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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 6 Documents
 1 
Search results for , issue "Vol. 9 No. 1 (2024)" : 6 Documents clear
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.

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