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Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor Al Khowarizmi; Rahmad Syah; Mahyuddin K. M. Nasution; Marischa Elveny
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2696-2703


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.
Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas Rahmad Syah; Mahyuddin KM Nasution; Erna Budhiarti Nababan; Syahril Efendi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 4: August 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i4.18872


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.
Managing Schema of Knowledge Acceleration Estimator (KAE) Model to Customer Behavior Using Business Metrics Rahmad Syah; M.K.M. Nasution; Marischa Elveny; Erna B Nababan; Sajadin Sembiring
International Journal of Supply Chain Management Vol 9, No 5 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

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Business metrics in Financial Technology 1500 users spread across North Sumatera Province. The impact of commercial digital business (commercial enterpreneurship and social entrepreneurship) is very large on users who are currently increasing in number. To produce Knowledge Acceleration (KAE) Model using Business Metrics on the impact of Commercial Entrepreneurship and Social Entrepreneurship in their utilization.Uncertainty arising from sustainable business operators by considering aspects of Business Metrics related. MARS an linear regression analysis method nonparametrics intended for statistics with the aim of facilitating research and modeling the relationships of each of the multi variables that arise