International Journal of Engineering and Emerging Technology
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.
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<![CDATA[Analysis Of Data Warehouse Design Using Powell Method ]]>
<![CDATA[Hisyam Rahmawan Suharno]]>;
<![CDATA[Nyoman Gunantara]]>;
<![CDATA[Made Sudarma]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p03
<![CDATA[With the evolution in this digital era, many industrial organizations and companies have begun to move towards digitization to increase the company's business opportunities. Data is something that is very useful in a company's business. If the dataprocessed correctly can provide a variety of information needed by the company to continue to grow. Now Data also becomes digital and data processing have many techniques and can provide us with a decision support for the information generated by the data. The data processing is usually called Data Warehouse. In running a business, business owners must certainly analyze a number of things so that the business continues to run and grow, including one of which is a fabric business in Bali, namely CV Phalani Bali. CV Phalani Bali still needs a centralized system that integrates sales data from online stores and offline stores, therefore a data warehouse is needed that can help manage all these data and make it a new information needed by CV Phalani Bali. With the data warehouse, it can help the owner of CV Phalani Bali in reporting and historical information of the business they run. Helps manage historical data and provides strategic information to support evaluation and take decision analysis at the executive level. So one of the data warehouse design methods is used, is the Powell method. This Powell method focuses on the ETL (Extract, Transform, Load) process to become a data warehouse that is ready to be processed by OLAP (Online Analytical Processing). This Powell method will be assisted by Microsoft SQL Server Business Intelligence as a tool that will design and process the sales data into a data warehouse that will produce the information needed by CV Phalani Bali for analyze and make decisions to bring cv phalani bali even more advanced.]]>
<![CDATA[A Deep Learning Approach For COVID 19 Detection Via X-Ray Image With Image Correction Method ]]>
<![CDATA[I Gede Totok Suryawan]]>;
<![CDATA[I Putu Agus Eka Darma Udayana]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p018
<![CDATA[In the mitigation effort for reducing the spread of the SARS-CoV-2 pandemic in Indonesia, finding, detecting, and containing the suspect be a very crucial step to contain the virus. One of the ways that this can be detected is by thorax x-ray examination by the expert. Transferring the doctor's knowledge to a computer makes the task more scalable and precise. This can be done by building a small artificial intelligence using a simple CNN model to detect COVID biomarkers' presence in x-ray images. As the AI relies heavily on the x-ray dataset as the system's underlying basis has a good quality dataset is very important. However, the x-ray data tend to have a noise problem that will affect their overall system quality. We did a little comparative study with the objective to improve the quality of the dataset with three techniques of image enhancement, namely color denoising, mean denoising, and contrast enhancement, with the mean denoising outperform the other image manipulation method by 4%, which yield the accuracy of the system to 95% with 100 pieces of real-world test data. Hopefully, this study would inspire future studies improving the tech-based pandemic mitigation technology In the future.]]>
<![CDATA[Systematic Review of Text Mining Application Using Apache UIMA ]]>
<![CDATA[Purwania Ida Bagus Gede]]>;
<![CDATA[I Nyoman Satya Kumara]]>;
<![CDATA[Made Sudarma]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p08
<![CDATA[Companies are often faced with a number of data and information in the form of unstructured texts. The unstructured data set can be processed / extracted so that it can benefit the company in the decision making process or strategy that must be carried out by the company. Text Mining is one solution to overcome these problems. Text Mining can be defined as the process of retrieving information sourced from several documents. One of the most commonly used text mining tools is Apache UIMA. This study aims to systematically study literature on the implementation of text mining and Apache UIMA by using several related databases, including reviewing text mining, Apache UIMA, and reviewing journal of text mining and Apache UIMA. These journals are reduced using certain criteria. The results obtained are the 20 journals that discuss the implementation of text mining and Apache UIMA. Based on the analysis of these journals, it can be concluded that the application of Text Mining is more widely used in the field of Classification with the method often used is Naive Bayes Classifiers. The average accuracy of the method reaches more than 85%, which means the method is very effective for classification. Specifically, Apache UIMA is more widely implemented in the Information Extraction and NLP fields. The main component of Apache UIMA that is often used is the Annotator Engine and is very effectively implemented for information extraction.]]>
<![CDATA[Solar PV Plant as a Replacement for Power Supply of Irrigation Water Pump ]]>
<![CDATA[I Made Agus Sastradiangga]]>;
<![CDATA[Ida Ayu Dwi Giriantari]]>;
<![CDATA[I Wayan Sukerayasa]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p23
<![CDATA[The southern side of Subak Babakan Yeh Kuning has an agricultural irrigation water shortage due to the dry season’s drought. In 2016, the Ministry of Public Works and Public Housing resolved that by giving diesel-powered irrigation water pump system and groundwater irrigation network, however the high operations cost resulted an abandonment of it. This study conducted a solar power plant design for submersible pumps at the southern side of Subak Babakan Yeh Kuning by calculating the amount of irrigation water needed, designing the solar power plant and the investment costs, and comparing the operations cost between the new and old system. Based on the design, the capacity of the solar power plant was set at 12,54 kWp using 33 solar module units and a 18,5 kW inverter unit. The pump could operate for 5 hours and 30 minutes on a sunny weather with a water discharge of 253 m3 or fulfill 17,5% of water needs for an area of 16-hectare. The investment costs of the solar power plant pump system were IDR 171.193.500. The operations cost of the new system (solar-powered) per hectare was IDR 6.495/day. Meanwhile, the operations cost of the old system (diesel-powered) per hectare was IDR 15.950/day.]]>
<![CDATA[Classification Of Loyality Customer Using K-Means Clustering, Studi Case : PT. Sucofindo (Persero) Denpasar Branch ]]>
<![CDATA[Charolina Devi Oktaviana Soleman]]>;
<![CDATA[Nyoman Pramaita]]>;
<![CDATA[Made Sudarma]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p28
<![CDATA[The success of the company in developing its services can be seen from the number of customers who use these services, customer loyalty in using services can be seen from customer loyalty. Customer loyalty is an important factor in the development of a business in the company, repeated use of services can be used as an indicator in determining the level of customer loyalty, by paying attention to customer loyalty, of course the company will be able to develop customer focus on an ongoing basis. Clarification of customers needs to be done to find out how demographics customers use services, can be seen from how many customers and the level of transactions made. K-Means clustering is one method that can be used for the classification process of customer data through transactions carried out by forming several clusters, this classification process is divided into 5 clusters with the results of which include 1) A few small number transactions with many customers, 2) Many transactions small number with many customers, 3) a few transactions with a medium number of customers begin to decrease, 4) a few large number transactions a few customers, 4) a moderate number of transactions with a number of customers.]]>
<![CDATA[Spatial Data Analysis using DBSCAN Method and KNN classification ]]>
<![CDATA[I Putu Sugi Almantara]]>;
<![CDATA[Ni Wayan Sri Aryani]]>;
<![CDATA[Ida Bagus Alit Swamardika]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p013
<![CDATA[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.]]>
<![CDATA[the Data Warehouse Design for the Bank X with Inmon Approach ]]>
<![CDATA[Lanang Bagus Amertha]]>;
<![CDATA[Rukmi Sari Hartati]]>;
<![CDATA[Made Sudarma]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p04
<![CDATA[Various activities related to financial transactions such as the process of deposit and loan funds require the role of the Bank. Along with a large number of customer needs in current technological developments, the banking industry is undergoing many changes. The products offered by the Bank are becoming more diverse. Customers needs for services offered by banks are becoming increasingly high for example internet banking needs. Banks require high data storage so that the bank's business processes can run efficiently. High data requirements by banks also require ease of exchanging data. One concept that can be applied is Data Warehouse. There are various approaches in developing Data Warehouse, one of which is Inmon. This study aims to build a database design from Bank X Data Warehouse with the Inmon approach. The results of the design of the Data Warehouse Bank X database design consists of three fact tables, namely loan fact, saving fact, and portfolio fact, and two data marts, namely loans and saving.]]>
<![CDATA[Detection of Covid Chest X-Ray using Wavelet and Support Vector Machines ]]>
<![CDATA[Ni Wayan Sumartini Saraswati]]>;
<![CDATA[Ni Wayan Wardani]]>;
<![CDATA[I Gusti Ayu Agung Diatri Indradewi]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p019
<![CDATA[The study of digital image processing is still a hot topic in the realm of research, especially in medical research. The presence of various digital image processing methods and machine learning also contributes to the progress of research in this field. Detecting Covid Chest X-Ray is a prediction problem solving with a supervised classification method. In this study, the SVM method was chosen because it is proven to function as a good classifier as has been done in previous studies. Where previously the chest x-ray image feature extraction was carried out using wavelet transform. Feature extraction using wavelets has given the distinctive features of normal lung X-rays and distinguishes them from the distinctive features of Covid lung X-rays. The measurement results of the average classification model for the approximate, vertical, horizontal and diagonal dataset are 93.91% accuracy, 6.09% error rate, 98.75% recall, 89.06% specificity, and 91.26% precision. The vertical dataset is the best dataset to get a classification model because it has the best value in the accuracy and recall variables, but still provides good performance in measuring precision.]]>
<![CDATA[Analysis of Shopping Cart in Retail Companies Using Apriori Algorithm Method and Model Profset ]]>
<![CDATA[putri agung permatasari]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p09
<![CDATA[This research needed a priori algorithm method to produce association rules, with a "if-then" pattern. Apriori algorithm uses an iterative approach known as level-wise search, where k-groups of products are used to explore (k + 1) product groups or (k + 1) -itemset (Han and Kamber, 2001). A priori algorithm method is expected to provide vacancy information on one of the stock items that are often purchased simultaneously by the customer. Product optimization is carried out using the profset model, which evaluates the profit margins generated per product and is developed to maximize cross-selling opportunities. Based on this background, research will be conducted under the title Shopping Cart Analysis in Retail Companies Using the Apriori Algorithm Method and Model Profset]]>
<![CDATA[The purpose of this research is Analysis And Design Of Data Warehouse At Warung Asri ]]>
<![CDATA[I Putu Agus Priska Suryana]]>
<![CDATA[ International Journal of Engineering and Emerging Technology Vol 5 No 2 (2020): July - December ]]>
Publisher : <![CDATA[Doctorate Program of Engineering Science, Faculty of Engineering, Udayana University ]]>
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DOI: 10.24843/IJEET.2020.v05.i02.p24
<![CDATA[The purpose of this research is to help provide information in supporting the decision making process in the field of sales, purchasing and food inventory control at Warung Asri. With the support of a data warehouse, management can be assisted in making decisions faster and more precisely. The research methods carried out include ongoing system analysis, library research, designing data warehouses using the star schema method. The results of this study are the availability of a data warehouse that can produce fast and accurate information both about data sales and material stock, thus helping the owner in making decisions in determining the amount of stock and sales targets. The conclusion of this research is the application of this data warehouse can be a media to assist the management of Warung Asri Index Terms—Application, Information, Data warehouse, Star Schema]]>
