Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Wibawa, Aji Prasetya

doi:10.47738/jads.v4i3.97

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…A lower MAPE indicates a more accurate model [43]. RMSE calculates the square root of the average squared difference between the predicted and actual energy usage values [44]. RMSE is used to determine how sensitive the existing DL model can detect outliers in the energy forecasting value compared to the original value, as in (7).…”

Section: F Data Analysismentioning

confidence: 99%

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Wibawa,

Utama,

Akbari

et al. 2023

Knowl. Eng. Data Sci.

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Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.

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Section: F Data Analysismentioning

confidence: 99%

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Wibawa,

Utama,

Akbari

et al. 2023

Knowl. Eng. Data Sci.

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“…Event Management, fundamentally, relies on early detection theory, focusing on detecting and responding to events or potential issues before they impact services [13]. Finally, Continual Service Improvement, as an approach involving continuous improvement, adheres to the theory of continuous improvement, where evaluation and improvement are consistently performed to optimize information technology services.…”

Section: Itil Version 3 Domainsmentioning

confidence: 99%

Analysis of the Implementation of ITIL V3 Domain Service Operation in Enhancing the Quality of Information Technology Services

Fauzan

2023

Int. J. Appl. Inf. Manag.

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The rapid growth in the hotel industry in Purwokerto has given rise to new challenges related to the management and provision of information technology services. In the midst of increasingly fierce competition, the success of a hotel depends not only on the quality of service but also on the effectiveness of the information technology systems that support its operations. To overcome this complexity, Hotel ABC Purwokerto has adopted the ITIL V3 Domain Service Operation approach in managing its information technology services. This article aims to provide comprehensive insight into how hotels can optimize their operations through effective information technology service management. The use of the ITIL V3 Domain Service Operation framework at Hotel ABC is not only a solution to respond to rapid changes in information technology but also an effort to meet the high expectations of guests who are increasingly smart and technology-savvy. Through careful analysis, this article will reveal how implementing ITIL V3 at the operational level can have a positive impact on the efficiency, reliability and innovation of information technology services provided by Hotel ABC. By understanding the context and problems faced by Hotel ABC Purwokerto, we can explore in more depth how ITIL V3-based information technology service management strategies can be the key to success in improving service quality and maintaining competitiveness in this dynamic hotel industry.

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“…Following Fig. 1, forecasting methods can be grouped into several categories: traditional statistical methods [24], [25], Machine Learning (ML) methods [26], [27], and Deep Learning (DL) [28], [29]. A more complete explanation of these categories is in the next sub-chapter according to each category.…”

Section: Fig 1 Basic Components Of Forecastingmentioning

confidence: 99%

Forecasting learning in electrical engineering and informatics: An ontological approach

Utama,

Patmanthara,

Wibawa

et al. 2023

IJELE

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This research explores the vital role of ontology in learning forecasting in electrical engineering and informatics. As formally defined models of knowledge, ontologies are critical in organizing concepts for predictive learning. More than just an inquiry, our research reveals complex interconnections centered on Internet of Things (IoT) design, the semantic web, and knowledge modeling. Applications demonstrate the practical significance of ontologies in fostering intelligent connections, advancing information production, and improving interactions between computers, devices, and humans. This research introduces a comprehensive forecasting learning ontology to highlight the importance of ontologies in education, scientific inquiry, and developing systems for predictive analysis. Ontologies provide a structured framework for understanding the essence of predictive learning, encompassing key elements such as ideas, terminology, methodology, algorithms, data preprocessing, assessment, validation, data sources, application environments, interactions with technology, and learning processes. Emphasizing ontologies as indispensable instruments that drive technological development, our work underscores structured representation, semantic interoperability, and knowledge integration. In summary, this research improves the understanding of ontologies in forecasting by explaining practical applications and revealing new perspectives. Its unique contribution lies in its specific applications and natural consequences, laying the foundation for the future progress of ontology and learning forecasting, especially in educational contexts.

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Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Cited by 10 publications

References 44 publications

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Analysis of the Implementation of ITIL V3 Domain Service Operation in Enhancing the Quality of Information Technology Services

Forecasting learning in electrical engineering and informatics: An ontological approach

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