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Science in Information Technology Letters
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27224139     DOI : https://doi.org/10.31763/SiTech
Core Subject : Science,
Computer Science & IT
Science in Information Technology Letters (SITech) aims to keep abreast of the current development and innovation in the area of Science in Information Technology as well as providing an engaging platform for scientists and engineers throughout the world to share research results in related disciplines. SITech is a peer reviewed open-access journal which covers four (4) majors areas of research that includes 1) Artificial Intelligence, 2) Communication and Information System, 3) Software Engineering, and 4) Business intelligence Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers. Finally, accepted and published papers will be freely accessed in this website.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 3 Documents
 1 
Search results for , issue "Vol 5, No 2 (2024): November 2024" : 3 Documents clear
Enhancing the performance of heart arrhythmia prediction model using Convolutional Neural Network based architectures Ismi, Dewi Pramudi; Khoirunnisa, Ninda
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1794

Abstract

Heart disease is one of the diseases that exposes high mortality worldwide. This conventional way of predicting heart disease is usually expensive, time-consuming, and prone to human error. Early detection of heart disease is important as it helps to prevent deaths caused by this disease.  Machine learning utilization as the non-invasive means for predicting heart disease is considered as a fast and affordable method to prevent the fatality of heart disease. This work aims at utilizing  Convolutional neural network (CNN)  to enhance the performance of an Arrhythmia prediction model. We have built an Arrythmia prediction model using neural networks comprising multiple convolutional layers and maxpooling layers. Our proposed model is trained using the MIT-BIH Arrhythmia dataset. The model performance has been evaluated and the model achieves  98.43% of performance  accuracy
Comparative analysis of decision tree and random forest classifiers for structured data classification in machine learning Kinasih, Agnes Nola Sekar; Handayani, Anik Nur; Ardiansah, Jevri Tri; Damanhuri, Nor Salwa
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1746

Abstract

This study explores the application of machine learning techniques, specifically classification, to improve data analysis outcomes. The primary objective is to evaluate and compare the performance of Decision Tree and Random Forest classifiers in the context of a structured dataset. Using the Elbow Method for optimal clustering alongside decision tree and random forest for classification algorithms, this research investigates the effectiveness of each method in accurately categorizing data. The study employs K-Means clustering to segment the data and Decision Trees and Random Forests for classification tasks. Dataset used in this research was obtained from Kaggle consisting of 13 attributes and 1048575 rows, all of which are numeric. The key results show that Random Forest outperforms Decision Trees in terms of classification accuracy, precision, recall, and F1 score, providing a more robust model for data classification. The performance improvement observed in Random Forest, particularly in handling complex datasets, demonstrates its superiority in generalizing across varied classes. The findings suggest that for applications requiring high accuracy and reliability, Random Forest is preferable to Decision Trees, especially when the dataset exhibits high variability. This research contributes to a deeper understanding of how different machine learning models can be applied to real-world classification problems, offering insights into the selection of the most appropriate model based on specific data characteristics.
Retaining humorous content from marked stand-up comedy text Supriyono, Supriyono; Wibawa, Aji Prasetya; Suyono, Suyono; Kurniawan, Fachrul; Voliansky, Roman; Cengiz, Korhan
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1812

Abstract

Identifying humor in stand-up comedy texts has distinct issues due to humor's subjective and context-dependent characteristics.  This study introduces an innovative method for humor retention in stand-up comedy content by employing a pre-trained BERT model that has been fine-tuned for humor classification.  The process commences with the collection and annotation of a varied assortment of stand-up comedy writings, categorized as hilarious or non-humorous, with essential comic elements like punchlines and setups highlighted to augment the model's comprehension of humor.  The texts undergo preprocessing and tokenization to be ready for input into the BERT model. Upon refining the model using the annotated dataset, predictions regarding humor retention are generated for each text, yielding classifications and confidence scores that reflect the model's certainty in its predictions.  The criterion for prediction confidence is set to categorize texts as "retaining humor."  The results indicate that prediction confidence is a dependable metric for humor retention, with elevated confidence scores associated with enhanced accuracy in comedy classification.  Nonetheless, the analysis reveals that text length does not affect the model's confidence much, contradicting the presumption that lengthier texts are more prone to comedy.  The findings underscore the significance of environmental and linguistic elements in comedy detection, indicating opportunities for model enhancement.  Future efforts will concentrate on augmenting the dataset to encompass a broader range of comic styles and integrating more contextual variables to improve prediction accuracy, especially in intricate or ambiguous comedic situations

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