Proceeding of The International Conference on Electrical Engineering and Informatics
Proceeding of the International Conference on Electrical Engineering and Informatics, Its a collection of papers or scientific articles that have been presented at the National Research Conference which is held regularly every two years by the Asosiasi Riset Teknik Elektro dan Informatika Indonesia.The paper topics published in the Proceeding of the International Conference on Electrical Engineering and Informatics the sub-groups of Electrical Engineering, Electronics Engineering, Electrical Power Engineering, Telecommunication Engineering, Computer Engineering, Information Systems, Information Technology and other relevant fields and published twice a year (January and July).
Articles
30 Documents
<![CDATA[An Enhancement of Harris Corner Detector Algorithm Applied in Signature Forgery Detection System ]]>
<![CDATA[Dzelle Faith R Tan]]>;
<![CDATA[Pauline Regina J Obispo]]>;
<![CDATA[Jonathan C Morano]]>;
<![CDATA[Khatalyn E Mata]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 1 (2024): January : Proceeding of the International Conference on Electrical Engineering ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i1.27
<![CDATA[Signature verification is crucial for confirming the authenticity of identities in both administrative and financial transactions, where signature forgery can lead to significant security risks. The Harris Corner Detector Algorithm is a widely used method for feature extraction in image processing; its application spans various domains, such as detection of signature forgery. While effective in identifying key features, noise significantly affects performance, especially with impulse noise like salt-and-pepper noise commonly found in signature images. To solve this problem, this study enhances the Harris Corner Detector Algorithm by applying a median filter before gradient calculation. This method removes noise without sacrificing the integrity of key features important in signature forgery detection. The study evaluates the original and the enhanced algorithm using standard image quality metrics. Peak Signal-to-Noise Ratio (PSNR) surged from an average of 13.6 dB to 43.28 dB, the Structural Similarity Index (SSIM) improved significantly from 78% to 94%, and the Mean Squared Error (MSE) dropped substantially from 16.74 to 3.84. These advancements resulted in a more reliable algorithm, exhibiting excellent resistance to noise while maintaining image structure, making the enhanced algorithm highly effective for accurate signature forgery detection.]]>
<![CDATA[An Artificial Intelligence Based Recommendation Model for Personalizing Students' Learning Interest Paths at Universities ]]>
<![CDATA[Safrizal Safrizal]]>;
<![CDATA[Chaerul Anwar]]>;
<![CDATA[Augury El Rayeb]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i2.28
<![CDATA[This study explores the integration of artificial intelligence (AI) in education, particularly in supporting personalized learning. AI presents new opportunities through adaptive learning platforms, virtual tutors, and intelligent assessment systems that have the potential to revolutionize teaching and learning methods. By conducting in-depth data analysis, AI can identify student performance patterns and provide tailored recommendations, enabling educators to deliver more targeted interventions. Furthermore, personalized learning plays a crucial role in enhancing student motivation and engagement by customizing learning experiences to meet individual needs and learning styles. This study aims to implement personalized learning strategies in educational settings and offers insights into best practices for their integration. It also examines their impact on student engagement and academic achievement. The findings highlight the importance of personalized learning in fostering an inclusive and effective educational environment. By leveraging AI, educators can optimize learning, empower students, and address achievement gaps. This study provides practical recommendations for educators and policymakers to implement AI-based learning strategies effectively.]]>
<![CDATA[Enhanced Named Entity Recognition Algorithm for Filipino Cultural and Heritage Texts ]]>
<![CDATA[Jhan Lou P Robantes]]>;
<![CDATA[Andreo A Serrano]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i2.29
<![CDATA[Named Entity Recognition (NER) is a crucial natural language processing task that extracts and classifies named entities from unstructured text into predefined categories. While existing NER methods have shown success in general domains, they often face significant challenges when applied to specialized contexts like Filipino cultural and historical texts. These challenges stem from the unique linguistic features, and diverse naming conventions. This research introduces an enhanced rule-based NER approach that specifically addresses these challenges. At its core, the system utilizes curated Corpus of Historical Filipino and Philippine English (COHFIE), which serves as both training and evaluation data. This research presents an enhanced rule-based approach for NER using a Corpus of Historical Filipino and Philippine English (COHFIE) building on pattern-learning methods, incorporating character and token features, and by using positive and negative example sets. To enrich the classification process, we used the International Committee for Documentation – Conceptual Reference Model (CIDOC-CRM), a cultural heritage framework, to provide a more nuanced categorization of entities based on their historical and cultural significance. Tested across existing Filipino based models (calamanCy and RoBERTa Tagalog), the enhanced model shows improvement on identifying entities related to Filipino culture (CUL) and history terms (PER, ORG, LOC).]]>
<![CDATA[Enhancement of Markov Chain-Based Linguistic Steganography with Binary Encoding for Securing Legal Documents ]]>
<![CDATA[Halili A B]]>;
<![CDATA[Salangsang G A]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 1 (2024): January : Proceeding of the International Conference on Electrical Engineering ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i1.30
<![CDATA[The Markov Chain-based linguistic steganography algorithm can effectively hide information within human-like cover text, but it is highly limited in processing speed. A traditional implementation relying on Huffman tree-based encoding mainly suffers from slow processing due to the computational overhead of building the tree itself. To address this issue, this study proposes an enhanced algorithm using binary indexing for constant time complexity. The results were experimentally calculated using models of varying state sizes derived from the same text corpus as a control variable. Perplexity analysis was also employed to evaluate imperceptibility and ensure there were no drawbacks to the cover media’s integrity. The results indicate that the enhanced algorithm improves processing speed by up to 54 times across all state sizes without compromising imperceptibility. This establishes that the enhancements yielded a significantly faster processing speed for the existing algorithm while remaining secure in its concealment. In practice, the algorithm was applied in legal document storage to strengthen its security.]]>
<![CDATA[Exploratory Data Analysis and Machine Learning Approaches for Early Detection of Student Depression ]]>
<![CDATA[Muhammad Fikry]]>;
<![CDATA[Bustami Bustami]]>;
<![CDATA[Ella Suzanna]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i2.31
<![CDATA[This study conducts an exploratory data analysis combined with machine learning techniques to identify early signs of student depression. We investigated various factors affecting mental health among students, including sleep duration, dietary patterns, history of suicidal thoughts, family history of mental illness, and their relationships with depression across age groups and academic pressure. The study also examined the influence of gender on academic stress levels. Three machine learning models such as Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were utilized to predict depression. The performance of these models was evaluated, achieving accuracy rates of 84.97% for Random Forest, 84.85% for SVM, and 81.16% for KNN. The findings highlight the effectiveness of these models in predicting student depression and underscore the importance of targeted mental health interventions based on key factors influencing mental health among students.]]>
<![CDATA[An Enhancement of Optical Character Recognition (OCR) Algorithm Applied in Translating Signages to Filipino ]]>
<![CDATA[Shieryl E. Tendilla]]>;
<![CDATA[Ivan Rey R. Dumago]]>;
<![CDATA[Francis Arlando L Atienza]]>;
<![CDATA[Dan Michael A Cortez]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i2.32
<![CDATA[Optical Character Recognition (OCR) systems often struggle to extract text accurately from images captured at various distances, particularly under challenging conditions such as blurriness, noise, or poor lighting. These issues are common in real-world scenarios and limit the effectiveness of existing OCR technologies. This study addresses these challenges by applying Gaussian blur after the grayscale conversion. This method reduces noise for the image's clarity without sacrificing the original algorithm's key features. Results revealed that the enhanced OCR algorithm significantly outperformed existing methods in terms of accuracy and confidence levels. It demonstrated the ability to read signages with higher precision, even in difficult conditions such as intricate designs, poor lighting, and long distances. This advancement enables more reliable text recognition and translation, offering practical applications for public signage translation, cross-cultural communication, and improved accessibility in multilingual environments.]]>
<![CDATA[Comparison of K-Means Clustering Method with Hierarchical Clustering in Senior High School Clustering (SMA) in Surakarta ]]>
<![CDATA[Fidi Febriani]]>;
<![CDATA[Amelia Shinta Dewi]]>;
<![CDATA[Muqorobin Muqorobin]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i2.33
<![CDATA[Clustering is one of the methods in data mining used in grouping data based on certain characteristics. This study aims to compare the performance of the K-Means Clustering and Hierarchical Clustering methods in clustering Senior High Schools (SMA) in Surakarta based on the parameters of the number of students, facilities, accreditation scores, and school achievements. In this study, a comparison was made between two popular clustering methods, namely K-Means Clustering and Hierarchical Clustering, to group Senior High Schools (SMA) in Surakarta City based on various relevant attributes. The attributes used include graduation rates, number of students, teaching quality, and available facilities. The results of the study show that both methods have their own advantages and disadvantages. K-Means is more efficient in terms of processing time, while Hierarchical Clustering provides a deeper understanding of the structure of relationships between SMAs. K-Means clustering provides better clustering results in terms of separation between clusters, with a higher Silhouette Score (0.52) and a lower Davies-Bouldin Index (0.88). This shows that K-Means is more efficient and better in clustering SMA based on the given data.]]>
<![CDATA[Revolution Technology Digital Change Aspects of Life ]]>
<![CDATA[Jukhri Syah Putra Bancin]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 1 (2024): January : Proceeding of the International Conference on Electrical Engineering ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i1.34
<![CDATA[Various aspect life man has changed in a way significant by revolution technology digital. Digital technology has created opportunities and new obstacles in many ways, such as the way people interact One The same other, Work, Study, And get service health. In article This, we will discuss how the digital technology revolution impacts areas such as economics, education, social interactions, and data security and privacy issues. This study finds ways in which technology digital increase productivity, efficiency, And inclusion. They Also find problem that need to be considered. They use literature study methods and secondary data analysis.]]>
<![CDATA[An Enhancement of Jiang, Z., et al.’s Compression-Based Classification Algorithm Applied to News Article Categorization ]]>
<![CDATA[Cid Antonio F Masapol]]>;
<![CDATA[Sean Lester C Benavides]]>;
<![CDATA[Jonathan C Morano]]>;
<![CDATA[Khatalyn E Mata]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 1 (2024): January : Proceeding of the International Conference on Electrical Engineering ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i1.35
<![CDATA[This study enhances Jiang et al.'s compression-based classification algorithm by addressing its limitations in detecting semantic similarities between text documents. The proposed improvements focus on unigram extraction and optimized concatenation, eliminating reliance on entire document compression. By compressing extracted unigrams, the algorithm mitigates sliding window limitations inherent to gzip, improving compression efficiency and similarity detection. The optimized concatenation strategy replaces direct concatenation with the union of unigrams, reducing redundancy and enhancing the accuracy of Normalized Compression Distance (NCD) calculations. Experimental results across datasets of varying sizes and complexities demonstrate an average accuracy improvement of 5.73%, with gains of up to 11% on datasets containing longer documents. Notably, these improvements are more pronounced in datasets with high-label diversity and complex text structures. The methodology achieves these results while maintaining computational efficiency, making it suitable for resource-constrained environments. This study provides a robust, scalable solution for text classification, emphasizing lightweight preprocessing techniques to achieve efficient compression, which in turn enables more accurate classification.]]>
<![CDATA[Emotion Classification in Sundanese Text Using LSTM and BERT Models ]]>
<![CDATA[Mahazzam Afrad]]>;
<![CDATA[Fauzi Irfan Syaputra]]>;
<![CDATA[Gilang Fibarkah]]>;
<![CDATA[Tectonia Nurul Silvani]]>
<![CDATA[ Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 1 (2024): January : Proceeding of the International Conference on Electrical Engineering ]]>
Publisher : <![CDATA[Asosiasi Riset Teknik Elektro dan Informatika Indonesia ]]>
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DOI: 10.62951/iceei.v1i1.36
<![CDATA[The Sundanese language, once spoken by 48 million individuals, has experienced a significant decline in speakers, losing 2 million in the past decade. This decline is attributed to weakened intergenerational transmission and the dominance of more widely used languages. The challenges in developing Natural Language Processing (NLP) tools for Sundanese stem from the lack of annotated corpora, trained language models, and adequate processing tools, complicating efforts to preserve and enhance the language's usability. This research aims to address these challenges by implementing emotion classification in Sundanese text using Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) models. The study utilizes a dataset of annotated Sundanese tweets, applying preprocessing techniques such as cleansing, stopword removal, stemming, and tokenization to prepare the data for analysis. The results indicate that the BERT model significantly outperforms the LSTM model, achieving an accuracy of approximately 80% compared to LSTM's 70%. These findings highlight the potential of advanced NLP techniques in enhancing the understanding of emotional nuances in Sundanese communication and contribute to the revitalization of the language in the digital age.]]>
