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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 7 Documents
 1 
Search results for , issue "Vol 9, No 1 (2025): June" : 7 Documents clear
Prediction Modeling of Capacity Factor of Rembang Coal-Fired Steam Power Plant Based on Machine Learning to Improve the Accuracy of Primary Energy Planning Perdana, Ery; Sulardjaka, Sulardjaka; Warsito, Budi
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1394

Abstract

The Rembang Coal-Fired Power Plant (PLTU Rembang), with a capacity of 2 x 315 MW, is a key power plant in Central Java, where fuel expenses represent the largest cost component. Accurate fuel procurement planning, which relies on projecting electricity sales, is essential to reduce these costs. This study develops and compares four machine learning-based Capacity Factor (CF) prediction models: random forest regression, support vector regression, multiple polynomial regression, and multiple linear regression. The independent variables are selected from internal and external sources using F-tests and t-tests. Among the four models, the multiple linear regression model demonstrated the smallest Mean Absolute Percentage Error (MAPE) of 7.83%. Using this model, the annual CF for PLTU Rembang in 2024-2026 is predicted to be between 82% and 84%, while the CF for February-June 2024 is expected to range from 87% to 91%. With a monthly CF prediction accuracy classified as very good (MAPE of 2.35%), these predictions are valuable for optimizing monthly fuel purchase allocations, considering initial fuel stock and target inventory age (17-30 Days of Plant Operation).
Evaluating the Impact of Ai-Generated Outputs on Student Assessment: Educator's Perspective Dolba, Sammy Q.; Inoncillo, Frederick A; Nunez, Jayrome L.
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1347

Abstract

This study investigates educators' perceptions of the impact of artificial intelligence (AI)-)-generated outputs on student assessment in the Philippine educational context. With the rapid integration of AI technologies in education, understanding how educators view these tools is crucial for effective implementation. A descriptive quantitative research design was employed, utilizing a structured survey distributed to a diverse group of 93 educators across various disciplines. The findings reveal a generally positive perception of AI's role in enhancing teaching practices, with a mean score of M = 3.42 indicating high perceived value. However, concerns regarding the reliability and fairness of AI-generated output were noted, with mean scores of M = 3.30 and M = 3.28, respectively. Additionally, educators expressed moderate confidence in using AI tools, reflected by a mean score of M = 3.24. Qualitative responses highlighted ethical considerations and the need for continuous professional development to equip teachers with the necessary skills to effectively integrate AI into their assessments. This research underscores the potential benefits and challenges associated with AI in education, emphasizing the importance of addressing educators' concerns to maximize the advantages of AI technologies in student learning outcomes.
The Expert Validation of Virtual Reality-Based Learning Media of Flexible Manufacturing Systems (FMS) Hatmojo, Yuwono Indro; Azis, Satria Muhammad; Fauzan, Muhammad Nur
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1470

Abstract

The advancement of digital technology has had a significant impact on the world of education, including vocational education that requires in-depth technical understanding. In this study, Virtual Reality (VR)-based learning media is an alternative to help the process of introducing engineering components more interactively and realistically. This study aims to validate VR-based learning media called Virtual Reality Distributing Station as a learning media in learning distribution station components in the Flexible Manufacturing System (FMS). Validation is carried out through evaluation by experts with a focus on two main aspects, namely, the content aspect and the media display aspect. Material experts are experts who are experienced in teaching FMS, while media experts come from experts in the development of computer-based learning media. Data analysis is carried out based on quantitative and qualitative results obtained from expert validation. The evaluation results show that the content aspect of the material, which includes suitability and usefulness, scored 87 out of 96 (90.27%) and is categorized as "very good." Meanwhile, the media aspect, which includes display design and usefulness, scored 61.5 out of 72 (85%) and is also categorized as "very good". These findings indicate that the VR Distributing Station learning media is suitable for use as a supporting tool for engineering learning in vocational education.
LSTM Model Using Adam’s Optimizer for Indonesian – Bugis Bidirectional Translation System Fajarwati, Erliana; Wibawa, Aji Prasetya; Hernandez, Leonel
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1272

Abstract

The purpose of this research is to develop a machine translation of Bugis to Indonesian and vice versa in order to preserve the Bugis language. This research utilizes a recent dataset consisting of 30,000 Bugis-Indonesian sentence pairs from the online Bible. This research conducts scraping to compile the corpus which is then followed by manual and automatic pre-processing. The method chosen is Neural Machine Translation (NMT) while for training and testing models Long Short-Term Memory (LSTM) is used. The performance of the model is evaluated by Bilingual Evaluation Understudy (BLEU) score to measure the translation accuracy at various epochs. In addition, this study also compared the use of Adam's optimizer with non-optimizer. The results showed that the use of Adam's optimizer significantly improved the performance of the model where at epoch 2000 the model achieved the highest BLEU score of 0.996261 indicating highly accurate translation quality. In contrast, the model without the optimizer showed lower performance. Other results also found that the translation from Bugis to Indonesian was more accurate than from Indonesian to Bugis. This is due to the more balanced word count difference in the Bugis to Indonesian translation, which makes it easier for the model to match words. In conclusion, the use of NMT with Adam optimizer effectively improves the accuracy of two-way translation from Bugis-Indonesian.
Data-Driven Insights Into Underdeveloped Regencies: SHAP-Based Explainable Artificial Intelligence Approach Oktora, Siskarossa Ika; Matualage, Dariani; Notodiputro, Khairil Anwar; Sartono, Bagus
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1399

Abstract

Classification analysis in high-dimensional data presents significant challenges, particularly due to the presence of complex non-linear patterns that traditional methods, such as logistic regression, fail to capture effectively. This limitation is often reflected in relatively low model accuracy. One approach to addressing this issue is through machine learning-based classification methods, such as Random Forest and Support Vector Machine (SVM). While these models generally achieve higher accuracy than logistic regression, their black-box nature limits interpretability, making it difficult to explain their classification decisions. As machine learning models continue to advance, interpretability has become a crucial concern, especially in data-driven decision-making. Post-hoc explainable artificial intelligence (XAI) techniques offer a viable solution to enhance model transparency. This study applies SHAP to machine learning models to gain insights into the underdevelopment status of regencies in Indonesia. The results indicate that SVM outperforms both logistic regression and Random Forest. SHAP values estimated from SVM, using various permuted variable subsets, exhibit stability. Clustering analysis identifies five optimal clusters of underdeveloped regencies. Based on average SHAP values, underdevelopment alleviation strategies should focus on social factors (Cluster 1), infrastructure (Cluster 2), accessibility (Cluster 3), and a combination of infrastructure, accessibility, education, and healthcare (Cluster 4), while Cluster 5 requires improvements in accessibility and economic conditions.
Determining Quality of Service (QoS) of End-User Internet Networks with Data Sniffing and Classification Algorithms Rosyidin, Zulkham Umar; Muladi, Muladi; Handayani, Anik Nur
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1444

Abstract

The development of telecommunications technology in this world has changed very rapidly. Changes are made to access technology using the transmission media, which uses fiber optic technology, which has the advantage of being free from interference, large and fast data delivery capacity. An Internet Service Provider (ISP) is a provider of construction services and management of network infrastructure that always meets customer needs. Customer satisfaction with the services provided by ISP is also important in the era of increasingly tight market competition. Quality of Service (QoS) testing in internet networks needs to be done so that customers get optimal service. This study analyzes the quality of internet networks with fiber optic media on the end user side with the data sniffing method using Wireshark software that records video data traffic on the YouTube platform. The results of the data recording are processed using the QoS method with Throughput, Packet Loss, Delay, and Jitter parameters. The QoS assessment index is divided into Excellent, Good, Fair, and Poor classes according to the TIPHON standard. Data from these parameters is classified using the Naive Bayes, KNN, and Decision Tree methods. The results of applying the algorithms show the highest Accuracy value in the Decision Tree algorithm of 97%, while the highest Precision and Recall are in the KNN algorithm with values of 94% and 85%.
Research on the Application of Artificial Intelligence in Hand Rehabilitation by Estimating Hand Grip Force using EMG Data Nguyen, Tien Manh; Takagi, Motoki; Nguyen, Trung Thanh; Tran, Hieu Huy; Dao, Khanh Viet Trong
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1381

Abstract

The human hand is a complex and functionally significant anatomical structure, playing a critical role in daily activities, communication, and professional tasks. Any impairment due to injury, neurological disorders, or musculoskeletal diseases can severely affect an individual's quality of life. Conditions such as stroke-induced hemiparesis, arthritis, carpal tunnel syndrome, and tendon injuries often necessitate rehabilitation to restore function, minimize pain, and prevent secondary complications. Traditional rehabilitation approaches, while beneficial, generally follow a standardized methodology, failing to account for individual variations in muscle strength, neuroplasticity, and adaptive capacity.Modern rehabilitation methods leverage advanced technologies such as electromyography (EMG) and hand grip force measurement to enhance therapy effectiveness. Additionally, artificial intelligence (AI) applications, particularly Long Short-Term Memory (LSTM) networks and Transformer models, have emerged as promising tools for personalized rehabilitation. These models analyze EMG signals to predict hand movement intentions, enabling adaptive rehabilitation strategies tailored to individual needs.  This study focuses on the construction of a real-time EMG signal acquisition system and uses them as input to LSTM and Transformer models to compare and analyze the performance of the two types of models. By demonstrating the superiority of applying AI for personalization over the general AI approach, this study highlights the potential of AI in hand rehabilitation in particular and healthcare in general with its ability to specialize for each individual patient.

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