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International Journal of Artificial Intelligence for Digital Marketing
Published by PT. Antis International Publisher
ISSN : -     EISSN : 30472903     DOI : https://doi.org/10.61796/ijaifd
Core Subject : Economy, Social,
Economics, Econometrics & Finance Environmental Science
International Journal of Artificial Intelligence for Digital Marketing proposes and fosters discussion on cutting-edge system theory and grounded research and practice addressing new ways of thinking, models and methodologies for understanding and acting within the complexities of market and organisational environments. The journal seeks to contribute to debates concerning the challenges of today regarding local and global economies and society. International Journal of Artificial Intelligence for Digital Marketing - provides a pragmatic view of the future of this area of marketing and focuses on what they describe as the tangible benefits offered by AI solutions. International Journal of Artificial Intelligence for Digital Marketing is designed to accelerate the adoption of AI technologies in the rapidly evolving digital marketing landscape and employs a comprehensive approach, incorporating surveys, exploration of existing AI solutions, and in-depth analysis of the results obtained.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 54 Documents
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SENTIMENT ANALYSIS OF INDRIVE APP USAGE REVIEWS ON GOOGLE PLAYSTORE USING SUPPORT VECTOR MACHINE (SVM) AND NAÏVE BAYES ALGORITHM Romadhoni, Afifani Aulida; Rachmadany, Andry; Prasojo, Bayu Hari
International Journal of Artificial Intelligence for Digital Marketing Vol. 2 No. 10 (2025): International Journal of Artificial Intelligence for Digital Marketing
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijaifd.v2i10.421

Abstract

Objective: This study aims to analyze user sentiment toward the InDrive application on Google Play Store by employing Support Vector Machine (SVM) and Naïve Bayes algorithms, motivated by the increasing number of user reviews that are difficult to evaluate manually, thus requiring a text mining approach to efficiently classify opinions into positive and negative categories. Method: A dataset of 30,000 reviews was collected through web scraping, and the analysis involved several stages, including preprocessing (cleaning, case folding, normalization, tokenizing, stopword removal, and stemming), term weighting using TF-IDF, and classification using SVM and Naïve Bayes. Results: The results revealed that SVM outperformed Naïve Bayes with an accuracy of 78%, precision of 0.80, and recall of 0.74, whereas Naïve Bayes achieved 76% accuracy, 0.79 precision, and 0.70 recall, indicating that SVM is more effective in handling complex user review data compared to Naïve Bayes. Novelty: The novelty of this research lies in applying a comparative study of the two algorithms to InDrive application reviews, which has not been extensively explored, and is expected to provide insights for developers to better understand user perceptions and improve the quality of application services.
THE EFFECT OF LIVE STREAMING, RATINGS, AND PRODUCT REVIEWS ON PURCHASING DECISIONS FOR COMPASS SHOE PRODUCTS IN THE SHOPEE APPLICATION Khaqi, M. Ivan Imanulloh; Yulianto, Mochamad Rizal; Rachmadany, Andry
International Journal of Artificial Intelligence for Digital Marketing Vol. 2 No. 10 (2025): International Journal of Artificial Intelligence for Digital Marketing
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijaifd.v2i10.422

Abstract

Objective: This study aims to analyze the effect of the live streaming feature, product ratings, and product reviews on purchasing decisions for Compass shoes in the Shopee application. Method: Using multiple linear regression techniques, the study identified that all independent variables (live streaming, ratings, and product reviews) have a positive and significant relationship to purchasing decisions, and the research instrument proved to be reliable with Cronbach's Alpha and Composite Reliability values above 0.7 for all constructs. Results: The analysis results show that the product review variable (X3) has the most significant influence, while live streaming (X1) has the smallest influence, and the research model shows moderate predictive power of purchasing decisions (R Square=0.528). Novelty: This finding confirms the importance of reviews, ratings, and real-time interactions in encouraging consumers to make purchasing decisions in digital marketplaces.
THE INFLUENCE OF CONTENT MARKETING, E-WOM, AND BRAND AWARENESS ON PURCHASE INTENTION OF EIGER PRODUCTS ON TIKTOK AMONG GENERATION Z Nafi’, Ahmad Rosyidun; Yulianto, Mochamad Rizal; Rachmadany, Andry
International Journal of Artificial Intelligence for Digital Marketing Vol. 2 No. 10 (2025): International Journal of Artificial Intelligence for Digital Marketing
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijaifd.v2i10.423

Abstract

Objective: This study aims to analyze the influence of Content Marketing, Electronic Word of Mouth (E-WOM), and Brand Awareness on the Purchase Intention of Eiger products among Generation Z on TikTok, focusing on identifying which factors most effectively drive consumer decisions in the digital-native segment. Method: A quantitative descriptive approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM), with data collected from 96 Generation Z respondents who actively use TikTok and have been exposed to Eiger promotional content. A purposive sampling method was applied, and measurements were conducted using a Likert-scale questionnaire based on established indicators for each variable. Results: The findings reveal that Brand Awareness significantly and positively influences purchase intention, while Content Marketing and E-WOM do not show a significant impact. The R-square value of 0.778 indicates that 77.8% of the variance in purchase intention can be explained by the tested variables, with brand awareness emerging as the dominant factor. Novelty: Unlike many prior studies highlighting the role of content marketing and E-WOM in driving Generation Z consumer behavior, this research demonstrates that on TikTok, purchase intention is primarily shaped by brand awareness. This highlights the strategic importance of enhancing brand presence, consistent messaging, and digital engagement to strengthen consumer trust and encourage purchasing decisions.
THE INFLUENCE OF TIKTOK SHOP’S PREDATORY PRICING ON CONSUMER ATTRACTION AND PURCHASING DECISIONS OF FASHION PRODUCTS AMONG GENERATION Z Priyanto, Ramadhani Dwi; Pebrianggara, Alshaf; Yulianto, Mochamad Rizal
International Journal of Artificial Intelligence for Digital Marketing Vol. 2 No. 10 (2025): International Journal of Artificial Intelligence for Digital Marketing
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijaifd.v2i10.424

Abstract

Objective: This study aims to empirically examine the effect of predatory pricing strategies on consumer attraction and purchasing decisions of fashion products on TikTok Shop among Generation Z, with consumer attraction serving as a mediating variable. Method: This study employed a quantitative approach with an explanatory research design and analyzed the data using Partial Least Squares Structural Equation Modeling (PLS-SEM). The sample consisted of 100 Generation Z respondents in Indonesia who actively shop for fashion products on TikTok Shop. Data were collected through a five-point Likert scale questionnaire distributed online and analyzed using SmartPLS. Result: The findings indicate that predatory pricing has a significant positive effect on both consumer attraction and purchasing decisions. Consumer attraction was also found to positively influence purchasing decisions and partially mediates the relationship between predatory pricing and purchasing decisions. The R² values for consumer attraction (0.871) and purchasing decisions (0.847) demonstrate very strong explanatory power, while the Q² values for each construct (>0.35) indicate high predictive relevance. Novelty: This study confirms that although extreme pricing strategies can trigger impulsive purchases and increase purchase urgency among Generation Z, long-term reliance on such practices may erode consumer trust and undermine market sustainability. Unlike previous studies that focused primarily on regulatory aspects and SMEs, this research provides empirical evidence of the paradoxical effects of predatory pricing on digital-native consumer behavior.

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