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Musthofa Galih Pradana
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Journal of Computing Innovations and Emerging Technologies
Published by Nova Mind Press
ISSN : -     EISSN : 31097111     DOI : 10.64472
Core Subject : Science,
Computer Science & IT
JCIET welcomes contributions that explore theoretical foundations, practical implementations, and innovative applications across a broad range of topics, including but not limited to: Artificial Intelligence and Machine Learning Data Science and Big Data Analytics Internet of Things (IoT) and Embedded Systems Cloud Computing and Edge Computing Cybersecurity and Cryptography Computer Vision and Image Processing Human-Computer Interaction Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality Software Engineering and System Development Web and Mobile Application Development Blockchain Technology and Decentralized Systems Natural Language Processing Robotics and Automation Educational Technology and E-Learning Platforms Smart Systems and Intelligent Environments JCIET is committed to supporting innovation, ethical research practice, and open science by ensuring a transparent and fair peer-review process. Articles published in JCIET are freely accessible to researchers worldwide.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
 1   2 
Predicting YouTube Video Viewership Using Multi-Feature Random Forest Modeling: A Case Study on the Warganet Life Official Channel Meiza Alliansa; Nur Hafifah Matondang; Rifka Dwi Amalia
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 2 (2025): Volume 1 No 2
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i2.23

Abstract

This study presents a viewer prediction model for the YouTube channel “Warganet Life Official” using the Random Forest algorithm and multi-feature engagement metrics obtained from YouTube Studio. The dataset includes impressions, likes, dislikes, shares, watch time, and subscriber changes, which were processed using the CRISP-DM framework. The model achieved its best performance under a 70:30 train–test split, producing a MAPE of 12.20%, an RMSE of 204,890.42. Random Forest outperformed Linear Regression and XGBoost baselines, confirming its suitability for modeling nonlinear engagement behavior in dynamic digital-media environments. The novelty of this work lies in its multi-feature, engagement-driven modeling applied to a large Southeast Asian entertainment channel, offering localized evidence for viewer-performance forecasting. Theoretically, this study strengthens recent findings that multi-modal engagement metrics yield more accurate digital-media performance predictions. Practically, the deployment of a Streamlit-based prediction tool enables creators to perform real-time content evaluation and early performance diagnostics, providing actionable insights for improving content strategies and long-term channel optimization.
Application of Computational Thinking as an Effort to Optimize Business Processes in Samudera Motor Showroom Sales Eunike Octavia Nababan; Nasywa Rakha Arrafi; Agnes Kurnia Gulo; Fawwaz Nabila Zulanifa; Nabila Zulanifa; Karina Ghaisani; Adinda Nuril Ashfiya; Nindy Irzavika
Journal of Computing Innovations and Emerging Technologies Vol. 1 No. 2 (2025): Volume 1 No 2
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v1i2.24

Abstract

The integration of computational thinking into the sales workflow of Samudera Motor provides significant opportunities for optimizing business processes. Through decomposition, key issues were identified clearly; pattern recognition highlighted recurring operational inefficiencies; abstraction helped isolate essential system components; and algorithm design produced structured solutions that can be implemented digitally. These findings demonstrate that computational thinking offers both a theoretical and practical framework for transforming traditional manual workflows into efficient, data-driven, and customer-oriented business processes. Implementing the proposed solutions can enhance operational accuracy, streamline decision-making, and significantly improve customer service performance at Samudera Motor. Furthermore, the application of computational thinking in the vehicle sales process provides an innovative foundation for enhancing efficiency and responsiveness to customer needs. By applying decomposition, pattern recognition, abstraction, and algorithm design, the study successfully identified major challenges in the manual sales workflow, such as difficulty in tracking sales history, lack of system integration, and a high risk of data errors. Proposed recommendations including data entry automation, the use of barcode or RFID technology, data analytics for identifying purchasing trends, and the development of an integrated centralized system are expected to improve operational efficiency, enrich customer experience, and support more adaptive sales strategies. Computational thinking serves not only as an analytical tool but also as a foundation for comprehensive business transformation, strengthening the competitiveness and long-term sustainability of Samudera Motor's showroom operations amid an increasingly dynamic market landscape.

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