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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 35 Documents
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Sentiment analysis of wayang climen using naive bayes method Fitriana Kurniawati; Aji Prasetya Wibawa; Agung Bella Putra Utama
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1220

Abstract

This research focuses on sentiment analysis of Wayang Climen performances in Indonesia using the Naïve Bayes algorithm. Wayang, a traditional puppet show, holds cultural significance and has persisted alongside modern entertainment options. The study collected public comments from Dalang Seno and Ki Seno Nugroho's YouTube channels, classified them into positive, negative, and neutral sentiments, and employed a translation process to align comments with program language objectives. Preprocessing steps included case folding, removing punctuation, tokenizing, stopword removal, and post-tagging. To address data class imbalances, resampling was performed using the Synthetic Minority Oversampling Technique (SMOTE). The Naïve Bayes algorithm was utilized for data classification, exploring various translation scenarios. Evaluation involved the confusion matrix method and metrics like accuracy, precision, recall, and f-measure. Results demonstrated that the Dalang Seno train data scenario outperformed Ki Seno Nugroho's, with higher precision, recall, accuracy, and f-measure values. Additionally, the translation scenario from Indonesian to English yielded the most effective results. In conclusion, this study highlights the suitability of the Naïve Bayes algorithm for sentiment analysis in the context of Wayang Climen performances, with practical implications for understanding public sentiment in the digital age.
Forging a learner-centric blended-learning framework via an adaptive content-based architecture Arnold Adimabua Ojugo; Christopher Chukwufunaya Odiakaose; Frances Uche Emordi; Patrick O. Ejeh; Winifred Adigwe; Kizito Eluemonor Anazia; Blessing Nwozor
Science in Information Technology Letters Vol 4, No 1 (2023): May 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i1.1186

Abstract

The covid-19 pandemic was reported with significant negative impact on global education with shocks that disrupted the learning processes via the closure of traditional classrooms/schools from 2020 to March 2022. These effects have continued to ripple across even with advances in media literacy. The Nigerian frontier has also witnessed a paradigm shift in the adoption/integration of the information and communication tech as tools for both digital revolution and advancement of alternative education delivery. Today’s education which aspires for growth and progressive development is assured of positive changes if priority for educational values and ICT is harnessed. Past educational theories seem not to cope with the ever-changing, information society. Nigeria must develop strategies to address education reforms with frameworks to bridge these gaps vid post covid-19 era. Our study implements a hybrid a(synchronous) learning framework for Nigerian Tertiary education. Result shows improved learner cognition, engaged qualitative learning, and a learning scenario that ensures a power shift in the educational structure that will further equip learners to become knowledge producer, help teachers to emancipate students academically, in a framework that measures quality of engaged student’s learning
Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis Nanda Lailatul Humairoh; Tuti Purwaningsih; Shoffan Saifullah; Felix Andika Dwiyanto; Ilyos Rabbimov
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1212

Abstract

Economic development often brings prosperity to communities, but it can also be accompanied by growing disparities that, when unaddressed, lead to increased crime rates. Central Java, an Indonesian province, has been grappling with a persistent high crime rate, necessitating an in-depth examination of the factors underlying this phenomenon. In this study, we employ a rigorous research methodology, incorporating data sources from the Central Java Central Statistics Agency (BPS) and utilizing key independent variables, including population, unemployment, poverty, Age-Dependency Ratio (APS), and Relative Location Quotient (RLS). Through the application of advanced spatial analysis techniques such as the Local Indicator of Spatial Association (LISA) and the Spatial Autoregressive Model (SAR), this research offers a nuanced exploration of the spatial relationships and regression analysis of these variables. Notably, the study presents a tree map highlighting crime distribution in Central Java's districts and cities. The findings reveal that these five variables exhibit a 75.48% accuracy in predicting crime in Central Java. Through this comprehensive analysis, our research aims to provide valuable insights for policymakers, law enforcement, and the community at large, enabling informed strategies for crime reduction and the promotion of a safer, more prosperous Central Java
Advanced product review summarization in e-commerce marketplaces: elevating beyond tf-idf and lexrank method Rita Melina Anggraeni; Dewi Pramudi Ismi
Science in Information Technology Letters Vol 3, No 2 (2022): November 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i2.1225

Abstract

In the fiercely competitive domain of online product sales, wherein engendering trust among prospective buyers assumes paramount significance, the role of product reviews cannot be understated. However, a prevailing issue in online marketplaces resides in the presence of product reviews that do not consistently align with the overall product rating. Furthermore, the sheer abundance of comments often leads potential consumers to confine their scrutiny to the initial comments, thus leaving a substantial volume of reviews unexplored. To rectify this challenge, this study introduces an automated text summarization system for product reviews, leveraging the LexRank methodology. This system underwent rigorous evaluation using the Rouge metric, with results manifesting substantial promise. At a threshold of 0.1, Rouge-1 exhibited an accuracy of 16.67%, while Rouge-2 scored 3.01%, and Rouge-L reached 16.50%. At a threshold of 0.2, Rouge-1 yielded a score of 16.08%, Rouge-2 registered 2.64%, and Rouge-L scored 16.57%. The second evaluation, performed with a distinct test dataset, notably excelled, emphasizing the system's competence. Specifically, at the 0.2 threshold, the system displayed superior performance, underscoring its efficacy in refining product review summarization within online marketplaces
Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification Awang Hendrianto Pratomo; Nur Heri Cahyana; Septi Nur Indrawati
Science in Information Technology Letters Vol 4, No 1 (2023): May 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i1.1182

Abstract

Convolutional Neural Networks (CNNs) have gained significant traction in the field of image categorization, particularly in the domains of health and safety. This study aims to categorize the utilization of face masks, which is a vital determinant of respiratory health. Convolutional neural networks (CNNs) possess a high level of complexity, making it crucial to execute hyperparameter adjustment in order to optimize the performance of the model. The conventional approach of trial-and-error hyperparameter configuration often yields suboptimal outcomes and is time-consuming. Genetic Algorithms (GA), an optimization technique grounded in the principles of natural selection, were employed to identify the optimal hyperparameters for Convolutional Neural Networks (CNNs). The objective was to enhance the performance of the model, namely in the classification of photographs into two categories: those with face masks and those without face masks. The convolutional neural network (CNN) model, which was enhanced by the utilization of hyperparameters adjusted by a genetic algorithm (GA), demonstrated a commendable accuracy rate of 94.82% following rigorous testing and validation procedures. The observed outcome exhibited a 2.04% improvement compared to models that employed a trial and error approach for hyperparameter tuning. Our research exhibits exceptional quality in the domain of investigations utilizing Convolutional Neural Networks (CNNs). Our research integrates the resilience of Genetic Algorithms (GA), in contrast to previous studies that employed Convolutional Neural Networks (CNN) or conventional machine learning models without adjusting hyperparameters. This unique approach enhances the accuracy and methodology of hyperparameter tuning in Convolutional Neural Networks (CNNs). 

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