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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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Design of an FTTH (Fiber To The Home) network for improving voice, broadband, and television services in hard-to-reach areas the Colombian case Leonel Hernandez; Juan Albas; Jair Camargo; César De La Hoz; Fachrul Kurniawan; Andri Pranolo
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.v3i2.1001

Abstract

This project establishes the process of designing a fiber optic Ftth network that reaches the homes of each end customer, which allows providing voice services, broadband internet, and television, the above using GPON technology, based on the tree architecture through passive elements, where the node or central is connected to other nodes through a common link, which is shared by all the nodes (ONTs) of the network. This network will be designed in two levels, the first level that starts from the OLT to the level one splitter and the second level that begins from the level one splitter to the OTB element that the level two Splitters have. The entire design will be subject to standards that must be met to achieve the percentage of attenuation allowed. At the design level, it has two directions: one from left to right, where the nodes insert traffic, and another from right to left, where the nodes only have two functions: read or read and delete traffic. It is nothing more than the convergence of the primary communication services of today, such as fixed telephony, the internet, and television. The FTTH Network is designed for the Municipality of Usiacurí of the Department of Atlántico, using the Top-Down Design methodology, where the requirements are analyzed, the designs are developed, and the tests are carried out. The operation of this network is monitored.
Motivational aspects of digital games in learning process Aminu Usman Jibril; Nabila Musa Abdullahi; Auwal Shehu Ali; Hamidatu Abdulkadir; Ugochukwu Okwudili Matthew; Khalid Haruna
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.v3i1.885

Abstract

With the advent of digital games and its rapid evolution, it is almost impossible for a lot of people especially the young children to go a day without coming into contact with them. One of the impacts of these digital games is that it is changing the way these young children think and learn. It is therefore important to carefully examine the influence of digital games on children’s education. The purpose of this research is to identify and examine the factors that motivate children to play digital games and to determine the effect of such games to the children’s learning abilities. Responses from 172 students of ages between 11 and 16 are analysed in this research. A questionnaire is used to capture the children’s motivation towards digital gaming. Also, an intellectual test was carried out to determine the effect of digital games on the children’s learning abilities. The findings have revealed that competitive spirit is the major factor that influences children to play digital games because of the challenge and the competition that comes along with it. Furthermore, a critical view from the results of the intellectual test has shown that the children that play digital games score higher results and were able to finish within a short period of time as against the children that do not play. The outcome of this research could be used to explore the possibilities of using digital games as tools for learning, especially to the young ages.
Gender inequality in HDI and per capita expenditure: A probabilistic distribution and spatial data analysis Zainal Fadilah; Tuti Purwaningsih; Rochmad Novian Inderanata; Siaka Konate; Cicin Hardiyanti P
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.v3i2.1214

Abstract

Men and women have different habits or lifestyles, which inevitably leads to variances in other areas. As a result, gender statistics emerged. In this example, researchers seek to discover if there are discrepancies in HDI and per capita expenditure in Indonesia between men and women. To determine this, data from reliable sources is required; thus, researchers use data from the official BPS website, bps.go.id. The data comes from many tables, so the researcher will join them so that they may be studied. The data used in this scenario are HDI data by gender in 2020 and Per Capita Expenditure data by gender in 2020. Researchers employed graphical tools, such as boxplots and thematic charts, to examine whether there are differences in HDI and per capita expenditure between men and women in Indonesia. Aside from that, researchers used the two-sample t-test approach to see if there were variations in HDI and per capita expenditure between men and women. Researchers will utilize Python software to run this hypothesis test. According to the findings of the investigation, there is still gender imbalance in Indonesia in terms of HDI and per capita expenditure. As a result, it is intended that this research can be utilized as a reference in analyzing existing policies to ensure that there is no gender discrepancy in terms of HDI and per capita expenditure between men and women. It is also envisaged that this research would be beneficial to many people.
YOLOv3 and YOLOv5-based automated facial mask detection and recognition systems to prevent COVID-19 outbreaks Md Asifuzzaman Jishan; Ananna Islam Bedushe; Md Ataullah Khan Rifat; Bijan Paul; Khan Raqib Mahmud
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.v4i1.1199

Abstract

Object detection system in light of deep learning have been monstrously effective in complex item identification task images and have shown likely in an extensive variety of genuine applications counting the Coronavirus pandemic. Ensuring and enforcing the proper use of face masks is one of the main obstacles in containing and reducing the spread of the infection among the population. This paper aims to find out how the urban population of a megacity uses facial masks correctly. Using YOLOv3 and YOLOv5, we trained and validated a brand-new dataset to identify images as "with mask", "without mask", and "mask not in position". In the YOLOv3 we carried out three pre-trained models which are: YOLOv3, YOLOv3-tiny, and SPP-YOLOv3. In addition, we utilized five pre-trained models in the YOLOv5: YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The dataset is included 6550 pictures with three classes. On mAP, the dataset achieved a commendable 95% performance accuracy. This research can be used to monitor the proper use of face masks in various public spaces through automated scanning.
Text classification of traditional and national songs using naïve bayes algorithm Triyanti Simbolon; Aji Prasetya Wibawa; Ilham Ari Elbaith Zaeni; Amelia Ritahani Ismail
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.v3i2.1215

Abstract

In this research, we investigate the effectiveness of the multinomial Naïve Bayes algorithm in the context of text classification, with a particular focus on distinguishing between folk songs and national songs. The rationale for choosing the Naïve Bayes method lies in its unique ability to evaluate word frequencies not only within individual documents but across the entire dataset, leading to significant improvements in accuracy and stability. Our dataset includes 480 folk songs and 90 national songs, categorized into six distinct scenarios, encompassing two, four, and 31 labels, with and without the application of Synthetic Minority Over-sampling Technique (SMOTE). The research journey involves several essential stages, beginning with pre-processing tasks such as case folding, punctuation removal, tokenization, and TF-IDF transformation. Subsequently, the text classification is executed using the multinomial Naïve Bayes algorithm, followed by rigorous testing through k-fold cross-validation and SMOTE resampling techniques. Notably, our findings reveal that the most favorable scenario unfolds when SMOTE is applied to two labels, resulting in a remarkable accuracy rate of 93.75%. These findings underscore the prowess of the multinomial Naïve Bayes algorithm in effectively classifying small data label categories.
Placement model for students into appropriate academic class using machine learning Khalid Haruna; Anadi Stella Uju; Ibrahim Alhaji Lawal; Raliya Abubakar
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.v4i1.1024

Abstract

Choosing the right academic major for junior secondary students into senior secondary school will assist both students and their teachers toward achieving the academic goal. Traditionally, students seeking admission into senior classes (Gambia, Sierra-leone, Ghana, Liberia and Nigeria) must have passed stipulated examinations like Basic Education Certificate Examination (BECE) and/or West Africa Junior Certificate Examination, which are done at the end of year three (at a sitting). They must pass the exam(s) satisfactorily with no emphasis on any of Science, Art or Commercial related subjects. Some schools use “Mock exam” or “Placement exam” as the basis for their placement of students but all are done at a sitting (end of year three). Though this method is to an extent valid but associated with some challenges (bias) as it does not carry along the student’s academic history in making decision for placement into appropriate class. However, we proposed a model that predicts appropriate academic class of Science, Art or Commercial for Junior students based on their progressive academic performances (history) of their predecessors on related subjects using ten supervised machine learning techniques. Two evaluation techniques were applied (70/30 splitting and 10-fold cross validation). The highest results of this research showed accuracy of 93% with Random forest, 98% precision with random forest, 99% recall with Decision tree and 94% f1 score with Random forest and KNN (cross validation). The correlation coefficient of the proposed model recorded 0.3 higher than that of the existing method. This research will benefit all stakeholders in education and students in particular because their academic performances over time stands a better chance for appropriate placement.
Factors Influencing open unemployment rates: a spatial regression analysis Tuti Purwaningsih; Rochmad Novian Inderanata; Sendhyka Cakra Pradana; Aissa Snani; Sarina Sulaiman
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.v3i1.1202

Abstract

The present study employed spatial regression analysis as a methodological approach to get insights into the unemployment rates across Indonesian provinces in the year 2016. The official website of the Bureau of Labor Statistics (BPS) offers secondary data pertaining to several socio-economic indicators, including the Total Open Unemployment Rate, Economic Growth Rate, Human Development Index, Severity of Poverty Index, and School Participation Rates. The investigation employed the Geoda software package and encompassed Ordinary Least Squares (OLS) regression, Dependency/Correlation investigation, and Spatial Autoregressive Model. The data presented in the study revealed the existence of three distinct provincial groupings characterized by varying levels of unemployment rates. In the context of unemployment variance, the traditional regression model accounted for 30 percent of the observed variation. However, the spatial regression model used spatial dependencies to enhance accuracy in capturing the phenomenon. The aforementioned findings have the potential to assist policymakers in formulating strategies to address unemployment in regions characterized by distinct spatial attributes, hence offering a potential blueprint for other nations.
Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV Fuad Izzudin Ariwibowo; Dewi Pramudi Ismi
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.v3i1.1216

Abstract

CCTV systems, while ubiquitous for traffic surveillance in Indonesian roadways, remain underutilized in their potential. The integration of AI and Computer Vision technologies can transform CCTV into a valuable tool for law enforcement, specifically in monitoring and addressing helmet non-compliance among motorcycle riders. This study aims to develop an intelligent system for the accurate detection of helmetless motorcyclists using image analysis. The approach relies on deep learning, involving the creation of a dataset with 764 training images and 102 testing images. A deep convolutional neural network with 23 layers is configured, trained with a batch size of 10 over ten epochs, and employs the YOLO method to identify objects in images and subsequently detect helmetless riders. Accuracy assessment is carried out using the mean Average Precision (mAP) method, resulting in a notable 82.81% detection accuracy for riders without helmets and 75.78% for helmeted riders. The overall mAP score is 79.29%, emphasizing the system's potential to substantially improve road safety and law enforcement efforts
Suicide and self-harm prediction based on social media data using machine learning algorithms Abdulrazak Yahya Saleh; Fadzlyn Nasrini Binti Mostapa
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.v4i1.1181

Abstract

Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigor
Performance analysis of naive bayes in text classification of islamophobia issues Faiz Mohammad Ridho; Aji Prasetya Wibawa; Fachrul Kurniawan; Badrudin Badrudin; Anusua Ghosh
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.v3i1.1211

Abstract

In the aftermath of the 2013 Woolwich attack, a disturbing surge in hate crimes against the Muslim community emerged both offline and on social media platforms, prompting concerns about the widespread issue of Islamophobia. To systematically evaluate and quantify the presence of Islamophobic sentiment in online spaces, this study employed sentiment analysis, a robust method for deriving insights from textual data. Two classification models, Bernoulli Naive Bayes and Multinomial Naive Bayes, were selected to conduct a thorough analysis. Bernoulli Naive Bayes, specialized in handling binary data, was used for binary sentiment analysis, while Multinomial Naive Bayes, well-suited for data with multiple occurrences, was applied for more comprehensive analysis. The research encompassed nine meticulously designed test-train data scenarios, ranging from a 10:90 test-train data ratio to a 20:80 ratio. Surprisingly, both models exhibited a maximum accuracy rate of 68% in their respective optimal scenarios, raising intriguing questions about the potential and limitations of sentiment analysis and Naive Bayes models in the complex task of identifying and quantifying Islamophobic content on social media

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