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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 19 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.
Sentiment Analysis of Customer Satisfaction with the Services of the Waikelo Port Class III Administrative Office Using the CAN Order Method Kamalia Ahmad; Friden Elefri Neno; Alexander Adis
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.14

Abstract

This study aims to analyze customer satisfaction levels with the services provided by the Waikelo Port Class III Administrative Office using the CAN (Cumulative Agreement Normalized) Order method and sentiment analysis. Data was collected from customer comments on service aspects such as speed of service, staff friendliness, facilities, and registration procedures. The comments were processed through text pre-processing, including case folding, tokenization, stopword removal, and stemming, then analyzed using the VADER Sentiment Analyzer to obtain positive, neutral, and negative scores. These sentiment values were used as input to calculate the CAN value, which determines the satisfaction ranking of each alternative objectively. The results show that service alternatives focusing on speed and staff friendliness have the highest CAN value of 0.83, while alternatives related to facilities and queues have the lowest CAN value of 0.33, indicating aspects that need improvement. Evaluation of the consistency of the CAN ranking with the respondent ranking using Spearman Rank Correlation yields a value of 0.92, indicating a high degree of conformity between the CAN method and customer preferences. This analysis proves that the integration of sentiment analysis and the CAN Order method can provide a quantitative picture of customer satisfaction levels and serve as a basis for practical recommendations to improve service quality.
Analysis of a Decision Support System for Selecting the Best Gaming Smartphone Using the Simple Additive Weighting (SAW) Method Kevin Gray Dasmasela; Wahit Desta Prastowo
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.19

Abstract

The rapid development of mobile hardware continues to reshape the performance landscape of gaming smartphones. Users increasingly require devices that balance high processing capability, thermal stability, display responsiveness, and price affordability. This study evaluates five gaming smartphones released in 2025 using the Simple Additive Weighting (SAW) method to determine the best performance-to-price option. The analysis considers five key criteria: benchmark performance, battery capacity, refresh rate, cooling system, and price. Each device is scored using normalized and weighted values to compute a final preference ranking. The findings show that the RedMagic 10 Pro achieves the highest score due to its strong performance metrics, advanced cooling system, and competitive pricing, making it the most balanced gaming smartphone in this comparison. The ROG Phone 9 Pro follows in second place, offering superior hardware but at a higher cost. Meanwhile, the RedMagic 10 Air emerges as a strong budget alternative, outperforming premium flagship phones in value-based analysis. This study demonstrates that multi-criteria decision-making provides clearer and more objective insights for consumers compared to single-parameter comparisons.
Analysis of The Most Effective Advertising Platform Selection Decision Support System Using The Analytical Hierarchy Process (AHP) Method Laela Rahmawati; Wahit Desta Prastowo
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.20

Abstract

This study aims to determine the most effective digital advertising platform among Google Ads, Meta Ads, and TikTok Ads by applying a Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) method. The research addresses the growing challenge faced by digital marketers in allocating advertising budgets effectively across multiple platforms with different strengths and audience characteristics. Analytical Hierarchy Process (AHP) was selected because of its ability to decompose complex multi-criteria decision-making problems into a structured hierarchical model. The analysis employed three main criteria: Cost, Target Audience, and Conversion. Pairwise comparisons and consistency testing were conducted based on expert judgments to ensure reliable evaluation results. The findings revealed that Conversion was the most influential criterion with the highest Eigen Vector (EV) value of 0.545, followed by Target Audience (0.273) and Cost (0.182). Through Global Synthesis analysis, Meta Ads achieved the highest total EV value of 0.3832, slightly outperforming Google Ads (0.3806), while TikTok Ads ranked third (0.2366). The results indicate that strong audience-targeting capabilities combined with competitive conversion performance make Meta Ads the most effective platform overall. This study provides strategic recommendations for advertisers and digital marketers to optimize advertising budget allocation based on business priorities and campaign objectives based on decision support system.
Application of Convolutional Neural Network Student Reviews for Lecture Facilities at Stella Maris University Sumba Emilia Kuba; Friden Elefri Neno; Karolus Wulla Rato
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.21

Abstract

The development of artificial intelligence technology, particularly in the field of deep learning, has opened up new opportunities in text-based data analysis, including student reviews of lecture facilities. This study aims to apply the Convolutional Neural Network (CNN) method in conducting sentiment analysis of student reviews of lecture facilities at Stella Maris University Sumba. Through this approach, it is hoped that the system can automatically and accurately classify student opinions into positive, negative, or neutral categories. The research data was obtained from surveys and student comments on various internal campus platforms. The data processing involved data preprocessing stages such as text cleaning, tokenization, stopword removal, and word embedding using the Word2Vec method. The CNN model was then built with an architecture involving an embedding layer, convolutional layer, max pooling, and fully connected layer to produce the final prediction. The test results show that the CNN model is capable of achieving a high level of accuracy in identifying sentiment polarity, with an average accuracy value of 90.2%. This performance proves that CNN is effective in extracting semantic features from unstructured student review texts. Analysis of the classification results also provides important insights into aspects of campus facilities that received positive and negative responses, such as classroom quality, internet network, and learning environment comfort. These findings can be used as a basis for universities in making strategic policies for the continuous improvement of lecture facilities. Thus, the application of CNN in student review analysis has been proven to support the evaluation and decision-making processes in data-driven academic environments.
Assessing Neural Machine Translation in Speech: Problems and Solutions in AI-Powered Translations Ahmad Zaki Munibi
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.27

Abstract

The swift progress of artificial intelligence (AI), particularly in Neural Machine Translation (NMT) systems, has substantially transformed the landscape of translation practices. While AI-powered applications such as Monica AI (Powered by ChatGPT) demonstrate a high degree of linguistic accuracy, there remains a notable research gap regarding the specific translation challenges encountered by professional translators when working with AI-generated outputs, especially in speech texts. The theoretical novelty of this study lies in integrating Nord’s text-typological translation problem framework with Molina and Albir’s translation technique model to evaluate NMT output, an approach rarely applied to spoken oratory texts. Empirically, the study provides a fine-grained error analysis of a ChatGPT-powered NMT system on formal political speech, quantifying problem types and mapping them to specific post-editing strategies. Utilizing a qualitative content analysis approach, this research examines a formal English-language speech text translated using Monica AI. A total of 282 source sentences, along with their AI-generated and post-edited versions, served as the corpus. Findings reveal that 91.1% of the translated sentences were accurate, while 8.9% contained identifiable issues, predominantly within pragmatic, conventional, and text-specific domains. This study emphasizes the indispensable role of human translators in ensuring cultural and contextual appropriateness in AI-assisted translation.
The Relationship Between Blended Learning Experience and Students' Problem-Solving Skills: A Quantitative Correlational Study Novialdi Ashari; Muhammad Akbar Syahbana Pane; Ulfah Oktarida Sihaloho
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.29

Abstract

   
An Analysis of the Impact of Zoom on Online Learning Using the Technology Acceptance Model Zatin Niqotaini; Budiman Budiman; Fahreja Ramadhan; Artika Arista; Esa Prakasa; Arafat Febriandirza; Nur Alamsyah; Rezza Novian Noor Rochmat; Henki Bayu Seta
Journal of Computing Innovations and Emerging Technologies Vol. 2 No. 1 (2026): Volume 2 No 1
Publisher : novamindpress

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64472/jciet.v2i1.30

Abstract

This study aims to analyze the effect of using the ZOOM application at the University of Informatics and Business Indonesia (UNIBI) using the Technology Acceptance Model (TAM) approach, which is often used by some researchers to examine user acceptance of technology. This research is quantitative using descriptive method. The data analysis technique was carried out using SEM (Structural Equation Model) with AMOS (Analysis of Moment Structure) software. The population in this study were UNIBI students. Determination of the sample is carried out by proportional sampling, which is a proportional sampling method based on sub-populations. The results of this study prove that only 4 hypotheses are accepted from a total of 6 hypotheses proposed. The following is the percentage of the influence of each variable: a) Perceived Ease of Use (PEOU) is 28%, b) Perceived Usefulness (PU) is 74%, c) Attitude Toward Using (ATU) is 57%, d) Behavioral Intention to Use (BITU) is 65%, and e) Actual system usage (AU) is 75%. This proves that the use of the ZOOM application as an online learning medium cannot be fully explained by the Technology Acceptance Model.

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