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All Journal Jurnal Mantik Journal of Artificial Intelligence and Digital Business
Limbong, Yohannes France
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Pattern recognition of 5G device serial number using K-Nearest Neighbors (K-NN) machine learning algorithm Sitorus, Zulham; Antoni, Robin; Limbong, Yohannes France; Sitompul, Jelly Rolley; Br Tarigan, Sella Monika
Jurnal Mantik Vol. 8 No. 4 (2025): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i4.6079

Abstract

5G networks are the latest generation of mobile communications technology that offer significant improvements in speed, capacity, and connectivity. However, along with the benefits it brings, it also brings a new set of challenges in the form of security breaches. Many 5G devices have been lost on-site. These devices are ABIA, AMIA, ASIB. Each of these devices has a serial number as identification data for each device. The rise of theft cases is due to the existence of collectors who are able to buy expensive stolen 5G devices for resale. So, the research will make the introduction of 5G device serial numbers using the Machine Learning (ML) with K-Nearest Neighbors (K-NN) algorithm. This pattern recognition is success to be done then become a guidance to recognizing stolen 5G devices. Next, this device cannot be used (deactivated) and be sold by system. This can break the demand and supply chain for stolen 5G devices. Based on the testing, there are 6 mismatches of 20 data testing or 70% data match
Enhanced Rainfall Forecasting Through Deep Learning Optimization Using Long Short-Term Memory Networks Harefa, Ade May Luky; Antoni, Robin; Sitepu, Andri Ismail; Limbong, Yohannes France; Novelan, Muhammad Syahputra
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.487

Abstract

This study aims to develop a rainfall prediction system using Deep Learning with the Long Short-Term Memory (LSTM) method to improve prediction accuracy and efficiency. The model was built using rainfall data from Gunung Sitoli, covering the period from October 16 to December 14, 2004. The dataset was divided into 90% for training and 10% for testing. The LSTM model was configured with 1 hidden layer and trained for 50 epochs. To evaluate its performance, the Mean Squared Error (MSE) metric was applied. The model achieved an MSE of 0.03 on the test data, indicating a low prediction error and good accuracy. This result shows that LSTM is capable of learning rainfall patterns over time and producing reliable forecasts. Furthermore, the model was integrated into a system to streamline the forecasting and evaluation process. This integration provides an efficient alternative to manual calculations, offering users faster and more accessible predictions. The implementation of this system is especially beneficial for early warning and decision-making processes in regions like Gunung Sitoli, where rainfall patterns can significantly impact on daily activities and disaster preparedness.
Co-Authors Antoni, Robin Br Tarigan, Sella Monika Harefa, Ade May Luky Muhammad Syahputra Novelan Sitepu, Andri Ismail Sitompul, Jelly Rolley Sitorus, Zulham