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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 5 Documents
 1 
Search results for , issue "Vol 4, No 2 (2023): November 2023" : 5 Documents clear
Mapping dengue vulnerability: spatial cluster analysis reveals patterns in Central Java, Indonesia Fithriyyah, Anisahtul; Purwaningsih, Tuti; Konate, Siaka; Abdalla, Modawy Adam Ali
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1203

Abstract

In Indonesia, where the interplay between climate variability and infectious diseases is pronounced, Dengue Fever poses a significant threat, particularly in Central Java, ranking as the province with the third-highest incidence of Dengue cases nationwide. This study adopts a proactive approach, employing cluster analysis techniques—single linkage, average linkage, and Ward’s method—to categorize cities and regencies in Central Java based on their susceptibility to Dengue outbreaks. The comparative analysis, facilitated by standard deviation values, reveals nuanced vulnerability patterns, with the single linkage method presenting the most refined categorization, yielding four distinct vulnerability clusters: very low (0.097), low (0.150), medium (0.205), and high (0.303). Furthermore, spatial analysis utilizing Moran’s Index indicates a positive spatial autocorrelation among Dengue cases (Moran’s I = 0.62, p 0.05), underscoring the spatial homogeneity in case distribution across regions. These findings emphasize the critical need for targeted interventions and evidence-based policymaking to effectively combat Dengue transmission in Central Java and mitigate its public health impact.
Hand image reading approach method to Indonesian Language Signing System (SIBI) using neural network and multi layer perseptron Bagaskoro, Muhammad Cahyo; Prasojo, Fadillah; Handayani, Anik Nur; Hitipeuw, Emanuel; Wibawa, Aji Prasetya; Liang, Yoeh Wen
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1362

Abstract

Classification complexity is the main challenge in recognizing sign language through the use of computer vision to classify Indonesian Sign Language (SIBI) images automatically. It aims to facilitate communication between deaf or mute and non-deaf individuals, with the potential to increase social inclusion and accessibility for the disabled community. The comparison of algorithm performance in this research is between the neural network algorithm and multi-layer perceptron classification in letter recognition. This research uses two methods, namely a neural network and a multi-layer perceptron, to measure accuracy and precision in letter pattern recognition, which is expected to provide a foundation for the development of better sign language recognition technology in the future. The dataset used consists of 32,850 digital images of SIBI letters converted into alphabetic sign language parameters, which represent active signs. The developed system produces alphabet class labels and probabilities, which can be used as a reference for the development of more sophisticated sign language recognition models. In testing using the neural network method, good discrimination results were obtained with precision, recall and accuracy of around ±81%, while in testing using the multi-layer perceptron method around ±86%, showing the applicative potential of both methods in the context of sign language recognition. Testing of the two normalization methods was carried out four times with comparison of the normalized data, which can provide further insight into the effectiveness and reliability of the normalization technique in improving the performance of sign language recognition systems.
Vision-based chicken meat freshness recognition system using RGB color moment features and support vector machine Sutarman, Sutarman; Avianto, Donny; Wibowo, Adityo Permana
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1230

Abstract

Chicken meat is a highly sought-after food product among various segments of the general population, known for its high nutritional value and easy accessibility. Presently, meat identification is primarily conducted manually, relying on visual inspection or tactile assessment of the meat's color and texture. However, this approach presents several limitations, particularly when consumers lack the discernment to differentiate the quality of chicken meat freshness. This research aims to identify the freshness level of chicken meat using the Support Vector Machine method, employing the extraction of RGB color moment features to determine the freshness of the meat. The feature extraction process involves calculating the percentage of intensity values for R (Red), G (Green), and B (Blue) in each chicken meat image. Based on the image processing results, the percentage of intensity values, particularly in the R and B parameters, can be used as determining factors. The study involves software testing using fresh and non-fresh chicken meat. The developed system can identify the freshness level of fresh chicken meat with an accuracy rate of 71.6% using the linear kernel SVM and 60.5% using the RBF kernel SVM.  This research represents a significant step toward the automation of chicken meat freshness assessment, potentially reducing food waste and enhancing food safety in the food industry. Further research and development could improve the system's accuracy and expand its applications in various food quality control settings.Chicken meat is a highly sought-after food product among various segments of the general population, known for its high nutritional value and easy accessibility. Presently, meat identification is primarily conducted manually, relying on visual inspection or tactile assessment of the meat's color and texture. However, this approach presents several limitations, particularly when consumers lack the discernment to differentiate the quality of chicken meat freshness. This research aims to identify the freshness level of chicken meat using the Support Vector Machine method, employing the extraction of RGB color moment features to determine the freshness of the meat. The feature extraction process involves calculating the percentage of intensity values for R (Red), G (Green), and B (Blue) in each chicken meat image. Based on the image processing results, the percentage of intensity values, particularly in the R and B parameters, can be used as determining factors. The study involves software testing using fresh and non-fresh chicken meat. The developed system can identify the freshness level of fresh chicken meat with an accuracy rate of 71.6% using the linear kernel SVM and 60.5% using the RBF kernel SVM.  This research represents a significant step toward the automation of chicken meat freshness assessment, potentially reducing food waste and enhancing food safety in the food industry. Further research and development could improve the system's accuracy and expand its applications in various food quality control settings.
Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling Jayadianti, Herlina; Arianti, Berliana Andra; Cahyana, Nur Heri; Saifullah, Shoffan; Dreżewski, Rafał
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1184

Abstract

This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews.
Web log augmented analytics and extraction for e-learning environment Mokhtar, Nur Azizah Mohammad; Sulaiman, Sarina; Pranolo, Andri
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1224

Abstract

E-Learning is a commonly used platform by most institutions, especially during the pandemic Covid-19. E-learning services include viewing, submitting, and uploading files, attempting quizzes, viewing forums, and downloading files. The data store in the servers grow on par with the increment of users in e-Learning@UTM every semester. As a result, the data have become extremely huge. These web log data can be used in augmented analytics to find meaningful insights. The web log data extracted are the log files of the history engagement of users and students’ grades. Data obtained are used in augmented analytics to study the pattern of the data and insights into meaningful information. This research focuses on classification of data through predictive analytics. Hence, predictive models are required. To prove a better outcome, building the model consists of three types of algorithms; Decision Tree, Artificial Neural Networks and Support Vector Machine which are used and compared. After extracting data from e-learning, the first step in building a predictive model is to do data collection, data pre-processing, and data transformation. These three classifiers use the pre-processed data and split the data into training and test sets afterwards. Each classifiers techniques are built and a confusion matrix is applied as a performance measurement to summarise the performance of a classification algorithm, respectively.

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