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p-Index From 2021 - 2026
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All Journal International Journal of Research and Applied Technology (INJURATECH)
Septiani, Riska Endah
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Optimizing Digital Content Strategy Based on User Interaction Patterns Using Machine Learning Algorithms Septiani, Riska Endah
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

In the attention economy era, generic digital content strategies are no longer effective in increasing user engagement. This study aims to optimize content strategies by analyzing user interaction patterns through a machine learning approach. Interaction data including engagement metrics, access time, and topic preferences are processed using the K-Means Clustering algorithm for audience segmentation and Random Forest to predict future content performance. The results show that automatically identifying user behavior patterns can increase the accuracy of content type recommendations by up to [X]% and upload time efficiency by [X]%. These findings prove that integrating intelligent algorithms in creative decision-making can minimize speculation in content production. This study provides practical contributions for digital marketers in designing more personalized, relevant, and data-driven strategies to achieve sustainable organic growth on digital platforms.
Accuracy Analysis of Automated Grammar Checkers: A Comparative Study between Grammarly and Quillbot in Detecting Syntactic Errors Septiani, Riska Endah
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

The use of Automated Grammar Checkers (AGC) has become standard in global academic writing, but their effectiveness in detecting complex syntactic structures remains questionable. This study aims to conduct a comparative study between Grammarly and Quillbot in detecting syntactic errors in English text. Using descriptive qualitative methods, the study examined ten specific syntactic error categories, including subject-verb agreement, dangling modifiers, and sentence fragments. Data were analyzed based on the accuracy of detection and the accuracy of correction suggestions provided by both platforms. Initial results indicate that Grammarly is better in detecting formal structural errors, while Quillbot focuses more on fluency of lexical variations, sometimes ignoring purely syntactic rules. These findings are expected to provide guidance for students and academics in selecting writing evaluation tools that suit their grammatical needs.
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