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INNOVATIVE KNOWLEDGE MANAGEMENT STRATEGIES TRANSFORMING ORGANIZATIONAL SUCCESS Wijaya, Yoana Sonia; Haryani, Calandra A.; Hery, Hery; Widjaja, Andree E.; Tarigan, Riswan E.
Proceeding National Conference Business, Management, and Accounting (NCBMA) 7th National Conference Business, Management, and Accounting
Publisher : Faculty of Economics and Business Universitas Pelita Harapan

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In the current information technology era, knowledge management plays a critical role in the growth and success of enterprises. Knowledge management techniques are not used in the same manner as previously due to advancements in technology. Current knowledge management practices can benefit greatly from the application of emerging technological trends like web 2.0, social networks, wikis and blogs, discussion forums, and artificial intelligence. This will increase organizational performance and efficiency. The purpose of this study is to investigate the growing influence of knowledge management techniques on organizational effectiveness. This study examines earlier research on the subject of how organizational performance is impacted by emerging trends in knowledge management strategies. The literature review approach, or literature study from earlier research publications linked to this topic, was the research method used in this study. The research findings are presented in the form of a model, which suggests that increasing organizational performance and competitiveness is positively and significantly correlated with the introduction of new trends in knowledge management methods. Organizations can increase their performance and competitiveness in a market that is becoming more and more competitive by implementing effective knowledge management methods, according to the implications of this research.

INNOVATIVE KNOWLEDGE MANAGEMENT STRATEGIES TRANSFORMING ORGANIZATIONAL SUCCESS Wijaya, Yoana Sonia; Haryani, Calandra A.; Hery, Hery; Widjaja, Andree E.; Tarigan, Riswan E.
Proceeding National Conference Business, Management, and Accounting (NCBMA) 7th National Conference Business, Management, and Accounting
Publisher : Faculty of Economics and Business Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar

In the current information technology era, knowledge management plays a critical role in the growth and success of enterprises. Knowledge management techniques are not used in the same manner as previously due to advancements in technology. Current knowledge management practices can benefit greatly from the application of emerging technological trends like web 2.0, social networks, wikis and blogs, discussion forums, and artificial intelligence. This will increase organizational performance and efficiency. The purpose of this study is to investigate the growing influence of knowledge management techniques on organizational effectiveness. This study examines earlier research on the subject of how organizational performance is impacted by emerging trends in knowledge management strategies. The literature review approach, or literature study from earlier research publications linked to this topic, was the research method used in this study. The research findings are presented in the form of a model, which suggests that increasing organizational performance and competitiveness is positively and significantly correlated with the introduction of new trends in knowledge management methods. Organizations can increase their performance and competitiveness in a market that is becoming more and more competitive by implementing effective knowledge management methods, according to the implications of this research.

Anime Segmentation Based on User Preferences: Applying Clustering to Identify Groups of Anime with Similar Genres, Themes, and Popularity Tarigan, Riswan E; Wijaya, Yoana Sonia
International Journal for Applied Information Management Vol. 5 No. 1 (2025): Regular Issue: April 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i1.99

The anime industry has experienced significant growth, with an increasing focus on user preferences for content discovery and engagement. This study applies clustering techniques, specifically K-means, to segment anime based on user preferences, genres, themes, and popularity. By analyzing a comprehensive dataset containing attributes such as user ratings, popularity, genres, and themes, the research identifies distinct groups of anime that align with varying viewer tastes. The clustering results reveal that anime can be categorized into several groups, including highly popular but critically less-acclaimed titles, well-regarded but moderately popular anime, and niche, critically acclaimed series that appeal to smaller but dedicated audiences. This segmentation allows streaming platforms to offer more personalized recommendations, enhancing user experience and engagement by matching viewers with content that best fits their preferences. Although clustering techniques provide valuable insights into anime content, the study acknowledges certain limitations, such as overlap between clusters, indicating that some anime may not fit perfectly into a single category. This highlights the need for further improvements in segmentation accuracy. The study suggests exploring hybrid clustering methods, combining K-means with other techniques, and integrating demographic data, such as age, gender, and geographic location, to refine recommendations. Overall, the application of clustering algorithms to better understand user preferences in anime offers a promising approach to developing more effective and personalized recommendation systems. This can ultimately improve user satisfaction and engagement in the rapidly growing and competitive anime streaming market.

A Quantitative Study on User Experience Dimensions and Their Impact on User Satisfaction in Indonesian Mobile E-Commerce Saputra, Afif Dwi; Tarigan, Riswan E.; Wijaya, Yoana Sonia
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.217

This research examines how user experience (UX) dimensions influence user satisfaction in Indonesia’s mobile e-commerce ecosystem. As mobile shopping continues to dominate digital transactions, understanding the relationship between UX and user satisfaction becomes crucial for maintaining platform competitiveness. Adopting a quantitative explanatory approach, the study gathered data from 100 active users of leading e-commerce platforms such as Shopee, Tokopedia, and Lazada through an online questionnaire. The instrument was based on the User Experience Questionnaire (UEQ) framework, encompassing six dimensions—Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty—with user satisfaction serving as the dependent variable measured via validated Likert-scale indicators. Analytical procedures included descriptive analysis, reliability and validity tests, and multiple linear regression using SPSS version 26. The findings reveal that five out of six UX dimensions significantly and positively affect user satisfaction (p 0.05). Among them, Perspicuity and Efficiency exert the strongest influence, underscoring the importance of intuitive interface design and smooth, error-free transaction processes. Dependability, Attractiveness, and Stimulation also play notable roles, indicating that both functional performance and emotional engagement contribute to favorable user experiences. Conversely, Novelty—though positively associated—does not reach statistical significance, implying that while users appreciate innovation, they prioritize clarity and reliability. The regression model yields an R² value of 0.742, suggesting that UX dimensions collectively account for 74.2% of the variance in user satisfaction. Overall, the study affirms that UX is a decisive factor in shaping user satisfaction and loyalty in mobile e-commerce environments. It enriches existing UX scholarship by providing empirical evidence from Indonesia’s fast-growing digital market. Practically, the results encourage developers to emphasize usability, dependability, and aesthetic design to maintain user engagement. Future studies are recommended to integrate trust, emotional attachment, and emerging technologies such as artificial intelligence and augmented reality to obtain a more comprehensive understanding of user satisfaction in digital commerce.

Forecasting Coffee Sales Using Time-Based Features and Machine Learning Models Wijaya, Yoana Sonia; Wawolangi, Ariel Christopher
International Journal of Informatics and Information Systems Vol 9, No 1: Regular Issue: January 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i1.294

Sales forecasting is a critical component of operational and strategic decision-making in retail and coffee businesses, where demand exhibits strong temporal variability and product-level heterogeneity. Accurate hourly-level forecasts enable effective inventory management, workforce scheduling, and data-driven promotional strategies. However, existing studies predominantly rely on aggregated sales data and provide limited comparative analyses between traditional statistical models and machine learning approaches using real transaction-level data. This study addresses this gap by conducting an empirical comparison between a traditional ARIMA model and ensemble machine learning models, namely Random Forest and XGBoost, for hourly coffee sales forecasting. The analysis is based on a real-world dataset comprising 3,547 transaction records enriched with temporal and product-related features. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results demonstrate that machine learning models significantly outperform the ARIMA baseline, with XGBoost achieving the best performance and explaining approximately 83% of the variance in sales data, while ARIMA shows limited explanatory power due to its inability to capture non-linear and highly volatile demand patterns. Feature importance analysis further reveals that product-specific attributes are the dominant drivers of sales predictions, complemented by seasonal and intra-day temporal effects. These findings provide both scientific and practical contributions by offering empirical evidence on the superiority of machine learning models for granular sales forecasting and supporting data-driven decision-making in coffee retail analytics

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