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All Journal Journal of Electrical Engineering and Computer (JEECOM) Sisfo: Jurnal Ilmiah Sistem Informasi
Tarigan, Anjasmara
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mechanical Engineering

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Analisis Algoritma Naïve Bayes Classifer Untuk Mendeteksi Berita Hoax Pada Dinas Kominikasi Informatika Dan Persandian Tarigan, Anjasmara; Sahputra, Ilham; Multazam, Teuku
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 8 No. 1 (2024): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2024
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v8i1.18083

Abstract

Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima. Oleh karena itu, penulis membahas tentang bagaimana mengklasifikasikan berita hoax menggunakan algoritma Naïve Bayes Classifier sehingga mampu memprediksi sebuah berita hoax atau fakta. Dataset yang dikumpulkan sebanyak 200 diantaranya 100 fakta dan 100 hoax. Dalam proses data split dengan pembagian data testing dan data trainning dengan pembagian 60% data training dan 40% data testing yaitu 120 data trainning dan 80 data testing. Dengan menggunakan algoritma Naive Bayes Classifer mendapatkan nilai Akurasi Sebesar 97%. Pada penelitian ini juga membahas tentang pemanfaatan TF-IDF dalam melakukan Klasifikasi Berita Hoax dengan menggunakan Algoritma Naive Bayes Classifer. Dengan nilai Akurasi 97% menyatakan bahwa Algoritma Naive Bayes Classifer efektif dalam melakukan klasifikasi. Algoritma in mengandalkan teorema Bayes untuk menghitung probabilitas berita hoax berdasarkan kemunculan kata-kata atau fitur-fitur lainnya dalam berita.
Application of Backpropagation Artificial Neural Networks for Optimizing Corn Production Prediction in Karo Regency Pratama, Angga; Yulisda, Desvina; Tarigan, Anjasmara
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i2.13065

Abstract

Corn production in Karo Regency, North Sumatra, plays a crucial role in supporting regional food security and the local economy. However, fluctuations in production caused by unpredictable environmental conditions and limited data-driven forecasting methods have made it difficult for policymakers and farmers to plan effectively. This study aims to address this problem by developing a model to predict corn production using the Backpropagation Neural Network (BPNN) method. The study utilized 302 cleaned datasets, with Planted Area and Harvested Area as input variables, and Production as the output variable. The dataset was divided into 70% for training and 30% for testing. Five BPNN architectures (ranging from 2-4-1 to 2-12-1) were tested using three activation functions (Sigmoid, ReLU, and Tanh), with a maximum of 200 iterations and a learning rate of 0.01. The best results were achieved by the 2-12-1 architecture with the Tanh activation function, obtaining an R-squared value of 94.86% and a Mean Squared Error (MSE) of 0.0039. These findings demonstrate that the Backpropagation Neural Network is effective for forecasting corn production and can serve as a valuable decision-support tool for sustainable agricultural planning in the region.
Co-Authors Angga Pratama Sahputra, Ilham Teuku Multazam Yulisda, Desvina