https://jurnal.polgan.ac.id/index.php/sinkron/issue/feed Sinkron : jurnal dan penelitian teknik informatika 2023-02-02T10:27:33+07:00 Muhammad Khoiruddin Harahap choir.harahap@yahoo.com Open Journal Systems <p><a href="https://sinta.kemdikbud.go.id/journals/detail?id=3320"><strong>Sinkron</strong> <strong>: Jurnal dan Penelitian Teknik Informatika</strong></a> is<strong> The<a href="http://polgan.ac.id/jurnal/sinkrons3.pdf"> Kemdikbud Accredited National Scientific Journal Rank 3 (Sinta 3), Number: 148 / M / KPT / 2020 on August 3, 2020</a></strong>. Start from 2022, SinkrOn is published Quarterly, namely in January, April, July and October. SinkrOn aims to promote research in the field of Informatics Engineering which focuses on publishing quality papers about the latest information about computer science. Submitted papers will be reviewed by the Journal and Association technical committee. All articles submitted must be original reports, previously published research results, experimental or theoretical, and will be reviewed by colleagues. Articles sent to the Sinkron journal may not be published elsewhere. The manuscript must follow the writing style provided by SinkrOn and must be reviewed and edited.</p> <p>Sinkron is published by <strong><span style="text-decoration: underline;"><a href="https://www.polgan.ac.id">Politeknik Ganesha Medan</a></span></strong>, a Higher Education in Medan, North Sumatra, Indonesia. </p> <p><strong>E- ISSN: <a href="https://issn.brin.go.id/terbit/detail/1472194336">2541-2019</a> </strong>(Indonesian | LIPI)<strong> | </strong><strong>P-ISSN: <a href="https://issn.brin.go.id/terbit/detail/1474367655">2541-044X</a> </strong>(Indonesian | LIPI)<strong> | </strong><strong>DOI Prefix: 10.33395</strong></p> <p><strong>E- ISSN: <a href="https://portal.issn.org/resource/ISSN/2541-2019">2541-2019</a> </strong>(International)<strong> | </strong><strong>P-ISSN: <a title="International ISSN" href="https://portal.issn.org/resource/ISSN/2541-044X">2541-044X</a> </strong>(International)</p> https://jurnal.polgan.ac.id/index.php/sinkron/article/view/11843 - Comparison of Selection for Employee Position Recommended MCDM-AHP, SMART and MAUT Method 2022-10-15T22:42:31+07:00 Akmaludin Akmaludin akmaludin.akm@nusamandiri.ac.id <p>In a company, employees are high-value assets, therefore it is necessary to select employees for the continuity of the company, of course, by getting quality human resources. The purpose of this paper is to refute the difference in the number of rankings in selecting the best employees through a comparison of the SMART and MAUT methods. Many methods can be used in the selection process. This article describes in detail about the selection of employee promotions using the MCDM-AHP collaboration method which is used to provide an assessment of the main criteria through eigenvector values ​​based on joint decisions by company leaders based on consistent and optimal questionnaire instrumentation which is not set based on percentages manually based on wishes leader. The SMART method is used to provide a sub-criteria assessment based on a balanced weighting utility according to the number of criteria used, with an assessment weight starting from zero as the lowest value and one as the highest value. The MAUT method will be used as a comparison against the results of the SMART method, where the MAUT method has differences in determining the weights on the sub-criteria based on the perception of understanding the criteria, so that they are arranged in an orderly manner and then determine the utility value of the criteria, so that there are similarities between the two methods. The ranking results obtained from the comparison of the two methods are that they have the same rating, so that the decision support taken also has similarities between the two SMART methods and the MAUT method. This can happen if the standard of measurement is carried out consistently through the MCDM-AHP method by not changing the assessment range in determining the interval range of each criterion.</p> 2023-02-02T00:00:00+07:00 Copyright (c) 2022 Akmaludin Akmaludin https://jurnal.polgan.ac.id/index.php/sinkron/article/view/12081 COMPARISON OF K-N EAREST NEIGHBOR AND NAÏVE BAYES ALGORITHMS FOR PREDICTION OF APTIKOM MEMBERSHIP ACTIVITY EXTENSION IN 2023 2022-12-30T11:10:57+07:00 Kannisa Adjani kannisaadjani68@gmail.com Fathia Alisha Fauzia fathiaalisha@uniga.ac.id Christina Juliane christina.juliane@likmi.ac.id <p><em>So far APTIKOM as the Informatics and Computer Higher Education Association has provided many opportunities for registered members to participate in discussions on the development of science among fellow association members, access to various professional experts, as well as technical and non-technical guidelines in the field of education. With the various opportunities above, it is hoped that all members will support the activities of each member who has joined or has just joined so that a good association can be created. This study aims to find out about the problems that occur in APTIKOM, namely members who have registered as members but rarely renew their membership which results in data accumulation in APTIKOM. This research method uses the k-nn and naïve Bayes algorithms by using data sets from 2012 to 2022. The dataset used is APTIKOM member data and has 5 attributes namely name, gender, last education, institution and validation secret. To calculate the research test using a rapid miner. The purpose of this study is to predict whether in the following year there will be a membership renewal process for all APTIKOM members who have been recorded from 2012 to 2022. Furthermore, the results of this study have a different level of accuracy. Where for k-nn the resulting accuracy is 94.00% and for the result of naïve Bayes is 91.35%.</em></p> 2023-02-02T00:00:00+07:00 Copyright (c) 2023 Kannisa Adjani, Fathia, Anne https://jurnal.polgan.ac.id/index.php/sinkron/article/view/12123 Optic Disc Detection on Retina Image using Extreme Learning Machine 2023-01-12T09:02:23+07:00 Sutikno Sutikno sutikno@lecturer.undip.ac.id Helmie Arif Wibawa helmie@if.undip.ac.id Priyo Sidik Sasongko priyoss@undip.ac.id <p>Optic disk detection on retina image has become one of many initial steps in evaluation of Diabetic Macular Edema (DME) severity.&nbsp; As much as early the step is, the result of the step is extremely essential. This article discusses the optic disk detection on retina image based on the color histogram value. The detection is done by using color histogram value which is taken from window sliding process with the size of 50x50 pixels. First, the candidates of optic disc were detected using Extreme Learning Machine towards the histogram value. Then the optic disc was selected form the candidates of optic which has highest average intensity. 4 retina image datasets were employed in the evaluation, including Drions dataset, DRIVE dataset, DiaretDB1 dataset, and Messidor dataset. The result of evaluation then validated by medical expert. The model outcome reaches the accuracy as much as 85,39 % for DiaretDB1 dataset, 95% for DRIVE dataset, 98,18% for Drions and 99% for Messidor dataset.</p> 2023-02-02T00:00:00+07:00 Copyright (c) 2023 Sutikno, Helmie Arif Wibawa, Priyo Sidik Sasongko https://jurnal.polgan.ac.id/index.php/sinkron/article/view/11845 Decision Support System for SmartPhone Selection with AHP-VIKOR Method Recommendations 2022-10-15T22:40:15+07:00 Akmaludin Akmaludin akmaludin.akm@nusamandiri.ac.id <p>Produce products that have various features and diverse functions, which are able to provide convenience with the reliability of their features and functions. The advantages possessed by SmartPhone become more confident for users to assess the level of product intelligence, the more trustworthy. The purpose of this research is to provide additional knowledge on the selection of SmartPhone to the user in having a product with various benefits. The more criteria that become a barometer, the more difficult it is to choose a product in the form of a SmartPhone. Thus, the right method is needed to perform the selection of the SmartPhone. There are several methods offered to carry out the selection process for SmartPhones, namely the Analytic Hierarchy Process (AHP) method combined with the VIKOR elimination method. Both of these methods are very supportive in the selection process with many types of criteria and their meanings against these criteria. A number of criteria that serve as a barometer for selecting object-based applications are Operating System, Processor, Internal Memory, External Memory, Back Camera, Front Camera, Battery, Cassing Model, Screen Size, Wight and Price. Of the eleven criteria have two different characteristics of understanding. The results of this study can be seen explicitly on the selection of SmartPhones through the acquisition of the smallest Qi index with the three highest ratings, namely the first ranked Samsung Galaxy A3 (0.00) the second is the Xiaomi Mi 4C with an index of 0.19, the third is the Lenovo Vibe K5 Plus with index 0.31. Thus it can be said that the collaboration of the AHP and VIKOR Elimination methods is able to provide optimal decision-making support.</p> 2023-02-02T00:00:00+07:00 Copyright (c) 2022 Akmaludin Akmaludin https://jurnal.polgan.ac.id/index.php/sinkron/article/view/12090 Detect Fake Reviews Using Random Forest and Support Vector Machine 2023-01-10T10:17:03+07:00 Zulpan Hadi zlpnhadi@students.amikom.ac.id Ema Utami ema.u@amikom.ac.id Dhani Ariatmanto dhaniari@amikom.ac.id <p>Tokopedia is one of the most popular online stores or e-commerce in Indonesia. So that Tokopedia is one of the most widely used e-commerce applications for online shopping. This causes many sellers to spam or create fake reviews to improve or knock down their competitors. Meanwhile, reviews are a very important source of information for consumers to decide whether to buy a product or not at an online store. Therefore, the researcher discusses the detection of fake reviews in Tokopedia product reviews. Here researchers use the pos tagging method for detection. Where tagging posts are used to get tags or word markers in a review. Using post tagging there are 856 fake reviews and 4478 real reviews. In the fake reviews, there were 628 reviews written to increase product sales or brand names from store owners, while there were 228 reviews aimed at dropping their competitors or competitors. Furthermore, the classification is carried out using the random forest algorithm model and the support vector machine. By dividing the dataset for training data by 80% and 20% for data testing. Here it is known that the support vector machine gets much higher accuracy than the random forest. The support vector machine gets an accuracy of 98% while the random forest gets an accuracy of 60%.</p> 2023-02-02T00:00:00+07:00 Copyright (c) 2023 Zulpan Hadi, Ema Utami, Dhani Ariatmanto https://jurnal.polgan.ac.id/index.php/sinkron/article/view/11994 Implementation Opinion Mining For Extraction Of Opinion Learning In University 2022-12-13T18:20:35+07:00 Mariana Purba riagalihprasojo@gmail.com Yadi Yadi yadimkom@gmail.com <p>Opinion mining is a field of Natural Language Processing (NLP) that is used to carry out the process of extracting and processing textual data which functions to obtain information through sentiment analysis from a document in the form of text, among others, to detect attitudes towards objects or people. Sub-processes in opinion mining can use documents of subjectivity, opinion orientation, and detection targets to find out the data used as sentiment analysis, sentiment analysis aims to assess emotions, attitudes, opinions, and evaluations conveyed by a speaker or writer towards a product or towards a public figure. In this study, an opinion mining system was developed to analyze learning in college. The methodology used is quantitative descriptive, while the processing of sentiment analysis data uses Azure machine learning. Sentiment analysis results are very good at assessing opinions or opinions and emotions, and attitudes conveyed by someone. The learning process is the main element that must run well so that competency and achievement in learning can be maximally conveyed to students. Documents that identified opinions were then classified into negative, neutral, and positive opinions based on the results. In general, it can be concluded that the value obtained by sentiment analysis using Azure Machine Learning tools is quite good, judging from the results of a positive class of 0.79 and a neutral class of 0.53. The use of cleaning and labeling techniques and other classifications is still very possible to use. To get a better accuracy value.</p> 2023-02-02T00:00:00+07:00 Copyright (c) 2022 Mariana Purba, Yadi https://jurnal.polgan.ac.id/index.php/sinkron/article/view/12096 Scrum Framework Implementation in Mobile Application Development Iwak Market Fish Auction Module 2023-01-04T14:49:08+07:00 Dewi Putri Ayuningsih 112201906284@mhs.dinus.ac.id Ika Novita Dewi ikadewi@research.dinus.ac.id Asih Rohmani aseharsoyo@dsn.dinus.ac.id <p><em>Lelang Ikan mobile application is an online auction in the marketplace platform of Pasar Iwak based on Android platform. Scrum framework is applied and consists of determining the product backlog, creating sprint planning and sprint backlogs, and conducting sprint reviews and sprint retrospectives. The product backlog resulted 14 backlog items based on the results of system and user requirements for user auctioneers. Sprint planning and sprint backlog are divided into four sprints, namely front-end and back-end development, system integration process and system implementation. Sprint reviews are carried out by implementing two types of testing, namely blackbox testing and user acceptance testing (UAT). Blackbox testing emphasizes testing application functions or features, while UAT is applied to measure the level of user acceptance. The results of blackbox testing showed that the features provided by the application are in accordance with the predetermined requirements. Whereas UAT showed the result of 66.8%, which means that the application is in the appropriate category and can be accepted by users. The application development process ends at the sprint retrospective stage which is a suggestion or feedback after the application testing. The suggestions obtained are in the form of adding tracking features, payment features with payment gateways, and application development with the iOS platform</em>.</p> 2023-02-02T00:00:00+07:00 Copyright (c) 2023 Dewi Putri Ayuningsih, Ika Novita Dewi, Asih Rohmani