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International Journal of Engineering and Emerging Technology
Published by Universitas Udayana
ISSN : 25795988     EISSN : 2579597X     DOI : https://doi.org/10.24843/IJEET.2017.v02.i01
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering
International Journal of Engineering and Emerging Technology is the biannual official publication of the Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University. The journal is open to submission from scholars and experts in the wide areas of engineering, such as civil and construction, mechanical, architecture, electrical, electronic, and computer engineering, and information technology as well. The scope of these areas may encompass: (1) theory, methodology, practice, and applications; (2) analysis, design, development and evaluation; and (3) scientific and technical support to establishment of technical standards.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 17 Documents
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Search results for , issue "Vol 4 No 1 (2019): January - June" : 17 Documents clear
Data Center Data Warehouse Development at Z Bali Clinic Using the Kimball Nine-Step Method I Gusti Ngurah Wira Partha; Philipus Novenando Mamang Weking; Putu Arya Mertasana
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

Z Bali Clinic has implemented a clinical information system as an operational system. However, the system applied is only limited to carrying out registration and payment functions. Over time, the number of patients from the Z Bali Clinic is increasing and causes more and more complex patient visit data to be managed. The data is managed to be used in making reports. However, the process of making the report is still done through manual calculations, causing it to become an obstacle in the data processing and reporting process. In this study, the process of developing a Data center data warehouse at Z Bali Clinic Using the Kimball Nine-Step Method, where the data warehouse can be used as a center for processing and analyzing outpatient visit data, and to produce reports that can facilitate the executive in analyzing data on outpatient visits as consideration in making decisions at the Z Bali Clinic.
Implementation of Apriori Algorithm in Determining Tourism Visit Patterns to Bali Dewa Agung Krishna Arimbawa P; I Nyoman Angga Prabawa; Putu Arya Mertasana
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

Bali is a tourist destination terknal destinations internationally. Every month there are hundreds of thousands of tourists who visit Bali. With a high tourist visits, stakeholders must prepare all matters related to tourism in Bali. Because problems can occur when a visit in a particular month high but there is no readiness of the parties involved. The purpose of this study is to look for patterns of tourists visiting Bali using Apriori algorithm. Apriori algorithm is one method of Data Mining Based Rule-based Assossiation that can be used to identify patterns of events. Training data used is the visit of foreign tourists to Bali every month from 1982 to 2018. The calculation of Apriori algorithm also tested using an application that is Weka Data Mining. The results obtained are the rules of association which is a pattern of tourists visiting Bali. For example, "If tourists visiting Bali in September and November among the highest in the current year, the visit in November will not enter into the highest visit". By knowing the pattern of tourist arrivals in Bali, expected that the stakeholders can have a reference for decision making
Design of Data Warehouse for University Library using Kimball and Ross 9 Steps Methodology I Nyoman Darma Kotama; Anak Agung Gede Oka Kessawa Adnyana; Komang Oka Saputra
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

Udayana University has a library that purposed to serve all the student and lecturer needs in Post graduate program. To help achieve University goals to be a world class university, improvement of library management and assessment of its value and function is needed. Data Warehouse concept is a long proven solution that exist to help the upper management know the state of the institution through its own data. Effective design of Data Warehouse can help the management of institution to decide critical evaluation for its institution. On past research Kimball & Ross propose a technique to build an effective design of Data Warehouse named “9 Steps Methodology”. This paper is aiming to build an effective design of Udayana Postgraduate Library Data Warehouse using Kimbal & Ross Methodology. We hope by this blueprint we can contribute to the future of the library by providing additional choice of Data Warehouse Design. After we follow through all the 9 steps and using past 3 years data of the library, we successfully designed model of Data Warehouse that consist of 8 dimension tables and 9 facts tables
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
Design General Hospital Data Warehouse Base on Nine Step Methodology Ida Bagus Leo Mahadya Suta; I Gusti Ngurah Agung Surya Mahendra; Yanu Prapto Sudarmojo
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

Data warehouse is a method used to hold and analyze data in large number. The analysis can be viewed from several different dimensions or points of view. The application of data warehouse technology can overcome the problems and needs of executive at Genral Hospital in conducting analysis of large amounts of patient data, especially data inpatient and outpatient care. The data warehouse design method is done by applying 9 step (Nine-Step Methodology) used by Kimball. The 9 stages are the selection process, selection, grain, identification and adjustment, fact selection, pre-calculation storage in the fact table, ensure dimension tables, select database durations, track changes from dimensions slowly, determination of priority and query model. The results of this study are in the form of schema design and data simulation warehouse by displaying inpatient and outpatient data based on time dimensions, patients reference, diagnosis, patient status, room, type of patient, condition of return, and district in the form of a table and graph
University Library Data Analysis to Help Book Collection Procurements using C4.5 Algorithm (Case Study: Udayana University Postgraduate Library) Anak Agung Gede Oka Kessawa Adnyana; I Nyoman Darma Kotama; Yanu Prapto Sudarmojo
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

Library is one of many requirements for a university to be labeled as qualified university. Udayana University’s Library collections is one major concern. To keep a good collections that can fulfill the student and other academic purposes fulfilled. To create an efficient procurement, we need to keep tracks on every transactions that happening, to determine whether it is important to procure certain books from a certain study field by going through number of lending, number of novelty and book counts. Using C4.5 to learn about past procurement we are going to create an effective procurement
Data Warehouse Analysis to Support UMKM Decisions using the Nine-step Kimball Method I Gede Wira Darma; Komang Sri Utami; 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

Micro, small and medium enterprises are one of the drivers of the economy. In the current technological era, companies have supported information systems to process business transactions. Naris Mart is one of the MSMEs engaged in retail with a business that has been running for 6 years. During the span of 6 years, of course, many buying and selling transactions were carried out. To help analyze how the business work, will be designed a data warehouse. Data warehouses need to be made so that they can receive information, reports, and can carry out multi-dimensional analysis that can ultimately help business owners. The design of the data warehouse uses the Nine-step Kimball methodology. Results obtained in the form of star schema and retail data warehouse analysis. The data warehouse can provide fast, accurate and continuous information that can help management in making policies for the future to come. In general, the benefit of this research is that additional references in building a data warehouse use the Nine-step Kimball methodology
Application of Data Mining with Support Vector Machine (SVM) in Selling Prediction Trend of Spiritual Goods (Case Study: PT. X Bali) Philipus Novenando Mamang Weking; I Gusti Ngurah Wira Partha; Antonius Ibi Weking
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

Spiritual activities become an inseparable part of human life. To support these spiritual activities, the need for spiritual equipment that supports the process of running a spiritual activity. PT. X Bali is a company that runs the business of selling spiritual goods. The tight competition and economic factors faced make PT. X Bali wants to predict the sale of goods so that they can see whether the sale of spiritual goods is up or down in order to increase the efficiency. The prediction process can be done with data mining technique using the Support Vector Machine (SVM) method. Data that used for prediction is based on stock data and data on goods sold from the total sales results of the last two years. Based on the results of SVM calculations, the level of prediction accuracy results reached 62.5% and the need for spiritual goods in the following year will be predicted to decline
Designing a Virtual Data Warehouse in Supporting Sales Information Needs (Case Study: National Scale Building Material Store X) Andrew Sumichan; I Made Gede Yudiana; Muhammad Ridwan Satrio; I Made Sudarma
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

National-scale building material stores that have branches in various regions certainly want the business to run smoothly. This must be supported by good sales activities at the central store and also branch stores spread in various places. However, there is no solution to collect sales data from branch shops quickly, causing a slow response to the analysis of the increasing trend in sales of goods and goods whose sales trend has dropped. This can reduce sales competitiveness in this national scale business with similar competitors. Virtual data warehouses can help collect scattered operational data, in this case sales data located at branch stores in various locations are then collected into a data warehouse that is stored in the cloud server so that it can be processed for strategic decisions. Virtual data warehouse is a data warehouse that connects operational databases regardless of where the database is located and regardless of the format, looks as if somewhere and in a consistent format. This study produces a virtual data warehouse structure that applies the kimball nine step design method so that it produces a data warehouse schema model with the star scheme. This study explains the design of a virtual data warehouse that can facilitate analysis of sales data at national scale building material stores that have branches in various places
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

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