Articles
24 Documents
Search results for
, issue
"Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020"
:
24 Documents
clear
<![CDATA[Analysis of Multi-attribute Utility Theory for College Ranking Decision Making ]]>
<![CDATA[Perdana, Adidtya]]>;
<![CDATA[Budiman, Arief]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (524.022 KB)
|
DOI: 10.33395/sinkron.v4i2.10232
<![CDATA[Ranking of a tertiary institution, both state and private universities, can be the basis of the tertiary institutions of interest to prospective new students. The better the ranking of the college, the more popular the campus. In this study the author discusses the case of campus ranking in the city of Medan where the results to be received are the best campus decision making with the method used is the MAUT (Multi Attribute Utility Theory) method. The aim is to see what results can be given by using the MAUT method in determining the best campus in the city of Medan which results in ranking the campus in Medan. Does it provide optimal results or not. But every case that is solved using the methods in artificial intelligence, in this case the MAUT method is a method of the Decision Support System, certainly provides optimal results even though the results given are not complete or complete. Therefore, the writer has a vision going forward, conducting research in this field, especially for the case of campus ranking. In this study the variables used in determining campus ranking are Institutional, Student Activities, Lecturer HR, Research and Community Service, and Innovation. These five variables in the future can be added or subtracted as needed. The results obtained are optimal ranking results but are still limited to the reference model for internal institutions.]]>
<![CDATA[Analysis of Taklinear Performance and Integer Linear Programming Models in Nurses Scheduling Problems ]]>
<![CDATA[Nababan, Junerdi]]>;
<![CDATA[Tulus, Tulus]]>;
<![CDATA[Situmorang, Zakarias]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (382.278 KB)
|
DOI: 10.33395/sinkron.v4i2.10528
<![CDATA[Almost all hospitals schedule nurses' work shifts manually, which is not effective, mistakes in scheduling nurses in hospitals can make them not work optimally so that they are prone to making mistakes and will endanger every patient in the hospital. In the research will be proposed integer linear programming model and branch and bound method, the purpose of this study is to develop an optimization model of nurse scheduling problems at the hospital. The optimization model will minimize the total deviation of nurses' working days from the standard workdays, the optimization model implemented in hospitals is processed using LINGO software.]]>
<![CDATA[Pythagoras Tree Applied For Determined Instagram Usage Habit Decision ]]>
<![CDATA[Ambarsari, Erlin Windia]]>;
<![CDATA[Herlinda, Herlinda]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (591.998 KB)
|
DOI: 10.33395/sinkron.v4i2.10517
<![CDATA[In previous studies, Pythagoras Tree constructed using the Regression Method, namely ID3 of Standard Deviation Reduction (SDR). The study using SDR for Classification. Data obtained from previous research about Instagram Usage. The result of the study that SDR is useful for the classification method for constructing Pythagoras Tree. However, the target attribute is must use a numerical variable to gain the Standard Deviation and the Mean. Empty data does not affect calculations. Although instances must be discarded, thereby reducing the amount of data. For the case of the Instagram Usage Habit itself, not getting the right pattern to deciding due to the data obtained is less. However, Construct Pythagoras Tree successfully done.]]>
<![CDATA[Deep Neural Networks Approach for Monitoring Vehicles on the Highway ]]>
<![CDATA[Husein, Amir Mahmud]]>;
<![CDATA[Christopher, Christopher]]>;
<![CDATA[Gracia, Andy]]>;
<![CDATA[Brandlee, Rio]]>;
<![CDATA[Hasibuan, Muhammad Haris]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (760.998 KB)
|
DOI: 10.33395/sinkron.v4i2.10553
<![CDATA[Vehicle classification and detection aims to extract certain types of vehicle information from images or videos containing vehicles and is one of the important things in a smart transportation system. However, due to the different size of the vehicle, it became a challenge that directly and interested many researchers . In this paper, we compare YOLOv3's one-stage detection method with MobileNet-SSD for direct vehicle detection on a highway vehicle video dataset specifically recorded using two cellular devices on highway activities in Medan City, producing 42 videos, both methods evaluated based on Mean Average Precision (mAP) where YOLOv3 produces better accuracy of 81.9% compared to MobileNet-SSD at 67.9%, but the size of the resulting video file detection is greater. Mobilenet-SSD performs faster with smaller video output sizes, but it is difficult to detect small objects.]]>
<![CDATA[Decision Support System for Achieving Scholarship Selection by Using Profile Matching Method ]]>
<![CDATA[Handayani, Rani Irma]]>;
<![CDATA[Triningsih, Triningsih]]>;
<![CDATA[Putri, Melia]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (302.873 KB)
|
DOI: 10.33395/sinkron.v4i2.10530
<![CDATA[Learning is one of the obligations of students to do in every school activity where they study. However, sometimes many students are less able to digest the subject matter delivered by the teacher. Therefore, the school held a scholarship program for outstanding students. In order to motivate students to study harder. Achievement scholarships are given with the aim of motivating students to study harder. Currently the scholarship is not right on target because it is still done manually and it is not clear the criteria for a student to get an achievement scholarship. To conduct an assessment in awarding scholarships to high achieving students use a decision support system to help solve a problem. For this reason, to conduct an assessment in the awarding of scholarships, a decision support system using the Profile Matching method is used. Profile Matching method is one of the methods used in decision making. In this study, there are several aspects of the assessment for awarding achievement scholarships, namely the KKM Aspect, the Attendance Aspect, the Behavior Aspect, the Craft Aspect or the Discipline, the Neatness Aspect.]]>
<![CDATA[Decision Support System For Determining the Best College High Private Using Topsis Method ]]>
<![CDATA[Lestari, Yuyun Dwi]]>;
<![CDATA[Mardiana, Mardiana]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (473.651 KB)
|
DOI: 10.33395/sinkron.v4i2.10235
<![CDATA[In this study, the Best Private Universities in Medan will be discussed with the TOPSIS method (Techniques for Other References with Similarities to Solutions). TOPSIS is one method of Decision Support Systems that is used to accept decisions that have many criteria. The general objective of this study is to use the TOPSIS method which is expected to be able to provide optimal results in determining the best universities. And specific targets of this study can provide estimates or indicators that can be used by Universities in Medan in determining whether the College is the best or not. The criteria used in this study were Institutional, Student Activities, Lecturer HR, Research and Community Service, and Innovation. In the future these criteria can be added according to the needs and requirements provided by the Ministry of Research, Technology and Higher Education. By applying the TOPSIS Method to Decision Supporters in the selection of the Best Private Universities in Medan City can provide optimal results based on the criteria and weighting that has been determined.]]>
<![CDATA[Evaluation of the Satisfaction of Users of Weather Forecast Systems with the Service Quality Method ]]>
<![CDATA[Rianti, Eva]]>;
<![CDATA[Rizky, Syafrika Deni]]>;
<![CDATA[Nugraha, Fariz Haris]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (511.757 KB)
|
DOI: 10.33395/sinkron.v4i2.10512
<![CDATA[This research was conducted to test the user's satisfaction of the weather forecast system that had been previously applied to the Teluk Bayur BMKG. The weather forecasting service at the Padang Bayur Padang BMKG has basically fulfilled the objectives of the company. However, over time, evaluation is needed so that the effectiveness of the use of the system can be known, so the system can continue to be developed. To overcome these problems, it is necessary to evaluate the performance of the system. One way is to measure user satisfaction by using Service Quality. This method is one of the techniques used to measure the level of user satisfaction with the system. Based on the results of the measurement of the quality of the weather forecast system, the results are obtained that the system user is satisfied with the system performance, but there are several indicators on the system whose performance is not fully maximized, so that there needs to be improvements and improvements of the system performance.]]>
<![CDATA[Selection of Outstanding Lecturers with Simple Additive Weighting Method ]]>
<![CDATA[Wati, Embun Fajar]]>;
<![CDATA[Istikharoh, Istikharoh]]>;
<![CDATA[Tuslaela, Tuslaela]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (530.741 KB)
|
DOI: 10.33395/sinkron.v4i2.10513
<![CDATA[Lecturers who excel have the right to be selected and get promotions and awards according to their academic performance. However, Sriwijaya State Buddhist College in Tangerang still uses manual assessment so it requires a lot of time to process the assessment data. Therefore, we need a method of calculation of SAW (Simple Additive Weighting) that can be used as a media for performance appraisal of outstanding lecturers who can facilitate an objective assessment. The SAW calculation aims to assist in the calculation of several criteria in the assessment of the right achieving lecturer. This assessment includes performance appraisals that include commitment, integrity, service orientation, discipline, cooperation, and leadership. In addition to the criteria, the assessment also includes the Employee Performance Target (SKP) assessment. The Employee Performance Target is in the form of a total assessment of lecturer performance. Two criteria, namely performance and SKP will be used as a guide in calculating the selection of outstanding lecturers. Samples were taken as many as 20 lecturers at Sriwijaya State Buddhist College in Tangerang. The simple additive weighting method is effectively used in the selection of outstanding lecturers with an assessment limit of more than 0.88. Of the many candidates, there are three lecturers with adequate performance and SKP, with grades 0.922, 0.88, 0.94. So that the highest achieving lecturer with the highest score is 0.94]]>
<![CDATA[PENERAPAN DATA MINING PENJUALAN PIZZA DENGAN MENGGUNAKAN METODE APRIORI ]]>
<![CDATA[Rusdiansyah, Rusdiansyah]]>;
<![CDATA[suharyanti, Nining]]>;
<![CDATA[Triningsih, Triningsih]]>;
<![CDATA[Murniyati, Murniyati]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (546.108 KB)
|
DOI: 10.33395/sinkron.v4i2.10500
<![CDATA[Pizza is a processed food originating from Italy and has been spread in various other countries including one of them in Indonesia. Pizza is a processed food that is currently sought after by various groups of people so as to make the pizza business opportunity very profitable, if it is run in a food business. Currently the pizza business has very favorable prospects when compared to other businesses. Moreover, the targeted target can be from all walks of life from children to adults. Pizza sales transactions that produce sales data every day, have not been able to maximize the use of sales data. Sales data is only stored as an archive, so it becomes a pile of data. Therefore the use of data mining is used to solve this problem. A priori algorithm is a data mining method by using minimum support parameters, minimum confidence and will analyze in the period of every month of sales transactions. This study produces data on the results of the process of association rules from the data collection of sales transactions. From the association rules it can be concluded that the pattern of pizza sales, where consumers more often buy Meatzza and Cheese Mania, as evidenced by the results of calculations using Apriori Algorithm and Rapidminer 5.3, with support of 30% and 60% confidence.]]>
<![CDATA[Optimizing Genetic Algorithms for Sentiment Analysis of Apple Product Reviews Using SVM ]]>
<![CDATA[Indrayuni, Elly]]>;
<![CDATA[Nurhadi, Acmad]]>
<![CDATA[ Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (503.377 KB)
|
DOI: 10.33395/sinkron.v4i2.10549
<![CDATA[Online reviews have the potential to provide buyers with insights about products such as quality, performance and recommendations. Website is one of the media that contains information or reviews provided by individuals, groups or organizations about an object or topic, one of which is Apple products. This study analyzes consumer sentiment reviews of Apple product users consisting of 200 reviews which will be classified into positive opinions and negative opinions using the Support Vector Machine algorithm and the application of genetic algorithms (GA) to obtain optimal accuracy values. The stages of this research are, firstly collecting a dataset, the second is preprocessing data. Third, the sentiment analysis process uses SVM and GA as optimization techniques. Fourth, do the validation process on the accuracy results obtained using the Confusion Matrix and ROC Curve. The results of this study indicate that Apple product review sentiment analysis produces the best accuracy of 70.00% and AUC 0.924 for SVM algorithm. Whereas the SVM + GA algorithm produces 85.76% accuracy and AUC 0.945, so that the accuracy value increases by 15.76% and the AUC 0.021 on the SVM model when compared before optimization with genetic algorithms (GA) is performed]]>
