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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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Precision agriculture: exploration of deep learning models for farmland mapping Anjela Cabrera Tolentino; Thelma D. Palaoag
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp592-601

Abstract

Precision is required for agricultural advancements to be sustainable. Traditional farming lacks effective monitoring, resulting in resource waste and environmental problems. Farmland mapping is important for agricultural management and land-use planning. The use of deep learning techniques in farmland mapping is increasing rapidly. Excellent results have been generated from deep learning approaches in a number of applications, such as image processing and prediction. Agricultural agencies are now considering different applications of deep learning including land mapping, crop classification, and monitoring of paddy fields. This paper shall explore different deep learning models that are commonly used for image processing specifically in land mapping. The three deep learning models convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were evaluated to find out which among the deep learning models is best for land mapping. It compares the classification accuracy of the models on image processing and it can be concluded that CNN algorithm normally makes better results when compared to other deep learning models. This study offers guideline and suggestions to researchers who are interested in contributing to the field of precision agriculture with the used of deep learning techniques.
Recognizing gender from images with facial makeup Micheal, Annie; Palanisamy, Geetha
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1201-1209

Abstract

Recognizing the sex of an individual is a difficult task due to pose variation, occlusion, illumination effect, facial expression, plastic surgery, and makeup. In this manuscript, a novel approach for gender recognition with facial makeup is proposed. A novel Log-Gabor COSFIRE (LG-COSFIRE) filter is a shape-selective filter that is trained with prototype patterns of interest. The geometrical structure of the faces is acquired using the dual-tree complex wavelet transform (DT-CWT). Dense SIFT descriptor extracts the shape attributes of an image by building local histograms of gradient orientation. Finally, least square support vector machine (LS-SVM) is utilized to recognize the gender of an individual. The experiment was performed on self-built facial makeup for male and female (FMMF) database and achieves 89.7% accuracy.
Improving web-oriented information systems efficiency using Redis caching mechanisms Maksim Vladimirovich Privalov; Mariya Valerevna Stupina
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1667-1675

Abstract

The responsiveness of a web application with minimum latency time and maximum web pages loading speed is determined by its overall performance. When dealing with a large number of users and amount of data, the performance of web applications is significantly affected by ways of data processing, storage and access. This paper identifies the significance of data caching process to speed up access to relational database. The study examines approaches to improve the performance of web applications through the joint use of MySQL relational database management system (DBMS) and Redis NoSQL DBMS. The practical part of the study presents a description of a web application built based on Java and Spring Boot framework. The paper proposes the implementation of the caching strategies that take into account the principles of aspect-oriented programming. Made experiments on performance testing of the developed web application with and without caching are presented. The presented results of the study allowed us to conclude that it is possible to improve the performance of web applications by the optimal use of caching strategies when performing database queries.
Free space optical communication system in the presence of atmospheric losses Vijayashri V. Belgaonkar; Sundaraguru Ramakrishnan
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp159-166

Abstract

Free space optical communication is gaining importance in the field of optical communication due to its high speed and high bandwidth applications. Free space optical communication system (FSOCS) provides many benefits as compared to traditional wireless communication system and fiber optic cables. This makes this technology the reasonable extension of metropolitan area network and also provides the quick recovery during natural disaster. This system performance is limited due to the atmospheric turbulence effect and various atmospheric losses such as rain, and fog. Gamma gamma atmospheric turbulent model is used to analyze the system performance in the presence of moderate to strong atmospheric turbulence. We have designed the FSO gamma gamma turbulent model with non-return to zero (NRZ) modulation format employing wavelength division multiplexing (WDM), spatial diversity multiple input multiple output (MIMO) (8×8) at various atmospheric turbulence levels and attenuation loss of 10 dB/km at the distance of 2-4 km. Using the proposed model, the link distance is enhanced up to 4km in the presence of turbulence and atmospheric losses with minimum laser transmitted power.
Evaluating machine learning models for precipitation prediction in Casablanca City Tricha, Abdelouahed; Moussaid, Laila
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1325-1332

Abstract

Accurate precipitation forecasting is a vital task for many domains, such as agriculture, water management, flood prevention, and crop yield estimation. The use of machine learning (ML) approaches has improved precipitation forecasting accuracy, exhibiting promising results in capturing the intricate connections between various meteorological variables and precipitation patterns. However, given the vast array of available ML models, a comparative analysis is imperative for identifying the most effective models for precipitation prediction. This study aims to examine the capacities of ML algorithms to forecast precipitation based on weather data for the city of Casablanca, Morocco, which faces challenges in water management and climate change adaptation. Eight different ML models’ performances are compared: linear regression, polynomial regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), XGBoost, and an ensemble learning model. These models are evaluated based on their mean absolute error (MAE), mean squared error (MSE), and R-squared (R2 ) value to determine their effectiveness. The study showcases the potential of ML models in predicting precipitation by utilizing meteorological parameters such as temperature, humidity, wind speed, and pressure.
A data-driven analysis to determine the optimal number of topics 'K' for latent Dirichlet allocation model Goyal, Astha; Kashyap, Indu
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp310-322

Abstract

Topic modeling is an unsupervised machine learning technique successfully used to classify and retrieve textual data. However, the performance of topic models is sensitive to selecting optimal hyperparameters, the number of topics 'K' and Dirichlet priors 'α' and 'β.' This data-driven analysis aims to determine the optimum number of topics, 'K,' within the latent Dirichlet allocation (LDA) model. This work utilizes three datasets, namely 20-Newsgroups news articles, Wikipedia articles, and Web of Science containing science articles, to assess and compare various 'K' values through the grid search approach. The grid search approach finds the best combination of hyperparameter values by trying all possible combinations to see which performs best. This research seeks to identify the 'K' that optimizes topic relevance, coherence, and model performance by leveraging statistical metrics, such as coherence scores, perplexity, and topic distribution quality. Through empirical analysis and rigorous evaluation, this work provides valuable insights for determining the ideal 'K' for LDA models.
Efficient deep learning architecture for the classification of diseased plant leaves Muniyandi Sadhasivam; Manoharan Kalaiselvi Geetha; James Gladson Maria Britto
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp198-206

Abstract

The classification of plant leaf diseases via machine learning and deep learning algorithms has a great deal of potential for enhancing agricultural operations by allowing the early and accurate diagnosis of diseases. These systems can potentially develop into useful instruments for environmentally responsible farming and increased food safety as technological advancements continue. In this work, an efficient deep learning architecture has been developed to classify the diseased plant leaves. A ten-layer architecture is designed, which includes 5-convolutional layers using different numbers of filters (32, 64, 128, 256, and 512) and for dimension reduction, five max-pooling layers are used. The PlantVillage dataset which consists of more than 50,000 plant leaf samples is used to analyze the proposed architecture's performance. The performances are evaluated across different training and testing configurations and different dropout configurations. When compared to well-known transfer learning methods using visual geometric group (VGG16), AlexNet, and GoogleNet architectures, the proposed architecture obtains a higher level of performance with 98.18% classification accuracy.
Characterization of the electrical properties of an optical device manufactured with CMOS 0.35 μm technology Ricardo Yauri; Vanessa Gamero; Marco Alayo
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1346-1352

Abstract

Currently, the relevance of optical devices has increased due to the physical limitations of the electrical transmission medium and the proximity of the limit of Moore's Law. Furthermore, the fabrication of optical devices on monolithic silicon substrates has gained importance in recent years thanks to manufacturing technologies in the microelectronics industry. For this reason, this paper aims to carry out the electrical characterization of an optical device manufactured with commercial austria micro syste m technology of complementary metal oxide semiconductors of 0.35 μm. The methodology consists of implementing an optical device, with an incandescent optical source called a microlamp, a waveguide and a photodiode. The microlamp was projected between two m etal layers connected by tungsten vias that act as filaments covered by SiO 2 dielectric to prevent oxidation. The results of the electrical characterization of the optical device show that the microlamp reaches a maximum current of 48 mA and stops working at higher currents. The waveguide was designed with a SiO 2 core and it was discovered that the TiN layers were found to be part of the waveguide causing it to behave as an emitter in the 2.5 - 5 µm region.
Stacking classifier method for prediction of human body performance Noer Rachmat Octavianto; Antoni Wibowo
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1832-1839

Abstract

A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data.
Non-linear control for enhanced solar power under partial shading and AC load variations Sabri Khadija; El Maguiri Ouadia; Farchi Abdelmajid
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1347-1362

Abstract

This paper solves the control problem to track the maximum power in grid-connected PV systems to catch up with the changes and meet the energy demand, given the irregular and arbitrary nature of the solar source. Our work addresses the following objectives: i) extracting the maximum available power under partial shading, ii) and having a unit power factor. To achieve the above objectives, we have integrated two control components. The first one is dedicated to the extraction of the maximum power point (MPPT) particle swarming algorithm (PSO) with a backstepping controller, by shaking on the DC/DC converter duty cycle to increase the robustness and stability of the system. The backstepping control of the three-phase voltage source inverter is the second part. To verify the effectiveness of the introduced system, modelling and simulation are verified in MATLAB/Simulink.

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