2 architecture. The dataset utilized comprises 4,072 images of potato leaves. categorized into three groups: non-infected leaves (healthy ), Early Blight-infected leaves, and Late Blight-infected leaves. The dataset is processed through data augmentation and normalization to enhance data quality. The resulting model demonstrates excellent performance, achieving an accuracy of 95.31%, a precision of 95.81%, a recall of 95.31%, and an F1-Score of 95.38%. These findings indicate the approach demonstrates its ability to identify the condition of potato leaves with a low classification error rate, especially in the healthy category. However, there are challenges in classifying between Early Blight and Late Blight that require further analysis and method improvement. This study contributes to the development of efficient and accurate plant disease detection systems.
Co-Authors Filamori, Refly Fauzan Gunawan, Rahmad Wahyudhy, Adhe Indra Wibowo, Angga Yudha Yordan, Gibril