Big data forecasting using evolving multi-layer perceptron

Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.

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“…Then in this paper also measured by mean absolute percent error (MAPE). The (3) of MAPE is as [25,33]:…”

Section: Sensitivity Accuracymentioning

confidence: 99%

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

Al-Khowarizmi

Syah

Nasution

et al. 2021

IJECE

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“…In this paper we will test the ant colony algorithm with several normalized distance formula formulas. The normalized Hamming distance formula is shown (3) [29]:…”

Section: Sensitivity Of Shortest Distance With Normalized Distance Formulasmentioning

confidence: 99%

Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas

et al. 2021

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The ant colony algorithm is an algorithm adopted from the behavior of ants which naturally ants are able to find the shortest route on the way from the nest to places of food sources based on footprints on the track that has been passed. The ant colony algorithm helps a lot in solving several problems such as scheduling, traveling salesman problems (TSP) and vehicle routing problems (VRP). In addition, ant colony has been developed and has several variants. However, in its function to find the shortest distance is optimized by utilizing several normalized distance formulas with the data used in finding distances between merchants in the mercant ecosystem. Where in the test normalized distance is superior Hamming distance in finding the shortest distance of 0.2875, then followed by the same value, namely the normalized formula Manhattan distance and normalized Euclidean distance with a value of 0.4675 and without using the normalized distance formula or the original ant colony algorithm gets a value 0.6635. Given the sensitivity in distance search using merchant ecosystem data, the method works well on the ant colony Algorithm using normalized Hamming distance.

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“…Therefore the Euclidean distance can be directly calculated. Since distance in the range of [0, 1], input data should be normalized [29]. In general, feedforward networks have an activation function.…”

Section: Secos Development On Normalized Euclidean Distancementioning

confidence: 99%

Classification of skin cancer images by applying simple evolving connectionist system

Al-Khowarizmi

Suherman

2021

IJ-AI

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<span id="docs-internal-guid-eea5616b-7fff-5d26-eeb4-1d8c084ec93d"><span>Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.</span></span>

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Big data forecasting using evolving multi-layer perceptron

Cited by 12 publications

References 12 publications

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

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

Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas

Classification of skin cancer images by applying simple evolving connectionist system

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