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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 62 Documents
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Search results for , issue "Vol 34, No 1: April 2024" : 62 Documents clear
Fuzzy expert system design for detecting stunting Linda Perdana Wanti; Oman Somantri; Nur Wachid Adi Prasetya; Lina Puspitasari
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.pp556-564

Abstract

Stunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis.
MDVC corpus: empowering Moroccan Darija speech recognition Boumehdi Ahmed; Yousfi Abdellah
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.pp290-301

Abstract

Automatic speech recognition (ASR) technology has significantly transformed human-machine interactions, but it remains limited in its representation of diverse languages and dialects. Moroccan Darija, the lively Moroccan dialect, has long been underrepresented in the realm of language technology. To address this gap, we present a novel corpus of audio files accompanied by meticulously transcribed Moroccan Darija speech. The corpus comprises 1,000 hours of diverse content, featuring multiple Moroccan accents, extracted from 80 YouTube channels. To standardize the representation of Moroccan Darija in our corpus, we made efforts to establish consistent writing norms and conventions. In addition to the dataset creation, we applied fine-tuning using the Wav2Vec2 model on the Moroccan Darija voice corpus (MDVC) dataset achieving a remarkable word error rate (WER) of 9%. This article discusses the current state of Moroccan Darija research, highlighting the scarcity of resources and the need for robust ASR systems. Our contribution offers a valuable resource for researchers and developers, and by standardizing the Darija language, we strive to improve ASR system for this low resource language.
Sentiment analysis and classification of Ghanaian football tweets from the 2022 FIFA World Cup Eshun Michael; Gyening Mensah Rose-Mary Owusuaa; Takyi Kate; Appiahene Peter; Peasah Ofosuhene Kwame; Banning Amoako Linda
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.pp497-507

Abstract

Football as an attractive sport generates huge volumes of tweets concerning fans' opinions, feelings, and judgments during prime events. Such data can be leveraged in sentiment analysis, an algorithmic approach to analyzing text in tweets by extracting emotional tones. This paper presents a novel benchmark dataset of 132,115 tweets collected during the 2022 world cup on ???? (formerly Twitter) for football-related sentiment classification. We also performed sentiment analysis on the dataset using lexicon-based tools, traditional machine learning algorithms, and pre-trained models, robustly optimized bidirectional encoder representations from transformers (BERT)- pretraining approach RoBERTa and distilled version of BERT (DistilBERT) to understand the emotions and reactions of football fans during different phases of the football matches. Results from the study indicate that most tweets had neutral sentiments in both context-aware and context-free analysis. We also describe our novel GhaFootBERT, a sentiment classification model based on transfer learning on BERT, which provides an effective approach to sentiment classification of football-related tweets. Our model performs robustly, outperforming the traditional models with 92% accuracy.
Agile fusion: developing 'Eat at Right Place' sentiment analysis tool Akash Prabhune; Vinay R Srihari; Neeraj Kumar Sethiya; Mansi Gauniyal
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.pp602-619

Abstract

This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.
Research of static and dynamic characteristics of a process system ‘electric drive-fluid-handling machine-pipeline’ Ayman Y. Al-Rawashdeh; Vlademer E. Pavlov; Khalaf Y. Al Zyoud; Mohammad S. Khrisat
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.pp119-127

Abstract

The paper offers the obtained quantitative assessment of the performance and energy parameters of a process system composed of an asynchronous motor, a fluid-handling machine and a pipeline when using two methods of performance control, in particular, throttling and speed control methods. Taking into account nonlinearity of mathematical representation of fluid-handling machines and asynchronous drive motors, the starting conditions were analysed using nonlinear differential calculus. The calculations for the models were performed using the MATLAB software package. Transient profiles of flow and head, stator current, angular frequency and torque of an asynchronous motor were obtained at pump startup and control of pump capacity. It has been found that the developed mathematical model of a process system composed of an asynchronous motor, a fluid-handling machine and a pipeline allows obtaining quantitative estimation of the performance and energy parameters of the unit when using two methods of the pump capacity control. The use of frequency method allows to decrease the pump rotation speed and significantly reduce the power consumed by the unit and provide energy-saving mode of operation, the economic efficiency of which depends on the range of feed control.
DNA based phenotype optimization of oryza sativa using machine learning and MolCNN Nikita Soren; Paramasivan Selvi Rajendran
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.pp575-583

Abstract

The prediction of phenotype from the genotype of oryza sativa (rice) is very crucial for optimizing the crop management. utilizing molecular convolutional neural networks (MolCNNs) and machine learning for crop management in oryza sativa provides a data-driven method for phenotype prediction based on DNA data, improving farming techniques. Data gathering, preparation, and integration of phenotypic and DNA data are all part of this process. Meaningful DNA features are extracted by MolCNN, and phenotypic qualities are predicted by a machine learning algorithm. Making educated decisions is ensured by assessing the model’s effectiveness, applying it to crop management, and updating it frequently. Choosing crop varieties, planting schedules, and management techniques are guided by molecular insights, which support sustainable agriculture and increase yields and quality. In the proposed research we are calculating pearson correlation coefficients between anticipated and actual trait values and the model’s performance on a test set. Additionally, it determines the (PCC) for every characteristic in the model and we have received a binary accuracy of 0.9998 in 139 seconds.
A novel fuzzy logic-based approach for textual documents indexing Latifa Rassam; Imane Ettahiri; Ahmed Zellou; Karim Doumi
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.pp254-263

Abstract

In the evolving landscape of information retrieval and natural language processing, the quest for more effective automatic keyword extraction (AKE) techniques from textual documents has become a pivotal research focus. Existing methodologies, while offering valuable insights, often grapple with the challenges posed by the imprecision and variability inherent in human language. This has led to a growing recognition of the need for innovative approaches to navigating textual content’s nuances more adeptly. In response to this imperative, this paper proposes a novel fuzzy indexing approach designed specifically for the indexing of textual documents. Fuzzy indexing, grounded in the principles of fuzzy logic, provides solutions for handling the inherent uncertainty and imprecision in natural language, especially when confronted with the intricacies of linguistic ambiguity and variability. By leveraging the power of fuzzy logic, we aim to enhance the precision of keyword extraction. This paper unfolds the intricacies of our fuzzy indexing approach, detailing the theoretical methodology through empirical evaluation and comparative analysis; we seek to demonstrate the efficacy of our approach in outperforming traditional methods in the context of fuzzy indexing for textual documents.
Safety hysteresis comparator design for transient overvoltage detection Ukrit Kornkanok; Sansak Deeon; Saktanong Wongcharoen
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.pp69-80

Abstract

This research introduces a safety hysteresis comparator that detects transient overvoltage in the track circuit relay interlocking of railway signaling system. This overvoltage is caused by voltage faults transmitted through the electric conductor on the track feed unit to the receiver equipped with the track relay, which acts as the occupied track circuit controller. The circuit was designed using safety system design principles and concepts. Findings illustrated that the transient overvoltage detection of the safety hysteresis comparator in the track circuit activated when the input voltage (Vin) was higher than the high hysteresis signal level (Vhyst_H). When Vin was less than low hysteresis signal level (VVhyst_L), the output voltage (Vo) state was low. Otherwise, it was high. The hysteresis voltage was 4.4 V. The installation of the transient overvoltage detector in the track circuit was to monitor the transient overvoltage fault in the track circuit and to confirm that the hysteresis comparator was in the safety failure mode, which was the safety function for the track circuit to be compliant with the IEC 61800-5-2 standard to ensure the system stability and reliability. It would maximize the performance of the controlling function and commanding of train arrangement of State Railway of Thailand (SRT).
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.
Machine learning approach for intrusion detection system using dimensionality reduction Deepa Manikandan; Jayaseelan Dhilipan
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.pp430-440

Abstract

As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.

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