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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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Nusantara capital city sentiment analysis using support vector machine and logistic regression Angelie Tania, Valencia Eurelia; Oetama, Raymond Sunardi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1708-1721

Abstract

The decision to move position the capital city of Indonesia to East Kalimantan has drawn people’s opinions, both pro and con, among the public, especially ahead of the presidential and vice-presidential elections. Discussions relevant to the relocation and construction of the capital city are increasingly crowded on social media, especially Twitter or X. This research aims to determine public sentiment regarding the development of the national capital to help the government and policymakers improve communication strategies, evaluate existing policies, and make more informed decisions based on public feedback. Public sentiment related to developing the Capital city of the Nusantara, including the presidential palace, toll road, and government offices, is analyzed. Support vector machine (SVM) and logistic regression (LR) algorithms are utilized for the sentiment classification. The results reveal that the SVM performs better in classifying sentiments in X data relevant to developing the Capital city of Nusantara, achieving an average accuracy of 91.97%.
An efficient segmentation using adaptive radial basis function neural network for tomato and mango plant leaf images Smitha, Jolakula Asoka; Shadaksharappa, Bichagal; Parvathy, Sheela; Veena, Kilingar; Jenifer, Albert; Nirmala, Baddala Vijaya; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp202-213

Abstract

Agriculture has become simply to feed ever-growing populations. The tomato is arguably the most well-known vegetable in agricultural areas and plays a significant role in the growth of vegetables in our daily lives. However, because this tomato has multiple diseases, image segmentation of the diseased leaf shows a key role in classifying the disease by the leaf's symptoms. Therefore, in this paper, an efficient plant disease segmentation using an adaptive radial basis function neural network (ARBFNN) classifier. The proposed radial basis function (RBF) neural network is enhanced by using the flower pollination algorithm (FPA). Firstly, the noise is detached by an adaptive median filter and histogram equalization. Then, from every leaf image, different kind of color features is extracted. After the extraction of features, those are fed to the segmentation phase to section the disease serving from the input image. The efficiency of the suggested method is analyzed based on various metrics and our technique attained a better accuracy of 97.58%.
Evolution of the optical add/drop multiplexer in dense wavelength division multiplexing optical networks Mkhwanazi, Mnotho P.; Mpofu, Khumbulani; Malele, Vusumuzi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp247-257

Abstract

Mobile network operators are facing ever-increasing traffic demands because of the numerous data-hungry applications used by subscribers nowadays. As a result, technologies that support high bandwidth and network availability have become essential. One such technology is dense wavelength division multiplexing (DWDM). This study investigated the evolution of an optical add/drop multiplexer (OADM), which is one of the key components of DWDM technology. The goal of this research was to investigate how the evolution of an OADM has contributed to network survivability and bandwidth enhancement in DWDM optical networks. A thorough search of the literature on an OADM was undertaken using data sources like Google Scholar, Elsevier, ResearchGate, ScienceDirect, Springer, and DWDM vendor manuals. The study found that in order to address present and future DWDM optical network demands, a reconfigurable optical add/drop multiplexer (ROADM) deployed over flexgrid spectrum is essential. The most advanced iteration of a ROADM supports colorless, directionless, contentionless, and flex-grid functionalities, resulting in the most robust, flexible, and future-proof DWDM optical network. The study further found that flex-grid technology supports uplinks with high line rates and has superior spectral efficiency.
Internet of things based autonomous robot system architecture for home automation and healthcare services Karuna Sagar, Bhimunipadu Jestadi Job; Latha, Garapati Swarna; Bolla, Sreenivasulu; Nanajkar, Jyotsna Amit; Patnala, Pattabhirama Mohan; Mande, Praveen; Kharde, Mukund Ramdas; Narasimharao, Jonnadula
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1624-1633

Abstract

The internet of things (IoT) is playing a major role in the development of the health industry by enabling more accessible and affordable virtual and distant patient contacts through applications that are easy to use. The IoT and automated homes are becoming more popular in recent days. A network of connected devices, including hardware, equipment, and technical support, is known as the IoT. Their purpose is to allow data exchange with other systems through the internet. This paper presents, internet of things based autonomous robot system architecture (IoT-ARSA) for home automation and healthcare services. The primary goal of this secure home automation system is to help the elderly and disabled people by allowing them to operate home appliances. Additionally, the system uses a cloud server to predict the health conditions of patients and the elderly people, providing information to a guardian. The patient's health condition is determined using sensors like temperature, pulse, blood pressure, and oxygen level. Ultrasonic sensor and face detection are used for home automation. Each sensor will interact with the Raspberry Pi 4 to record data, which will then be processed and stored in the cloud. From results it is clear that described (IoT-ARSA) for home automation and healthcare services model is very efficient with high accuracy and high security. Health monitoring is achieved with this model continuously with great efficiency.
Long-term user engagement in recommender systems: a review Edem, Swathi; Naresh Yadav, B. V. Ram
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2050-2058

Abstract

The purpose of recommender systems (RS) is to facilitate user collaboration and communication on the platform. Nevertheless, there is limited knowledge regarding the extent of this relationship and the techniques by which RS could promote persistent user engagement with the platform. In order to fill this void, the present study investigates the role of RS in transforming users’ short-term engagement with the RS into long-lasting involvement with the platform. We present a theoretical framework by reviewing relevant literature in the domains of RS and user engagement to probe these issues. We provide open challenges in this field along with metrics in the present study.
Classification model for infectious lung diseases using convolutional neural networks on web and mobile applications Okokpujie, Kennedy; Agamah, Alvin K.; Orimogunje, Abidemi; Adaora, Ijeh Princess; Omolara, Olusanya Olamide; Daramola, Samuel Adebayo; Awomoyi, Morayo Emitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp410-424

Abstract

Accurate lung disease diagnosis in infected patients is critical for effective treatment. Tuberculosis, COVID-19, pneumonia, and lung opacity are infectious lung diseases with visually similar chest X-ray presentations. Human expertise can be susceptible to errors due to fatigue or emotional factors. This research proposes a real-time deep learning-based classification system for lung diseases. Three models of convolutional neural networks (CNNs) were deployed to classify lung illnesses from chest X-ray images: MobileNetV3, ResNet-50, and InceptionV3. To evaluate the effect of high interclass similarity, the models were evaluated in 3-class (Tuberculosis, COVID-19, normal), 4-class (lung opacity, tuberculosis, COVID-19, normal), and 5-class (tuberculosis, lung opacity, pneumonia, COVID-19, normal) modes. The best classification accuracy was attained by retraining MobileNetV3, which obtained 94% and 93.5% for 5-class and 4-class, respectively. InceptionV3 had the lowest accuracy (90%, 89%, 93% for 5-, 4-, and 3-class), while ResNet-50 performed best for the 3-class setting. These findings suggest MobileNetV3's potential for accurate lung disease diagnosis from chest X-rays despite the interclass similarity, supporting the adoption of computer-aided detection systems for lung disease classification.
A novel (????, ????) multi-secret image sharing scheme harnessing RNA cryptography and 1-D group cellular automata Abdul, Yasmin; Ramasamy, Venkatesan; Kukaram, Gaverchand
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp700-709

Abstract

In the modern landscape, securing digital media is crucial, as digital images are increasingly disseminated through unsecured channels. Therefore, image encryption is widely employed, transforming visual data into an unreadable format to enhance image security and prevent unauthorized access. This paper proposes an efficient (????, ????) multi-secret image sharing (MSIS) scheme that leverages ribonucleic acid (RNA) cryptography and one-dimensional (1-D) group cellular automata (GCA) rules. The (????, ????) MSIS scheme encrypts ???? images into ???? distinct shares, necessitating all ???? shares for decryption to accurately reconstruct the original ???? images. Initially, a key image is generated using RNA cryptography, harnessing the extensive sequence variability and inherent complexity of RNA. This secret key is then used to encrypt ???? images in the primary phase. In the secondary phase, pixel values are transformed through multiple processes, with randomness achieved by executing a key function derived from GCA, known for its reversible properties, computational efficiency, and robustness against cryptographic attacks. The proposed model, implemented in Python, is validated through experimental results, demonstrating its effectiveness in resisting a broad spectrum of attacks, including statistical, entropy, differential, and pixel parity analyses. These findings affirm the model's durability, security, and resilience, underscoring its superior performance compared to existing models.
The variety of phosphor Ca2MgSi2O7:Eu2+ emission color affect white light LEDs Van De, Pham; Nguyen Thi, My Hanh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1471-1478

Abstract

The conventional phosphor-converted white light emitting diode (WLED) suffers from several drawbacks relevant to heat generation and low rendered performance. Thus, using ultraviolet LEDs was introduced as a solution. It is essential to choose the phosphors with high stability that can activated under 350-410 nm to be compatible with the chips. Rare-earth-doped silicate phosphor is among the most reserched materials for solid-state light devices, thanks to its high stability and low-cost production. This work presents the Eu2+ -doped Ca2MgSi2O7 green phosphor to serve the pursuit of comprehensively enhancing the WLED performances. The f–d transitions and Eu2+ ions mixture take possession of two seperate cation spots in main grids with the help of two emission peaks, one at 465 nm and another at 520 nm. The composition of YAG:Ce3+ and Ca2MgSi2O7:Eu2+ phosphors, and a near-UV chip of 370 nm were utilized to compose WLEDs. Results show that by increasing the Ca2MgSi2O7:Eu2+ phosphor amount, the lumen output, correlated color homogeneity, and color rendering factors can be improved. The paper emphasizes the necessity for the optimal selection of the Ca2MgSi2O7:Eu2+ phosphor concentration, which would be about 10 wt%. The phosphors could be promising in making green-induced white luminous materials for white pc-LEDs with near UV-base.
ClearNet: auto-encoder based denoising model for endoscopy images Shokeen, Vikrant; Kumar, Sandeep; Mathur, Vidhu; Sharma, Amit; Gupta, Indrajeet; Jain, Parita
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1990-2000

Abstract

Gastrointestinal (GI) endoscopy images play a crucial role in the detection and diagnosis of diseases within the digestive tract. However, the development of effective computer vision models for automated analysis and denoising of endoscopy images faces challenges arising from the diverse nature of abnormalities and the presence of image artefacts. In this work, the utilization of an encoder-decoder network for denoising GI endoscopy images using the HyperKvasir dataset has been analyzed. This approach involves training a custom encoder-decoder model on this extensive multiclass endoscopy image dataset and assessing its performance across 23 prevalent classes of digestive tract issues. Here experiments showcase the model’s ability to learn robust visual representations from endoscopic data, enabling accurate disease prediction. The achieved promising results highlight the potential of encoder-decoder architectures as a foundational framework for computer-aided endoscopy analysis with a specific focus on denoising applications. Our model manages to increase the peak signal-tonoise ratio (PSNR) of original-noisy pair from 19.118954 to 69.892631 for original-reconstructed pair showcasing almost perfect reconstruction.
Deep learning-based secured resilient architecture for IoT-driven critical infrastructure Vaddadi, Srinivas A.; Vallabhaneni, Rohith; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1819-1829

Abstract

While enabling remote management and efficiency improvements, the infrastructure of the smart city becomes able to advance due to the consequences of the internet of things (IoT). The development of IoT in the fields of agriculture, robotics, transportation, computerization, and manufacturing. Based on the serious infrastructure environments, smart revolutions and digital transformation play an important role. According to various perspectives on issues of privacy and security, the challenge is heterogeneous data handling from various devices of IoT. The critical IoT infrastructure with its regular operations is jeopardized by the sensor communication among both IoT devices depending upon the attacker targets. This research suggested a novel deep belief network (DBN) and a secured data dissemination structure based on blockchain to address the issues of privacy and security infrastructures. The non-local means filter performs pre-processing and the feature selection is achieved using the improved crystal structure (ICS) algorithm. The DBN model for the classification of attack and non-attack data. For the non-attacked data, the security is offered via a blockchain network incorporated with the interplanetary file system.

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