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Register: Jurnal Ilmiah Teknologi Sistem Informasi
Published by Universitas Pesantren Tinggi Darul ulum
ISSN : 25030477     EISSN : 25023357     DOI : https://doi.org/10.26594/register
Core Subject : Science,
Computer Science & IT
Register: Scientific Journals of Information System Technology is an international, peer-reviewed journal that publishes the latest research results in Information and Communication Technology (ICT). The journal covers a wide range of topics, including Enterprise Systems, Information Systems Management, Data Acquisition and Information Dissemination, Data Engineering and Business Intelligence, and IT Infrastructure and Security. The journal has been indexed on Scopus (reputated international indexed) and accredited with grade “SINTA 1” by the Director Decree (1438/E5/DT.05.00/2024) as a recognition of its excellent quality in management and publication for international indexed journal.
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Articles 219 Documents
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Fuzzy-AHP MOORA approach for vendor selection applications Al Khoiry, I’tishom; Gernowo, Rahmat; Surarso, Bayu
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2356

Abstract

Vendor selection is a critical activity in order to support the achievement of company success and competitiveness. Significantly, the company has some specific standards in the selection. Therefore, an evaluation is needed to see which vendors match the company's criteria. The purpose of this study is to evaluate and select the proposed vendor in a web-based decision support system (DSS) by using the fuzzy-AHP MOORA approach. The fuzzy-AHP method is used to determine the importance level of the criteria, while the MOORA method is used for alternative ranking. The results showed that vendor 4 has the highest score than other alternatives with a value of 0.2536. Sensitivity analysis showed that the proposed DSS fuzzy-AHP MOORA concept was already solid and suitable for this problem, with a low rate of change.
Effect of information gain on document classification using k-nearest neighbor Perwira, Rifki Indra; Yuwono, Bambang; Siswoyo, Risya Ines Putri; Liantoni, Febri; Himawan, Hidayatulah
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2397

Abstract

State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.
An empirical study on the various stock market prediction methods Pandya, Jaymit Bharatbhai; Jaliya, Udesang K.
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2533

Abstract

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods.
The Impact of Personal, Environmental, and Information Platform Factors on Disaster Information Sharing on Twitter Amriza, Rona Nisa Sofia; Ngafidin, Khairun Nisa Meiah; Ratnasari, Wiwit
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 2 (2022): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i2.2540

Abstract

Twitter has become a major platform for disseminating disaster news, providing people with disaster information quickly and precisely. A lot of essential and valuable information can be obtained from this online platform. Twitter users might be able to help with warnings and submit specific and accurate information in a disaster situation. This research attempts to examine factors that affect disaster information-sharing behavior. Furthermore, this study aims to integrate personal, environmental, and information platform factors to gain more insight into the factors influencing Twitter users' willingness to share disaster information. The hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The result showed that Altruism, Self-efficacy, Community Identity, and Information Platforms significantly influence people's decisions to share disaster information on Twitter.
Detecting violent scenes in movies using Gated Recurrent Units and Discrete Wavelet Transform Imah, Elly Matul; Laksono, Ivan Kurnia; Karisma, Karisma; Wintarti, Atik
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 2 (2022): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i2.2541

Abstract

The easiness of accessing video on various platforms can negatively impact if not done wisely, especially for children. Parental supervision is needed so that movies platforms avoid inappropriate displays such as violence. Violent scenes in movies can trigger children to commit acts of violence, which is not desired. Unfortunately, it is not easy to supervise them fully. This study proposed a method for automatic detection of violent scenes in movies. Automatic violence detection assists the parents and censorship institutions in detecting violence easily. This study uses Gated Recurrent Units (GRU) algorithm and wavelet as feature extraction to detect violent scenes. This paper shows comparative studies on the variation of the mother wavelet. The experimental results show that GRU is robust and deliver the best performance accuracy of 0.96 while combining with mother wavelet Symlet and Coiflets8. The combination of GRU with wavelet Coiflets8 shows better results than the predecessor.
Predicting the Number of Passengers of MRT Jakarta Based on the Use of the QR-Code Payment Method during the Covid-19 Pandemic Using Long Short-Term Memory Jayadi, Riyanto; Indriasari, Taskia Fira; Chrisna, Charis; Fanuel, Putri Natasya; Afita, Rayhana
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 2 (2022): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i2.2546

Abstract

The trend of using public transportation has been rising over the last several decades. Because of increased mobility, public transportation has now become more crucial. In modern environments, public transportation is not only used to carry people and products from one location to another but has also evolved into a service company. In Jakarta, Mass Rapid Transit Jakarta (MRTJ) started to operate in late 2019. Recently, they updated their payment gateway system with QR codes. In this study, we predicted the hourly influx of passengers who used QR codes as their preferred payment method. This research applied machine learning to perform a prediction methodology, which is proposed to predict the number of passengers using time-series analysis. The dataset contained 7760 instances across different hours and days in June 2020 and was reshaped to display the total number of passengers each hour. Next, we incorporated time-series regression alongside LSTM frameworks with variations in architecture. One architecture, the 1D CNN-LSTM, yielded a promising prediction error of only one to two passengers for every hour.
Drowsy Eyes and Face Mask Detection for Car Drivers using the Embedded System Budiarti, Rizqi Putri Nourma; Nugroho, Bagoes Wahyu; Ayunda, Nisa; Sukaridhoto, Sritrusta
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.2612

Abstract

Efforts to prevent the spread of the COVID-19 virus have underscored the critical importance of mask-wearing as a preventive measure. Concurrently, road traffic accidents, often resulting from human error, have emerged as a significant contributor to global mortality rates. This study endeavors to address these pressing issues by employing advanced Deep Learning techniques to detect mask usage and identify drowsy eyes, thus contributing to the prevention of COVID-19 and accidents due to driver fatigue. To achieve this objective, an embedded system was developed, utilizing the integration of hardware and software components. The system effectively utilizes MobileNetV2 for face mask detection and employs HOG and SVM algorithms for drowsy eye detection. By seamlessly integrating these detection systems into a single embedded device, the simultaneous detection of both mask usage and drowsy eyes is made possible. The results demonstrates a commendable accuracy rate of 80% for face mask detection and 75% for drowsy eye detection. Furthermore, the mask detection component exhibits a remarkable training accuracy of 99%, while the drowsy eye detection component demonstrates an 80% training accuracy, affirming the system's efficacy in precisely identifying masks and drowsy eyes. The proposed embedded system offers potential applications in enhancing road safety. Its capability to effectively detect drowsy eyes and mask usage in car drivers contributes significantly to preventing accidents due to driver fatigue. Additionally, it plays a vital role in mitigating COVID-19 transmission by promoting widespread mask-wearing among individuals. This study exemplifies the potential of integrating Deep Learning methodologies with embedded systems, thus paving the way for future research and development in the realm of driver safety and virus prevention.
A Bibliometric Analysis of Metaheuristic Research and Its Applications Hendy, Hendy; Irawan, Mohammad Isa; Mukhlash, Imam; Setumin, Samsul
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.2675

Abstract

Metaheuristic algorithms are generic optimization tools to solve complex problems with extensive search spaces. This algorithm minimizes the size of the search space by using effective search strategies. Research on metaheuristic algorithms continues to grow and is widely applied to solve big data problems. This study aims to provide an analysis of the performance of metaheuristic research and to map a description of the themes of the metaheuristic research method. Using bibliometric analysis, we examined the performance of scientific articles and described the available opportunities for metaheuristic research methods. This study presents the performance analysis and bibliometric review of metaheuristic research documents indexed in the Scopus database between the period of 2016-2021. The overall number of papers published at the global level was 3846. At global optimization, heuristic methods, scheduling, genetic algorithms, evolutionary algorithms, and benchmarking dominate metaheuristic research. Meanwhile, the discussion on adaptive neuro-fuzzy inference, forecasting, feature selection, biomimetics, exploration, and exploitation, are growing hot issues for research in this field. The current research reveals a unique overview of metaheuristic research at the global level from 2016-2021, and this could be valuable for conducting future research.
Associate Editors, Reviewers, & Table of Contents Register, Register
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2803

Abstract

Student Satisfaction with Online Learning: A Multigroup Analysis Santosa, Paulus Insap
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 2 (2022): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i2.2804

Abstract

The Coronavirus disease 2019 pandemic “forced” students to attend online classes roughly from mid-March 2020. This situation, which caused universities, among other institutions, to deal with an overnight change in course delivery from traditional face-to-face to online mode, has resulted in many students facing difficulties. They must cope with the available infrastructure, unstable and limited Internet connection, course delivery, and their self-discipline. Male and female students may have different preferences regarding technology use. This study focused on student satisfaction with the above situation and determined whether a difference exists between male and female students using Technology Acceptance Model as the main theoretical background. Seven hypotheses were proposed and tested with the whole dataset and comparisons between the two groups. Due to the strict health protocol, an online survey was employed using Google Form to collect data. Respondents were 327 undergraduate students from one higher institution in Yogyakarta, comprising 140 male and 187 female students. The population consisted of undergraduate students who have been attending online classes since March 2022. A multigroup analysis was performed using SmartPLS 3.3.3. Results indicated no gender difference in all hypothesized relationships. The theoretical contribution can be seen from the use of Internet Quality, User Interface Quality, and Delivery Quality as the three exogenous variables of the proposed model. The practical contribution is that technology designers must pay attention to the different preferences of user groups.

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