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All Journal International Journal of Computer Science and Humanitarian AI
Manalu, Sonya Rapinta
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Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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From Sign-Up to Cancellation: A Comparative Study of Dark Patterns in Spotify and Netflix Chow, Irene Ancilla; Santana, Carolyn; Manalu, Sonya Rapinta; Ario, Muhamad Keenan
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 2 (2026): IJCSHAI (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i2.15112

Abstract

Subscription-based platforms such as Spotify and Netflix have transformed digital content access by offering convenience, personalization, and seamless integration, yet often employ dark patterns—deceptive interface designs that manipulate user behavior and limit autonomy. While previous studies have explored dark patterns broadly, few have quantitatively compared their prevalence across major subscription services. This study addresses this gap by examining the sign-up and cancellation flows of Spotify and Netflix, focusing on asymmetry, friction, and manipulative design elements. Using structured observation, click-count tracking, timing measurements, and interface coding based on established dark pattern taxonomies, the study identifies and classifies manipulative strategies. Results show that cancellation flows consistently involve more complex dark patterns, including hierarchical button placement, information hiding, redundant steps, forced actions, and confirm-shaming messages, which increase user effort and reduce control. Spotify exhibits a higher concentration of manipulations than Netflix, though both leverage cognitive biases like default settings and effort avoidance. Findings underscore ethical concerns and provide recommendations for interface design that enhances transparency, fairness, and user autonomy in subscription services.
The Impact of Parameter Scaling: Analysis of Specific Large Language Model Capabilities Putera, Ariya Uttama; Marcellino, Felix; Manalu, Sonya Rapinta; Muhamad , Keenan Ario
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i1.15119

Abstract

Large Language Models (LLMs) are currently very diverse. Some of the largest include Chat-GPT, Gemini, Microsoft Copilot, Claude Sonet, Grok, and DeepSeek. Based on this, the plan of this research is to determine how efficient these AI models can be, based on their strengths in LLM training. In this study, we will examine the impact of LLM scaling parameters on the results of each local model we will test. This study also limits the number of parameters and classifies the questions to be asked. From these questions, we can identify and classify which local LLM models perform better when asked the same questions. Then, we will objectively evaluate each of them based on the results of the study. Thus, this study aims to establish a known correlation between scaling parameters and results. We also hope that it will be useful for improving work efficiency in selecting AI that suits user needs and expanding users' knowledge of AI so they can perform their jobs more efficiently and accurately. From this research, we conclude, aware of the results of the work that has been done, that local LLMs with large scaling are not entirely good and efficient. As with Gemma3, even with 12B parameters, the results weren't better than the Gemma3 model with 4B parameters. Alternatively, if you're using similar hardware to ours, you can use GPT-oss (openai/gpt-oss-20B) and Qwen3 (Qwen/Qwen3-4B & Qwen/Qwen3-8B), which offer good results in terms of reasoning and inference speed.
PoseTracker: Accuracy Evaluation of AI-Based Mobile Application for Exercise Posture Feedback Collhins, Billy; Mitta, Kalyana; Gunawan, Christian; Manalu, Sonya Rapinta
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i1.15123

Abstract

In recent years, the rising of public health awareness has increased fitness activities participation. However, improper exercise form remains a significant contributor to injuries, particularly in unsupervised environments. To address this, PoseTracker’s accuracy was evaluated as a native Android application that provides real time feedback on exercise posture through MediaPipe based Human Pose Estimation (HPE) model. The system extracts 33 3D body landmarks, normalizes them to account for body scale, and employs cosine similarity to compare user movements against a reference dataset. Evaluations involving participants aged between 17 to 50 years old and 240 repetitions across four exercises demonstrated high detection accuracy: 88.33% for jumping jacks, 85% for squats, 83.33% for push-ups and 82% for sit ups. While performance can be influenced by environmental factors such as inconsistent lighting, camera positioning and incomplete body visibility, these results highlight the potential for lightweight, AI driven tools to support safe and self-guided fitness routines. Overall, the evaluations indicate that PoseTracker achieves reliable detection accuracy in distinguishing correct and incorrect exercise posture across multiple movement types under realistic conditions. Although performance variability exists due to environmental and system constraints, the accuracy levels observed demonstrate the feasibility of MediaPipe based Human Pose Estimation (HPE) for practical posture assessment in mobile fitness applications.
An End-to-End Architecture for Stock Market Prediction Integrating Mobile Application, Backend Services, and ML/DL Models Wilham, Abraham Kefas; William, William; Manalu, Sonya Rapinta
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i1.15154

Abstract

Prior research on stock market prediction has predominantly focused on algorithmic accuracy, leaving a significant research gap in the system-level realization required for real-world delivery. This paper addresses this disparity by presenting an end-to-end stock prediction delivery system that operationalizes trained machine learning models within a mobile-centric architecture. Unlike model-centric studies limited to offline evaluation, this work focuses on the rarity of system-level implementation. Market data are periodically ingested into a managed relational database, where predictions are generated using a fixed historical window and persisted for downstream access. A cross-platform mobile application serves as the primary user interface, providing structured access to historical prices, predictions, and accuracy metrics via backend APIs without local model inference. A key novelty is the implementation of an in-memory caching layer to optimize responsiveness for repeated mobile access. Experimental results demonstrate that this architecture significantly improves efficiency, reducing average API response times by approximately 94% from 817 ms to 48,7778 ms compared to direct database queries. These findings underscore the critical role of mobile-oriented system design in bridging the gap between predictive modeling and practical deployment, while also highlighting the importance of scalable infrastructure, efficient data synchronization, and reliable service integration for sustainable real-world financial technology applications.
Augmented Reality in Mobile Gaming and Player Engagement: A Quantitative Study Jeffry, Gaelen; Wong, Juan Felix; Manalu, Sonya Rapinta
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 2 (2026): IJCSHAI (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i2.16032

Abstract

Augmented Reality (AR) technology has had a profound effect on the gaming industry in recent years by enabling game designers to create interactive virtual environments that overlay upon a player’s physical space. Through an in-depth investigation of the effects of AR on mobile game player engagement this paper will explore four dimensions of player engagement: immersion, interaction, enjoyment and the user experience. In order to collect and analyze data, a quantitative methodology was utilized that includes a standardized Likert scale survey designed for the mobile AR gaming community as well as the examination of secondary market and industry data reports from 2022-2026. Survey results indicate that AR positively impacts player engagement on all four measures, most notably with respect to augmented, geospatial and location-based mechanics, and this finding is corroborated by relevant industry and market reports. In 2024, the global AR gaming market was valued at USD 14.2 billion and is projected to increase at a compound annual growth rate (CAGR) of 28.1% through to 2033 to reach USD 141.7 billion. Moreover, when Scopely bought Pokemon Go for $3.5 billion in 2025, it was clear to see that businesses were willing to pay significant capital in exchange for AR technology to use as an engagement mechanism. The contribution to knowledge made by this study is to bring together both primary source survey data collected from real-world users and secondary sources of market data to offer evidence-based insights into the impact of AR on player engagement that previous research had not previously accomplished, thereby offering practical guidance to researchers and industry developers interested in the development of both more user engaging and economically profitable AR-based games.
Employee Aptitude & Personality testing using Gamification for more accurate results Alexander, Raymond; Manalu, Sonya Rapinta
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 2 (2026): IJCSHAI (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i2.16043

Abstract

The concept of "gamification" and how it is applied to make boring, tedious work seem exciting, rewarding, and fun is becoming more prevalent in many industries. Gamification has made many mundane tasks seem enjoyable and rewarding through the use of simple reward systems, which is something we typically find in games. In addition to enhancing employee motivation to complete tasks, there are indications that gamification could be an effective method to evaluate an individual's psychological makeup. This research will investigate the possibility of using gamified systems as a way to assess an employee's potential to perform at a high level (aptitude) and their personality characteristics. It is suggested that game-based assessments will yield more reliable information regarding an employee's ability to think critically, make decisions based upon data, and manage resources than conventional testing methods. Because employees are evaluated through their performance in the game, the employee does not experience the anxiety associated with traditional testing, the employee does not have to worry about providing socially desirable responses, and the employee does not have to worry about answering questions honestly. The proposed approach allows for evaluation of employee competencies, including problem solving, adaptability, risk assessment, and strategic planning in simulated environments related to specific jobs.
Co-Authors Ario, Muhamad Keenan Chow, Irene Ancilla Collhins, Billy Gunawan, Christian Jeffry, Gaelen Marcellino, Felix Mitta, Kalyana Muhamad , Keenan Ario Putera, Ariya Uttama Raymond Alexander Santana, Carolyn Wilham, Abraham Kefas William William Wong, Juan Felix