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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Informatics and Computation Bima Abdi: Jurnal Pengabdian Masyarakat Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology International Journal of Informatics Engineering and Computing Jurnal Informatika
Selamet Riadi
Universitas Teknologi Mataram
Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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Journal : international journal of informatics engineering and computing

 1 
Intrusion Detection System in Network Security Using Naive Bayes and Support Vector Machine Selamet Riadi; Mohammad Nur Fawaiq
International Journal of Informatics Engineering and Computing Vol. 1 No. 1 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i1.24

Abstract

An Intrusion Detection System (IDS) is designed to detect suspicious activities or security threats within a network, necessitating continuous advancements in the field. Both implementation techniques and algorithmic research play pivotal roles in enhancing IDS capabilities. This study addresses this need by focusing on the implementation and comparison of two prominent classification models: Naive Bayes and Support Vector Machine (SVM). The study is centered within the domain of Intrusion Detection System (IDS) tailored for network security. In the course of this research, a relevant dataset sourced from Kaggle serves as the foundation for training and testing both classification models. The findings of this study underscore the models' efficacy in intrusion detection. The SVM model, in particular, emerges as a standout performer, showcasing an accuracy rate that approaches 100%, thus exemplifying its potential in real-world scenarios. Meanwhile, the Naive Bayes model delivers commendable accuracy, surpassing 88%. This investigation not only contributes to the advancement of intrusion detection methodologies but also highlights the viability of these classification models for bolstering network security against the ever-evolving threat landscape.
Enhancing Rainfall Prediction Using LSTM Algorithm Selamet Riadi; Trisna Jamil
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.86

Abstract

Rainfall is an important factor that influences various aspects of human life, including agriculture, transportation, and urban planning. With climate change, the need for accurate rainfall prediction systems is becoming increasingly urgent. Traditional methods, such as statistical or physical models, often struggle to deal with the complex and nonlinear nature of weather data. This research proposes the use of Long Short-Term Memory (LSTM), a deep learning model capable of processing sequential data, to predict rainfall based on historical data. The model can capture long-term dependencies, making it suitable for analyzing meteorological data such as temperature, humidity, wind speed and rainfall intensity. This paper investigates the performance of an LSTM-based rainfall prediction system, and compares it with traditional forecasting methods. Evaluation metrics such as Root Mean Square Error (RMSE) are used to assess the accuracy of predictions. These findings indicate that LSTM-based models provide a more reliable solution for rainfall prediction, especially in detecting extreme weather events early.
Co-Authors AA Sudharmawan, AA Ahmad Yani Aulia Riswanti Naya Efendi, Muhamad Masjun Ema Utami Erfan Wahyudi Imran, Bahtiar Karina Nurwijayanti Liana Trihardianingsih M. Zulpahmi Mohammad Nur Fawaiq Nurkholis, Lalu Moh. Rudi Muslim San Sudirman Supardianto Suryadi, Emi Trisna Jamil Uska, Muhammad Zamroni Zaeniah Zaeniah Zulpan Hadi