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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
 872   873   874   875   876 
Optimal land distribution for ambiguous profit vegetable crops using multi-objective fuzzy linear programming Dixit, Pranav; Tyagi, Sohan Lal
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.pp1162-1169

Abstract

Decisions in agriculture had been driven by methodical planning to increase yields to cater to the needs of overwhelming populations while also allowing farmers to prosper. Allocating land to various crops by making use of limited resources is becoming a crucial challenge for achieving higher profits. To make cropping pattern decisions, farmers traditionally rely on experience, instinct, and comparisons with their neighbors. Since profit varies depending on many factors, intuition and experience usually cannot guarantee optimal (maximum) profits. A number of research studies on linear programming (LP) have shown optimum cropping patterns when crop prices (profits) are fixed. Vegetable crops, also known as cash crops, are subject to a high degree of price volatility owing to the fact that their production is costly and they carry a significant risk of not being profitable, despite the fact that they provide higher earnings than food crops. The net returns of crops in agriculture are greatly impacted by price uncertainty. With the use of the optimization tool TORA, a step-by-step process is shown in this paper to solve the model and manage the volatility in vegetable crop profitability using fuzzy multi-objective linear programming (FMOLP).
A review on power transformer failures: analysis of failure types and causative factors Abdugaffar Ugli, Abdullaev Abduvokhid; Zokirovich, Tuychiev Zafarjon; Kamolovich, Jabborov Tulkin; Khamidjon Ugli, Kobilov Mirodil; Zukhriddin Ugli, Najmitdinov Zikrillo; Ziyodulla Ugli, Sharipov Mukhriddin
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.pp713-722

Abstract

This article analyzes power transformers and their components, types of damage, factors causing them. The advantage of this review article is that it was initially conducted a theoretical analysis based on published articles on power transformer damage in recent years. Then a statistical analysis was carried out on damaged power transformers in real condition. In the theoretical analysis, the articles published in the databases in recent years were first identified by keywords, and then sorted according to their relevance to the topic. A statistical examination of the damaged power transformers was performed utilizing the theoretical approach. According to the results of the analysis, damage to power transformers in 6(10) kV networks occurs mainly in 100 kVA, 160 kVA and 250 kVA power transformers. One of the factors that cause the power transformer to fail is the irregular implementation of restrictions on the power supply to electrical consumers. And these failures mainly damage the windings of the power transformer. We hope that the materials in this analytical article will serve as a crucial resource.
Intelligent voice control system for UAV with mobile robot Atanov, Sabyrzhan; Moldamurat, Khuralay; Bakyt, Makhabbat; Zinagabdenova, Dariga; Moldamurat, Aibek; Zhumazhanov, Berik; Maidanov, Adil
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.pp1061-1072

Abstract

The article presents a voice control system for unmanned aerial vehicles (UAVs) and an integrated mobile robot, based on artificial intelligence (AI). The system recognizes voice commands in the Kazakh language, converted into Latin transliteration, providing intuitive control of the UAV and robot. The performance of the system in various scenarios including agriculture, environmental monitoring and search and rescue operations is investigated. The system showed high accuracy of command recognition (95%) and efficient control of the UAV and robot. The proposed system opens up new possibilities for the use of UAVs and robots in various fields, increasing their autonomy, flexibility and ease of use.
A tag-based recommender system for tourism using collaborative filtering Selmi, Afef; Alawadh, Maryah; Alotaibi, Raghad; Alharbi, Shrefah
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.pp960-974

Abstract

Recommender systems have garnered significant attention from researchers due to their potential for delivering personalized recommendations in light of the vast amount of information available online. These systems have found applications in various domains, including financial services, movies, and research articles. Their implementation in the tourism industry is particularly promising. Travelers often face the daunting task of selecting the right tourist attractions from a plethora of options, which can consume considerable time and energy. By leveraging personalized recommendation technologies, it is possible to provide highly accurate travel suggestions tailored to individual preferences. This study proposes the development of a customized recommendation system (RS) aimed at assisting travelers in the Qassim region of the Kingdom of Saudi Arabia. By using this region as a case study, the proposed RS consists of two main modules: a user registration and login module and a recommendation technique and tag module. The system will capture users’ interests and prompt them to select from various options, subsequently presenting them with tailored recommendations based on their preferences. This approach aims to enhance the travel experience by offering relevant suggestions that align with the interests of each traveler.
A comparative analysis of GPUs, TPUs, DPUs, and QPUs for deep learning with python Allali, Ayoub; El Falah, Zineb; Sghir, Ayoub; Abouchabaka, Jaafar; Rafalia, Najat
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.pp1324-1330

Abstract

In the rapidly evolving field of deep learning, the computational demands for training sophisticated models have escalated, prompting a shift towards specialized hardware accelerators such as graphics processing units (GPUs), tensor processing units (TPUs), data processing units (DPUs), and quantum processing units (QPUs). This article provides a comprehensive analysis of these heterogeneous computing architectures, highlighting their unique characteristics, performance metrics, and suitability for various deep learning tasks. By leveraging python, a predominant programming language in the data science domain, the integration and optimization techniques applicable to each hardware platform is explored, offering insights into their practical implications for deep learning research and application. the architectural differences that influence computational efficiency is examined, parallelism, and energy consumption, alongside discussing the evolving ecosystem of software tools and libraries that support deep learning on these platforms. Through a series of benchmarks and case studies, this study aims to equip researchers and practitioners with the knowledge to make informed decisions when selecting hardware for their deep learning projects, ultimately contributing to the acceleration of model development and innovation in the field.
Performance evaluation of transdermal optical wireless communication using spatial diversity techniques Almajdoubah, Rawan; Hasan, Omar
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.pp865-875

Abstract

Active medical implants and other contemporary medical applications need a dependable, high-speed communication channel between external and internal transceivers. Optical wireless communications have demonstrated advantages over widely used radio frequency technology in terms of data speeds, bandwidth abundance, and immunity to interference. Regretfully, this advantage implies strict alignment requirements for both sending and receiving ends. This study focuses on the effects of using multiple transmitters or receivers under the influence of pointing error on the transcutaneous link's overall performance measured by the outage probability and outage rate. Spatial diversity techniques have demonstrated their viability in increasing the link's reliability in free space optical communications. This drives the investigation of improvement transdermal communication system by adding numerous transmitters or receivers. Various misalignment severities are used to represent different operating circumstances, and these analyses result in explicit closed-form formulas for the relevant metrics. The findings clearly show the benefits of employing multiple transmitters and receivers on the link's outage performances. A notable improvement in the average signal-to-noise ratio values for the outage probability and outage rate compared to the single input single output setup was achieved. Furthermore, the theoretical conclusions are subsequently confirmed by MATLAB-based Monte-Carlo simulation for several instructive cases.
Emerging approaches of artificial intelligence tools for distance learning: a review Faouzi, Ghita; Amrous, Naila; El Faddouli, Nour-Eddine; Khabouze, Mostafa
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.pp1219-1230

Abstract

Learning management system (LMS) is the best way to deliver educational content in the context of higher education, by settings students worldwide with high-quality educational material. This paper principally seeks to examine the use of e-learning platforms in the last years from 2019 to 2023, which has coincided with the pandemic period, by elucidating the benefits and limitations of e-learning platforms, analyzing the real-world artificial intelligence (AI) algorithms used and their operating context. A comprehensive literature search was conducted on different electronic databases to identify relevant studies related to e-learning and AI tools used during this period by applying inclusion, exclusion criteria and preferred reporting items for systematic reviews and meta-analysis (PRISMA) process. Based on this review the tools were necessary social media and free communication platforms that offer the flexibility and build autonomy to students. On the other hand, many challenges are arisen due to the lack of experience in the term of using those tools or due to technical problems, for this reason, the use of AI tools to enhance learning experience still one of the approved solutions.
An embedded system for the classification of sleep disorders using ECG signals Rajani Kumari, Lavu Venkata; Daravath, Babishamili; Sai, Yarlagadda Padma
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.pp767-773

Abstract

Sleep apnea (SA) is a well-known sleep disorder. It predominantly appears due to lack of oxygen in humans. Identifying SA at an early stage can help early diagnosis. The primary motto of our research is to identify SA using electrocardiogram (ECG) signals. Here, three classes are considered for classification. One is normal (N), and the other two are SA classes obstructive sleep apnea (OA) and central sleep apnea (CA). ECG signals are accumulated for MIT-BIH polysomnographic dataset. The ECG data divided into ECG segments and labelled using annotation file. The proposed deep long short-term memory (LSTM) model is then trained using ECG segments and further tested. The model is then finetuned and optimized to obtain the best accuracy. An accuracy of 98.51% is obtained. In addition, performance measures like precision, sensitivity, specificity, F-score are also evaluated. The model is then deployed on NVIDIA’s Jetson nano board to build a prototype. Our model is effective, promising and outperformed existing state of art techniques.
Deep learning-based cryptanalysis in recovering the secret key and plaintext on lightweight cryptography Fatma, Yulia; Remli, Muhammad Akmal; Mohamad, Mohd Saberi; Al Amien, Januar
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.pp1115-1123

Abstract

The development of machine learning (ML) technologies provide a new development direction for cryptanalysis. Several ML research in the field of cryptanalysis was carried out to identify the cryptographic algorithm used, find out the secret key, and even recover the secret message The first objective of this study is to see how much influence optimization and activation function have on the multi-layer perceptron (MLP) model in performing cryptanalysis. The second research objective, which is to compare the performance of cryptanalysis in recovering keys and the plaintext. Several experiments have been carried out, the observed parameters found that the use of the rectified linear unit (ReLU) activation function and the ADAM optimizer improves the performance of deep learning (DL)-based cryptanalysis as evidenced by a significantly smaller error rate. DL-based cryptanalysis works more effectively in recovering keys than recovering plaintext. DL-based cryptanalysis managed to recover the keys with an average loss of 0.007, an average of 49 epochs, and an average time of 0.178 minutes.
End-user software engineering approach: improve spreadsheets capabilities using Python-based user-defined functions Elserwy, Tamer Bahgat; Aly, Tarek; El-Demerdash, Basma E.
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.pp1024-1032

Abstract

End-user computing enables non-developers to handle data and applications, boosting collaboration and productivity. Spreadsheets are a key example of end-user programming environments that are extensively utilized in business for data analysis. However, the functionalities of Excel have limitations compared to specialized programming languages. This study aims to address this shortcoming by proposing a prototype that integrates Python's features into Excel via standalone desktop Python-based user-defined functions (UDFs). This method mitigates potential latency concerns linked to cloud-based solutions. This study employs Excel-DNA (dynamic network access) and IronPython; Excel-DNA facilitates the creation of custom Python functions that integrate smoothly with Excel's calculation engine, while IronPython allows these Python UDFs to run directly within Excel. Core components include C# and visual studio tools office (VSTO) add-ins, which enable communication between Python and Excel. This approach grants users the chance to execute Python UDFs for various tasks such as mathematical computations and predictions — all within the familiar Excel environment. The prototype showcases seamless integration, enabling users to invoke Python-based UDFs just like built in Excel functions. This study enhances the capabilities of spreadsheets by harnessing Python's strengths within Excel. Future work may focus on expanding the Python UDF library and examining user experiences with this innovative approach to data analysis.

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