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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 632 Documents
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IOT AND MACHINE LEARNING INTEGRATION TO PERSONALIZE SHOPPING EXPERIENCES IN SMART MALLS Fajri, Teuku Irfan
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.v9i1.1.1609

Abstract

The development of digital technology is driving significant transformation in the retail industry, one of which is through the application of the Internet of Things (IoT) and Machine Learning (ML) in smart mall management. This research aims to analyze how the integration of IoT and ML can create a more optimal personalized shopping experience for visitors. The method used is qualitative research with a literature review approach, namely reviewing various scientific studies, research reports, and academic publications related to IoT implementation, ML analytics, and personalization concepts in the context of smart retail. The study results show that IoT enables real-time customer data collection through sensors, beacons, mobile devices, and smart camera systems. The data is then processed using ML algorithms to produce personal recommendations, predictions of consumer preferences, visitor movement patterns, and automatic service adjustments. The integration of these two technologies is proven to improve the quality of the shopping experience through relevant offers, navigation efficiency, optimization of tenant management, and increased customer interaction with the mall environment. In addition, the literature shows that the success of smart malls depends on the quality of system integration, data security, privacy transparency, and the readiness of human resources in managing technology. This research concludes that the use of IoT and ML has great potential in forming a smart retail ecosystem that is responsive and customer-centered. However, implementation needs to pay attention to ethical, technical and operational challenges so that personalization can be achieved without reducing consumer comfort and trust. It is hoped that this study can become a conceptual basis for the development of further research and digital transformation strategies in the modern retail sector.
Festival Stakeholder Strategic Mapping: Power-Interest Matrix Analysis for Cap Go Meh Singkawang Inclusive Governance Tohirin, Ahmad; Widianingsih, Ida; Pancasilawan, Ramadhan
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.v9i1.1.1688

Abstract

This study aims to analyze the power-interest configuration of stakeholders in the governance of the Cap Go Meh Singkawang Festival to assess the level of inclusivity and formulate appropriate engagement strategies. The research employs a qualitative case study method, collecting data through in-depth interviews, participatory observation, and document analysis involving seven key stakeholder groups. Findings reveal a tripartite dominance of the Festival Committee, Cultural Community, and Sponsors as Key Players with high power and interest. At the same time, MSME actors and affected residents are marginalized as Subjects with high dependency but low influence. This configuration reflects an elitist and less inclusive governance model. Based on these findings, the study recommends reconstructing the collaborative architecture through a more representative festival board, reforming data-based economic participation systems, and developing a long-term collaborative ecosystem that empowers marginalized groups. This research suggests a systematic redistribution of power to achieve more equitable and sustainable festival governance.
Governance-Based SPBE Model Framework Design in Multi-Level Government Agung Gumilar Saputra; Ika Sartika
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.v9i1.1.1689

Abstract

This research aims to develop a governance-based framework model for the implementation of the Electronic-Based Government System (ESG) in Indonesia. Although ESG is a national strategic agenda, its implementation still faces various challenges, such as institutional fragmentation, low interoperability, minimal public participation, and weak adaptation to technological change. Through a qualitative approach with a literature review and conceptual construction, this research formulates four interrelated governance models: the Transparent Digital Governance Framework, the Collaborative ESG Ecosystem Model (CSEM), the Participatory E-Governance Model (PEGM), and the Adaptive ESG Governance Framework (ASGF). Each model is built on the governance principles of transparency, collaboration, participation, and adaptability as a response to structural weaknesses in current ESG practices. The results show that effective and sustainable digital governance requires the synergy of these four principles in an integrated and contextual manner, particularly to bridge the gap between national policy and local implementation.
Exploring Indicators and Developing the Initial DigiGOVQUAL Model: An Exploratory Factor Analysis in Banjar City Ramdhani, Hudzaifah Nuruzzaman; Giri, Refi Rifaldi Windya
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.v9i1.1.1510

Abstract

The digital transformation of government through the Electronic-Based Government System (Sistem Pemerintahan Berbasis Elektronik—SPBE) has become a foundational element of bureaucratic reform in Indonesia. However, the absence of a standardized instrument for assessing SPBE service quality—particularly within the Government-to-Government (G2G) domain—has hindered systematic evaluation and continuous improvement efforts. This study aims to develop a preliminary model called DigiGOVQUAL, designed to measure the quality of SPBE services from the perspective of internal users within government institutions. The model adapts and refines relevant indicators from the E-Government Service Quality (EGSQUAL) framework to better reflect the specific characteristics of digital public services in the Indonesian bureaucratic context. Employing a quantitative research approach, the study utilizes Exploratory Factor Analysis (EFA) to validate the construct dimensions. Data were collected via an online survey distributed to over 200 active civil servants in the Banjar City government who have experience using G2G SPBE services. The findings are expected to contribute to the development of a valid and contextually appropriate evaluation tool for digital government service quality. Ultimately, the DigiGOVQUAL model is intended to support more accountable and user-centered public service delivery policies in the digital era
Business Intelligence–Based Digital Marketing Strategy for SME Market Expansion Supriyadi, Supriyadi; Pranyoto, Edi
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.v9i2.1682

Abstract

Small and medium-sized enterprises (SMEs) face increasing challenges in expanding their markets due to limited resources, intense competition, and rapidly changing digital consumer behavior. This study proposes a Business Intelligence (BI)–based digital marketing strategy as a data-driven approach to support SME market expansion by integrating data from multiple digital channels, including social media, e-commerce platforms, CRM systems, and website analytics, to generate actionable insights for strategic decision-making. Using a mixed-method approach that combines quantitative analysis of digital marketing performance with qualitative managerial perspectives, the study applies key BI components—such as data warehousing, dashboards, predictive analytics, and customer segmentation—to enhance targeting, personalization, campaign effectiveness, and resource allocation. The findings indicate that SMEs adopting BI-supported digital marketing achieve significantly higher customer acquisition, engagement, and conversion rates than those relying on traditional approaches, while predictive analytics enables more accurate demand forecasting and identification of high-potential market segments. The study also identifies critical success factors, including data integration capability, analytical skill development, and strategic alignment between business objectives and marketing activities. Despite constraints related to technical expertise and budget, scalable BI tools and cloud-based platforms make advanced analytics increasingly accessible. Overall, the research contributes a practical framework demonstrating that Business Intelligence is a key enabler of marketing efficiency, evidence-based decision-making, and sustainable market expansion for SMEs in the digital economy.
Returns to Postgraduate Education in Indonesia: Province-Level Evidence and Policy Implications Farisy, Ahmad Arib Al; Sartika, Devi; Elfianty, Lena; Wahyudi, Jusuf; Mayangsari, Chisillia; Amany, Sarah Ulfah Al
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.v8i1.1.1708

Abstract

This study investigates the relationship between postgraduate education (Master's and Doctoral degrees, designated S2/S3 in Indonesia's educational system) and provincial-level economic outcomes using comprehensive 2022 data from Indonesia's Central Statistics Agency (BPS). Drawing on human capital theory (Becker, 1964; Mincer, 1974) and signaling theory (Spence, 1973), we examine how the concentration of advanced degree holders correlates with key development indicators across Indonesia's 34 provinces. Our cross-sectional analysis employs Pearson correlations with heteroscedasticity-robust standard errors and multiple regression specifications controlling for urbanization, industrial composition, and geographic factors. Results reveal significant positive correlations between postgraduate education rates and economic prosperity. Provinces with higher concentrations of S2/S3 degree holders demonstrate substantially higher GDP per capita (r = 0.52, p < 0.01), Human Development Index scores (r = 0.71, p < 0.001), and lower poverty rates (r = -0.48, p < 0.01). DI Yogyakarta leads with a postgraduate rate of 1.11%, while Nusa Tenggara Timur lags at 0.21%, highlighting significant regional disparities. These findings are robust to the exclusion of outliers (DKI Jakarta, Papua) and persist across alternative model specifications. While cross-sectional data precludes causal inference, the strength and consistency of associations provide empirical support for expanding government scholarship programs such as LPDP (Lembaga Pengelola Dana Pendidikan). We recommend targeted scholarship investments in underrepresented provinces and fields with high economic multiplier effects to address regional disparities and maximize returns from educational investment
Implementation of K-Means Clustering Algorithm for Inventory Management Optimization at Putra Mart Maryaningsih, Maryaningsih; Sapri, Sapri; Akbar, Abdussalam Al
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1712

Abstract

The rapid advancement of Information Technology has become a crucial element in enhancing business decision-making efficiency. Putra Mart, a prominent wholesaler in Bintuhan City, faces significant operational challenges due to its reliance on manual inventory recording and supply management systems. These manual processes often lead to data inaccuracies, stock imbalances, and difficulties in identifying market demand patterns. This research aims to address these issues by implementing data mining techniques using the K-Means Clustering algorithm to categorize inventory data into strategic groups based on stock levels and supply frequency.The system development follows the structured Waterfall model, which includes requirements analysis, system design using Data Flow Diagrams (DFD), coding with Visual Basic .Net 2010, and database management using SQL Server 2008. The clustering process utilizes the Euclidean Distance formula to measure the proximity between data points and centroids, effectively partitioning the items into two main clusters: "Fast Moving" and "Slow Moving" goods.The results of the analysis on 2020 transaction data successfully identified a clear distribution of products, with Cluster 1 accounting for 40% of the tested items. System testing through the Blackbox method confirmed that all functional features operate correctly, while a user satisfaction survey yielded a score of 84%, categorized as "Good." This study concludes that the implementation of the K-Means algorithm provides a reliable, data-driven solution for Putra Mart to optimize its inventory management, minimize deadstock, and improve overall service quality for its partner stores
Implementation of the Additive Ratio Assessment (ARAS) Method for Evaluating Company Leadership Performance Sartika, Devi; Sari, Venny Novita; Beti, Ila Yati
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.1710

Abstract

Evaluating the performance of company leaders is an important process in determining leadership effectiveness and organizational success. Performance assessments that rely on subjective judgments often lead to less accurate decision-making results. Therefore, a systematic and objective decision-making method is required. One method that can be used is the Additive Ratio Assessment (ARAS) method. ARAS is a multi-criteria decision-making method used to determine the best alternative based on the utility value obtained from each evaluation criterion. This study aims to implement the ARAS method in a decision support system to evaluate the performance of company leaders. The research process involves several stages, including determining evaluation criteria, assigning weights to the criteria, normalizing the decision matrix, calculating optimal values, and determining utility values. The criteria used in this study include leadership, decision-making ability, communication, responsibility, and organizational performance. The results of the study indicate that the ARAS method can provide objective and systematic evaluation results in determining the best leadership performance. With the implementation of a decision support system based on the ARAS method, companies can obtain more accurate evaluation results that can be used as a basis for decision-making in leadership development and improving the quality of company management
VISUAL HISTORICAL DATA-BASED TRAFFIC MOVEMENT AND DENSITY PATTERN EXTRACTION FOR ADAPTIVE PATTERN DETECTION BASE ON VEHICLE TYPE Angellia, Filda; Merlina, Nita; Subekti, Agus; Handayanto, Rahmadya Trias
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.v8i2.1715

Abstract

Traffic congestion in urban areas has become a crucial issue, impacting time efficiency, energy consumption, and quality of life. One of the main causes of difficulties in traffic management is the lack of optimal predictive systems capable of detecting and adaptively responding to vehicle movement patterns. This study proposes a historical digital image-based approach to extract traffic movement patterns and density based on vehicle type and dimensions. The developed model utilizes historical traffic video footage from CCTV systems as a visual data source, which is then processed using the YOLOv5 algorithm to detect the number, size, and type of vehicles. After the detection process, vehicle information is converted into a sequential format that reflects vehicle movement in the temporal dimension. This data is then analyzed using a Long Short-Term Memory (LSTM) model to generate traffic density prediction patterns. This study also compares the performance of LSTM with other algorithms such as Random Forest and XGBoost in terms of prediction accuracy. Model evaluation is conducted using MSE and RMSE metrics to measure accuracy against actual data.The research results show that the integration of dimension-based vehicle detection with a visual historical data-driven prediction approach can improve the accuracy and flexibility of modeling future traffic conditions. This approach significantly contributes to the development of intelligent transportation systems that can adapt to dynamic environmental conditions and traffic patterns
Forecasting the Number of Visitors to Rahmat Museum And Gallery Using the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) Model tajrin, tajrin tajrin; Wintania, Evelyn; Purba, Musa Ferdianshah; Kholik, Fatimatuzzahro
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.v9i1.1.1696

Abstract

Fluctuations in monthly visitor numbers present challenges for tourism management in planning operational and promotional strategies. Rahmat International Wildlife Museum & Gallery, a leading educational tourism destination in Medan, experiences dynamic variations in visitor numbers influenced by seasonal patterns and external conditions. This study aims to forecast monthly visitor numbers using the SARIMAX model. Monthly visitor data from January 2021 to November 2025 were analyzed, incorporating national holidays and the COVID-19 period as external variables. The modeling process included data preprocessing, stationarity testing using the Augmented Dickey-Fuller (ADF) test, parameter identification through ACF and PACF analysis, and model evaluation using RMSE and MAPE metrics. The results indicate that the SARIMA(1,0,1) model without exogenous variables provides the best predictive performance. Forecasting for the next 12 months suggests relatively stable visitor numbers, ranging from approximately 1,800 at the beginning of the forecast period to around 1,500 toward the end, indicating a gradual declining trend. These findings provide a data-driven foundation for strategic planning and visitor management at the museum

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