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All Journal International Journal of Engineering and Emerging Technology
Ni Wayan Sri Aryani
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Journal : International Journal of Engineering and Emerging Technology

 1 
Stock management using K-means method and Time Series method as Stock Order Komang Sri Utami; I Gede Wira Dharma; Ni Wayan Sri Aryani
International Journal of Engineering and Emerging Technology Vol 4 No 1 (2019): January - June
Publisher : Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University

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Abstract

Good stock management is one of the keys to success for sales businesses. A stable stock flow will affect the cost of purchasing goods and income. This condition can be achieved when the prediction of the required stock is right, so there is no accumulation of stock or empty stock. The case to be taken is for drug management of a pharmacy. This study uses the K-means method and time series method. The K-means method is a grouping method that is very easy to use and implement. Drug groupings will be made into 3 types, namely the best-selling, selling, and less-selling groups. While the regression time series method is used to predict the stock to be purchased that will be used in two weeks so that there is no stock buildup. Both of these methods are used to provide a grouping of drugs and the right amount of medicine to buy so that the management of drug stocks can be done well. The results of the tests carried out using 1000 test data, in which the K-means grouping test was C1 = 13, C2 = 29, C3 = 958 which was obtained from 11 iterations that had been done. In addition, each drug item has been predicted for the number of drugs to be purchased according to the sales performance of the last 3 months. From both of these results, it can be a reference in making order decisions to better manage stocks
Application of Data Mining in Optimization of Hotel's Food and Beverage Costs I Wayan Surya Pramana; Putu Risanti Iswardani; Ni Wayan Sri Aryani
International Journal of Engineering and Emerging Technology Vol 4 No 1 (2019): January - June
Publisher : Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University

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Abstract

Optimized costs could increase hotel revenue. However, based on observations, there are various methods that can be used in cost optimization, this indicating the possibility that there are other methods that can be used for this purpose. This study aims to propose application of data mining using the K-Nearest Neighbor (KNN) method to optimize costs by classifying feasibility of addition of raw materials for food and beverages based on data such as number of requests, supplies, usage, and purchases. Data used in this study is raw materials data for hotel food and beverage during January and February 2019 which amount to 152 data. Furthermore, data cleaning process applied to eliminate incomplete and duplicated data. This process produces 99 data that has been clean. Based on results of application and testing of the KNN method using confusion matrix, it is known that the value of k = 3 gives the best classification accuracy results of 80%. Then the classification results are represented in the form of graphs that are used as a basis for consideration of cost control. Based on this study, it was concluded that data mining using KNN method can be used in optimization of Hotel's Food and Beverage Costs
Spatial Data Analysis using DBSCAN Method and KNN classification I Putu Sugi Almantara; Ni Wayan Sri Aryani; Ida Bagus Alit Swamardika
International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December
Publisher : Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/IJEET.2020.v05.i02.p013

Abstract

Spatial Data Clustering is one of the most important technical techniques used to obtain information about knowledge about number boundaries in databases from various applications. This technique can determine groups of forms that cannot be arranged and can be used effectively with a budget. Exploring interesting and useful spatial boundary patterns is more difficult to extract traditional and categorical numerical polymers because of the difficulty of species, the relationship between autocorrelation of spatial boundaries. One of the pioneering techniques in the development of facial and technical grouping technologies is DBSCAN. This technique can determine groups of shapes that cannot be arranged and can be arranged in an effective way. the groups that have already received the next classification process are carried out in order to obtain information on the classes already formed. The K-Nearest Neighbour classification technique is based on learning by analogy. When there is new data, K-Nearest Neighbor will look for a class of data from the learning sample that is closest to the new data. This closeness can be defined using the Euclidean Distance calculation method.
Co-Authors I Gede Wira Dharma I Putu Sugi Almantara I Wayan Surya Pramana Ida Bagus A. Swamardika Komang Sri Utami Putu Risanti Iswardani