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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 64 Documents
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Search results for , issue "Vol 33, No 3: March 2024" : 64 Documents clear
Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification Aziz Jihadian Barid; Hadiyanto Hadiyanto; Adi Wibowo
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.pp1632-1640

Abstract

Rapid economic development, industrialization, and urbanization in Indonesia have caused a large increase in air pollution with negative impacts on the environment and public health. The aim of this research is to use machine learning techniques to categorize air quality and generate an air quality index (AQI) using a dataset that includes six prevalent air pollutants. Next steps are preprocessing and data extraction, K-nearest neighbors (KNN) classification, support vector machine (SVM), and random forest (RF) models are implemented. Furthermore, synthetic minority oversampling technique (SMOTE) is incorporated into the ensemble learning process to improve the results. This research uses K-fold cross validation for improve classification accuracy and reduce overfitting. Research findings show that the application of SMOTE brings a significant increase in model accuracy, effectively solving the problem of imbalanced data sets. These insights provide direction for effective air quality monitoring systems and informed decision making in air pollution management.
Virtual analysis of machine learning models for diseases prediction in muskmelon Deeba Kannan; Balakrishnan Amutha; Sattianadan Dasarathan; Daniel Rosy Salomi Victoria; Vikas Maheshkar; Ravindran Ramkumar; Dhandapani Karthikeyan
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.pp1748-1759

Abstract

Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real-time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability.
Mitigating ransomware attacks through cyber threat intelligence and machine learning Mamady Kante; Vivek Sharma; Keshav Gupta
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.pp1958-1965

Abstract

In the face of escalating cyber threats, particularly the rampant and sophisticated nature of ransomware attacks, organizations are compelled to adopt a proactive and multi-faceted strategy for mitigation. The fusion of machine learning (ML) algorithms enables the system to dynamically adapt and evolve in response to evolving attack vectors and tactics employed by cybercriminals. This paper presents a comprehensive approach that synergistically integrates ML and cyber threat intelligence (CTI) to fortify defenses against ransomware assaults. The proposed methodology incorporates three distinct machine learning techniques, namely random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Empirical evidence derived from the study affirms the efficacy of this approach in effectively discriminating between malicious and ransom software, achieving a notable identification rate of 98.55%. The incorporation of CTI enhances the strategic posture by providing actionable insights into the threat landscape. The proposed focuses on identifying and neutralizing ransomware, aligning with contemporary cybersecurity imperatives, offering a proactive defense against ransomware attacks, ultimately safeguarding critical assets, and preserving the integrity of digital ecosystems.
Interface design features and evaluation of batik 4.0 mobile application Nova Suparmanto; Anna Maria Sri Asih; Andi Sudiarso; Paulus Insap Santosa
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.pp1604-1619

Abstract

The use of information and communication technology could increase the quantity and quality of small medium enterprises (SME) production, including batik industry. This study focuses on the development of batik 4.0, a custom batik mobile-based interface that makes it easier for customer which can be used to quickly produce high-quality digital batik designs. The findings of this study simplify the ordering process for potential clients who want to acquire custom batik designs. Ease of transactions, namely down payments (DP) where users can make advance payments, so that users are relieved in terms of payment transactions. In designing mobile devices, applications, and user interfaces (UI), it is important to consider the user experience (UX). This paper focuses on UX design rooted in the user-centered design (UCD) approach, placing emphasis on understanding user requirements and prioritizing empathy for users. This ensures the recognition of user needs and the creation of a high-fidelity prototype. Then it was validated by the UI experts to identify problems and user difficulties in interacting with the UI. The experts responded positively towards the application and suggest for prototype improvement. Lastly, UX testing; based on the user experience questionnaire (UEQ)-S benchmark results, the batik 4.0 mobile is included in the “Excellent” category.

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