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All Journal Agridevina : Berkala Ilmiah Agribisnis EDUCARE Empowerment : Jurnal Pengabdian Masyarakat CIVICUS : Pendidikan-Penelitian-Pengabdian Pendidikan Pancasila dan Kewarganegaraan JURNAL ILMIAH MAHASISWA AGROINFO GALUH JURNAL AGRIBISAINS Journal of Civic Education PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat IJIIS: International Journal of Informatics and Information Systems Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia International Journal for Applied Information Management Indonesian Journal of Business Analytics (IJBA) Jurnal Pendidikan, Sains dan Teknologi Jurnal Praktik Baik Pembelajaran Sekolah dan Pesantren KOLONI International Journal Research on Metaverse
Eko Priyanto
Universitas Lampung
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : International Journal for Applied Information Management

 1 
Analysis of Demographic and Consumer Behavior Factors on Satisfaction with AI Technology Usage in Digital Retail Using the Random Forest Algorithm Priyanto, Eko; Saekhu, Ahmad; Prasetyo, Priyo Agung
International Journal for Applied Information Management Vol. 4 No. 4 (2024): Regular Issue: December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i4.91

Abstract

The rapid integration of artificial intelligence (AI) into digital retail has reshaped consumer interactions, enabling personalized services and operational enhancements. This study investigates the demographic and behavioral factors influencing consumer satisfaction with AI technologies in digital retail, using the Random Forest classification algorithm for predictive modeling. After comprehensive preprocessing and hyperparameter tuning through grid search cross-validation, the Random Forest model achieved an overall accuracy of 83%. While the model showed strong performance for predicting satisfied consumers yielding a precision of 0.84, recall of 0.97, and F1-score of 0.90, it performed poorly in identifying dissatisfied users, with a recall of only 0.27 and F1-score of 0.39, highlighting a class imbalance issue. Feature importance analysis revealed that experiential factors, particularly enhanced AI experience and preference for online services, significantly influenced satisfaction levels, whereas demographic variables such as age and gender had limited predictive value. These findings emphasize the need for digital retailers to focus on user-centric design and service personalization, rather than demographic segmentation alone, to enhance customer satisfaction and loyalty. Furthermore, the study contributes methodologically by demonstrating the effectiveness of Random Forest in handling complex consumer datasets and theoretically by validating TAM and Customer Satisfaction Theory in the context of AI adoption. Despite limitations related to class imbalance and sector-specific data, this research offers actionable insights for retailers, marketers, and system developers aiming to improve AI-driven service quality and consumer engagement. Future studies are encouraged to address these limitations through the inclusion of emotional and contextual variables and by expanding the analysis to other industries for broader applicability.
Clustering Netflix Shows Based on Features Using K-means and Hierarchical Algorithms to Identify Content Patterns Hayadi, B Herawan; Priyanto, Eko
International Journal for Applied Information Management Vol. 5 No. 2 (2025): Regular Issue: July 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i2.102

Abstract

This study explores clustering patterns within Netflix's movie catalog by applying K-means and hierarchical clustering algorithms. The primary objective is to identify distinct content groups based on features such as movie duration, release year, and content ratings. The dataset, which includes 5,185 Movies, was preprocessed by handling missing values, one-hot encoding categorical variables, and standardizing numerical features. Four distinct clusters were identified, with each cluster exhibiting unique characteristics. Cluster 0 primarily consists of longer, family-friendly Movies rated TV-14, while Cluster 1 contains shorter, mature Movies with a TV-MA rating. Cluster 2 represents a diverse range of TV-MA Movies with moderate durations, and Cluster 3 focuses on adult-oriented, longer Movies with an 'R' rating. These findings offer valuable insights into Netflix's content strategy, highlighting the platform's ability to cater to different audience segments based on content type and viewer preferences. The results suggest that Netflix can leverage clustering patterns to improve its recommendation system and content acquisition strategy. However, the study is limited by the absence of user-specific data and the reliance on basic metadata features. Future research could explore the integration of additional features like user ratings and apply deep learning techniques for more sophisticated clustering.
Co-Authors Achmad Affandi Ahmad Khoiri Ahmad, Aghniawati Ani Kusbandiyah Aprilia, Rennanti Lunnadiyah Aryani, Ine Kusuma B. Herawan Hayadi Berlilana Berlilana Budi Prabowo Chatarina Umbul Wahyuni Efi Miftah Faridli, Efi Miftah Eka Handriani Elly Hasan Sadeli Fungkiana, Heni Hengky Widhiandono, M.Si. S.E. Heru Irianto Hono Sejati, Hono Imam Tahyudin Iwan Fakhruddin Lea, Mella Angella Maemunah, Titi Mubarokah Mubarokah Mudjiyanti, Rina Nurlaili Dina Hafni Prasetyo, Priyo Agung Pratama, Satrya Fajri Puspitasari, Winarni Ragil Setiyabudi, Ragil Ratna Kartika Wati Risdianto Hermawan Ristiana Dyah Purwandari Saekhu, Ahmad Satibi, Imam Septiningtyas, Sisilia Sika Sri Harmianto Sri Tjondro Winarno, Sri Tjondro Sukaryorini, Pancadewi Sukawi, Zaenal Sutomo Sutomo Suyoto Suyoto Tukiran Tukiran Utomo, Ahmar Dwi Velenita, Nandisa Wildan Nurul Fajar Yogi Ageng Sri Legowo