– k-Nearest Neighbor (k-NN) is one of the popular classification algorithms and is widely used to solve classification cases. This is because the k-NN algorithm has advantages such as being simple, easy to explain, and easy to implement. However, the k-NN algorithm has a lack of classification results that are strongly influenced by the scale of input data and Euclidean which treats attribute data evenly, not according to the relevance of each data attribute. This causes a decrease in the classification results. One way to improve the classification accuracy performance of the k-NN algorithm is the method of weighting its features when measuring the Euclidean distance. The exponential function of the optimized Euclidean distance measurement is applied to the k-NN algorithm as a distance measurement method. Improving the performance of the k-NN method with the Exponential function for weighting features on k-NN will be carried out by experimentation using the Data Mining method. Then the results of the performance of the objective method will be compared with the original k-NN method and the previous k-NN weighting research method. As a result of the closest distance decision, taking the closest distance to k-NN will be determined with a value of k=5. After the experiment, the goal algorithm was compared with the k-NN, Wk-NN, and DWk-NN algorithms. Overall the comparison results obtained an average value of k-NN 85.87%, Wk-NN 86.98%, DWk-NN 88.19% and the k-NN algorithm given the weighting of the Exponential function obtained a value of 90.17%.
Carbon has an amorphous structure and a crystalline structure. The amorphous structure of carbon is usually found in charcoal, while the crystalline structure of carbon can be obtained from heat treatment. In the present study, the synthesis of carbon nanocrystals based on corn cob charcoal was successfully carried out. The synthesis began with the carbonization process of corn cobs to produce charcoal. Corn cob charcoal powder was then put into 80 mL of HCl solution and stirred using a magnetic stirrer by a speed of 750 rpm at room temperature and 80 mL of NH4OH solution was titrated into it. After the synthesis, the carbon powder was calcined at 400°C and activated using PEG 2000 template. The samples were tested using XRD (X-ray Diffraction) and SEM-EDX (Scanning Electron Microscope-Energy Dispersive X-ray). The carbon component (C) from the EDX test after the synthesis and carbonization process had an atomic percentage of 56.89% and increased by 81.06 % after PEG 2000 activation. The results of the X-ray diffraction pattern show that in all samples a broad and weak diffraction pattern was the characteristic of amorphous carbon. However, on carbon heated for 5 hours at 400°C and the addition of PEG 2000 activator, the crystal structure pattern with higher diffraction peaks was obtained and the peaks of diffraction were matched with CIF data 9008569 from phase C Graphite which had a space group P of 63 mc. SEM data on the morphology of the material showed that after receiving PEG activator, the carbon particles were split into smaller ones so that it increased in surface area and showed fairly even distribution of pores which was also seen in the surface morphology of the carbon
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Erratum: Roihatur Rohmah, M. Zainuri. Jurnal Neutrino:Jurnal Fisika dan Aplikasinya. 2020 April: 12(2):49-56. (Doi: 10.18860/neu.v12i2.8208: The title "The Influence of Silica Calcination Temperature Variation to The Hydrophobicity and Transmittance on Glass Substrate" was deleted because it is the same as the author's thesis.Therefore, the title was replaced with "Study of Hydrophobic Surface by Silica Calcination Temperature Variation."
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