An Intelligent Modeling of Oil Consumption

Chiroma, Haruna; Kareem, Sameem Abdul; Muaz, Sanah Abdullahi; Abubakar, Adamu; Sutoyo, Edi; Mungad, Mungad; Saadi, Younes; Sari, Eka Novita; Herawan, Tutut

doi:10.1007/978-3-319-11218-3_50

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Opinions play an important role as product feedback, services, and other topics. Various text mining and sentiment analysis using classification approaches [19][20][21][22] such as Naïve Bayes classifier [23][24][25], logistic regression [26,27], support vector machine [28,29], and K-nearest neighbor [30] has been applied for finding the best result and accuracy. Therefore, in this study, sentiment analysis will be conducted to find out public opinion about the discourse of relocation of the capital city from Jakarta to Penajam Paser Utara and Kutai Kartanegara.…”

Section: Introductionmentioning

confidence: 99%

Twitter sentiment analysis of the relocation of Indonesia's capital city

Sutoyo

Almaarif

2020

Bulletin EEI

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Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.

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Section: Introductionmentioning

confidence: 99%

Twitter sentiment analysis of the relocation of Indonesia's capital city

Sutoyo

Almaarif

2020

Bulletin EEI

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“…The new emerging technique of data mining is educational data mining that can be applied to the data related to the field of education (Romero & Ventura 2007), including students' performance evaluation. Machine learning (Chiroma et al 2015;Kotsiantis et al 2004;Pal 2012;Sutoyo et al 2017;, association rule mining (Borkar & Rajeswari 2013), decision tree , attribute selection (Mohammed et al 2016;Saedudin et al 2017), and genetic algorithm (Minaei-Bidgoli et al 2003) have been proposed in the domain of evaluating students' performance. However, all those methods do not consider the missing values and can only be applied if all the preference values are completely available.…”

Section: Introductionmentioning

confidence: 99%

A Relative Tolerance Relation of Rough Set in Incomplete Information

Saedudin¹,

Kasim²,

Mahdin³

et al. 2019

JSM

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University is an educational institution that has objectives to increase student retention and also to make sure students graduate on time. Student learning performance can be predicted using data mining techniques e.g. the application of finding essential association rules on student learning base on demographic data by the university in order to achieve these objectives. However, the complete data i.e. the dataset without missing values to generate interesting rules for the detection system, is the key requirement for any mining technique. Furthermore, it is problematic to capture complete information from the nature of student data, due to high computational time to scan the datasets. To overcome these problems, this paper introduces a relative tolerance relation of rough set (RTRS). The novelty of RTRS is that, unlike previous rough set approaches that use tolerance relation, non-symmetric similarity relation, and limited tolerance relation, it is based on limited tolerance relation by taking account into consideration the relatively precision between two objects and therefore this is the first work that uses relatively precision. Moreover, this paper presents the mathematical properties of the RTRS approach and compares the performance and the existing approaches by using real-world student dataset for classifying university's student performance. The results show that the proposed approach outperformed the existing approaches in terms of computational time and accuracy.

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“…In order to avoid the limitations associated with the individual models; researchers suggested a hybrid model which combines linear and nonlinear methods to yield high prediction accuracy rates [32,39]. Several studies investigated hybrid modeling to optimize the parameters of the ANN [40]. Hence the improved performance of artificial bee colony (ABC-LM) over other alternatives has been demonstrated on both benchmark data and OC time series.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model

Khairalla

AL-Jallad

et al. 2018

Energies

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In the real-life, time-series data comprise a complicated pattern, hence it may be challenging to increase prediction accuracy rates by using machine learning and conventional statistical methods as single learners. This research outlines and investigates the Stacking Multi-Learning Ensemble (SMLE) model for time series prediction problem over various horizons with a focus on the forecasts accuracy, directions hit-rate, and the average growth rate of total oil demand. This investigation presents a flexible ensemble framework in light of blend heterogeneous models for demonstrating and forecasting nonlinear time series. The proposed SMLE model combines support vector regression (SVR), backpropagation neural network (BPNN), and linear regression (LR) learners, the ensemble architecture consists of four phases: generation, pruning, integration, and ensemble prediction task. We have conducted an empirical study to evaluate and compare the performance of SMLE using Global Oil Consumption (GOC). Thus, the assessment of the proposed model was conducted at single and multistep horizon prediction using unique benchmark techniques. The final results reveal that the proposed SMLE model outperforms all the other benchmark methods listed in this study at various levels such as error rate, similarity, and directional accuracy by 0.74%, 0.020%, and 91.24%, respectively. Therefore, this study demonstrates that the ensemble model is an extremely encouraging methodology for complex time series forecasting.

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An Intelligent Modeling of Oil Consumption

Cited by 6 publications

References 14 publications

Twitter sentiment analysis of the relocation of Indonesia's capital city

Twitter sentiment analysis of the relocation of Indonesia's capital city

A Relative Tolerance Relation of Rough Set in Incomplete Information

Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model

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