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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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Plant leaf disease detection and classification using artificial intelligence techniques: a review R, Kusuma; Rajkumar, R.
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.pp1308-1323

Abstract

Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.
Calibration of phased array antenna with the minimum point finding method of the array factor Luong, Nguyen Xuan; Nhan, Nguyen Trong; Thanh, Tran Van; Thanh Thuy, Dang Thi
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.pp854-864

Abstract

The problem of phased array antenna calibration with the minimum point finding method of the array factor is investigated. A mathematical model of the minimum point finding method is presented. Then, the proposed method is applied to the phased array antenna and compared with the traditional rotating-element electric-field vectors method. Experimental verification of the mathematical model of the proposed method showed the following: the minimum point finding method determines the phase shift more accurately than the maximum point finding method of the array factor; the proposed method showed a better detection range per phase change corresponding to a 35 dB higher resolution. The error ranges of the minimum and the maximum point finding methods were 50 and 700 , respectively. The peak of the combined beam when using the minimum point finding method is higher than the maximum point finding method which is 3.7 ... 4.1 dB. One can use the research results in large-scale phased array antenna calibration systems during the production phase.
Artificial intelligence approaches for cardiovascular disease prediction: a systematic review Hammadi, Jasim Faraj; Abdul Latif, Aliza Binti; Che Cob, Zaihisma Binti
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.pp1208-1218

Abstract

Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis. By consistently maintaining comprehensive patient-related data, healthcare providers can anticipate the emergence of potential diseases. Our study conducts a meticulous comparative analysis of CVD prediction methods, focusing on various artificial intelligence (AI) algorithms, particularly classification and predictive algorithms. Scrutinizing approximately sixty papers on cardiovascular disease through the prism of AI techniques, this study carefully assesses the selected literature, uncovering gaps in existing research. The outcomes of this study are expected to empower medical practitioners in proactively predicting potential heart threats and facilitating the implementation of preventive measures.
Internet of things meteorological station for climate monitoring and crop optimization in Carabayllo-Perú Rumiche-Cardenas, Jeremy Jared; Figueroa-Guevara, Axel Walter; Gamarra-Pahuacho, Deyvis Jhosmar; Quiroz-Grados, Josue Daniel; Segovia-Ojeda, Jamil; Cabana-Cáceres, Maritza; Castro-Vargas, Cristian
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.pp755-766

Abstract

In the agricultural sector, monitoring environmental variables such as temperature, humidity, and atmospheric pressure is crucial for efficient and sustainable agriculture. However, conventional monitoring systems are expensive and need more autonomy, making their implementation difficult in small- and medium-scale agricultural operations. This study presents the design, implementation, and evaluation Internet of things (IoT)-based autonomous for watch remote critical climate variables in the Carabayllo region, Peru. The system uses a data acquisition, processing, and transmission architecture based on the ESP32 microcontroller, DHT22 sensors for measure climatic aspects, BMP180 for detection barometric, and the ThingSpeak cloud platform for data storage and visualization. Results show that the proposed system achieves accuracy comparable to commercial weather stations, making it accessible to small farmers. The implementation demonstrated the system’s ability to detect feasible local microclimates to monitor and predict weather patterns for proper crop growth. This approach enables farmers to monitor conditions in real time, receive early alerts on adverse weather events, and optimize agricultural practices such as irrigation and fertilization. The study concludes that the proposed IoT weather station represents a viable and cost-effective solution to improve agricultural decision-making in developing regions, potentially contributing to increasing crops.
Android malware detection through opcode sequences using deep learning LSTM and GRU networks Lakshmanarao, Annemneedi; Mantena, Jeevana Sujitha; Thota, Krishna Kishore; Chandaka, Pavan Sathish; Murali Krishna, Chinta Venkata; Jetty, Madhan Kumar
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.pp1106-1114

Abstract

Android malware detection was a complex task due to the intricate structure of Android applications, which consisted of numerous Java methods and classes. Effective detection required the extraction of meaningful features and the application of advanced machine learning (ML) or deep learning (DL) algorithms. This paper presented a novel approach to detecting Android malware by leveraging opcode sequences extracted from Android applications. These opcode sequences, which differed between malicious and benign apps, formed the basis of the detection model. The methodology involved extracting opcode sequences from decompiled Android APK files using the “Androguard” tool and applying recurrent neural networks (RNN) with long short-term memory (LSTM), Bi-LSTM, and gated recurrent unit (GRU) architectures to classify the apps as either malware or benign. The combination of these advanced DL techniques allowed for capturing temporal dependencies in opcode sequences, resulting in a significant improvement in detection capabilities. This work underscored the potential of using opcode sequences in conjunction with RNN, LSTM, and GRU for robust and accurate malware detection, while also highlighting the importance of further exploring additional features for comprehensive classification.
Data mining and cardiac health: predicting heart attack risks Paucar, Inoc Rubio; Andrade-Arenas, Laberiano
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.pp1010-1023

Abstract

In a context where heart attacks continue to be a global health concern, the lack of precision in predicting who is at higher risk poses a critical challenge due to the variability of risk factors and complex interactions among them. The research aims to develop predictive models for heart attack risks using data mining techniques, employing the knowledge discovery in databases methodology (KDD) and the k-means algorithm with RapidMiner studio. The primary objective is to identify patterns and risk profiles, allowing for early identification of at-risk individuals, considering factors like obesity, diabetes, alcoholism, and stress, to reduce preventable deaths and improve cardiac healthcare. This innovative approach combines cardiac health, data mining, and KDD methodology to address the challenge of predicting heart attack risks and has the potential to enhance medical care and save lives. The predominant results obtained were that cluster 1 with a fraction of 0.312 and a percentage of 31.2% of the attribute diabetes was one of the most prevalent causes of cardiac risk. Finally, the research concluded that people with diabetes are more likely to have cardiac risk associated with dietary factors or consumption of other substances.
Discrete wavelet transform and convolutional neural network based handwritten Sanskrit character recognition Shelke, Shraddha V.; Chandwadkar, Dinesh M.; Ugale, Sunita P.; Chothe, Rupali V.
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.pp1367-1375

Abstract

Sanskrit is one of the ancient languages from which the majority of present Indian languages are developed. Although the national mission for manuscripts (NMM) is digitizing handwritten Sanskrit manuscripts, there are still a lot of papers that need to be digitized. Recognition of handwritten script is a challenging task due to individual differences in writing styles and how those variations alter over time. The Sanskrit language is written in Devanagari script. A novel approach using discrete wavelet transform (DWT) and convolutional natural network (CNN) is proposed in this paper. Devanagari handwritten character dataset which includes 2000 handwritten images of 36 classes (2000*36=72000) is used in this research. Fine-tuned GoogLeNet model implemented here gave optimum values of epochs and learning rate of 15 and 0.01 respectively. Classification accuracy obtained by proposed DWT – CNN model is 98.97% with a loss of 0.098. Fine-tuned GoogLeNet model achieves 99.68% accuracy with a 0.0635 loss. Results obtained are also compared with existing approaches and found superior.
Geographic information system-based approaches for evaluating CO2 storage in Kalimantan basins, Indonesia Susantoro, Tri Muji; Sugihardjo, Sugihardjo; Suliantara, Suliantara; Widarsono, Bambang; Usman, Usman; Setiawan, Herru Lastiadi; Romli, Mohamad; Sukarno, Panca W.; Nurkamelia, Nurkamelia; Suhartono, Rudi
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.pp904-914

Abstract

To achieve the energy transition towards more environmentally friendly energy, various approaches must be taken, one of which is CO2 source-to-sink matching. A basin evaluation study has been carried out through classifying, weighting, and scoring in the geographic information system (GIS) for screening and ranking basins for CO2 storage on the island of Kalimantan, Indonesia. The region covers 13 sedimentary basins that have the potential to serve as CO2 sinks. As many as 21 parameters have been analyzed through classification and weighting using a pairwise comparison matrix method to produce scores and ranks for each basin. The results show that the Kutai, Tarakan, and Barito basins are the top three basins for CO2 storage potential. Singkawang, Nangapinoh, Pangkalanbun Utara, and Embaluh Selatan basins have been found to have the lowest sink potential.
An efficient load balance using virtual machine migration hybrid optimization technique in cloud computing Sivalingam, Saravanan Madderi; Prathapagiri, Pavan Kumar
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.pp1265-1272

Abstract

Cloud computing is becoming increasingly important to developers and companies because to the rapid development of information technology and the wide availability of internet applications. Every information technology industry has a significant role for cloud computing. Numerous multinational technology businesses, like Google, Microsoft, and Facebook, have established data centers across the world to offer processing and storage capabilities. Customers can submit their jobs to cloud centers directly. Reducing overall power usage is the primary goal, which was overlooked in the early stages of cloud development. Using gene expression programming (GEP), symbolic regression models of virtual machines (VMs) are developed using measured VM loads and the corresponding resource parameters. In order to minimize resource use, multidimensional resource load balancing of all the physical machines within the cloud computing platform is the aim of this analysis. The VMH loads estimated and the genetic algorithm that considers the current and the future loads of VMHs and decides an optimal VM-VMH for migrating VMs and performing load-balance. Hence, an efficient load balance using virtual machine migration hybrid optimization technique (HOT) in cloud computing shows better results in terms of accuracy, energy consumption, migration cost.
Optimizing photovoltaic system performance through MPPT synergetic adaptive control Hadjadj, Kamel; Attoui, Hadjira
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.pp808-820

Abstract

This paper investigates enhancement of energy conversion through the implementation of new MPPT control strategy based on synergetic adaptive control (SAC) for a photovoltaic system. The architecture of this system encompasses a photovoltaic module, a DC-DC boost converter, a resistive load, and an MPPT controller. The controller amalgamates two distinct methodologies: the initial algorithm deduces the peak power current through a perturbation and observation (P&O) method, which serves as the reference point for the subsequent algorithm founded on synergetic adaptive control. The parameters for the latter are refined through the particle swarm optimization (PSO) technique This innovative method is employed to ascertain the optimal power output across varying weather conditions, aiming to enhance power transmission performance irrespective of meteorological variations. The efficacy of this strategy was affirmed through a comparative study with the conventional P&O method using MATLAB/Simulink simulations, which verified the superior performance of the proposed algorithm.

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