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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.
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Articles 64 Documents
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Search results for , issue "Vol 9, No 1.1 (2025)" : 64 Documents clear
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; Arifianto, Teguh; Bahrawi, Ahmad; Sari, Dewi Ratna; Hariyanto, Didi; Sari, Putri Dya Citra Nur Kumala
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
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
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
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. 
Power Analysis Of A 100 Watt Micro Hydro Power Generator Using An Internet Of Things (IoT) Web Services Based On The Code Igniter Framework Yunior, Yudhis Thiro Kabul; Hartono, Hartono; Hariyadi, Slamet; Dwiyanto, Dwiyanto; Sukomardojo, Tekat; Widdana, Surya
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.v9i1.1.1633

Abstract

Small-scale Microhydro power plants (100 Watt) require a real-time and accurate power monitoring system to improve efficiency and maintenance. This research aims to develop an Internet of Things (IoT)-based electrical power analysis system by utilizing a Web Service based on the CodeIgniter Framework to process and display data online. The system consists of sensor modules (voltage and current) using ZMPT101B and ACS712, an ESP32 microcontroller for data transmission, and a CodeIgniter backend that provides a RESTful API for data storage and processing. Power (P), voltage (V), current (I), and energy (kWh) data are displayed on a web dashboard with graphic visualization using Chart.js. The research method uses a Research and Development (R&D) approach with stages of needs analysis, system design, implementation, and testing. The test results show that the system is able to monitor power with 95% accuracy compared to digital multimeter measurements, and has a data transmission latency of <2 seconds. This solution can be applied to small-scale Microhydro power plants for IoT-based monitoring with low cost and high scalability.
Performance Improvement Analysis of Design and Build Construction Project Managers of State Builidngs Kusumawati, Jujuk
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.v9i1.1.1654

Abstract

Project managers of planning and construction of government buildings are experts of the implementing contractor who determine the timeliness of the implementation of design and build construction. In order for timely implementation, project managers not only have higher education and long experience, but must have a good work culture and work behavior as well. Therefore, it is necessary to examine project performance based on the work performance of project managers
Performance of Deep Face Recognition Models under Adaptive Margin Loss: A Real-Time Evaluation Aditama, Kevin Muhammad Tegar; Nugroho, Anan; Subiyanto, Subiyanto; Pongoh, Arthur Gregorius
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.v9i1.1.1641

Abstract

Real-time face recognition systems encounter a critical trade-off between high-security demands and computational efficiency, particularly when deployed in unconstrained open-set environments. This study presents a comprehensive benchmarking of four distinct deep learning backbones ResNet100, GhostFaceNet, LAFS, and TransFace specifically trained using the Adaptive Margin Loss (AdaFace) function to handle image quality variations. The primary objective is to identify the optimal architecture for secure attendance systems operating on standard hardware with limited training data. The evaluation protocol employs a rigorous real-world open-set test to quantify performance using False Acceptance Rate (FAR) and False Rejection Rate (FRR). The experimental results demonstrate that ResNet100 establishes the highest security standard, achieving a 0.00% FAR at strict thresholds. Meanwhile, GhostFaceNet emerges as the most balanced solution for resource-constrained deployments, delivering competitive accuracy above 93% with significantly lower computational complexity. Conversely, the Vision Transformer (TransFace) fails to generalize in this low-data regime, resulting in unacceptable false acceptance rates. These findings definitively recommend GhostFaceNet for efficient edge-based implementations, while ResNet100 remains the superior choice for mission-critical security applications.
Evaluation of IoT Regulatory Readiness in Indonesia and Policy Recommendations to Support Safe and Effective Implementation Robie, Rizqon; Munadi, Rendy; Jumhur, Helni Mutiarsih
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.v9i1.1.1639

Abstract

The rapid development of Internet of Things (IoT) technology in Indonesia presents significant opportunities as well as regulatory challenges. Although IoT adoption continues to increase across various sectors, national policies remain fragmented and lack an integrated framework to support safe and effective implementation. This study assesses Indonesia's readiness for IoT regulation and formulates policy recommendations using a mixed-methods approach. The dataset used in this study comprises both secondary and primary data. Secondary data includes Indonesia's Cybersecurity data, Digital Infrastructure Status, IoT Regulations and Laws, Bappenas Studies, Data from Bappenas, and several policies in other countries, such as America, China, Japan, Korea, and Europe. Meanwhile, primary data was collected through questionnaires distributed to several elements, including 61.5% respondents from IoT users, 19.3% respondents from IoT business actors/IoT Startups, 15.8% academics, and 3.7% Government as regulators. The results of this data were then processed to determine government policy readiness by implementing DDPG, where the state space consists of 6 dimensions of leading regulatory readiness indicators (infrastructure, security, data protection, interoperability, institutional maturity, and economy). The action space is a 6-dimensional vector with continuous values in the range of [-1, 1], representing policy interventions in each dimension. The implementation applies reward functions, actor networks, and critic networks. Training data was applied for several episodes at 400 and 1000 episodes. The comparison results show that IoT regulations and policies in Indonesia should be designed with an adaptive approach based on Reinforcement learning, where the balance between data security, technology readiness, and market penetration can be dynamically adjusted to national and global conditions

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