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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Science in Information Technology Letters
Muhammad Haikal Satria
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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A novel energy-efficient dynamic programming routing protocol in wireless multimedia sensor networks Putra, Emansa Hasri; Satria, Muhammad Haikal; Azwar, Hamid; Rianda, Rendy; Saputra, Muhammad; Darwis, Rizadi Sasmita
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5855

Abstract

Wireless multimedia sensor networks (WMSNs) have characteristics that may influence the routing decisions, such as limited energy resources, storage and computing capacity. Therefore, a routing optimization needs to be done to match the characteristics of the WMSNs. Existing routing protocols only consider energy efficiency regardless of energy threshold, maximum energy, and link cost collectively as the primary basis of routing. In this work, the energy-efficient dynamic programming (EEDP) protocol is proposed to optimize routing decisions that take into account the energy threshold, the maximum energy, and the link cost. Then, the protocol is compared with the dynamic programming (DP), and the ant colony optimization (ACO) protocol. The simulation results show that the EEDP protocol can improve energy efficiency of nodes and network lifetime of the WMSNs. Then, the EEDP protocol is also implemented into a network topology of 10 NodeMCU ESP32 devices. As a result, the EEDP protocol can work very well by selecting routes based on nodes that have the remaining energy above 50 and has the shortest distance. The average delay in sending data for the entire route for the 10 iterations of sending data is 3.99 seconds.
Artificial Intelligence for Thyroid Disorders: A Systematic Review Al Hakim, Rosyid Ridlo; Satria, Muhammad Haikal; Arief, Yanuar Zulardiansyah; Setiawan, Antonius Darma; Pangestu, Agung; Hidayah, Hexa Apriliana
Science in Information Technology Letters Vol 2, No 2: November 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i2.694

Abstract

The thyroid gland plays a very important role in hormonal regulation in the human body. If the thyroid gland has a disorder, it can affect the performance of body functions. The development of artificial intelligence technology today allows an expert such as a doctor to be helped by his work. One of the important roles of artificial intelligence is helping doctors, among others, to diagnose a patient to determine appropriate post-diagnosis care. The study aims to shed light on the role of artificial intelligence in the treatment of thyroid disorders.The thyroid gland plays a very important role in hormonal regulation in the human body. If the thyroid gland has a disorder, it can affect the performance of body functions. The development of artificial intelligence technology today allows an expert such as a doctor to be helped by his work. One of the important roles of artificial intelligence is helping doctors, among others, to diagnose a patient to determine appropriate post-diagnosis care. The study aims to shed light on the role of artificial intelligence in the treatment of thyroid disorders.
Tailoring Data Storage Configuration for Efficient Fraud Detection Model Training Syarif, Abdusy; Satria, Muhammad Haikal; Gabteni, Hanene
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30013

Abstract

The rapid growth of e-commerce in Indonesia, with a record 88.1% growth rate, has been accompanied by a surge in online fraud, leading to an estimated loss of 4.62 trillion rupiahs. Current fraud prevention methods, such as the widely used 3D-Secure system, though effective, result in a high rate of transaction abandonment (approximately 16%), which is undesirable for merchants. To address this, we propose an AI-based fraud detection system that leverages machine learning models to identify potentially fraudulent transactions. By employing a combination of classification algorithms, including logistic regression and neural networks, security protocols are activated only for high-risk transactions, optimizing transaction processing efficiency and improving detection accuracy. Our study focuses on fine-tuning key parameters of the AI-Fraud Detector model, specifically some parameters such as ∆ttrain, ∆tlag and f rac hr pass, to enhance detection performance over time. Simulation performances using ROCAUC, false positive rate (fpr), and true positive rate (tpr) metrics show that a configuration with a training period (∆ttrain) of 180 days, a lag period (∆tlag ) of 90 days, and a high-risk pass fraction (f rac hr pass) of 10% yields a balance between detection efficiency (∼ 50%) and a reduced false positive rate. It means that the model is able to identify approximately 50% of the actual high-risk events while minimizing the number of times it incorrectly identifies a low-risk event as high-risk. However, further research is required to refine these results, explore parameter optimization strategies, and enhance the model’s adaptability to evolving fraud patterns. Future work will focus on optimizing thresholds, improving model robustness over time, and ensuring effective detection of new fraud schemes. This research improves model performance by optimizing key parameters and enhancing detection accuracy while minimizing false positives
Co-Authors Abdusy Syarif Agung Pangestu Azwar, Hamid Emansa Hasri Putra Gabteni, Hanene Hexa Apriliana Hidayah Rianda, Rendy Rizadi Sasmita Darwis, Rizadi Sasmita Rosyid Ridlo Al-Hakim Saputra, Muhammad Setiawan, Antonius Darma Yanuar Zulardiansyah Arief