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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 212 Documents
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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.
Classification of Land Suitability For Soybean Crops Using The Cart Method and Feature Selection Using an Algorithm ABC Efendi, Rusdi; Yusa, Mochammad; Hallatu, Stefani Tasya
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.13595

Abstract

The allocated area for soybean cultivation has been gradually decreasing, leading to a decline in both production and productivity. Consequently, the current level of soybean production and productivity falls short of meeting the demand within the community. One potential solution to augment soybean output and efficiency involves allocating specific parcels of land for soybean cultivation. It is essential to conduct land evaluations tailored to soybean cultivation, accounting for the land's inherent potential, in order to optimize land utilization. Thus, a comprehensive system is required to assess land suitability, particularly for soybean cultivation, and employ the results of this classification as recommendations for land allocation. This research employess combination the Classification and Regression Tree (CART) method and the Artificial Bee Colony (ABC) algorithm to classify suitable land for soybean cultivation. CART is used for classification and ABC is utilized for feature selection to identify the most relevant attributes in case of the algorithm improvement. Through a series of iterative experiments involving 5, 10, 25, 50, 75, and 100 iterations, the best attribute was determined following three attempts at each iteration. The Confusion Matrix test yielded an accuracy rate of 94.22% for the CART method in the second experiment, while the combined use of the best ABC and CART combination resulted in an accuracy rate of 97.11%. Therefore, it can be concluded that the integration of the artificial bee colony (ABC) algorithm with the classification and regression tree (CART) method outperforms the sole use of the CART method in terms of accuracy.
Enhancing Fishing Efficiency with Geographic Information System and Optimized Methods Santosa, Anisa Fitri; Arfianto , Afif Zuhri; Hasin, Muhammad Khoirul; Sutrisno, Imam; Sukoco, Didik; Riananda, Dimas Pristovani
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.13859

Abstract

Traditional fishing techniques frequently lack efficiency and optimization, resulting in fishermen obtaining unsatisfactory yields. This study presents a novel approach by incorporating Geographic Information System (GIS) technology, notably utilizing Leaflet, to improve fishing techniques. The suggested system incorporates a LoRa node tool that logs the journeys of fishermen, offering comprehensive itineraries and data on the distribution of fish and unfavorable weather conditions. Notable outcomes were attained by employing the haversine approach to compute distances between the LoRa Gateway and different data points. The approach exhibited a negligible error margin of 0.157% in contrast to the calculations performed in Excel. In addition, the GPS accuracy testing produced remarkable results, with latitude and longitude errors of 0.000116% and 0.000002%, respectively. The LoRa system demonstrated a range of RSSI performance, with values ranging from -57 dBM at 50 meters to -121 dBM at 1500 meters. This range of performance guarantees dependable transmission of data over significant distances. The findings underscore the GIS-based strategy's efficacy in enhancing the effectiveness and precision of conventional fishing methods, presenting a promising technical improvement for the fishing sector.
Automatic Vocal Completion for Indonesian Language Based on Recurrent Neural Network Prasetiadi, Agi; Dwi Sripamuji, Asti; Riski Amalia, Risa; Saputra, Julian; Ramadhanti, Imada
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.14171

Abstract

Most Indonesian social media users under the age of 25 use various words, which are now often referred to as slang, including abbreviations in communicating. Not only causes, but this variation also poses challenges for the natural language processing of Indonesian. The previous researchers tried to improve the Recurrent Neural Network to correct errors at the character level with an accuracy of 83.76%. This study aims to normalize abbreviated words in Indonesian into complete words using a Recurrent Neural Network in the form of Bidirected Long Short-Term Memory and Gated Recurrent Unit. The dataset is built with several weight confgurations from 3-Gram to 6-Gram consisting of words without vowels and complete words with vowels. Our model is the frst model in the world that tries to fnd incomplete Indonesian words, which eventually become fully lettered sentences with an accuracy of 97.44%.
Low-Cost Early Detection Device for Breast Cancer based on Skin Surface Temperature Arsyad Cahya Subrata; Sirajuddin, Muhammad Mar’ie; Salsabila, Sona Regina; Ibad, Irsyadul; Prasetyo, Eko; Yusmianto, Ferry
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.16034

Abstract

One of the deadly diseases that attacks many women is breast cancer. It was recorded that breast cancer cases in 2020 were 2.3 million, with deaths accounting for 29% of these cases. The BSE technique is an easy way of early identification of breast cancer that can be done independently. However, this technique often goes wrong when practiced, making it ineffective. An early breast cancer detection system is proposed to make it easier for women to carry out early identification independently. Detection is carried out based on the measured temperature of the breast surface. The temperature difference at each point is a reference for the potential for breast cancer. This system was built in a bra and tested with a mannequin as a simulator subject. The MLX90614 temperature sensor, as the primary sensor, succeeded in measuring the surface temperature of the dummy with 99% accuracy. Final testing of the proposed system can also differentiate the temperature differences in each zone.
Classification for Determining the Level of Drugs Dependence Using the Naïve Bayes Classifier Puspitasari, Novianti; Ajay, Muhammad; Wati, Masna; Septiarini, Anindita
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.16319

Abstract

Drug users or abusers are people who use narcotics or psychotropic drugs without supervision or medical indication from a doctor. Before undergoing rehabilitation, drug users must first undergo an examination to determine their level of drug dependence so that they can receive medical treatment according to their level of drug dependence. Determining the level of drug dependence requires a technique that can provide labels or categories of data for drug users based on the user's condition or influential criteria. This study applies the Naïve Bayes Classifier method to a system to determine the level of drug dependence. This study uses medical record data from 220 drug users. The user's medical record data is processed using data mining stages consisting of data selection, data cleaning, data transformation, and division of training and test data to produce 120 training data and 100 test data. The results of the Naive Bayes Classifier method calculation resulted in 29 users having a trial level of dependence (mild), 42 identified as having a regular level of dependence (moderate), and 29 others as users with a severe level of dependence. The confusion matrix testing was very accurate, namely, 94% accuracy, 95% precision value, and 92% recall. Meanwhile, the system that has been built can run very well. Based on the results of the research that has been conducted, this research can contribute to determining the level of dependence of drug addicts objectively so that related parties can provide rehabilitation or appropriate treatment to drug addicts.
Enhancing Cybersecurity through AI-Powered Security Mechanisms Akhtar, Zarif Bin; Tajbiul Rawol, Ahmed
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.16852

Abstract

In the rapidly evolving landscape of digital technology, the proliferation of interconnected systems has brought unprecedented opportunities and challenges. Among these challenges, the escalating frequency and sophistication of cyberattacks pose significant threats to individuals, organizations, and nations. In response, the fusion of Cybersecurity and Artificial Intelligence (AI) has emerged as a pivotal paradigm, offering proactive, intelligent, and adaptable defense mechanisms. This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures. By analyzing vast volumes of data at speeds beyond human capacity, AI-driven cybersecurity systems can identify subtle patterns indicative of potential threats, allowing for early detection and prevention. The exploration consolidates existing studies, highlighting the trends and gaps that this research addresses. Expanded results and discussions provide a detailed analysis of the practical benefits and challenges of AI applications in cybersecurity, including case studies that offer concrete evidence of AI's impact. Novel contributions are emphasized through comparisons with other studies, showcasing improvements in accuracy, precision, recall, and F-score metrics, which demonstrate the effectiveness of AI in enhancing cybersecurity measures. The synergy between AI and human expertise is explored, highlighting how AI-driven tools augment human analysts' capabilities. Ethical considerations and the "black box" nature of AI algorithms are addressed, advocating for transparent and interpretable AI models to foster trust and collaboration between man and machine. The challenges posed by adversarial AI, where threat actors exploit AI system vulnerabilities, are examined. Strategies for building robust AI security mechanisms, including adversarial training, model diversification, and advanced threat modeling, are discussed. The research also emphasizes a holistic approach that combines AI-driven automation with human intuition and domain knowledge. As AI continues to rapidly evolve, a proactive and dynamic cybersecurity posture can be established, bolstering defenses, mitigating risks, and ensuring the integrity of our increasingly interconnected digital world.
Text Mining Approach to Emotion Analysis in Translation of Surah Yusuf With NRC Emotion Lexicon Syafiqah, Annisa; Zaman, Syahiduz; Imamudin, Mochamad
IT Journal Research and Development Vol. 9 No. 2 (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2025.17765

Abstract

In the digital era, the accessibility of vast textual data, including the Quran, has facilitated broader comprehension of its teachings. This study analyzes the emotions in the English translation of Surah Yusuf using the NRC Emotion Lexicon. The findings show that trust is the most dominant emotion (22.89%), followed by joy (15.66%), anticipation (13.25%), sadness (12.05%), fear (10.84%), anger (9.64%), surprise (8.43%), and disgust (7.23%). These results confirm the text's diverse emotional expressions and the effectiveness of the lexicon-based method. The research aligns with the initial goals and highlights the potential of emotion analysis in understanding religious texts. Future research can expand the analysis to more verses and use machine learning for improved accuracy. This study aids scholars and students in exploring the Quran's emotional and spiritual dimensions and can be adapted to other texts for broader applications.

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