Measuring the Accuracy of Simple Evolving Connectionist System with Varying Distance Formulas

Suherman

2021

IJ-AI

<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>

Section: Secos Development On Normalized Euclidean Distancementioning

confidence: 99%

“…SECos is able to perform data classification in various fields [19]. The work in [20] used SECoS to classify stroke data texts to help patient treatment by determining how much fibrinogen should be consumed. The classification process is very helpful in assisting doctors and patients.…”

Section: Introductionmentioning

confidence: 99%

Classification of skin cancer images by applying simple evolving connectionist system

Suherman

2021

IJ-AI

“…In obtaining a small error value, of course, a technique of measuring accuracy must be carried out as is done by Lubis [24] to analyse bank customer data and measure the accuracy value looking for the smallest error with MAPE and MSE so that in his research the smallest error value was achieved using MAPE. In addition, reference [25] also applies the smallest error value accuracy in the Simple Evolving Connectionist System method using MAPE but is influenced by the normalized distance formula.…”

Section: Introductionmentioning

confidence: 99%

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

Syah

Nasution

et al. 2021

IJECE

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.

“…The process of success or failure at SECoS is measured using MAPE [23]. MAPE is the average of the overall percentage of error (difference) between the actual data with forecasting data that is measured and matched with time-series data and shown in percentage [24]. Meanwhile, to get the difference value based on the original value and forecast value can be calculated with the Detection rate formula [25].…”

Section: Introductionmentioning

confidence: 99%

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

Syah

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

2021

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%.