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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
 849   850   851   852   853 
A novel CNN-ANN fusion approach for improved facial emotion detection Sawant, Viraj; Shaikh, Husna; Palkar, Bhakti; Kazi, Sanam; Jasani, Wasim; Rampurawala, Lamiya; Naser Shaikh, Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp959-967

Abstract

In recent years, the field of emotion recognition has witnessed an increased interest due to the rise of deep learning techniques. However, one of the persistent difficulties in this domain, which we have attempted to address, is the variability in image sizes utilized. In this study, we have reviewed the work by different researchers and summarized their key findings. In our research, we introduce a novel technique that integrates the strengths of 1D convolutional neural networks (CNNs) and artificial neural networks (ANNs) through a late fusion model, leveraging CNNs' shared weights and automatic feature learning for spatial and temporal data, alongside ANN's comprehensive feature consideration. Our research findings highlight the effectiveness of this approach, which achieves a remarkable accuracy of 92.42%, along with other evaluation metrics demonstrating notable results. Furthermore, we conduct a comprehensive analysis of the proposed method, comparing it with advanced methods in the field of facial emotion recognition. Through this comparative analysis, we demonstrate the superiority of our proposed approach, addressing challenges that have not yet been addressed till date, thus leading to progress in this field.
Predicting student performance using Moodle data and machine learning with feature importance Rogers, Jamal Kay; Mercado, Tamara Cher; Cheng, Remelyn
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp223-231

Abstract

Despite the growing technological advancement in education, poor academic performance of students remains challenging for educational institutions worldwide. The study aimed to predict students’ academic performance through modular object-oriented dynamic learning environment (Moodle) data and tree-based machine learning algorithms with feature importance. While previous studies aimed at increasing model performance, this study trained a model with multiple data sets and generic features for improved generalizability. Through a comparative analysis of random forest (RF), XGBoost, and C5.0 decision tree (DT) algorithms, the trained RF model emerged as the best model, achieving a good ROC-AUC score of 0.77 and 0.73 in training and testing sets, respectively. The feature importance aspect of the study identified the submission actions as the most crucial predictor of student performance while the delete actions as the least. The Moodle data used in the study was limited to 2-degree programs from the University of Southeastern Philippines (USeP). The 22 courses still resulted in a small sample size of 1,007. Future research should broaden its focus to increase generalizability. Overall, the findings highlight the potential of machine learning techniques to inform intervention strategies and enhance student support mechanisms in online education settings, contributing to the intersection of data science and education literature.
Design and testing of a nutrient solution control system for soilless culture using mathematical models Chaila, Sirinya; Soemphol, Chaiyong
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp101-110

Abstract

The optimization of nutrient management is crucial for successful soilless plant cultivation, where precise control of fertilizer application significantly impacts plant growth. This research addresses the challenge of developing an effective nutrient control system tailored for soilless cultivation by focusing on regulating electrical conductivity (EC) levels in nutrient solutions. The proposed system utilizes mathematical models and linear regression techniques to manage the nutrient solution mixing ratio. To ensure accuracy, sensors were calibrated, achieving a 99.59% accuracy rate for pH measurement and 95.25% for EC measurement. Experimental validation of the system demonstrated that, with a target EC range of 1.5-2.3 mS/cm, a 10 L solution volume yielded a maximum error rate of 1.75% and an average error of 0.95%. In contrast, a 50 L solution volume showed a slight increase in maximum error rate to 2.89% and an average error of 2.08%. These results highlight the system’s capability to precisely adjust EC levels using a defined linear regression model for AB liquid fertilizer ratios. In conclusion, the developed system effectively controls nutrient levels, demonstrating its potential for enhancing nutrient management in hydroponic farming applications.
A predictive model for postpartum depression: ensemble learning strategies in machine learning Fazraningtyas, Winda Ayu; Rahmatullah, Bahbibi; Naparin, Husni; Basit, Mohammad; Razak, Nor Asiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp443-451

Abstract

Postpartum depression (PPD) presents a significant mental health challenge for mothers following childbirth. While the precise cause of this condition remains unknown, preventive measures and treatments are available. This study aims to employ ensemble learning techniques, utilizing C4.5 decision tree (DT), gradient boosting tree (GBT), and extreme gradient boosting (XGBoost), to predict the occurrences of PPD in the Banjarmasin, South Kalimantan, Indonesia. The predictive model developed encompasses a dataset comprising 317 records gathered from postpartum mothers in hospitals, community health services, and midwifery clinics (referred to as Model 1). Furthermore, resampling techniques (Model 2) were employed to address class imbalance. Additionally, feature selection including forward selection and backward elimination (Model 3) were implemented to enhance model performance. The findings reveal that XGBoost, combined with resampling methods, achieved the highest accuracy rate at 87.57%. Feature selection identified five crucial factors associated with PPD incidence: marital status, number of living children, history of depression, fear of delivery, and family relationships. The utilization of ensemble learning strategies for PPD prediction yields reliable outcomes that can be applied within clinical settings. Exploring alternative ensemble learning strategies such as random forest and adaptive boosting could further optimize model performance and warrant consideration in future research endeavours.
Proposed strategies for lightning performance improvement of 35 kV distribution lines in Vietnam Tung Tran, Anh; Son Tran, Thanh
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp689-699

Abstract

Lightning strikes frequently cause power outages on 35 kV distribution lines in Vietnam. These lines was normally protected with a sparse density of surge arrester and typically do not make use of shield wire. On the other hand, a single rod, that is frequently used as the grounding electrode, is not suitable for some region with high value of soil resistivity. Therefore, it is of particular of interest to investigate the solutions to protect the medium voltage lines from the back-flashover due to lightning strikes by analyzing the impact of the grounding impedance, the surge arrester density and the installation of the shield wire. The finite element method and the electromagnetic transient program EMTP-RV are combined in this work to propose techniques for increasing the critical flashover current of 35 kV lines in Hanoi city. The obtained results showed that the installation of shield wires combined with high density of surge arrester and reduced grounding impedance value can achive a very significant improvement of the overall lightning performance of the line.
Kimball data warehouse for the sales analysis process in a manufacturing business in Perú Vidal Carlos, Palomino; Obregon Patricia, Condori
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1093-1101

Abstract

The main goal of this research is to demonstrate that the use of innovative technology like business intelligence (BI) in a specific type of business significantly impacts their sales processes, enhancing decision-making, promotional strategies, and consequently customer loyalty and sales growth. The case study is a manufacturing business located in Lima, Peru. The information requirements of this business were analyzed, and a data mart model was created using the Kimball methodology. This multidimensional model enabled the comparison of client sales trends to propose new promotions and marketing strategies. The data analysis used to evaluate the results included hypothesis testing, analysis of employee responses to questionnaires to measure the impact of technology use on sales processes, and data reviews to assess sales increases both before and after the implementation of this technology. In both cases, the approval of the BI technology by the employees was satisfactory, and the increase in sales quantity was significant.
An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting Arora, Nidhi; Srivastava, Shilpa; Tripathi, Aprna; Gupta, Varuna
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp214-222

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs step–by-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved ‘AUC’ and ‘ROC’ values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as ‘accuracy’, ‘f1-score’, ‘precision’, and ‘recall’ significantly support the need for presented methodologies for qualitative NAFLD prediction modelling.
Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review Wahyuni, Retno Tri; Hanafi, Dirman; Tomari, M. Razali; Sihabudin Sahid, Dadang Syarif
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1317-1327

Abstract

Spatial modeling is commonly used to map research variables, including particulate matter 2.5 (PM2.5) concentrations, in specific areas. The article that surveys publications on the application of machine learning in spatial modeling of PM2.5 using bibliometric methods has not been identified yet. This paper aims to analyze trends in applying machine learning in the spatial modeling of PM2.5 using bibliometric methods. The review was conducted on publications indexed in the Scopus database over the decade (2014–2023) comprising 335 articles. The analysis included co-authorship and co-occurrence using VOSviewer. From the two stages of analysis, it can be concluded that research on this topic has constantly increased over the past 10 years, with the highest productivity coming from researchers in China. This research topic is multidisciplinary, with most publications appearing in environmental science. The research also shows a very high collaboration rate of 0.98. A deeper examination of the keywords reveals the most commonly used machine learning techniques by researchers. The random forest method is the most frequently found in the analyzed documents, followed by deep learning, long short-term memory (LSTM), extreme gradient boosting (XGBoost), and ensemble model.
ADALINE-based synchronous detection for enhanced shunt APF performance Mebarek, Abdesslam Ryad; Merabet, Leila; Rahli, Chouaib; Saad, Salah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp35-47

Abstract

Power quality issues caused by current harmonics from nonlinear and unbalanced loads are a growing concern. This paper presents a novel control strategy for four-wire shunt active power filters (SAPF) that surpasses existing conventional methods in mitigating harmonics and power factor correction. The strategy employs an improved synchronous detection method (SDM) enhanced by an adaptive linear neural network (ADALINE) trained using the least mean square (LMS) algorithm. This approach accurately estimates harmonic frequencies, enabling the SAPF to generate precise compensation currents. The effectiveness of the proposed method is validated through MATLAB-Simulink simulations under balanced supply conditions, encompassing diverse load scenarios. These simulation results are compared with those obtained using instantaneous power theory (IPT). They demonstrate the ability of the proposed method to achieve excellent harmonic identification and elimination, to comply with IEEE 519 harmonic limits, to ensure sinusoidal and balanced line currents, and to compensate for reactive power and neutral current. Furthermore, its simple architecture and noise robustness make it a promising solution for enhancing power quality.
Improving the MSMEs data quality assurance comprehensive framework with deep learning technique Sadikin, Mujiono; Katidjan, Purwanto S.; Dwiyanto, Arif Rifai; Nurfiyah, Nurfiyah; Pratama Yusuf, Ajif Yunizar; Trisnojuwono, Adi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp613-626

Abstract

In the year of 2022 the ministry of cooperatives and small and medium enterprises (SMEs) executed a complete data collection program for the cooperatives and micro small and medium enterprises (MSMEs) profile. As the complexity of the process and the uniqueness of the data characteristics, plenty of risks must be mitigated. The most challenging risk is the possibility of reduced data quality. This study is performed to validate the proposed comprehensive framework to ensure the quality data of cooperatives and MSME. The proposed framework aims to prevent, detect, repair, and recover dirty data to achieve the required data quality minimum standard. We investigated many techniques namely rule-based, selection-based, and deep learning-based. By applying the framework, 6,850,000 missing values are found and corrected, whereas the number of instant data containing attribute values that do not follow the domain constraints or integrity rule is 4,082,630. The first deep learning task applied in the framework is MSME activity image description (image captioning) generated by the convolutional neural network-recurrent neural network (CNN-RNN) model. By using 1000 MSME images as data training, the model’s performance is quite good, achieving the average BLEU score of Culinary 0,3149, Fashion 0,4868, and creative products 0,5086. So far, the proposed framework can contribute to supporting MSME one data as the Indonesian government program.

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