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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Lung cancer segmentation and classification using hybrid CNN-LSTM model Pradhan, Manaswini; Alkhayyat, Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp309-319

Abstract

A collection of genetic disorders and various types of abnormalities in the metabolism lead to cancer, a fatal disease. Lung and colon cancer are found to be main causes of death and infirmity in people. When choosing the best course of treatment, the diagnosis of these tumors is usually the most important consideration. This study's main objectives are to classify lung cancer and its severity, as well as to recognize malignant lung nodules. The suggested approach additionally classifies the stages of lung cancer in order to recognize lung nodules. The convolutional neural network (CNN) is used to detect lung nodules, identifying a nodule which is accurately segmented and classified. The suggested method is separated into dual parts: primarily, it classifies normal and abnormal behavior, and the subsequent one classifies the different stages of lung cancer. Texture and intensity-based features are extracted during the classification stage. When compared to other methods such as nested long short-term memory (LSTM)+ CNN, the hybrid CNN LSTM obtains superior outcomes in terms of accuracy (99.35%), specificity (99.30%), sensitivity (99.32%), and F1-score (99.29%).
Dynamic driver of digital devices for embedded systems design Kunle Akinde, Olusola; Adeola Ajagbe, Sunday; Abiodun Afe, Rotimi; Bethel Mutanga, Murimo
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1251-1260

Abstract

A wide-ranging exploration of the diverse applications of embedded systems (ES) is delved in in this study, tracing their evolution from early industrial control to their current pervasive influence on modern technological landscapes. The study underscores their crucial role in various sectors, including consumer electronics, automotive technology, medical and healthcare, education and research, industrial automation, telecommunications, smart cities, edge computing, and the convergence of 5G and artificial intelligence (AI). It accentuates the versatility and transformative potential of ES. The paper reviews the historical, current, and future contributions and evolution of ES in shaping contemporary technological landscapes. Emphasizing the broad impact of ES, the paper highlights their significance for researchers, practitioners, and enthusiasts navigating the dynamic intersection of technology and diverse disciplines.
Tool support for LoRaWAN development: a comparative perspective on simulation and emulation Koketso, Ntshabele; Isong, Bassey
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp233-249

Abstract

This paper explores the use of various long range wireless area network (LoRaWAN) simulation and emulation tools when designing and evaluating IoT networks. Simulation tools are often popular with researchers because they are less costly and can easily simulate large-scale networks, allowing for easy and faster tests of the scalability of various protocols and behaviors. However, they often lack the unpredictable nature of real deployments. Emulation and cloud-based tools fill this gap, but with their flexibility they provide a more realistic approximation of real-world performance and allow easier interfacing with actual network hardware infrastructure, although they generally incur a higher cost which is often controlled by technical skill level use. 
Enhancing evaluation practices for Islamic inheritance calculation systems: toward a standardized benchmark Reda Kurdi, Ghader; Mohammad Justanieah, Hala
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1549-1566

Abstract

Accurate estate distribution is a critical aspect in Islamic law, governed by complex rules that require precise inheritance calculations. Although numerous computerized inheritance calculation systems have been developed, their reliability remains questionable due to inadequate evaluation and unclear criteria for test case selection. This study addresses this gap by introducing a structured evaluation methodology to rigorously assess the functionalities of inheritance calculation systems. A new benchmark comprising 50 test cases was developed by reviewing the functionality of existing systems, collecting prior test cases and identifying coverage gaps through a detailed gap analysis. These benchmark cases were then used to assess the performance of leading online inheritance calculators, comparing their results to expert-validated solutions. Results revealed a significant drop in performance for calculators previously reported to achieve near-perfect accuracy, with scores declining to 68% and 58% compared to earlier reports of 100% and 90%. This demonstrates the effectiveness of the proposed test cases in exposing limitations within current systems. In contrast, the Almwareeth calculator, which had not been previously evaluated, demonstrated the highest accuracy (86%) and was able to handle a wider range of cases. This study lays a critical foundation for advancing the evaluation standards of Islamic inheritance calculation systems, thereby enhancing their reliability in real-world applications.
Prediction of permeability via nuclear magnetic resonance logging using convolutional neural networks Amusat, Islamia Dasola; Odekanle, Ebenezer Leke; Toluhi, Lanre Michael; Ajagbe, Sunday Adeola; Mudali, Pragasen; Arinkoola, Akeem Olatunde
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp168-179

Abstract

Permeability is a critical parameter in subsurface fluid flow analysis, reservoir management, hydrocarbon recovery, and carbon dioxide sequestration. Traditional permeability measurement methods involve costly and time-consuming laboratory tests or well-related data. Machine learning (ML), specifically convolutional neural networks (CNN), is proposed as a cost-effective and rapid permeability prediction solution, harnessing interrelationships of input-output variables. In this study, empirical permeability correlation was developed using CNN. Forty nuclear magnetic resonance (NMR) T2 spectrums and 89 logarithmic mean NMR T2 distributions (T2lm) were preprocessed, screened and key spectra were identified using the principal component analysis (PCA). To develop the correlations, a custom-designed CNN architecture was employed to leverage the spatial patterns and intricate relationships embedded in the NMR data. The model was trained and validated rigorously using k-fold cross validation scheme to ensure robustness and generalization. Performance metrics like R-squared (R2), root mean squared error (RMSE), mean absolute error (MAE), standard deviation (SD), absolute deviation (AD), average absolute deviation (AAD), average absolute percentage relative error (AAPRE), and maximum error (Emax) were deployed to evaluate the model’s accuracy and ability to predict permeability values accurately. Among the folds considered, the fold 1 emerged as the best-performing model with the highest R2 value of 0.9544. This CNN-based correlation outperformed conventional and other AI-based models in terms of R2, Emax, AD, AAD, AAPRE, among other metrics. Overall, the study demonstrates the effectiveness of CNN in predicting permeability, offering a superior alternative to costly and limited traditional methods, with fold 1 showing the most promising results.
An energy-efficient hardware module for edge detection using XNOR-Popcount in resource-constrained devices Pham, Van-Khoa; Le, Lai
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp73-82

Abstract

Edge detection is a fundamental building block in many embedded vision tasks, including drone navigation, IoT cameras, and wearable devices. However, traditional edge detectors based on multiply–accumulate (MAC) operations are poorly suited to the tight power and area budgets of such resource-constrained hardware. This work introduces a fully synthesizable Prewitt edge detector that replaces MAC operations with 1-bit XNOR– Popcount logic. Incoming 8-bit pixels and ±1 kernel coefficients are binarized, processed by parallel XNOR gates, and tallied by a lightweight Popcount adder tree, eliminating all multipliers and DSP slices. Prototyped on a Xilinx Zynq-7020 FPGA, the proposed design reduces lookup-table usage by 55% and flip-flop count by 26%, cuts dynamic power by about 60%, and supports clock frequencies up to five times higher than a MACbased core. Frame-level evaluations on the MNIST and ORL datasets show near-lossless edge fidelity, with per-image dissimilarity scores below 0.08 and throughput gains approaching four times. These results demonstrate that hardware-aware binary approximations can enable real-time, energyefficient edge detection for embedded AI systems without sacrificing functional accuracy.
Optimizing resume information extraction through TSHD segmentation and advanced deep learning techniques Abuhamdah, Anmar; Al-Shabi, Mohammed; Jawarneh, Sana
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1453-1465

Abstract

This research focuses on a significant factor in the natural language processing area, which is extracting information from unstructured textual data through efficient methods in order to pull useful insights and structured representations from this data. This research attempts to boost the effectiveness of information retrieval systems through computational analysis. This paradigm is explored in this work using question answering models in an extractive style, a modern information extraction approach, creating a new methodology combining the topic segmentation based on headings detection (TSHD) segmentation algorithm and deep learning methods. The TSHD algorithm breaks documents into sections in which certain topics are addressed. Refined extraction models are then used to process these disjoint segments leading to more accurate and contextjudicious extraction compared to naive whole-document extraction approaches. We empirically validate this approach using the stanford question answering dataset (SQuAD) 1.1 dataset, with a specific adaptation to resumes. Experimental results show that the performance metrics increase by 7.4% in exact match (EM) and by 7.8% in F1-score. This can be concluded from these results illustrating the feasibility of the proposed approach in the automated information extraction frameworks such as resume processing.
OFDM/CDMA channel quality estimation using K-means algorithm Abrous, Mohammed; Yagoubi, Benabdellah; Cherifi, Abdelhamid
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1391-1400

Abstract

This work aims to estimate the transmission channel quality and suggest a possible way to enhance the data rates to satisfy the increasing demand for higher data rates to a certain extent. The combination of any non-orthogonal subcarrier multiplexed (SCM) with CDMA needs a large bandwidth, hence a limited number of subcarriers and number of users as well as lower data rates. In contrast, orthogonal subcarriers such as the case of OFDM which are closely spaced due to their orthogonality property as well as to their reduced frequency selectivity fading are, therefore, crucial for increasing subcarriers and thus, increasing the data rates as well as the number of users. To describe the OFDM/CDMA technique in more detail, we performed a simulation using the software Scilab 5.5.2. In this simulation, we treat a simple example of a certain number of users using a bipolar orthogonal code, particularly, the Hadamard/Welsh code for the OCDMA, and the fast fourier transform (FFT) algorithm for the OFDM. For a more realistic simulation, we have introduced a gaussian white noise in the transmission channel and studied the effect of this noise on the eye diagram. Finally, to avoid the computational complexity in calculating the BER to study the OFDM/CDMA channel system quality, we have instead computed the bias and the variance of a noisy 16- quadrature amplitude modulation (QAM) constellation at the reception using the K-means algorithm.
Survey on plant disease detection via combination of deep learning and optimization algorithms with IoT sensors Govindapillai, Santhiya; A, Radhakrishnan
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp357-366

Abstract

Crop diseases are one of the main problems facing the farming sector. Detecting plant diseases using some automatic techniques is advantageous because it recognizes problems early and eliminates a significant amount of monitoring effort on massive farms. Numerous investigators have created various metaheuristic optimizing and an innovative technique for deep learning to recognize and classify plant illnesses. This research analyzes many IoT-based methods for automated plant disease identification and detection. The automatic module for detecting plant diseases provides data to a sink node that the system maintains to facilitate IoT-based monitoring. Numerous methods based on plant disease and computer vision exist. Thirty three papers in all are examined here. This research also offers a thorough understanding of how to enhance IoT-integrated plant disease detection and identification capabilities. In addition to this, various problems and research gaps are noted along with potential research.
Energy-efficient knapsack algorithm for intelligent cluster head selection in IoT enabled wireless sensor networks Aleem, Abdul; Thumma, Rajesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1735-1742

Abstract

The demand for wireless sensor networks (WSN) has grown rapidly with the development of the internet of things (IoT), which requires sensors that are both energy-efficient and scalable to support continuous data collection and real-time monitoring applications. The main challenge is limited battery life in network nodes, which necessitates effective energy management strategies to prolong network lifespan. This paper introduces an energyefficient knapsack algorithm (EEKA) for smart cluster head (CH) selection in IoT WSNs, aiming to optimize energy use while enhancing network stability and data transmission efficiency. The approach features a CH selection strategy based on residual energy, ensuring an even distribution of energy among sensor nodes. The incorporation of the knapsack optimization technique enhances resource allocation, thereby minimizing energy consumption and maximizing transmission reliability. Simulation results using NS2.34/2.35 show remarkable improvement in performance metrics compared to existing techniques: EEKA extends the network lifetime by 16% whereas throughput is enhanced by 17% with reduced latency by 14% under efficient data distribution. Moreover, adaptive CH selection strategy extends coverage by another 20% for wider and effective monitoring. All these results therefore confirm that EEKA has successfully focused on improving energy efficiency, stability, and scalability regarding IoT-driven WSNs to make it a practical solution for real-world applications like smart cities, environmental observation, and industrial automation.

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