Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Al-Khowarizmi, Al-Khowarizmi; Syah, Rahmad; Nasution, Mahyuddin K. M.; Elveny, Marischa

doi:10.11591/ijece.v11i3.pp2696-2703

Cited by 16 publications

(12 citation statements)

References 26 publications

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“…Meanwhile reference [16] compares the Support Vector Machine (SVM) and the Multi-Layer Perceptron (MLP) Neural Networks for emotion classification, using prosodic and voice quality features extracted from the Berlin Emotional Database, is reported. Classification is very helpful in the process of learning on computers in computer knowledge working automatically [17] [18].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Syah

Al-Khowarizmi

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Digital image processing, in general to makes images that appear converted to a function of light intensity represented in a two-dimensional plane. The function is a value that will be processed for classification so that the computer is able to recognize the image. Besides, classification requires training and testing to produce a small error value and optimal algorithm. The problem of optimization is closely related to the principles and findings of science. Getting the smallest error value by calculating using Mean Absolute Percent Error (MAPE) for that MAPE calculation is done by using the Detection Rate formula to generalize knowledge in order to find the optimal model. Thus, the application of Artificial Neural Network (ANN) is very suitable for optimizing classification using the Simple Evolving Connectionist System Method (SECoS), and as a result, the classification of images containing protein with test data is that the eggs work with optimal proof of achieving MAPE without modification of 0.1947% and MAPE which has been modified with the formula detection rate of 0.05554633%.

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Section: Introductionmentioning

confidence: 99%

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Syah

Al-Khowarizmi

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…However, the forecasting process gets an accuracy of 0.035% which is calculated using the MAPE formula. In addition, to get an accurate business process on CPO, Al-Khowarizmi [19] developed again by testing the accuracy of forecasting CPO prices on a different method, namely KNN by obtaining MAPE sensitivity which involves detection rates and obtaining more optimal results of 0.000361%.The two studies seem to have the same goal so that the application of business intelligence (BI) can be applied in the agricultural sector. In addition, BI can be relied on in business because BI focuses on systems and technology that can collect data from several sources that can be processed into a form of information for business needs [20].…”

Section: Introductionmentioning

confidence: 99%

Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices

Al-Bara

Al-Khowarizmi

Pradesyah

2021

International Journal of Science, Technology & Management

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Forecasting is one of the techniques in data mining by utilizing the data available in the data warehouse. With the development of science, forecasting techniques have also entered the computational field where the forecasting technique uses the artificial neural network (ANN) method. Where is the method for simple forecasting using the Time Series method. However, the ability to create data visualizations certainly hinders researchers from maximizing research results. Of course, with the development of the Power BI software, the data science process is more neatly presented in the form of visualization, where the data science process involves various fields so that in this paper the results of forecasting the price of crude palm oil (CPO) are presented for the development of the CPO business with the hope of implementing the Business Process. intelligence (BI) by involving ANN, namely the time series for forecasting. From the final results, accuracy in forecasting with time series involves 2 accuracy techniques, the first using MAPE and getting a result of 0.03214% and the second using MSE to get 962.91 results.

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“…Forecasting is also almost the same as predictions based on time series [16]. Forecasting is included in a technique that processes data into information that then becomes knowledge [17]- [19]. This forecasting uses supervised learning [20].…”

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confidence: 99%

FinTech forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior

Al-Khowarizmi,

Watts,

Efendi

et al. 2024

IJ-AI

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<span lang="EN-US">Financial Technology (FinTech) which is included in the development of digitalization in the financial sector in the industrial era 4.0. Fintech can make any transactions anywhere with the pillars of Peet-to-Peer (P2P) lending, merchants and crowdfunding. In the P2P Lending pillar, there are borrowers and lenders who are digitized in Fintech devices. Fintech in Indonesia is controlled by a state agency called the Otoritas Jasa Keuangan or Financial Services Authority (OJK). In the movement of P2P Lending, there are borrowers and lenders who can be said to be investors where these activities are reported to the OJK. This data can be forecasted using a neural network approach such as ECoS, which is a method capable of forecasting with learning that develops in the hidden layer. In this research article, we present results on forecasting borrowers with a Mean Absolute Percentage Error (MAPE) of 0.148% and forecasting lenders with an accuracy measurement with MAPE of 0.209% with a learning rate 1 = 0.6 and a learning rate 2 = 0.3. So, this forecasting model can be said as an optimization in FinTech activities on the behavior of borrowers and lenders.</span>

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Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor

Cited by 16 publications

References 26 publications

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein

Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices

FinTech forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior

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