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All Journal INOVTEK Polbeng - Seri Informatika
Luntungan Stephen Pieters
Universitas Pradita
Computer Science & IT

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Analysis of the Impact of the Implementation of IT Service Management on the Operational Efficiency of PT Sinergis Utama Informasi Transformasi Luntungan Stephen Pieters; Erick Dazki
INOVTEK Polbeng - Seri Informatika Vol. 9 No. 2 (2024): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tsnq8518

Abstract

This research aims to assess the impact of the implementation of IT Service Management (ITSM) on operational efficiency at PT Sinergis Utama Informasi Transformasi. The company is facing challenges in optimizing IT services, which is impacting delays, increasing operational costs, and decreasing service quality. We chose ITSM as the approach to address this issue, focusing on more structured service management. The research utilized simple linear regression analysis with a sample of 40 employees directly involved in the implementation of ITSM. The results indicate that ITSM has a positive and significant impact on operational efficiency, with a regression coefficient of 0.693 and a significance level of 0.000. The F-test showed an F value of 37.282, confirming a significant relationship between the implementation of ITSM and operational efficiency. This finding confirms that the implementation of ITSM enhances IT service performance, optimizes resources, and boosts company productivity. We recommend expanding the application of ITSM to other operational areas and consistently monitoring ITSM metrics to ensure continuous improvement in services and operational performance.
Development of Automatic Waste Classification System using CNN-Based Deep Learning to Support Smart Waste Management Luntungan Stephen Pieters
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wst8mh87

Abstract

This research develops an automatic waste classification system using deep learning based on Convolutional Neural Network (CNN) to support the implementation of Smart Waste Management (SWM). The main objective of this research is to design and test a CNN model that is able to classify various types of waste, such as plastic, paper, organic, and other non-organic waste, with high accuracy and efficiency. The developed CNN model successfully achieved an accuracy rate of 94.86% on the training dataset. The system performed very well in classifying recyclable waste with a precision of 56.6% and recall of 63.5%, although it still faces challenges in the classification of organic waste with a precision of 45.7% and recall of 38.8%. This research also includes model validation using cross-validation techniques to ensure the generalizability of the model on different datasets. In addition, tests were conducted on external datasets to evaluate the robustness of the model under real-world conditions. Data preprocessing techniques such as image normalisation and data augmentation were used to improve the performance of the model. The results show that a CNN-based automated waste classification system has great potential to be implemented in SWM systems, enabling more efficient and automated waste management. However, there are still some challenges such as high variation in litter images and dataset limitations that need to be addressed for future development of a more robust system
Co-Authors Dazki, Erick