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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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Investigation of Determinants of Financial Distress in Manufacturing Companies Mulatsih, Listiana Sri; Dharmawan, Donny; Tumiwa, Ramon Arthur Ferry; Judijanto, Loso; Alfiana, Alfiana
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1323

Abstract

Financial distress refers to a continuous decline in a company's financial performance, necessitating prediction and mitigation. It typically begins with the company’s inability to meet its short-term obligations, signaling a deterioration in financial condition. This study aims to examine the effects of Return on Assets (ROA), Current Ratio (CR), and Debt to Equity Ratio (DER) on financial distress, with company size serving as a moderating variable. The research employs regression analysis and quantitative methods, focusing on manufacturing firms listed on the Indonesia Stock Exchange that consistently published financial reports from 2020 to 2023. Using purposive sampling, the study selected 40 samples from 10 companies, with data analyzed through Partial Least Squares-Structural Equation Modeling (PLS-SEM). The findings reveal that ROA significantly impacts financial distress, while CR and DER have no such effect during the observed period. Furthermore, company size moderates the relationship between ROA and CR with financial distress but does not moderate the influence of DER on financial distress. These results provide managerial implications, serving as indicators for corrective actions to prevent financial distress or potential bankruptcy in manufacturing companies
Interest Rate Policy Moderate The Performance Impact On Banking Stock Price In Indonesia Sari, Laynita; Assagaf, Aminullah; Lusiana, Lusiana; Elfiswandi, Elfiswandi; Zefriyeni, Zefriyeni
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1352

Abstract

The stock price is a factor that investors must consider in investing. The stock price is an indicator of the successful management of the company. An increase in share price will reflect an increase in shareholder wealth as an investor. There is a phenomenon in banking stock prices in Indonesia from 2020-2021, where share prices have decreased yearly. This study aimed to investigate the effect of company performance on stock prices by using interest policy as a moderating variable. The method used in this research is panel data regression. The results show that performance positively affects stock prices, and interest policy moderates the relationship between performance and stock prices.
Understanding Tourism Behaviour Through Exploratory Data Analysis with Machine Learning on Search Engine Data: Case Study in Bangka Belitung Islands, Indonesia Divo Dharma Silalahi; Nidia Mindiyarti
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1372

Abstract

Analyzing tourist behaviour through Google search data offers a dynamic, real-time approach to understanding travel preferences. This study employs Exploratory Data Analysis (EDA) alongside machine learning techniques such as hierarchical clustering, Principal Component Analysis (PCA), Strength Variables Index (SVI), heat map generation, and correlation matrix analysis to explore the key tourism drivers in Bangka Belitung. These drivers are categorized into demand-side factors—including tourist preferences, curiosity, seasonality, and economic conditions—and supply-side factors, such as transportation, accommodation, activities, pricing, culinary tourism, and local attractions. The findings reveal that transportation and accommodation consistently emerge as the most influential drivers in both regions, highlighting the importance of accessibility and lodging availability. Bangka emphasizes culinary experiences and price sensitivity, while Belitung is more influenced by economic conditions and seasonality. Peak tourism periods are identified during Chinese New Year in February, New Year, and mid-year school holidays in June to July. In Belitung, culinary tourism and seasonal activities see increased interest during February and October, while Bangka shows steady interest in beach-related activities and culinary offerings throughout the year. . Misalignment between supply-side factors, such as limited affordable accommodation or transportation options, can impact tourism performance during these periods. These insights offer practical recommendations for local governments, tourism boards, and businesses to refine marketing strategies, enhance tourist experiences, and optimize tourism infrastructure. . Focusing on affordable travel and culinary experiences for Bangka seasonal tourism and economic preferences for Belitung can help maximize tourism potential and drive sustainable growth in the region
Bearing Capacity in Reinforced Soil: A Systematic Review of Modern Assesment Methods Putri, Lusi Dwi
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1301

Abstract

This systematic literature review  aims to thoroughly analyze the probabilistic methods used to assess the bearing capacity of reinforced soil , with a focus on identifying research trends, gaps, and evaluating various techniques such as Monte Carlo simulation, reliability-based design, and the stochastic finite element method (SFEM). The review follows established SLR protocols, employing purposive sampling from scientific databases such as Scopus to select peer-reviewed articles and conference papers. The dataset includes 113 full-text articles, 9 books, and 420 non-full-text entries, totaling 542 sources. Data collection was guided by predetermined inclusion and exclusion criteria, and a coding framework was utilized to categorize and compare key variables, including probabilistic methods and research outcomes. Qualitative synthesis was used for theme extraction, while quantitative assessment evaluated the effectiveness of the methods. The main contribution of this study lies in highlighting the strengths, limitations, and practical applicability of various probabilistic approaches, while advocating for the further integration of probabilistic and deterministic methods to enhance the reliability of soil reinforcement design. This review provides valuable insights for geotechnical engineers and researchers, advancing the understanding of probabilistic methods in improving the performance of reinforced soil.
ENHANCING COMMUNITY HEALTH CENTER PERFORMANCE THROUGH PUBLIC SERVICE INNOVATION AND DIGITAL TRANSFORMATION STRATEGIES Prianto, Arif; Rahayu, Agus; Gaffar, Vanessa; Adiwibowo, Lili
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1380

Abstract

The performance of the Community Health Center (Puskesmas) has been in the spotlight of all parties concerned with its ability to adopt the right innovation strategy and implement appropriate digital transformation. This research aims to uncover (1) how public service innovation strategies impact digital transformation implementation; (2) their effect on Puskesmas performance; (3) the impact of digital transformation implementation on Puskesmas performance; and (4) the mediating role of digital transformation in this relationship within West Java, viewed from a strategic management perspective. This quantitative study was conducted across 285 from 1,098 Puskesmas in West Java Province, utilizing proportional random sampling. The sample consisted of Puskesmas distributed across cities and districts in the region, ensuring representation from each area. A questionnaire with a 5-point rating scale was the primary research instrument, validated for accuracy and reliability. Data analysis included descriptive categorization and inferential analysis using Partial Least Square (PLS) via SmartPLS. The study found that public service innovation strategies positively impacted digital transformation implementation and Puskesmas performance. Additionally, digital transformation implementation positively influenced Puskesmas performance, with digital transformation partially mediating the relationship between innovation strategies and performance.
Public Service: Digital Transformation Building Smart City In Makassar City Maldun, Syamsuddin; Juharni, Juharni; Ridho, Muhmmad; Anas, Ali; Afrisal, Ade ferry
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1379

Abstract

The demands of the era of globalization of modern government systems require the management of changes in district/city governments to improve so that stakeholders can access maximum service. For this, an applicable work mechanism is needed through the design of a prototype model of Digital Transformation of Public Services e-Government. The type of research used is phenomenological and qualitative descriptive approach. The data collection method used is triangulation, namely: survey, interview, and document analysis. Data analysis uses data collection, data condensation, data presentation, verification and drawing conclusions. Research results: focus (1) The reality of the institutional structure and human resources of the Communication and Information Service still experiences various challenges and weaknesses that must be anticipated immediately, (2) Determinant factors in capacity development originating from internal sources, namely publication, polling up, and decision-making and external opportunities supported by the local government. Meanwhile, internal obstacles are the still strong interests of political parties and external challenges are that public participation is still lacking, and (3) Prototype model of Digital Transformation of Public Services e-Government, is actually a public right and at the same time an obligation of the government, Management of existing public services system, separately accommodates data exchange between institutions, and Management of public services according to regulations, which are guided by Law 37/2008, Law 25/2009 and Regulation of the Minister of State Apparatus Empowerment and Bureaucratic Reform 27/2014 is a combination of complementary nomenclature.
Sentiment Analysis And Topic Extraction Related To The 2024 Indonesian Presidential And Vice Presisdential Election Using Deep Learning Methods Tatulus, Raffael Lucas; Wulandhari, Lili Ayu
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1378

Abstract

This research examines public sentiment and discourse surrounding the 2024 Indonesian Presidential and Vice Presidential Election through analysis of YouTube comments. Using a combination of deep learning techniques, specifically Long Short-Term Memory (LSTM) networks for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic extraction, we analyzed public responses to the three presidential candidates. The LSTM model achieved varying accuracy rates across candidates: 58% for Anies Baswedan, 61% for Prabowo Subianto, and 71% for Ganjar Pranowo, with consistently high recall rates of 100% across all candidates. Topic extraction through LDA revealed distinct themes in public discourse, including leadership qualities, policy implementations, and campaign promises. The research methodology involved web scraping YouTube comments from January to October 2023, followed by comprehensive text preprocessing and analysis. Our findings provide valuable insights into public opinion dynamics and key discussion topics during the election period, contributing to the understanding of social media's role in Indonesian political discourse. This study demonstrates the effectiveness of combining deep learning approaches for analyzing large-scale social media data in the context of political communications
Transforming Learning in Primary Schools: The Role of AI and Flipped Classroom-based Apps Rindaningsih, Ida; Mustaqim, Ilmawan; Indra, Ika Ratna; Astuti, Ruli
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1371

Abstract

In today's digital era, the flipped classroom is gaining popularity for its ability to increase student engagement and meaningful learning. In the flipped classroom, there are learning videos and assignments given to students to study at home. For this reason, a simple and adaptive application is needed for students to use. Referring to this, it is known that in several regions in Indonesia there are still schools and communities that are not adaptive to technological developments and are still considered complicated and confusing. For this reason, this article aims to test the effectiveness of flipped classroom-based learning applications by applying joyfull learning for elementary school students. This research uses a quasiexperimental version of the control group design, the factorial analysis technique of 2x2 nonequivalent design used Analysis of Variance (ANOVA). The results showed that there was a very significant difference in learning outcomes for Aqidah akhlak subjects in the experimental class and control class. And there is an interaction between Joyfull learning and Flipped classroom application based on Flipped classroom on student learning outcomes. The findings of this study are that the kelasbalik.id application is very adaptive for teachers, parents, and students who are gaptek so that it can improve students' learning experience
Prediction of Performance and Emissions Diesel Engines Fueled-Biodiesel Using Artificial Neural Network (ANN) Resilient Backpropagation Algorithm (Rprop) Amrulloh, Riva; Widayat, Widayat; Warsito, Budi
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1265

Abstract

In order to increase energy security and improve environmental quality, the Indonesian Goverment set a target of 23% renewable energy mix in 2025, one of which is the Mandatory Bioediesel Program. A higher biodiesel blending ratio will affect the performance and emissions of diesel engines because biodiesel is chemically different from diesel oil. Research related to the prediction of diesel engine performance and emissions using Artificial Neural Network (ANN) has been conducted, but the author sees a research opportunity for the implementation of the ANN Resilient Backpropagation (Rprop) algorithm. The data used to create the ANN model prediction was secondary data from previous research. The model designed multi input and multi output (MIMO) with 4 input variables and 7 output variables. Model building done by varying the number of neurons and hidden layers. Model evaluation selected based on the largest coefficient of determination parameter R2  and the smallest RMSE or MAPE. The results showed that the ANN single layer 4-20-7 network architecture is the best model for predicting diesel engine performance and emissions with test data R2 , RMSE and MAPE of 0.962532, 6.699428 and 6.0% respectively, while for overall data testing has a performance of 0.982869, 3.908542 and 4.3%. The results also show that based on the ANN prediction results, the increasing biodiesel ratio can increase NOx emissions and decrease HC, CO and CO emissions2 . In terms of performance, the addition of biodiesel can increase BSFC and BP and decrease BTE. The results also show that the addition of ZnO concentration can reduce emissions while in terms of performance it will increase BTE and reduce BSFC and BP.
Optimizing Text Correction For Voice Based IoT Smart Building Virtual Assistants Shidiqi, Maulana Ahmad As; Hadi, Mokh Sholihul; Wibawa, Aji Prasetya; Mhd. Irvan, Mhd. Irvan
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1085

Abstract

The integration of Virtual Assistants (VAs) within Smart Building Internet of Things (IoT) ecosystems is increasingly critical, particularly for interpreting user commands via Automatic Speech Recognition (ASR). This paper presents an in-depth performance analysis of text correction algorithms on a Raspberry Pi 4—a cost-effective and widely used computing solution in smart building applications. Due to the absence of GPU acceleration for Python on ARM architecture, a specialized dataset was developed to benchmark algorithmic performance, focusing on correction times and accuracy. Our study utilized a near-real-world experimental setup, deploying Docker containers to simulate IoT MQTT brokers, a Smart Building Platform, and Rasa for dialogue management. Among the algorithms tested—Edit distance, Jaccard, FuzzPartialRatio, FuzzSortRatio, MLE, and Norvig Spell—the Edit distance and Norvig Spell emerged as leaders in accuracy, achieving an 84% success rate in text correction. Notably, the Edit distance algorithm demonstrated superior speed, vital for real-time processing demands. The Fuzz Sort Ratio algorithm distinguished itself with the fastest correction time at 31.6 milliseconds, albeit with a slight compromise on accuracy, attaining a 79% success rate. Consequently, the Edit distance algorithm is recommended for applications where accuracy and response time are paramount, while the Fuzz Sort Ratio is preferable for scenarios where speed is the overriding priority. This research paves the way for future exploration into the computational impacts of these algorithms and the exploration of neural network-based methods to further enhance text correction capabilities in smart building automation systems.

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