cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andri Pranolo
Contact Email
andri@ascee.org
Phone
+6281392554050
Journal Mail Official
andri@ascee.org
Editorial Address
Association for Scientific Computing Electrical and Engineering (ASCEE) Jl. Janti, Karangjambe 130B, Banguntapan, Bantul, Yogyakarta, Indonesia
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 1 (2023): May 2023" : 5 Documents clear
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.
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.
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
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
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). 

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 4, No 1 (2023): May 2023 Vol 3, No 2 (2022): November 2022 Vol 3, No 1 (2022): May 2022 Vol 2, No 2: November 2021 Vol 2, No 1: May 2021 Vol 1, No 2: November 2020 Vol 1, No 1: May 2020