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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 65 Documents
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Search results for , issue "Vol 38, No 2: May 2025" : 65 Documents clear
Assessment of cloud-free normalized difference vegetation index data for land monitoring in Indonesia Hadiyanto, Ahmad Luthfi; Sukristiyanti, Sukristiyanti; Hidayat, Arif; Pratiwi, Indri
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp845-853

Abstract

Continuous land monitoring in Indonesia using optical remote sensing satellites is difficult due to frequent clouds. Therefore, we studied the feasibility of monthly land monitoring during the second half of 2019, using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from Terra and Aqua satellites. We divide the Indonesian area into seven regions (Sumatra, Java, Kalimantan, Sulawesi, Nusa Tenggara, Maluku, and Papua) and examine NDVI data for each of the regions. We also calculated the cloud occurrence percentage every hour using Himawari-8 data to compare cloud conditions at different acquisition times. This research shows that Terra satellite provides more cloud-free pixels than Aqua while combining data from both significantly increase the cloud-free NDVI pixels. Monthly monitoring is feasible in most regions because the cloudy areas are less than 10%. However, in Sumatra, the cloudy area was more than 10% in October 2019. We need to include further data processing to improve the feasibility of continuous monitoring in Sumatra. This research concludes that monthly monitoring is still feasible in Indonesia, although some data require further processing. The use of additional data from other satellites in the monitoring can be an option for further research.
Optimizing cloud tasks scheduling based on the hybridization of darts game hypothesis and beluga whale optimization technique Chhabra, Manish; E., Rajesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1195-1207

Abstract

This paper presents the hybridization of two metaheuristic algorithms which belongs to different categories, for optimizing the tasks scheduling in cloud environment. Hybridization of a game-based metaheuristic algorithm namely, darts game optimizer (DGO), with a swarm-based metaheuristic algorithm namely, beluga whale optimization (BWO), yields to the evolution of a new algorithm known as “hybrid darts game hypothesis – beluga whale optimization” (hybrid DGH-BWO) algorithm. Task scheduling optimization in cloud environment is a critical process and is determined as a non-deterministic polynomial (NP)-hard problem. Metaheuristic techniques are high-level optimization algorithms, designed to solve a wide range of complex, optimization problems. In the hybridization of DGO and BWO metaheuristic algorithms, expedition and convergence capabilities of both algorithms are combined together, and this enhances the chances of finding the higher-quality solutions compared to using a single algorithm alone. Other benefits of the proposed algorithm: increased overall efficiency, as “hybrid DGH-BWO” algorithm can exploit the complementary strengths of both DGO and BWO algorithms to converge to optimal solutions more quickly. Wide range of diversity is also introduced in the search space and this helps in avoiding getting trapped in local optima.
High-efficiency multimode charging interface for Li-Ion battery with renewable energy sources in 180 nm CMOS Mamouni, Hajjar; El Khadiri, Karim; El Affar, Anass; Jamil, Mohammed Ouazzani; Qjidaa, Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp744-754

Abstract

The high-efficiency multi-source Lithium-Ion battery charger with multiple renewable energy sources described in the present paper is based on supply voltage management and a variable current source. The goal of charging the battery in a constant current (CC) mode and controlling the supply voltage of the charging circuit are both made achievable using a variable current source, which may improve the battery charger’s energy efficiency. The battery must be charged with a degraded current by switching from the CC state for the constant voltage (CV) state to prevent harming the Li-Ion battery. The Cadence Virtuoso simulator was utilized to obtain simulation results for the charging circuit, which is constructed in 0.18 μm CMOS technology. The simulation results obtained using the Cadence Virtuoso simulator, provide a holding current trickle charge (TC) of approximately 250 mA, a maximum charging current (LC) of approximately 1.3 A and a maximum battery voltage of 4.2 V, and takes only 29 minutes to charge.
A GRU-based approach for botnet detection using deep learning technique G., Suchetha; K., Pushpalatha
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1098-1105

Abstract

The increasing volume of network traffic data exchanged among interconnected devices on the internet of things (IoT) poses a significant challenge for conventional intrusion detection systems (IDS), especially in the face of evolving and unpredictable security threats. It is crucial to develop adaptive and effective IDS for IoT to mitigate false alarms and ensure high detection accuracy, particularly with the surge in botnet attacks. These attacks have the potential to turn seemingly harmless devices into zombies, generating malicious traffic that disrupts network operations. This paper introduces a novel approach to IoT intrusion detection, leveraging machine learning techniques and the extensive UNSW-NB15 dataset. Our primary focus lies in designing, implementing, and evaluating machine learning (ML) models, including K-nearest neighbors (KNN), random forest (RF), long short-term memory (LSTM), and gated recurrent unit (GRU), against prevalent botnet attacks. The successful testing against prominent Bot- net attacks using a dedicated dataset further validates its potential for enhancing intrusion detection accuracy in dynamic and evolving IoT landscapes.
Multi-camera multi-person tracking with DeepSORT and MySQL Raghavendra, Shashank Horakodige; Sorapalli, Yashasvi; Poojar S. V., Nehashri; Maddirala, Hrithik; Kumar P., Ramakanth; Nasreen, Azra; Trivedi, Neeta; Agarwal, Ashish; K., Sreelakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp997-1009

Abstract

Multi-camera multi-object tracking refers to the process of simultaneously tracking numerous objects using a network of connected cameras. Constructing an accurate depiction of an object’s movements requires the analysis of video data from many camera feeds, detection of items of interest, and their association across various camera perspectives. The objective is to accurately estimate the trajectories of the objects as they navigate through a monitored area. It has several uses, including surveillance, robotics, self-driving cars, and augmented reality. The current version of an object tracking algorithm, DeepSORT, doesn’t account for errors caused by occlusion or implementation of multiple cameras. In this paper, DeepSORT has been extended by introducing new states to improve the tracking performance in scenarios where objects are occluded in the presence of multiple cameras. The communication of track information across multiple cameras is achieved with the help of a database. The suggested system performs better in situations where objects are occluded, whether due to object occlusions or person occlusions.
Indonesian sentiment analysis in natural environment topics Octovianto, Christofer; Ibrohim, Muhammad Okky; Budi, Indra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1353-1366

Abstract

Indonesia is one of the countries that is rich in biodiversity and has a high population growth. This condition can cause Indonesia to have problems related to the natural environment that are more complex than other countries. Hence, this has created a lot of discussions regarding natural environmental issues in Indonesia on social media platforms. In this case, stakeholders like the government in general can utilize sentiment analysis (SA) to comprehend the public’s views to allow them to better fit the public’s expectations when formulating a particular policy that related to the environmental sustainability (ES) issues. This paper built the first open dataset of Indonesian SA dataset in ES topics collected from Instagram. As the benchmark of our dataset, we used IndoBERT model variant for constructing the model and the experiment result shows that model based on IndoBERT-large-p2 obtained the best performance with 72.44% of F1-score.
A recurrent network technique for energy optimization in 6G networks with dynamic device-to-device communication Aneesh, Sonia; Shaikh, Alam N.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp897-903

Abstract

Energy efficiency has become a paramount concern in the design and deployment of 6G networks, driven by the exponential growth of connected devices and increasing traffic demands. For domain experts grappling with dynamic device-to-device (D2D) communication scenarios, optimizing energy consumption while maintaining reliable connectivity poses a significant challenge. To address this issue, we propose a novel recurrent network technique that dynamically configures D2D communication patterns, adaptively allocating temporary base stations among network nodes to enable efficient data transmission while minimizing energy expenditure. Our simulations demonstrate substantial energy savings, extended node lifetimes, and reliable performance, with a 37% reduction in overall network energy consumption and a 65% increase in average node lifetime compared to traditional cellular communication scenarios. In conclusion, this innovative approach paves the way for sustainable and energy efficient 6G communication systems, benefiting society by reducing operational costs, minimizing environmental impact, and prolonging the usability of mobile devices.
Enhance big data security based on HDFS using the hybrid approach Zine-Dine, Fayçal; Alcabnani, Sara; Azouaoui, Ahmed; El Kafi, Jamal
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1256-1264

Abstract

Hadoop has emerged as a prominent open-source framework for the storage, management, and processing of extensive big data through its distributed file system, known as Hadoop distributed file system (HDFS). This widespread adoption can be attributed to its capacity to provide reliable, scalable, and cost-effective solutions for managing large datasets across diverse sectors, including finance, healthcare, and social media. Nevertheless, as the significance and scale of big data applications continue to expand, the challenge of ensuring the security and safeguarding of sensitive data within Hadoop has become increasingly critical. In this study, the authors introduce a novel strategy aimed at bolstering data security within the Hadoop storage framework. This approach specifically employs a hybrid encryption technique that leverages the advantages of both advanced encryption standard (AES) and data encryption standard (DES) algorithms, whereby files are encrypted in HDFS and subsequently decrypted during the map task. To assess the efficacy of this method, the authors performed experiments with various file sizes, benchmarking the outcomes against other established security measures.
Spatial-temporal data imputation for predictive modeling in intelligent transportation systems Widi Prasetyo, Yohanes Pracoyo; Linawati, Linawati; Wiharta, Dewa Made; Sastra, Nyoman Putra
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp794-807

Abstract

Data imputation is necessary to overcome data loss in intelligent transportation systems (ITS) due to the many sensors used to monitor traffic conditions. Sensor malfunction, hardware limitations, and technical glitches can lead to incomplete data, potentially leading to errors in traffic data analysis. This analysis investigated spatial-temporal data imputation approaches applied for predictive modeling in ITS. Each approach's strengths, weaknesses, and applicability in the context of ITS are evaluated. We analyzed various imputation approaches involving statistical, machine learning, and combined methods. Statistical methods are more straightforward but could effectively handle modern traffic's complexity. On the other hand, machine learning and combined approaches, such as hybrid convolutional neural network (CNN)- long short-term memory (LSTM), offer more robust capabilities in capturing non-linear patterns present in spatio-temporal data. This research aims to investigate the effectiveness of each approach in overcoming data incompleteness and the accuracy of predicting future traffic conditions with the widespread adoption of IoT, electric vehicles, and autonomous vehicles. The results of this investigation provide an understanding of the most suitable approaches to address the challenges of spatio-temporal data imputation and provide practical guidance for predictive modeling in ITS.
Enhancing uncollateralized loan risk assessment accuracy through feature selection and advanced machine learning techniques Salahudin, Shahrul Nizam; Dasril, Yosza; Arisandy, Yosy
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1149-1161

Abstract

Accuracy in evaluating the risk of credit applications is crucial for lenders, particularly when dealing with unsecured loans. Accuracy can be enhanced by selecting suitable features for a machine learning model. To better identify high-risk borrowers, this study applies an elaborate feature selection technique. This study uses the light gradient boosting machine (LGBM) Classifier model with boosting type gradient boosting decision tree (GBDT) algorithm and n_estimator value 100 for feature selection process. This work uses advanced machine learning techniques namely stacking to improve accuracy model perform. The dataset consists of 307,506 applicants from European lenders who have applied for loans in Southeast Asia. Each applicant is described by 126 different features. Using GDBT algorithm GBDT, 30 best features were selected based on their maximum accuracy compared to another feature. By employing a stacking technique that combines the LGBM, gradient boosting (GB), and random forest (RF) models, and utilizing logistic regression (LR) as the final estimator, an accuracy of 0.99637 was reached. This study demonstrates an improved the accuracy compared to previous research. This discovery indicates that utilizing feature selection and stacking method can provide one of the most precise choices for modelling the binary class classification among the current models.

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