cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Heri Nurdiyanto
Contact Email
Heri Nurdiyanto
Phone
-
Journal Mail Official
internationaljournalair@gmail.com
Editorial Address
-
Location
Kota metro,
Lampung
INDONESIA
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 12 Documents
 1   2 
Search results for , issue "Vol 8, No 1 (2024): June 2024" : 12 Documents clear
Assessing Performance Across Various Machine Learning Algorithms with Integrated Feature Selection for Fetal Heart Classification Amanda, Laura Rizka; Anasanti, Mila Desi
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1110

Abstract

The global concern over declining perinatal death rates, particularly in low- and middle-income nations, underscores the importance of adopting Cardiotocography (CTG) as a vital fetal monitoring method. Recent strides in machine learning (ML) present promising opportunities to enhance the accuracy of assessing fetal health, providing a viable alternative to traditional approaches. This study aims to evaluate various ML methodologies and feature selection techniques for predicting fetal health using CTG data. The primary objective is to improve ML algorithms' accuracy, precision, recall, and F1 score while selecting the most critical features. The dataset includes 2,126 expectant mothers in the third trimester, with 35 variables related to fetal heart rate (FHR) and uterine contractions (UC). Preprocessing involves feature scaling, data balancing, and outlier elimination. Additionally, a 10-fold stratified cross-validation approach is employed to ensure robust evaluation and generalizability of the model's performance. Six ML algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), and K-Nearest Neighbors (KNN)—are employed, optimized through grid search cross-validation. The RF algorithm outperforms with an impressive 99% accuracy, closely followed by DT at 98.7%. Optimizing 15 features from the original 35 using Simultaneous Perturbation Feature Selection and Ranking (spFSR) yields a remarkable accuracy of 99%, mirroring the full feature set. This underscores the vital role of selected features in improving predictive power and overall model performance. The study emphasizes the efficacy of tree-based classification algorithms, especially RF, in predicting fetal health and highlights the impact of preprocessing on model performance. These findings suggest avenues for future research, including exploring alternative feature engineering methods and assessing algorithm performance in diverse scenarios.
Machine Design and Development of CoreXY FDM 3D Printer for Learning Mustaqim, Ilmawan; Prianto, Eko; Husna, Amelia Fauziah; Pramono, Herlambang Sigit; Fattah, Husain Abdul
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.v8i1.1186

Abstract

This research aims to design and develop CoreXY FDM 3D Printer that can be optimized for learning purposes. By detailing aspects of design, hardware, and software, this research is expected to make a significant contribution in improving the quality of learning. Research using the Research and Development method, carried out based on the Machine Learning System Development model with stages in the form of Problem Understanding, Data Handling, Model Building, and Model Monitoring. The results showed that the lowest average depreciation value in the 3D printer developed was smaller than the 3D Printer machine from the previous study. The best print quality was produced in experiment number 3 where the print results were almost flat and smooth. So that the best print parameters are produced at a layer thickness of 0.1 mm and a print speed of 60 mm / minute. Blackbox Test results show that all components of the 3D printer machine have been able to function properly. The results of the user trial questionnaire showed that the average value of all aspects received a value of 3.55 from a range of values 1-4, indicating that this machine is very good to be used as a medium in the learning process. Comparison of FDM CoreXY 3D Print printing process time after development shows shorter print time than FDM CoreXY 3D Printer machine before development.
Using a Semi-supervised Learning Model for Recognition of Human Daily Activities from Wearable Sensor Data Nguyen, Tien Manh; Motoki, Takagi
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.v8i1.1146

Abstract

The application of Machine Learning (ML) and Artificial Intelligence (AI) is growing, and also becoming more important as the aging population increases. Smart support systems for distinguish Activities of Daily Living (ADL) can help the elders live more independently and safely. Many machine learning methods have been proposed for Human Activity Recognition (HAR), including complex networks containing convolutional, recurrent, and attentional layers. This study explores the application of ML techniques in ADL classification, leveraging wearable devices' time-series data capturing various parameters such as acceleration. The acceleration data obtained from sensors is so huge that it is difficult and expensive to accurately label every sample collected, so this study applies the Semi-supervised Learning model to unlabeled samples. Long Short-Term Memory (LSTM) has always been used for time series data such as acceleration, and recently, the Transformer model has emerged in many applications such as Natural Language Processing (NLP) or creating ChatGPT. In this study we proposed ADL classification method using the Self-Attention Transformer block and the Recurrent LSTM block and evaluated their results. After comparison, the model built with LSTM block gives better results than the model built with Transformer block.
Analysis of Information Technology Service Management for Radio Frequency Spectrum Licensing Services at Balmon Palembang Using ITIL V3 Andryani, Ade; Herdiansyah, Muhammad Izman; Negara, Edi Surya; Sutabri, Tata
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1132

Abstract

This research discusses the importance of timely, accurate, and relevant information technology services to support organizational activities, particularly in the context of radio frequency spectrum licensing services. The lack of government oversight of IT services often leads to complaints, necessitating evaluation using the ITIL framework. The study seeks to examine how ITIL V3 processes are applied within radio frequency spectrum licensing services at Balmon Palembang through a qualitative descriptive method. Findings suggest that the services have implemented ITIL V3 processes with a maturity level at level 3 Defined.
Knowledge Capture in Agile Organizations: Methods and Strategies for Enhancing Effective and Efficient Process Pratiwi, Maharani Eka; Ramadhan, Yudistira; Sensuse, Dana Indra; Lusa, Sofian; Safitri, Nadya; Elisabeth, Damayanti
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.v8i1.1164

Abstract

The competitive landscape of modern organizations relies on efficient knowledge utilization and management, underscoring the crucial role of knowledge capture in effective Knowledge Management (KM). This study explores the relationship between knowledge capture, organizational agility, and employee proficiency within KM, pinpointing critical gaps in understanding the optimal utilization of knowledge capture methods within agile-based setups. By addressing these gaps, the study aims to identify effective knowledge capture methods and propose strategies for their seamless integration into agile organizations. The research investigates five hypotheses, affirming the positive impacts of expert interviews, focus groups, interviews, surveys, and questionnaires on the efficiency and effectiveness of knowledge capture processes in agile contexts. Utilizing a mixed-method approach, this study evaluates qualitative and quantitative data derived from interviews and questionnaires. The results highlight the importance of various knowledge capture methods in augmenting the efficiency and efficacy of the knowledge capture process. Additionally, the study outlines implementation strategies customized for each method's application within agile-based organizations. The objective of this research is to provide practical solutions that narrow the disparity between the potential of knowledge capture and the particular needs of agile setups.
Analyzing Important Knowledge Management Sub-Processes, Mechanisms, and Technologies in PT XYZ using Contingency View of Knowledge Management Astrianty, Rani Aprilia; Sensuse, Dana Indra; Imanuddin, Kamila Alifia
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.v8i1.1181

Abstract

In the current competitive business landscape, having knowledge is crucial for gaining a competitive advantage, particularly in service-oriented firms where it significantly enhances organizational capabilities. However, effectively implementing knowledge management presents challenges for companies, as demonstrated by PT XYZ. The main challenges faced by PT XYZ include insufficient information dissemination among departments, complexities in accessing crucial business process information, resulting low level of employee knowledge, contributing to operational inefficiencies. By applying contingency factors theory, this study aims to prioritize essential KM processes at PT XYZ and recommend appropriate KM mechanisms and technologies for implementationThe research, which draws on interviews with three employees, finds that socializing for knowledge exchange is the most important knowledge management subprocess. Face-to-face meetings and on-the-job training serve as the main methods of instruction, while computer-based simulations are used as a supporting tool. Direction, socialization for knowledge discovery, and combination are more knowledge management subprocesses that can be added later to enhance KM procedures at PT XYZ.
Progress in Non-Invasive Cognitive Brain-Computer Interface and Implications for Mind-Uploading Astawa, I Wayan Aswin Dew; Purnomo, Hindriyanto Dwi; Sembiring, Irwan
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1133

Abstract

Mind-uploading, the vision of transferring human consciousness into a digital realm, relies on a profound comprehension of the brain and cutting-edge technology. Non-invasive cognitive Brain-Computer Interfaces (BCI) offer a promising avenue for delving into neural activity and bridging the brain-machine gap. This research explores the potential of non-invasive cognitive BCI in realizing mind-uploading through a systematic literature review (SLR), analyzing recent research that focuses on its current progress and implications for mind-uploading. The SLR unveils significant strides in non-invasive cognitive BCI, demonstrating increased precision in recording and decoding cognitive processes and fostering a deeper understanding of these processes. This progress is attributed to a diverse range of emerging feature extraction and decoding methods, transforming subtle neural signals into interpretable commands. Notably, advancements in signal processing and neuroimaging techniques enhance communication speed and clarity between the brain and computer. Furthermore, the development of cost-effective methods, frameworks, and hardware holds the promise of broader accessibility to BCI technology. However, significant hurdles remain. The computational demands of current cognitive BCI systems pose a substantial challenge, while the scarcity of high-quality training datasets hampers algorithm development and accuracy. The poor signal quality causes difficulties in recording neural complexity and hampers accuracy. In conclusion, non-invasive cognitive BCI has significant potential to pave the way for mind-uploading. However, its limitations, make their capabilities remain insufficient to fully realize this ambitious vision. This highlights the critical need for sustained research and innovation to bridge the gap between current understanding and the exciting realm of mind-uploading.
Training Need Analysis For Developing People: A Case Study of Market Development Division in PT XYZ Firdaus, Aditya Reza; Sensuse, Dana Indra
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.v8i1.1183

Abstract

The impact of Industry 4.0 on the Indonesian stock market, with a special focus on PT XYZ. PT XYZ has experienced a significant surge in individual investors, particularly from the younger generation, who are technologically savvy and prefer the stock exchange as an investment method. This paper discusses the critical role of the Market Development Division at PT XYZ in meeting the educational needs of prospective investors and companies planning to list. One significant challenge identified is the knowledge gap within PT XYZ, especially between employees with varying levels of experience, and the absence of a centralized knowledge repository. This gap hinders education for potential investors and listed companies, thereby impacting service quality. In response, PT XYZ acknowledges the need to enhance Knowledge Management (KM) practices and begins developing a tailored knowledge sharing strategy. This strategy aims to bridge the existing knowledge gap and prevent knowledge loss, utilizing the Zack Framework for gap analysis. Qualitative research methods, including semi-structured interviews with 3 experts, were employed in this study. It resulted in a gap analysis between the current state and PT XYZ's necessary knowledge regarding the implementation of knowledge sharing, leading to the recommendation of eight knowledge sharing strategies.
Exploiting Silhouette Principle Component For Dimension Reduction In Breast Ultrasound Images Classification Kartikadarma, Etika; Fanani, Ahmad Zainul; Pujiono, Pujiono; Affandy, Affandy; Wulandari, Sari Ayu
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.v8i1.1165

Abstract

This paper proposes the use of the Dimensional Reduction method with the Silhouette Principle Component (SPC) Approach to improve the classification of breast ultrasound images. The PCA method is used to reduce the dimensions of medical images to improve classification, with MobileNet-v2 and DenseNet-121 as the optimal classification algorithm choices. The results show that the SPC method succeeded in producing efficient feature representation with data sizes that are almost the same as the original data, while PCA produces greater dimensionality reduction. The SPC model also shows the best performance in terms of accuracy and loss. This research makes a significant contribution to the development of more sophisticated and efficient medical image analysis techniques to support the diagnosis and treatment of breast cancer.
Fuzzy Preference Relations-Based AHP for Multi-Criteria Supplier Segmentation Nurdiyanto, Heri; Fauzi, Chairani; Lestari, Sri
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.v7i1.1.1103

Abstract

Supplier segmentation is a strategic activity for businesses. It involves dividing suppliers into distinct categories and managing them differently. Various supplier typologies based on different dimensions and factors are available in the existing literature. By highlighting two main characteristics the skills and the desire of suppliers to work with a specific company this article integrates many typologies. Almost all of the supplier segmentation criteria stated in the literature are covered by these dimensions. These dimensions can be defined utilizing a multi-criteria decision-making process for each specific case. To account for the inherent ambiguities and uncertainties in human judgment, a fuzzy Analytic Hierarchy Process (AHP) is suggested as part of the technique. This approach makes use of fuzzy preference relations. A broiler firm uses the suggested process to divide up its suppliers. A categorization of vendors according to two aggregated criteria is the end outcome. Lastly, we offer some suggestions for future research, draw some conclusions, and talk about some techniques to address distinct sectors

Page 1 of 2 | Total Record : 12

 1   2 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 9, No 1.1 (2025) Vol 9, No 2 (2025): December Vol 9, No 1 (2025): June Vol 8, No 2 (2024): December 2024 Vol 8, No 1 (2024): June 2024 Vol 8, No 1.1 (2024) Vol 7, No 2 (2023): December 2023 Vol 7, No 1 (2023): June 2023 Vol 7, No 1.1 (2023) Vol 6, No 2 (2022): Desember 2022 Vol 6, No 1 (2022): June 2022 Vol 6, No 1.2 (2022) Vol 6, No 1.1 (2022) Vol 5, No 2 (2021): December 2021 Vol 5, No 1 (2021): June 2021 Vol 4, No 2 (2020): December 2020 Vol 4, No 1 (2020): June Vol 3, No 2 (2019): December 2019 Vol 3, No 1 (2019): June 2019 Vol 2, No 2 (2018): December 2018 Vol 2, No 1 (2018): June 2018 Vol 1, No 2 (2017): December 2017 Vol 1, No 1 (2017): June 2017 More Issue