cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota pekanbaru,
Riau
INDONESIA
IT JOURNAL RESEARCH AND DEVELOPMENT
Published by Universitas Islam Riau
ISSN : 25284061     EISSN : 25284053     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
Information Technology Journal Research and Development (ITJRD) adalah Jurnal Ilmiah yang dibangun oleh Prodi Teknik Informatika, Universitas Islam Riau untuk memberikan sarana bagi para akademisi dan peneliti untuk mempublikasikan tulisan dan karya ilmiah di Bidang Teknologi Informatika. Adapun ruang lingkup dalam jurnal ini meliputi bidang penelitian di teknik informatika, ilmu komputer, jaringan komputer, sistem informasi, desain grafis, pengelolaan citra dan multimedia.
Arjuna Subject : -
Articles 7 Documents
 1 
Search results for , issue "Vol. 8 No. 2 (2024)" : 7 Documents clear
Potensi akurasi Convolutional Neural Network (CNN) dalam mengenali pakaian adat Herwinsyah, Herwinsyah; Yuswanto Jaya, Dery
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.12690

Abstract

The diversity of cultures in Indonesia is proof that Indonesia is a country that is rich in cultural diversity. Many foreign tourists who want to know about culture in Indonesia are not directly proportional to the media to introduce culture in Indonesia. Therefore, this study aims to classify images of traditional clothing by detecting images of traditional clothing sent to the application to determine the name of the traditional soldier. These images will be converted into vectors and processed to find the closest similarity level. The Deep Learning method which currently has the most significant results in image recognition is the Convolutional Neural Network (CNN). The analysis carried out resulted in an accuracy of 0.7934 with an epoch of 20 and a data set of 700 data. The accuracy value is 0.7934 which is a large enough number to determine the correct classification of image objects. This is proven by testing on 10 different images and only 1 image is inaccurate with 90% accuracy.
Analisis Antarmuka Pengguna Aplikasi PeduliLindungi untuk Meningkatkan Pengalaman Pengguna dengan Metode Evaluasi Heuristik: - Andry, Johanes Fernandes; Clara, Monica; Chandra, William; Antonio, Marco; Bernanda, Devi Yurisca
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.12295

Abstract

The Covid-19 pandemic limits human activities every day. The spread of the virus is uncontrollable, it can attack anyone, anytime, and anywhere. As a solution, the use of the PeduliLindungi application technology developed by the Ministry of Communication and Information in collaboration with other relevant Ministries and Institutions is used to help track the spread of viruses and understand preventive measures to stop their spread. Research was conducted on the appearance of the application's User Interface using the Heuristic Evaluation method to measure its usefulness. However, there are several problems experienced by users, namely the information is not updated (vaccines and Covid-19 tests), the language used is inconsistent, and others. Questionnaire testing with 10 questions based on 10 Heuristic Evaluation criteria was distributed via Google Form to 33 respondents. Then, carry out validity and reliability tests with the research results obtained Valid and Reliable. as well as testing the hypothesis of all Learnability, Efficiency, Memorability, and Error Prevention factors, based on usability and user satisfaction aspects, questionnaire items all test results are accepted and there are no serious problems. However, there are a number of things that need to be improved so that the application can run more optimally.
The Role of the Principal as an Educator in Developing Capability Teacher Information And Communication Technology Sari, Putri Indah; Mustika, Dea; Wandri, Rizky
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.12970

Abstract

The demands of teachers in this era require teachers to continue to develop with the times. It is the responsibility of the principal to help teachers improve their abilities. This study aims to determine the role, obstacles and solutions of the principal as an educator in developing the Information and Communication Technology skills of teachers at SD Negeri 109 Pekanbaru. This study used descriptive qualitative method. Data collection techniques using interviews, observation and documentation review. Testing the validity of the data using triangulation. Data analysis techniques namely data collection, data reduction, data presentation and drawing conclusions. The results showed that the principal had tried to carry out his role as an educator in developing teachers' ICT skills. The conclusion from the research results is that the role of the principal as an educator in developing teachers' ICT skills is carried out with strategies that have been prepared such as, involving teachers in all ICT-based training and providing opportunities for teachers to increase knowledge and skills, creating a conducive school atmosphere by completing infrastructure ICT and complement teaching materials by checking ICT teaching materials used by teachers in learning, providing guidance and advice at regular meetings or meetings and providing motivation such as giving praise to teachers who have contributed a lot to the school.
Identifikasi Faktor Risiko Pada Tahap Perancangan Perangkat Lunak Menggunakan Algoritma C4.5 Azkiya, M. Akiyasul; Maulita, Deva Sindi; Jumanto
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.13251

Abstract

A strong design phase is necessary for good software. However, design errors in software can cause serious issues with its creation and use. Therefore, the goal of this study is to find risk variables that could have an early impact on software development. In this study, a machine learning technique called technique C4.5 is employed to create decision tree models. 100 respondents with software design experience participated in the online surveys and questionnaires that collected the data for this study in 2022. The C4.5 Algorithm was used in this study to analyze the data and determine the risk variables that affect the success of software design. The study's findings show that the C4.5 Algorithm-based model has a high level of accuracy (93.33%), which means that the data can offer crucial insights into understanding potential risks that may arise during the software design stage, enabling software developers to take the necessary precautions to lessen or eliminate these risks. In order to enhance the caliber and effectiveness of software design, this research is anticipated to provide a significant contribution to practitioners and academics in the field of software development.
Analisis Knowledge Graph Pada Halaman Wikipedia Berbahasa Inggris Menggunakan Algoritma Deep Learning Pratama, Yudistira Bagus; Haiyudi, Haiyudi
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.13459

Abstract

Analysis of social networks or online communities can be very difficult when working on large networks, as many measurements require expensive hardware. For example, identifying the community structure of a network is a very computationally expensive task. Embedded graph is a way to represent graphs with vectors, so that further analysis becomes easier. The purpose of this research is to analyze the knowledge graph from the wikipedia article data. This research aims to implement web scraping techniques on the wikipedia article search engine and display similar wikipedia pages and analyze them using a predetermined deep learning algorithm. Data collection in this research used scraping techniques to retrieve data from the unstructured wikipedia website and then processed it into structured data. The method used in this research is a standard cross-industry process for data mining by performing phases of data collection, data processing, proposed algorithms, testing and evaluation. The algorithm applied is deepwalk, kmeans, girvan newman. By doing this research, it is expected to provide knowledge about the deep learning approach for data representation of the wikipedia pages knowledge graph and can help users find similar wikipedia pages and enrich literacy on knowledge graph analysis.
A Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects Kommey, Benjamin; Isaac, Oniti Jesutofunmi; Tamakloe, Elvis; Opoku4, Daniel
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.13474

Abstract

Reinforcement Learning (RL) is fast gaining traction as a major branch of machine learning, its applications have expanded well beyond its typical usage in games. Several subfields of reinforcement learning like deep reinforcement learning and multi-agent reinforcement learning are also expanding rapidly. This paper provides an extensive review on the field from the point of view of Machine Learning (ML). It begins by providing a historical perspective on the field then proceeds to lay a theoretical background on the field. It further discusses core reinforcement learning problems and approaches taken by different subfields before discussing the state of the art in the field. An inexhaustive list of applications of reinforcement learning is provided and their practicability and scalability assessed. The paper concludes by highlighting some open areas or issues in the field
Enhancing Stock Price Prediction Using Stacked Long Short-Term Memory Diqi, Mohammad; Ordiyasa, I Wayan; Hamzah, Hamzah
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.13486

Abstract

This research explores the Stacked Long Short-Term Memory (LSTM) model for stock price prediction using a dataset obtained from Yahoo Finance. The main objective is to assess the effectiveness of the model in capturing stock price patterns and making accurate predictions. The dataset consists of stock prices for the top 10 companies listed in the Indonesia Stock Exchange from July 6, 2015, to October 14, 2021. The model is trained and evaluated using metrics such as RMSE, MAE, MAPE, and R2. The average values of these metrics for the predictions indicate promising results, with an average RMSE of 0.00885, average MAE of 0.00800, average MAPE of 0.02496, and an average R2 of 0.9597. These findings suggest that the Stacked LSTM model can effectively capture stock price patterns and make accurate predictions. The research contributes to the field of stock price prediction and highlights the potential of deep learning techniques in financial forecasting.

Page 1 of 1 | Total Record : 7

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 10 No. 2 (2025) Vol. 9 No. 2 (2025) Vol. 9 No. 1 (2024) Vol. 8 No. 2 (2024) Vol. 8 No. 1 (2023) Vol. 7 No. 2 (2023) Vol. 7 No. 1 (2022) Vol. 6 No. 2 (2022) Vol. 6 No. 1 (2021) Vol. 5 No. 2 (2021) Vol. 5 No. 1 (2020) Vol. 4 No. 2 (2020) Vol. 4 No. 1 (2019) Vol 3 No 2 (2019) Vol. 3 No. 2 (2019) Vol 3 No 1 (2018) Vol. 3 No. 1 (2018) Vol 2 No 2 (2018) Vol. 2 No. 2 (2018) Vol 2 No 1 (2017) Vol. 2 No. 1 (2017) Vol. 1 No. 2 (2017) Vol 1 No 2 (2017) Vol. 1 No. 1 (2016) Vol 1 No 1 (2016) Article In Press More Issue