Analysis Accuracy Of Forecasting Measurement Technique On Random K-Nearest Neighbor (RKNN) Using MAPE And MSE

Prayudani, Santi; Hizriadi, Ainul; Lase, Yuyun Yusnida; Fatmi, Yulia; Al-Khowarizmi, Al-Khowarizmi

doi:10.1088/1742-6596/1361/1/012089

Cited by 46 publications

(24 citation statements)

References 2 publications

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“…MAPE indicates how much error in prediction is compared to the measured value in the series. Additionally, MAPE is used for comparison of the precision of the same or different methods in two different series and measure the accuracy of the estimated value of the model expressed in terms of the absolute percentage error average [41]. In this case, the smaller the values of MAPE means the higher the efficiency of the model.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, sea surface temperature has been considered an essential indicator for coastal upwelling events influencing fish production reported for the region [40]. Prior studies showed that relative humidity is a significant climatic factor in fisheries studies due to its indirect impact on some environmental stressors [41][42][43]. Therefore, we have collected data of maximum and minimum air temperature, sea surface temperature, rainfall, rainfall duration, and humidity for building models using the different ML approaches.…”

Section: Variable Selectionmentioning

confidence: 99%

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Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production

et al. 2021

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The fishing industry is identified as a strategic sector to raise domestic protein production and supply in Malaysia. Global changes in climatic variables have impacted and continue to impact marine fish and aquaculture production, where machine learning (ML) methods are yet to be extensively used to study aquatic systems in Malaysia. ML-based algorithms could be paired with feature importance, i.e., (features that have the most predictive power) to achieve better prediction accuracy and can provide new insights on fish production. This research aims to develop an ML-based prediction of marine fish and aquaculture production. Based on the feature importance scores, we select the group of climatic variables for three different ML models: linear, gradient boosting, and random forest regression. The past 20 years (2000–2019) of climatic variables and fish production data were used to train and test the ML models. Finally, an ensemble approach named voting regression combines those three ML models. Performance matrices are generated and the results showed that the ensembled ML model obtains R2 values of 0.75, 0.81, and 0.55 for marine water, freshwater, and brackish water, respectively, which outperforms the single ML model in predicting all three types of fish production (in tons) in Malaysia.

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Section: Discussionmentioning

confidence: 99%

Section: Variable Selectionmentioning

confidence: 99%

Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production

et al. 2021

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“…e commonly used evaluation metric in machine learning regression problems is the RMSE, which is more intuitive as a model evaluation metric in terms of order of magnitude. In this paper, there are no zeros in the original data but to prevent the existence of zero in the interpolated values, MAPE is also chosen as the evaluation metric [32].…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery

Zou

Jin

2022

Journal of Advanced Transportation

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With advent of the postepidemic era, the development of digital logistics operations management is imminent. Among the various logistics delivery methods, same-city delivery is chosen by the vast majority of customers for its timeliness and safety. Online ordering and delivery methods for same-city delivery are also gaining increasing attention from enterprises which need to know the inventory balance of all same-city warehouses in time for early deployment and response. However, in practice, the inventory balance of each warehouse can be affected by other warehouses in the same city, and there is often a lack of data in the inventory management system due to equipment and other issues resulting in a poor response from the company to handle emergencies. To address these issues, an improved matrix decomposition model was designed to interpolate the missing data by taking into account the spatiotemporal correlation between warehouses. The L-curve criterion was used to select hyperparameter values, the spatiotemporal regularize was used to capture the time dependence of the time series, and the model performance was evaluated using root mean square error and mean absolute percentage error. Comparisons with classical interpolation techniques were made to validate the improved performance of the proposed method.

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“…However, Prayudani [26] compared the smallest error value with MAPE, MAE and MSE where the MAPE and MAE results have the same value except that the difference is in units using percent (%). In addition, by getting the value of accuracy and the smallest error value, it will increase productivity in business intelligence [20].…”

Section: Introductionmentioning

confidence: 98%

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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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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Analysis Accuracy Of Forecasting Measurement Technique On Random K-Nearest Neighbor (RKNN) Using MAPE And MSE

Cited by 46 publications

References 2 publications

Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production

Developing an Ensembled Machine Learning Prediction Model for Marine Fish and Aquaculture Production

A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery

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

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