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                  <text>VOL. 23, NO.1 2025</text>
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                <text>Simple RNN-LSTM hybrid deep learning model for Bitcoin and EUR_USD forecasting</text>
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                <text>Bitcoin&#13;
Deep learning&#13;
Gated recurrent unit&#13;
Long short term memory&#13;
Simple recurrent neural network&#13;
Time series forecasting</text>
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                <text>The popularity of deep learning in time series prediction has significantly increased compared to the past. In this article, we utilize deep learning methods, which encompass long short term memory (LSTM) networks, simple recurrent neural network (SimpleRNN) networks, and gated recurrent units (GRU) networks. This research introduces a hybrid foundational model for forecasting future closing prices of EUR_USD in financial time series and Bitcoin, combining SimpleRNN with LSTM, referred to as SimpleRNN-LSTM. To improve the precisions of our hybrid model, we incorporate twenty-one technical indicators into the training data. Then, we compute four measures to evaluate the performance of various prediction models. When predicting currency pairs EUR_USD and Bitcoin, our hybrid foundational model outperforms SimpleRNN, LSTM, and GRU models.</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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        <name>Bitcoin Deep learning Gated recurrent unit Long short term memory Simple recurrent neural network Time series forecasting</name>
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                  <text>VOL. 23, NO.1 2025</text>
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                <text>Swarm intelligence for intrusion detection systems in internet of things environments</text>
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                <text>Internet of things&#13;
Intrusion detection systems&#13;
Network security&#13;
Pigeon inspired optimization&#13;
Swarm intelligence</text>
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                <text>The rise of the internet of things (IoT) technology has brought new security challenges, necessitating robust intrusion detection systems (IDS). This research applies swarm intelligence (SI) principles, specifically the pigeon inspired optimization (PIO) algorithm, to enhance IDS effectiveness in IoT environments. Drawing on the behavior of social species, SI fosters decentralized control and emergent behavior from simple rules. These principles guide the PIO algorithm, making it apt for optimizing IDS. We utilize two comprehensive IoT datasets – the Canadian Institute for Cybersecurity (CIC) IoT dataset 2023 and the IoT dataset for IDS, aiming to boost the IDS’s capability to detect illicit attacks. By adapting the PIO algorithm, our IDS learns from the environment, adapts to evolving threats, and mitigates false-positive rates. Preliminary tests show that our SI-based IDS outperforms traditional systems’ accuracy, speed, and adaptability. This research advances SI applications in IoT security, contributing to developing more resilient IDS and ultimately enhancing IoT network security against a range of cyber threats.</text>
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                <text>Apri Siswanto1, Akmar Efendi1, Jaroji2, Fajar Ratnawati2</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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                <text>NAT64 vs SIIT: performance and scalability study for VoIP services</text>
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                <text>Graphical network simulator 3&#13;
Internet protocol version 6&#13;
Internet protocol service level agreement&#13;
Network address translation 64&#13;
Scalability&#13;
Stateless IP/ICMP translation&#13;
Voice over internet protocol</text>
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                <text>The growing demand for IP addresses, driven by the proliferation of devices, has depleted the internet protocol (IP) version 6 (IPv6) reserves of some regional internet registries (RIRs). It is imperative to migrate to IPv6, offering an extended addressing space. This transition is no longer a choice but a necessity due to the exhaustion of IP version 4 (IPv4) addresses. The internet engineering task force (IETF) has implemented various transition strategies, such as the use of dual stack, IPv6-in-IPv4 tunnels, and address translation, due to the inconsistency between the two versions of the IP (IPv4 and IPv6). IPv4/IPv6 address translation mechanisms are crucial for the coexistence of networks using both protocols, with scalability playing a central role. Although these mechanisms offer advantages such as optimizing addressing space, their ability to scale effectively must be evaluated, especially in demanding scenarios such as voice over IP (VoIP). This article examines the scalability of two mechanisms, network address translation 64 (NAT64) and stateless IP/internet control message protocol (ICMP) translation (SIIT), in terms of VoIP clients in the graphical network simulator 3 (GNS3) environment. The results indicate that the SIIT mechanism is more performant and scalable than NAT64 in all measured parameters.</text>
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                <text>Khalid El Khadiri2, Samir Elouaham1, Najib El Kamoun2, Boujemaa Nassiri3, Ali El Ksimi4, Said Amrane2, Said Dlimi2</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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        <name>Graphical network simulator 3 Internet protocol version 6 Internet protocol service level agreement Network address translation 64 Scalability Stateless IP/ICMP translation Voice over internet protocol</name>
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Power spectral density&#13;
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                <text>The conventional method for assessing the working memory performance of children is time-consuming and potentially inaccurate, especially when dealing with many samples. Therefore, an automated system that can produce swift and accurate results is required. Electroencephalograms (EEG) can be used to analyse the working memory status of children by extracting specific features from the EEG signal, which can be incorporated into an automatic system to reduce manpower and processing time for analysis. This project used EEG recording to identify children’s working memory status while they were performing working memory tasks. EEG signals were acquired from both children and adults using an automated computer-based working memory assessment tool, processed, and analyzed. The discrete wavelet transform (DWT) was then employed to identify five distinct working memory statuses: distracted, confused, daydreaming, losing focus, and active. DWT was also used to extract features that demonstrate these various statuses. The results showed that DWT could accurately identify the working memory status of both children and adults from their EEGs. This work has thus provided a more efficient method for extracting features from EEG signals to identify working memory statuses in both children and adults.</text>
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                <text>Muhammad Hilmi Khairul Azlan1,2, Wahidah Mansor1,2, Ahmad Ihsan Mohd Yassin1,2, Nabila Ameera Zainal Abidin1,2, Mirsa Nurfarhan Mohd Azhan1,2, Aisyah Hartini Jahidin3, Muhammad Fakharul Radzy Mohd Rozlan4, Zulkifli Mahmoodin5,6, Megat Syahirul Amin Megat Ali1,2</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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Optical communication&#13;
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Reconfigurable intelligent surfaces</text>
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                <text>Near terrestrial free-space optical (NT-FSO) communication refers to the use of light to transmit data wirelessly through the atmosphere over relatively short distances, typically within the earth’s atmosphere. It is an alternative to traditional radio frequency (RF) communication and fiber optics, providing high-speed data transmission without the need for physical cables. NT-FSO is an attractive solution for high-speed, short-distance wireless communications where physical infrastructure like fiber is impractical. Reconfigurable intelligent surfaces (RIS) are a cutting-edge technology that enhances wireless communication systems by dynamically controlling how electromagnetic waves propagate through an environment. RIS technology is increasingly relevant in modern communication systems like 5G and beyond, aiming to improve the efficiency, coverage, and energy usage of wireless networks. This study investigates the effects of atmospheric turbulence and RIS on near terrestrial optical link for internet of things. Several numerical outcomes obtained for different link distance and average electrical signal signal-to-noise ratio (SNR) are shown to quantitatively illustrate the average spectral efficiency.</text>
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                <text>Duong Huu Ai1, Hoang Huu Duc2, Khanh Ty Luong3, Ngoc Dat Vo3</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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                <text>PERI IRAWAN</text>
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        <name>Atmospheric turbulence Average channel capacity Optical communication Quadrature amplitude modulation Reconfigurable intelligent surfaces</name>
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                  <text>VOL. 23, NO.1 2025</text>
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                <text>Heart disease&#13;
Neural network&#13;
Prediction&#13;
Random forest&#13;
Support vector machine</text>
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                <text>The heart is a vital organ responsible for pumping blood throughout the human body. Machine learning has become an increasingly important tool in medical forecasting, improving diagnostic accuracy and reducing human errors. This study focuses on detecting heart disease using machine learning algorithms. It aims to compare the performance of three key algorithms random forest (RF), support vector machine (SVM), and neural networks (NN), in predicting heart disease. Using a patient dataset with both nominal and numeric attributes, record mining techniques were applied through Orange software. The target classes indicated the absence (0) or presence (1) of heart disorders. The evaluation was based on the prediction accuracy of each algorithm. Results show that SVM achieved the highest accuracy, with a rate of 85%, outperforming RF and NN. The findings suggest that the SVM algorithm is a reliable tool for heart disease prediction, helping reduce diagnostic errors and improve medical decision-making.</text>
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                <text>Didik Setiyadi1, Henderi2, Anrie Suryaningrat3, Rulin Swastika4, Saludin5, Muhamad Malik Mutoffar6, Imam Yunianto7</text>
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                <text>Journal homepage: http://telkomnika.uad.ac.id</text>
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