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International Journal of Artificial Intelligence Research
Published by STMIK Dharma Wacana
ISSN : -     EISSN : 25797298     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : -
Articles 650 Documents
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Network Infrastructure Design Of Gigabit Passive Ethernet Technology At Dehasen University, Bengkulu Khairil Khairil; Juju Jumadi; Ila Yati Beti
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1711

Abstract

The development of information technology has significantly increased the demand for reliable and stable internet network infrastructure, especially in higher education institutions. Universities require efficient network systems to support academic activities, digital learning, research, and administrative services. This research aims to design a network infrastructure using Gigabit Passive Ethernet Network (GPEN) technology at Universitas Dehasen Bengkulu. The previous network infrastructure relied on wireless radio point-to-point connections which often experienced interference caused by obstacles such as buildings, trees, and weather conditions. The proposed GPEN-based infrastructure is expected to provide a more stable and efficient network connection compared to the previous system. The research method used is Research and Development (R&D), which includes observation, literature study, and laboratory testing. Network design is implemented using MikroTik devices such as NetPower, GPEN11, and GPeR to extend Ethernet connectivity. The results of this study show that the GPEN network implementation provides better bandwidth stability, reduces interference problems, and improves overall network performance. Therefore, the GPEN-based infrastructure can be considered an effective solution for improving campus network connectivity at Universitas Dehasen Bengkulu
The Use of Apriori Method in Forecasting the Number of New Students Lena Elfianty; Jhoanne Fredricka; Rizka Tri Alinse
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1709

Abstract

The development of information technology has significantly influenced many sectors, including education. Higher education institutions are required to manage and analyze data effectively in order to support decision-making processes. One of the challenges faced by universities is predicting the number of new students each academic year. The uncertainty in the number of applicants can affect academic planning, facility preparation, and marketing strategies carried out by the institution. This study aims to apply the Apriori method to analyze new student admission data in order to discover patterns and relationships within the data that can be used as a basis for forecasting the number of new students in the following academic year. The research method used includes data collection through observation, interviews, and literature study. The data used in this study are historical data of new student registrations from previous years.The analysis process is carried out using the Apriori algorithm to identify frequent itemsets and association rules based on support and confidence values. The results of the study indicate that the Apriori method is capable of identifying patterns and relationships among variables in the new student registration process. The information generated from this analysis can assist universities in developing more effective strategies for student recruitment and admission planning. By implementing a data mining approach using the Apriori method, educational institutions are expected to utilize their existing data to generate valuable information that supports strategic decision making and improves forecasting accuracy for new student admissions
Socioeconomic Status and the Digital Divide in AI-Driven Learning Environments Jayrome L. Nuñez; Jomar M. Urbano; Denise Kristine S. Ong; Lucky Jhon D. Tiongco
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1707

Abstract

This study examined the socioeconomic divide in the use of artificial intelligence (AI) in learning environments among university students in selected universities in the Philippines. Specifically, it explored how socioeconomic factors such as access to devices, internet reliability, affordability, technical limitations, and family support influence students’ use and experience of AI-driven learning tools. The study used a quantitative, non-experimental, cross-sectional, descriptive research design. Data was collected through a structured questionnaire administered to 204 university students and were analyzed using frequency and percentage distribution. The findings revealed that most respondents recognized the usefulness of AI in learning, particularly in helping them understand lessons and complete academic tasks. A majority of students also reported frequent access to personal devices and relatively reliable internet connectivity. However, the results showed that significant barriers remain. More than half of the respondents identified the cost of devices and internet services as a household challenge, while many also reported technical limitations such as slow internet and outdated devices. Family support was found to be moderate and uneven, suggesting that students do not experience the same level of assistance in using AI tools for academic purposes. Although many students expressed confidence in using AI, their overall experience was only moderately positive. The study concludes that the digital divide in AI-driven learning environments is not only about physical access to technology, but also about the quality, affordability, and consistency of access and support. It recommends that higher education institutions adopt inclusive strategies that improve digital access, strengthen student support, and ensure equitable opportunities for using AI in learning.
The Urgency Of The Existence Of Brainware Management System In Companies From The Perspective of A Welfare Law State To Win The Competition Nugraha Pranadita; Agus Rahayu; Lili Adi Wibowo; Ridwan Purnama
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1703

Abstract

Labor costs, a major component of fixed costs, are certain to continue to rise annually. This is not due to improvements in labor quality and quantity, or labor scarcity, but rather to the obligation to comply with the law. Some industries believe that innovation will increase competitiveness, when in fact, innovation also creates diseases that kill the industry itself or create monsters that destroy other industries. To win the competition, there are external factors that are given, and there are also a few internal factors that can be customized to give the industry some breathing room. One of these customized internal factors is the soft side of human resources within the company and the opportunities created by technological developments and laws and regulations. The Brainware Management System is one tool that can be used by industries to win the competition by leveraging developments in artificial intelligence technology and legal protection in the current digital era. How urgent is the existence of a brainware management system in companies from the perspective of a welfare state to win the competition? The research method used here is qualitative research. In qualitative research, the population is referred to as the social situation, which consists of three elements: Place, actors, and activities interact with one another. The paradigm used is a methodological paradigm, which concerns how research should be conducted by researchers; understanding through the eyes of the researcher. In this case, specific phenomena can be explained by the researcher's a-priori knowledge. The important role of the brainware management system in companies is to reduce labor/worker honorarium/salary costs, which are carried out appropriately and legally. This ensures that companies can compete in the industry with a low-cost strategy, the public can obtain jobs with honorariums/salaries in accordance with statutory regulations, and the government can fulfill its constitutional duty to improve public welfare through the availability of employment opportunities
Performance Evaluation of Optical Character Recognition in SmartScan Rivai Based on Document Quality Variations Sulistiyanto Sulistiyanto; Bima Saputra; Krisna Natawijaya; Fitrianto Puja Kusuma
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1745

Abstract

Optical Character Recognition (OCR) plays a key role in this process by converting text contained in scanned documents into machine-readable information. However, OCR performance is highly dependent on document image quality, which may be affected by factors such as blur, low illumination, physical deterioration, and perspective distortion. This study evaluates the performance of the OCR module implemented in the SmartScan Rivai application under varying document quality conditions. Five document conditions were considered: normal, blurred, worn, low illumination, and skewed perspective. The evaluation focused on three performance indicators: Customer ID recognition accuracy, document classification accuracy, and OCR processing time. Experimental results show that the OCR system successfully recognized Customer IDs in 22 out of 25 test cases, achieving an overall accuracy of 88%. The highest recognition accuracy (100%) was obtained for normal and worn documents, whereas blurred documents, low illumination, and skewed perspectives reduced the accuracy to 80%. Document classification achieved an overall accuracy of 67%, indicating that this task is more challenging because it depends on the successful recognition of multiple textual features rather than individual characters. In addition, all OCR processes were completed in less than five seconds per document, demonstrating the operational feasibility of the proposed system. The findings confirm that document quality significantly influences OCR performance and highlight the importance of incorporating image preprocessing techniques to improve recognition accuracy under challenging document conditions. Overall, SmartScan Rivai provides an effective solution for operational document digitization while offering opportunities for further enhancement through advanced image processing and artificial intelligence-based OCR techniques.
Adaptive Q-Learning for Safety Message Priority in Vehicular Fog Computing Based on Indonesia Transportation and Weather Data Ceng Giap Yo; Riki Riki; Aditiya Hermawana; Yusuf Kurniaa; Satria Abadi
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1718

Abstract

The development of Intelligent Transportation Systems demands fast, adaptive, and reliable communication mechanisms between vehicles, especially when the system has to process multiple emergency events simultaneously. This article proposes an adaptation of the Adaptive Q-Learning model for the priority dissemination of safety messages in the Vehicular Fog Computing environment by utilizing the context of Indonesian data. Adaptation was made to the Q-learning design based on incident priority, but modified in terms of state, action, and reward variables to match the characteristics of Indonesia's urban transportation, especially DKI Jakarta and national data. The data sources mapped in the model come from the Central Statistics Agency's Land Transportation Statistics, Jakarta Statistics Profile, and BMKG Open Weather Forecast Data. State agents are built from a combination of bucket delay, traffic density level, weather conditions, trust node scores, and types of safety events such as ambulances, accidents, road hazards, and inundation. Action space is represented as a choice of different Quality of Service weights to balance delay, packet delivery ratio, trust, and energy efficiency. The reward function is designed to give higher priority to stacked emergencies while penalizing delays and energy consumption. The results in the tables and graphs in this article are presented as an illustrative simulation based on a methodology design, not the results of direct field tests. With this approach, this article offers a relevant, original, and contextual research framework for the development of intelligent transportation systems in Indonesia.
Innovation Opportunity, Social Media Utilization, And Customer Orientation: Empirical Study From The West Part Of Java Land Cipta Canggih Perdana; Fulgentius Danardana Murwani; David Sukardi Kodrat
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1697

Abstract

This study investigates the impact of social media utilization on innovation opportunities, mediated by customer orientation, in Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, focusing on information technology-based sectors in Banten, DKI Jakarta, and West Java. Using a purposive sampling technique and Partial Least Squares Structural Equation Modeling (PLS-SEM) for parameter estimation, the study finds that effective social media usage positively influences both innovation opportunities and customer orientation, which in turn impacts innovation opportunity. Moreover, customer orientation mediates the relationship between social media utilization and innovation opportunity. Data from 228 MSMEs confirm the validity and reliability of the tested variables. The study underscores that technology-based MSMEs should prioritize social media to enhance innovation and gather real-time customer data for market insights. With strong customer orientation and effective social media capabilities, MSMEs can convert information into valuable insights for high-value-added product and service development, bolstering competitiveness. While highlighting managerial implications, the study acknowledges limitations in regional scope and variables, recommending further research to explore the impact of social media capabilities on Innovation. 
Vulnerability Assessment and Mitigation Strategies Against Mozi Botnet Attacks in IoT Environments Walhidayat Walhidayat; Ahmad Zamsuri; Yuhelmi Yuhelmi; Muhamad Sadar
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1685

Abstract

The rapid proliferation of Internet of Things (IoT) devices has introduced significant security gaps, making them prime targets for large-scale botnet infiltrations. Among these threats, the Mozi botnet has emerged as a formidable challenge due to its decentralized peer-to-peer (P2P) structure and its ability to exploit weak credentials and unpatched vulnerabilities. This study conducts a comprehensive vulnerability assessment and proposes strategic mitigation frameworks to counter Mozi botnet attacks. Using a laboratory-controlled environment, we simulated Mozi’s propagation vectors, specifically focusing on its exploitation of Telnet brute-forcing and command injection vulnerabilities. The assessment identifies critical weaknesses in IoT firmware and network configurations that facilitate rapid infection. Furthermore, this research evaluates several mitigation strategies, including implementing customized Intrusion Detection System (IDS) rules, network micro-segmentation, and automated patch management. The results demonstrate that a multi-layered defense approach can reduce the probability of successful infiltration by up to [insert percentage, e.g., 85%]. This study provides actionable insights for network administrators and IoT manufacturers to enhance the resilience of IoT ecosystems against evolving P2P-based threats.
Prototype of IoT-Enabled RFID Door Lock System Using n8n Workflow Integration Ilmawan Mustaqim; Dzul Fadli Rahman; Heri Nurdiyanto; Abdul Khaliq Pangestu; Fauzi Wijaya
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1733

Abstract

This study aims to develop a Radio Frequency Identification (RFID)-based door lock system integrated with the n8n automation platform as an innovative solution to enhance access security within an academic envi-ronment. The research employs a Research and Development (R&D) method with a Waterfall Model approach, consisting of requirement analysis, system design, hardware and software implementation, and prototype test-ing stages. The system utilizes an RFID RC522 module, an ESP8266 microcontroller, an I2C LCD, and a 12V Li-Ion battery, integrated through an n8n workflow to automatically record access data into an Excel data-base. The test results show that the system can perform user authentication and real-time access logging with 100% accuracy and an average response time of five seconds. The casing, printed using a 3D printer with PLA filament, provides a lightweight, durable, and easily assembled design. Overall, the developed system functions effectively as both a control and monitoring tool, demonstrating potential as a model for smart security system development in higher education institutions
Development of an Intelligent Digital Dashboard Using Design Thinking to Improve Production Visibility in Automotive Aftermarket Manufacturing Ratna Sari Dewi; Mahendra Wardhana; Drajat Drajat
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1743

Abstract

The increasing complexity of automotive aftermarket manufacturing requires companies to improve production visibility and decision-making through digital transformation. However, many manufacturers still depend on fragmented information systems, manual data recording, and limited real-time monitoring, leading to production delays, inaccurate material planning, and inefficient delivery performance. This study aims to develop an intelligent digital dashboard that improves production visibility using a user-centered approach based on the Design Thinking methodology.A qualitative case study was conducted at PT Adyawinsa Stamping Industries, an automotive component manufacturer. The Design Thinking framework, consisting of the Empathize, Define, Ideate, Prototype, Test, and Reflect stages, was applied to identify user needs, design an interactive dashboard, and evaluate its usability. Data were collected through observations, semi-structured interviews, workshops, document analysis, and usability testing involving production managers, operators, administrators, marketing staff, purchasing personnel, and information technology specialists. The developed dashboard integrates purchase order management, material availability, production progress, barcode-based work-in-process monitoring, delivery scheduling, and key performance indicator visualization into a single digital platform. The evaluation results show that the dashboard improves production visibility, information transparency, communication between departments, and production monitoring efficiency. It also supports faster and more accurate managerial decision-making by providing real-time production information. The findings demonstrate that combining Design Thinking with intelligent dashboard development offers an effective strategy for digital transformation in automotive aftermarket manufacturing. This research contributes to the development of practical, user-centered decision support systems that enhance operational performance and support Industry 4.0 implementation in manufacturing environments.

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