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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 621 Documents
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Digitalization and Spiritual Transformation: A Phenemenological Study of Young Catholic Priest in Diocese of Palangka Raya Manik, Resmin; Jimmy, Andreas
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1636

Abstract

This research explores the phenomenological experiences of young Catholic priests in the Diocese of Palangkaraya in constructing their spirituality amid digital transformation and the cultural expectations of the Dayak community. Using a mixed-methods sequential explanatory design with Interpretative Phenomenological Analysis (IPA) involving 10 priests aged 30-40 years with 1-10 years of ordination, the research revealsfourmainthemes:thedualityofdigitalexperienceinspirituality, negotiationoftensionbetweentraditionandmodernity,reconstructionof priestlyidentity,andspiritualadaptationstrategies.Quantitativeanalysis shows significant correlations between digital competence (r=0.68, p<0.01) and cultural intelligence (r=0.72, p<0.01) with digital pastoral adaptation. Qualitative findings reveal that young priests experience "cultural-digital dissonance" between demands for physical presence in Dayakritualsanddigitalefficiency,aswellas"erosionofsilence"dueto continuous connectivity. In response, they develop a "trihybrid spirituality"thatintegratesCatholictradition,Dayakwisdom,anddigital technologythroughstrategiesofscheduleddigitallimitations,theological reinterpretation, and ritual hybridization. This research contributes to understandingspiritualtransformationinthedigitalerawithinindigenous contexts and offers practical implications for reformulating priestly formation and developing inculturation models that integrate digital dimensions without neglecting local wisdom.
Detection of SQL Injection Attacks on MariaDB Using Hybrid Long Short-Term Memory Khotimah, Khusnul; Hartono, Hartono; Apriando, Rama
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1547

Abstract

This study discusses the development of a SQL Injection attack detection system using the Long Short-Term Memory (LSTM) deep learning model. SQL Injection is a serious security threat to web applications that exploits vulnerabilities in user input to manipulate databases. The LSTM model was chosen due to its ability to process sequential data, which is relevant for analyzing the patterns and structure of SQL queries that are susceptible to attacks. The process begins by collecting and combining datasets from various sources, performing preprocessing to handle duplicate data, missing values, and gibberish queries, as well as analyzing the distribution of query lengths. The textual query data is then converted into a numerical representation through tokenization and padding. The processed dataset is divided into training and testing data. The Bi-directional LSTM model architecture is built with embedding, LSTM, dropout, and dense layers. The model is trained using the training data and its performance is evaluated using the test data, producing metrics such as accuracy, precision, recall, and F1-score. Evaluation results on the test data show a model accuracy of 99.99%, with precision of 99.99%, recall of 99.99%, and F1-score of 99.99% in distinguishing between normal queries and SQL Injection queries. The trained model and the tokenizer used are then saved for further testing purposes. This research demonstrates that the LSTM-based approach is highly effective in detecting SQL Injection attacks with high accuracy. Thus, the model can be deployed at the production level or production server.
IoT-Based Monitoring for Optimizing Yield of Gogo Rice (Oryza sativa, L.) Handayani, Etik Puji; Saputri, Tri Aristy; Sutomo, Budi
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1677

Abstract

Advancements in Internet of Things (IoT) technology have introduced new opportunities in precision agriculture, particularly for enhancing the productivity of upland rice (Oryza sativa, L.) cultivated on marginal lands. This study aims to integrate an IoT-based monitoring system with the application of biochar and Trichoderma harzianum to optimize soil parameters and water resource efficiency. The monitoring system utilizes Trico Master and Slave devices to measure real-time environmental parameters, including soil pH, soil moisture, soil temperature, and air temperature. The results reveal that the application of biochar at a dosage of 1 kg/m² increased soil pH from an average of 7.0 to 8.7, creating a conducive environment for the activity of Trichoderma harzianum. This microorganism demonstrated its ability to improve soil quality by decomposing organic matter and enhancing nutrient absorption by plants. Additionally, the IoT-based automated irrigation system maintained soil moisture levels above 45% while reducing water usage by up to 30% compared to manual irrigation methods. In conclusion, the integration of IoT technology with biochar and Trichoderma harzianum significantly improved upland rice yield, resource efficiency, and the sustainability of agricultural systems. This study presents an innovative and sustainable approach to supporting future food security, particularly in resource-limited environments
Optimization of Website Based Facility Service System at Politeknik Penerbangan Surabaya moonlight, lady silk
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1638

Abstract

Technological developments, particularly in the Industry 4.0 era, accompanied by increasing technological literacy among the public, have influenced business operations, making them easier to develop and more widely known. Digital transformation innovations in facility services at the Politeknik Penerbangan Surabaya were carried out to improve administrative efficiency, information, and service speed. This study aims to develop a web-based facility service information system with the Laravel framework and online payments using the Midtrans payment gateway that can be accessed by internal and external users of the institution more easily and transparently. The development of this system uses the Waterfall Software Development Life Cycle (SDLC) method. The results of the study show that this system is able to manage facility data, transactions, availability, and transaction history in an integrated and real-time manner. The integration of Midtrans into the system not only makes transactions fast and secure, but also provides convenience and security protection for customers. The implementation of this system has been proven to increase efficiency, speed up the service process, minimize recording errors, while expanding service access for users from outside the institution easily and effectively. In addition, with the institution's digital development system, strategic decision-making has also become easier
Modeling the Driving Factors of Educational Technology Innovation in Indonesian Universities: A Hybrid ISM–ANP Approach Abadi, Satria; Majid, Mad Helmi ab; Marwanta, Y. Yohakim; Susianto, Didi
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to model the critical enablers driving technological innovation in higher education institutions in Indonesia by integrating Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP). The hybrid approach provides both structural and quantitative insights into the interrelationships among eight identified enablers: policies and regulations, digital infrastructure, faculty competence, technology incentives, industry collaboration, student literacy, innovation culture, and data security. The ISM results classify policies and regulations and digital infrastructure as driving factors that form the foundational layer of innovation ecosystems. Meanwhile, faculty competence, technology incentives, and industry collaboration serve as linkage factors that bridge strategic policies and operational implementation, whereas student literacy, innovation culture, and data security emerge as dependent factors representing the system’s outcomes. The ANP results reinforce the ISM structure, revealing that policies and regulations (0.215) and digital infrastructure (0.187) have the highest influence, followed by faculty competence (0.142) and industry collaboration (0.130). The combined ISM–ANP framework demonstrates that sustainable educational technology innovation requires a synergistic interaction between governance, human resources, and digital culture. The findings provide a comprehensive model that can guide universities and policymakers in formulating evidence-based digital transformation strategies within the Indonesian higher education context
E-GOVERNMENT IN INNOVATION AND PUBLIC COMMUNICATION Boestam, Ambia B; Swastiningsih, Swastiningsih; Anggraini, Cyntia Dewi; Derivanti, Azizah Des
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1642

Abstract

Public communication has undergone major transformation since the early stages of digitalization of services as part of innovation. One of the main digital transformations in public communication, especially in government systems, is e-government. This study aims to discuss what e-government really is, what research is related to e-government, and what the practice of e-government applications is. This research is a library study research by taking documentation data about innovation and public communication from various sources. The data presented is in the form of the latest studies regarding innovation, especially in public communication. The data collected in this research will then be analyzed using narrative analysis. Narrative analysis refers to a set of methods for interpreting texts that take the form of exposition. The conclusions in this study show that globally, the implementation of e-government throughout the world still has many challenges, especially in Indonesia. Therefore, many improvements still need to be made and this also requires further study about what and how to improve innovation in e-government
Salt Quality Classification Using Backpropagation Neural Network and K-Nearest Mahmudi, Anas; Abidin, Zainul; Razak, Angger Abdul Razak Abdul
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1505

Abstract

Salt quality plays a vital role in determining its usability across various sectors, including food, pharmaceuticals, and industrial applications. Traditional methods of classifying salt quality, which rely heavily on manual inspection and laboratory testing, are often time-consuming, costly, and prone to human error. In response to these limitations, this study explores the implementation of machine learning techniques—specifically, Backpropagation Neural Network (BPNN) and K-Nearest Neighbor (K-NN)—to classify salt quality based on its physical and chemical properties. The features used in this research include NaCl concentration, moisture content, magnesium levels, sulfat, insoluble, calcium, NaCL(wb) and NaCL(db) which are commonly used indicators of salt purity and grade. The BPNN model is designed to handle complex and non-linear relationships within the dataset by adjusting weights through iterative backpropagation during training. Meanwhile, the K-NN algorithm serves as a simpler, instance-based learning method that classifies samples based on the majority class of their nearest neighbors in the feature space. Comparative experiments were conducted to evaluate the classification and computational efficiency of both models. Results indicate that both methods are effective in classifying salt into predefined quality categories. However, BPNN consistently outperforms K-NN in terms of time efficiency and generalization, particularly when handling noisy or overlapping data. The findings underscore the potential of integrating artificial intelligence into quality control systems in the salt industry, offering a faster, more objective, and scalable solution for ensuring product standards.
Interactive Dashboard Development for Student Performance Monitoring: Integrating Academic and Socio-Demographic Data Humaira, Fitrah Maharani; Yuwono, Wiratmoko; Asmara, Rengga; Widodo, Rusminto Tjatur; Susetyoko, Ronny; Adawiyah, Robi’Atul
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1590

Abstract

Strategic decision  making in institutional settings is often constrained by the fragmentation and heterogeneity of data across multiple sources. This study addresses this critical gap by developing and validating an interactive web-based dashboard designed to consolidate and transform heterogeneous institutional data from seven distinct sources into actionable insights. A complex feature engineering pipeline was necessitated, involving comprehensive data integration and structural consistency checks. Techniques like Text Normalization and Feature Mapping were applied to clean over a lot of inconsistent entries, alongside Feature Binning and Extraction to generate analytically robust metrics. The system was implemented using Python for data processing and ReactJS for the dynamic interface, and its viability was validated via structured User Acceptance Testing (UAT). The subsequent descriptive analysis provided key insights into student demographics, geographical reach, and enrollment compliance across academic levels. Crucially, the comprehensive UAT resulted in an outstanding overall acceptance score of very worthy. However, feedback analysis indicated a dominant user focus on visual aspects, with noted complaints regarding the suboptimal color scheme and contrast impacting user experience. The findings confirm that complex feature engineering is a viable and effective strategy for transforming fragmented institutional data into an immediately deployable strategic resource. This system offers a validated blueprint for data consolidation in higher education. Future work is accordingly  directed toward revising the color palette and contrast ratios to enhance visual clarity and user experience, alongside continuous optimization of data completeness to maintain the dashboard’s utility
Evaluation Of A Feature-Concatenated Model For Multiclass Diagnosis Of Pulmonary Diseases on An Imbalanced Dataset Ajitomo, Wahyu; Tyas, Dyah Aruming; Harjoko, Agus
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1519

Abstract

Lung diseases such as pneumonia, tuberculosis, and COVID-19 pose serious global health challenges, particularly in X-ray image classification where class distribution is often imbalanced. To address this issue, this study proposes a hybrid model based on concatenated CNN architectures and applies class weighting using focal loss multiclass. The dataset consists of 7,135 X-ray images divided into four main classes: pneumonia, tuberculosis, COVID-19, and normal. Focal loss with a gamma parameter of 2.0 is employed to enhance the model’s focus on minority classes. Evaluation results show that combined models such as DenseNet121 + VGG16 and VGG16 + ResNet50 achieve F1-scores of up to 0.87, outperforming single models. Grad-CAM visualizations also indicate that the combined models can recognize pathological areas more comprehensively and accurately. This approach proves effective in improving the accuracy and sensitivity of AI-based diagnostic systems.
Design and Development of a Competency Certificate Surveillance System for Electrical Technical Personnel using a Disruptive RSM Design Approach Mulyati, Rika; Lubis, Muharman; Suakanto, Sinung; Rahman, A. Taupik
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1660

Abstract

The competency certificate for electrical technical personnel serves as formal evidence that an individual is qualified to work in the electricity sector and has a validity period of three years, requiring periodic surveillance to ensure regulatory compliance. In practice, the surveillance process is still largely conducted manually by Competency Certification Bodies (LSK), resulting in administrative inefficiencies, delays in certificate renewal, fragmented documentation, and limited traceability of surveillance records. These challenges not only burden certification bodies and certificate holders but also affect regulatory supervision performance. This study aims to design and develop a competency certificate surveillance information system for electrical technical personnel using a disruptive Recognise–Scrutinize–Materialize (RSM) design approach. Data were collected through observations, semi-structured interviews, and document analysis to identify existing problems and system requirements. The RSM method was applied to systematically align stakeholder needs with national regulations and international standards, including ISO, IEEE, and NIST guidelines. The results of this research produce a regulation-based surveillance system design in the form of a structured mock-up that integrates automated reminders, digital document validation, standardized surveillance workflows, and real-time monitoring dashboards. The proposed system is expected to improve efficiency, data accuracy, transparency, and regulatory compliance in the surveillance and renewal process of competency certificates. This research contributes novel insights into the digitalization of competency certificate surveillance, a topic that has received limited attention in previous studies, particularly within the electricity sector. 

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