Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors

Wibawa, Aji Prasetya; Izdihar, Zahra Nabila; Utama, Agung Bella Putra; Hernandez, Leonel; Haviluddin, .

doi:10.1109/icaiic51459.2021.9415197

Cited by 13 publications

(11 citation statements)

References 33 publications

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“…MAPE measures the extent to which forecasting or prediction distinguishes between predicted and actual energy values in percentage terms as in (6). 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].…”

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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“…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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“…The dataset is a multivariate type that contains four attributes: sessions, page views, visitors, and new visitors. Sessions is the number of visitors from one IP who access a journal portal for the first time in a certain period [29]. Pageviews are the total number of web pages visited.…”

Section: Dataset Collectionmentioning

confidence: 99%

PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis

Utama

Wibawa

Muladi

et al. 2022

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Convolutional Neural Network (CNN) is an effective Deep Learning (DL) algorithm that solves various image identification problems. The use of CNN for time-series data analysis is emerging. CNN learns filters, representations of repeated patterns in the series, and uses them to forecast future values. The network performance may depend on hyperparameter settings. This study optimizes the CNN architecture based on hyperparameter tuning using Particle Swarm Optimization (PSO), PSO-CNN. The proposed method was evaluated using multivariate time-series data of electronic journal visitor datasets. The CNN equation in image and time-series problems is the input given to the model for processing numbers. The proposed method generated the lowest RMSE (1.386) with 178 neurons in the fully connected and 2 hidden layers. The experimental results show that the PSO-CNN generates an architecture with better performance than ordinary CNN.

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Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors

Cited by 13 publications

References 33 publications

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Forecasting learning in electrical engineering and informatics: An ontological approach

PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis

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