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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 51 Documents
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The hybrid metaheuristic scheduling model for garment manufacturing on-demand Мoch Sаiful Umam
Science in Information Technology Letters Vol 2, No 2: November 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i2.504

Abstract

The latest technology milestone drives the fashion industry to implement on-demand production services. This study introduces a decision-making scheme in the manufacturing on-demand production scheduling of the garment industry using a hybrid metaheuristic model to meet consumer demand in the digital economy as quickly as possible. Then we conduct computational experiments based on the real-world case study and compare the hybrid metaheuristic method with existing approaches. The experimental results demonstrate that the hybrid metaheuristic approach can yield very efficient solutions to the scheduling problem; it can save production completion time by 22.6%; it shows promising performance compared to the existing methods.
A fundamental overview of sota-ensemble learning methods for deep learning: a systematic literature review Klaiber, Marco
Science in Information Technology Letters Vol 2, No 2: November 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i2.549

Abstract

The rapid growth in popularity of Deep Learning (DL) continues to bring more use cases and opportunities, with methods rapidly evolving and new fields developing from the convergence of different algorithms. For this systematic literature review, we considered the most relevant peer-reviewed journals and conference papers on the state of the art of various Ensemble Learning (EL) methods for application in DL, which are also expected to give rise to new ones in combination. The EL methods relevant to this work are described in detail and the respective popular combination strategies as well as the individual tuning and averaging procedures are presented. A comprehensive overview of the various limitations of EL is then provided, culminating in the final formulation of research gaps for future scholarly work on the results, which is the goal of this thesis. This work fills the research gap for upcoming work in EL for by proving in detail and making accessible the fundamental properties of the chosen methods, which will further deepen the understanding of the complex topic in the future and, following the maxim of ensemble learning, should enable better results through an ensemble of knowledge in the future.
Knowledge representation of drug using ontology alignment and mapping techniques Herlina Jayadianti; Alisya Amalia Putri Hasanah; Yuli Fauziah; Shoffan Saifullah
Science in Information Technology Letters Vol 2, No 1: May 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i1.561

Abstract

Drug searches are still based on drug names and brands, making it difficult for patients to come looking for a cure by saying that they feel sick. Likewise, when looking for drugs and information about their content to avoid overdose errors when changing drugs when drugs are supposed to be unavailable. Based on the issues raised, a study was conducted on applying semantic web ontology to search for drugs that can appear based on patients’ names, compositions, or complaints of diseases. Protégé 5.5 serves to represent drug information based on knowledge. The application uses Netbeans with Jena API as a library and creates data and drug information on the semantic web. Drug search also uses similar in-formation meaning based on user knowledge. By representing knowledge on the search for drug and disease information with semantic web ontology technology, it can meet the purpose of research, namely to improve drug and disease information search following the user’s wishes.
Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes Khalid Haruna; Aminu Musa; Zayyanu Yunusa; Yakubu Ibrahim; Fa’iz Ibrahim Jibia; Nur Bala Rabiu
Science in Information Technology Letters Vol 2, No 1: May 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i1.610

Abstract

Recommender systems (RS) have been widely used to extract relevant and meaningful information from a vast body of data, to make appropriate suggestions to users with different preferences in various domains of applications. However, despite the success of the early recommendation systems, they suffer from two major challenges of cold start and data sparsity. Traditional RS consider an interaction between user and item (2D), neglecting contextual information such as location, until fairly recently. The contexts extend traditional RS to multi-dimension interaction and provides a useful information that allow recommendations to be more personalized. Surprisingly, taking these contexts such as location, into consideration eliminates the challenges of traditional RS. Location-Aware Recommender System (LARS) takes user's location into account as an additional context. The combination allows the prediction of spatial items, items closest to the users, to reduce information overload and was proved to be more effective than earlier RS. In this research, we provide a systematic literature of the existing literature in LARS from 2010 to 2021, focusing on the state-of-the-art methodologies, the domain of applications, and trends of publications in LARS. The paper proposed several models of LARS based on the traditional RS methodologies, providing future directions to researchers. Despite numerous reviews available on LARS, a review that proposed several LARS techniques were not found in the literature. The results indicated that the trend of publication in LARS is growing exponentially and that the field is getting attention rapidly with the number of publications on the rise every year.
Developing an integrated business model in the manufacturing industry – An AHP approach G K L Chien; F T S Chan
Science in Information Technology Letters Vol 2, No 1: May 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i1.611

Abstract

Due to the market competition in the current business environment, there are many pressures in the Small and Medium-sized Enterprise (SME) manufacturers. Most SMEs are facing challenges in the market change from Mass Production (MP) to Mass Customization (MC). The purpose of this study is mainly based on drilling down into an SME manufacturer, exploring the limitation in its current business model and determining the boundaries of its operation process. This research paper designs an Analytical Hierarchy Process (AHP) model to develop a new business model to resolve the MC issue in the SME manufacturers. The aims of the new business model should be to improve company profit. Thereby seven criteria in the AHP model are profit, Minimum Order Quantity (MOQ), flexibility, inventory control, delivery time, revenue from existing customers and revenue from new customers for customized products. The methodology is pilot-run the new business model and compares the results among the AHP, current and new business models. The results prove that the new business model not only solves the problems in MC but also create a synergic effect on the business. The study provides a method that uses an AHP model for developing an integrated business model for SME manufacturers.
Forecasting electrical power consumption using ARIMA method based on kWh of sold energy Gianika Roman Sosa; Moh. Zainul Falah; Dika Fikri L; Aji Prasetya Wibawa; Anik Nur Handayani; Jehad A. H. Hammad
Science in Information Technology Letters Vol 2, No 1: May 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i1.637

Abstract

Customer demand for electrical energy continues to increase, so electrical energy infrastructure must be developed to fulfill it. In order to generate and distribute electrical energy cost-effectively, it is crucial to estimate electrical energy consumption reasonably in advance. In addition, it is necessary to ensure that customer demands can be met and that there is no shortage of electricity supply. This study aims to determine the estimated long-term electricity use with a historical Energy Sold (T1) database in kW accumulated over several periods from 2008 to 2017. The ARIMA method with the Seasonal-ARIMA (SARIMA) pattern is used in forecasting analysis. The ARIMA method was chosen because it is considered appropriate for forecasting linear and univariate time-series data. The results of this study indicate that the MAPE (%) error rate is relatively low, with a result of 7,966, but the R-Square reaches a value of -0.024 due to the lack of observational data.
Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks Widiharso, Prasetya; Handoko, Wahyu Tri; Wibawa, Aji Prasetya; Handayani, Anik Nur; Teng, Ming Foey
Science in Information Technology Letters Vol 2, No 2: November 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i2.638

Abstract

Measuring the quality of river water used as industrial wastewater disposal is needed to maintain water quality from pollution. The chemical industry produces hazardous waste containing toxic materials and heavy metals. At specific concentrations, industrial waste can result in bacteriological contamination and excessive nutrient load (eutrophication). Using the Convolutional Neural Network (CNN), the method for measuring water quality processes remote sensing images taken via an RGB camera on an Unmanned Aerial Vehicle (UAV). The parameter measured is the change in the color of the river water image caused by the chemical reaction of the heavy metal content of industrial waste disposal. The test results of the Convolutional Neural Network (CNN) method in 2.01s/step obtained the value of training loss mode 17.86%, training accuracy 90.62%, validation loss 23.43%, validation accuracy 83.33%.
Image processing for student emotion monitoring based on fisherface method Awang Hendrianto Pratomo; Mangaras Yanu Florestyanto; Y I Sania; B Ihsan; H H Triharminto; Leonel Hernandez
Science in Information Technology Letters Vol 2, No 1: May 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i1.690

Abstract

Monitoring academic emotion is an activity to provide information from students' academic emotions in the class continuously. Some research in the image processing field had done for face recognition but had not been many studies on image processing to detect student emotions. This paper aims to determine the percentage of facial recognition with fisherface and academic emotional recognition by monitoring changes in students' facial expressions using facial landmarks in various distances, camera angles, light, and attributes used on objects. The proposed method uses facial image extraction based on fisherface method for presence. Furthermore, face identification will be made with Euclidean distance by finding the smallest length of training data with test data. Emotion detection is done by facial landmarks and mathematical calculations to detect drowsiness, focus, and not focus on the face. Restful web service is used as a communication architecture to integrate data. The success rate of applications with the fisherface method obtains 96% percent accuracy of face recognition. Meanwhile, facial landmarks and mathematical calculations are used to detect emotions, with 84 %.
Artificial Intelligence for Thyroid Disorders: A Systematic Review Al Hakim, Rosyid Ridlo; Satria, Muhammad Haikal; Arief, Yanuar Zulardiansyah; Setiawan, Antonius Darma; Pangestu, Agung; Hidayah, Hexa Apriliana
Science in Information Technology Letters Vol 2, No 2: November 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i2.694

Abstract

The thyroid gland plays a very important role in hormonal regulation in the human body. If the thyroid gland has a disorder, it can affect the performance of body functions. The development of artificial intelligence technology today allows an expert such as a doctor to be helped by his work. One of the important roles of artificial intelligence is helping doctors, among others, to diagnose a patient to determine appropriate post-diagnosis care. The study aims to shed light on the role of artificial intelligence in the treatment of thyroid disorders.The thyroid gland plays a very important role in hormonal regulation in the human body. If the thyroid gland has a disorder, it can affect the performance of body functions. The development of artificial intelligence technology today allows an expert such as a doctor to be helped by his work. One of the important roles of artificial intelligence is helping doctors, among others, to diagnose a patient to determine appropriate post-diagnosis care. The study aims to shed light on the role of artificial intelligence in the treatment of thyroid disorders.
CORONAVIRUS Diagnosis Based on Chest X-Ray Images and Pre-trained DenseNet-121 Yousra Kateb; Hocine Meglouli; Abdelmalek Khebli
Science in Information Technology Letters Vol 2, No 2: November 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i2.779

Abstract

A serious global problem called COVID-19 has killed a great number of people and rendered many projects useless. The obtained individual's identification at the appropriate time is one of the crucial methods to reduce losses. By detecting and recognizing contaminated individuals in the early stages, artificial intelligence can help many associations in these situations. In this study, we offer a fully automated method to identify COVID-19 from a patient's chest X-ray images without the need for a clinical expert's assistance. A new dataset was released, which consists of 300 chest X-ray images from 100 healthy individuals, 100 individuals who were infected with Covid 19, and 100 images of viral pneumonitis. 100 more for testing, too. In order to attain an F1 score of 0.98, a Recall of 0.98, and also an Accuracy of 0.98 with this dataset, a classification method deep learning-based learning algorithm DenseNet-121, transfer learning, as well as data augmentation techniques were implemented. Therefore, even though there are not enough training photos, these findings are far better than other state-of-the-art.

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