IJISTECH
IJISTECH (International Journal of Information System & Technology) has changed the number of publications to six times a year from volume 5, number 1, 2021 (June, August, October, December, February, and April) and has made modifications to administrative data on the URL LIPI Page: http://u.lipi.go.id/1492681220 IJISTECH (International Journal Of Information System & Technology) is a peer-reviewed open-access journal published two times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and practice-oriented papers dealing with advances in intelligent informatics. All the papers are refereed by two international reviewers, accepted papers will be available online (free access), and no publication fee for authors. The articles of IJISTECH will be available online in the GOOGLE Scholar. IJISTECH (International Journal Of Information System & Technology) is published with both online and print versions. The journal covers the frontier issues in computer science and their applications in business, industry, and other subjects. Computer science is a branch of engineering science that studies computable processes and structures. It contains theories for understanding computing systems and methods; computational algorithms and tools; methodologies for testing of concepts. The subjects covered by the journal include artificial intelligence, bioinformatics, computational statistics, database, data mining, financial engineering, hardware systems, imaging engineering, internet computing, networking, scientific computing, software engineering, and their applications, etc. • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis, and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High-Performance Computing • Information storage, security, integrity, privacy, and trust • Image and Speech Signal Processing • Knowledge-Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition, and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high-performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data
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<![CDATA[K-Medoids: Inflation Clustering of 90 Cities in Indonesia (January-October 2020) ]]>
<![CDATA[Mhd Ali Hanafiah]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.98
<![CDATA[Inflation affects society and the economy of a country. For the general public, inflation is a concern because inflation directly affects the welfare of life, and for the business world, the inflation rate is a very important factor in making various decisions. Therefore, the aim of this study is to cluster the inflation rate that occurs in 90 cities in Indonesia, so that it is known which cities have high, medium, or low inflation levels. The grouping algorithm used is K-Medoids data mining. The research data is quantitative data, namely inflation data that occurred in 90 major cities in Indonesia from January to October 2020. The data was obtained from the Indonesian Central Statistics Agency. The clustering in this study is divided into 5, among others: cities with very high inflation rates, cities with high inflation rates, cities with moderate inflation rates, cities with low inflation rates, and cities with very low inflation rates. Based on the results of clustering analysis using rapidminer, for cities with a very high inflation rate category consists of 1 city (available on Cluster_4), high category consists of 4 cities (Cluster_0), medium category consists of 4 cities (Cluster_3), low category consists of 79 cities (Cluster_2) and very low category consisted of 2 cities (Cluster 1). This can provide information for the Indonesian government to keep the inflation rate stable.]]>
<![CDATA[Face Recognition Using Tiny Yolo V2 Algorithm as Attendance System ]]>
<![CDATA[Hafidz Sanjaya]]>;
<![CDATA[Dony Susandi]]>;
<![CDATA[Sandi Fajar Rodiyansyah]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.79
<![CDATA[Nowadays many websites use the usual online attendance system which does not pay attention to safety and comfort factors so that attendance activities still have a gap of cheating. Therefore, in this study the study of the application of face recognition systems in real-time using the Tiny Yolo V2 algorithm in the online attendance system. The study was conducted with several stages starting from collecting face images, the process of image improvement (preprocessing), face detection, face recognition, and data integration using web service. The test results of 10 students, each of whom has a face image facing forward as a dataset with 4 variations of distance, each of which performs 10 different face position scenarios. Based on the test results it can be concluded that the farther the distance of the face image with the webcam, the success rate decreases, it is shown at a distance of 0.5 meters the percentage of success reaches 97% and at a distance of 2 meters 88% where 2 faces are not detected and identified at the distance is due to wearing glasses and having rather dark skin.]]>
<![CDATA[The Mapping Model is in the form of Clustering of Workers' Hourly Wages by Region in Indonesia using the K-Means Method ]]>
<![CDATA[S Suhendra]]>;
<![CDATA[Siti Aisyah]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.92
<![CDATA[Wages are a very important element in manpower activities because the main purpose of working people is to get wages or salaries which will be used to meet daily needs. The hourly wage system for workers in the Province affects the wages received by workers. The research objective is to create a cluster model of the hourly wages of workers in Indonesia by region. The data used in the mapping is data on workers' hourly wages for 2017-2018, which are managed by the Central Statistics Agency (abbreviated as BPS). The technique used is clustering with the k-means method, which is part of data mining. This process uses two cluster labels, namely the high wage cluster (C1) and the low wage cluster (C2), with a maximum Davies Bouldin value of 0.490. The research results were obtained from 34 regions in Indonesia, twenty-seven provinces were in the low category cluster (C2), and seven provinces were in the high category (C1). This can be used as input for the provincial government to make policies on hourly wages in Indonesia that have an impact on the welfare of the community.]]>
<![CDATA[Fuzzy Inference System In Predicting Unemployment Levels In Batam City ]]>
<![CDATA[Nanda Jarti]]>;
<![CDATA[Sestri Novia Rizki]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.73
<![CDATA[Batam city is known as an industrial city in Indonesia; this can be seen from a large number of Indonesians who come to the Riau Islands to work. The company standards in accepting industrial employees can be seen from various aspects, such as age, education, company, opportunities, and job vacancies. Each of these aspects can be used as a decision-making system in predicting unemployment in Batam City. The method in completing this research is using the Sugeno method by taking the highest score and using the operator and. Sugeno's work steps are four. The first is the determination of the input value, the second is the inference engine, the third is the application of the Implementation function, and the fourth is the definition to get the final value—application for determining job vacancies using Matlab software.]]>
<![CDATA[Model of Data mining Clustering Rules on Population Determination of Trade and Accommodation Facilities in Indonesia with K-Means ]]>
<![CDATA[Rino Subekti]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.86
<![CDATA[The research aims to conduct a mapping model in the form of the grouping of residents of trade and accommodation facilities according to regions in Indonesia using data mining techniques. This research is a reference specifically for the role of the government in increasing regional income in Indonesia evenly. The data source is obtained from the government statistical data provider website, namely the Central Statistics Agency (BPS) with the URL address www.bps.go.id. The mapping method used is K-Mens and tested with the Rapid Miner software. There are 3 clusters used in mapping the area to the population of trade and accommodation facilities, namely the high (C1), medium (C2), and low (C3) clusters. The results obtained are cluster C1 centroid data, namely ((1527), (810.4), (5865), (6655.3), (323), (315.1)); cluster C2, namely ((286), (199,591), (1327), (2240,227), (93,227), (140,955)); and cluster C3, namely ((139,25), (122,5), (508,833), (919,222), (64,417), (94,444)). The results of the mapping show that in cluster C3, there are 16 provinces with a low population of trade and accommodation facilities.]]>
<![CDATA[Simple Additive Weighting (SAW) Method on The Selection of New Teacher Candidates at Integrated Islamic Elementary School ]]>
<![CDATA[Rozi Meri]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.80
<![CDATA[A decision support system is a computer-based information system that produces various alternative decisions to assist management in dealing with various structured or unstructured problems using data or models, one of which is in the problem of recruiting new teachers so that decisions are made in the right direction. The research method used in this study is SAW (Simple Additive Weighting), which is one of the algorithms used for decision making. The SAW algorithm is also known as the weighted addition algorithm. This method requires a decision matrix normalization process (x) to a scale that can be compared with all available alternative ratings. The authors took a case study at SD IT Cinta Qur'an. SD IT Cinta Qur'an is one of the elementary schools managed by the As-Salam Ilal Jannah Foundation, which is located in Tanjung Balik Village, Solok Regency. The result of the calculation of the saw method is in the form of information that can be taken into consideration by the school in making decisions on new teacher selection. In this study, six candidates as samples with five criteria were used in the calculation of the SAW method to obtain the best candidate rankings to make decisions by ordering from maximum to minimum value. It can be concluded that the SAW method can be used to support decisions in the selection of new teacher candidates by building a decision support system modelling.]]>
<![CDATA[Decision Support System for The Selection of North Sumatra Atlet Drumband By Applying Elimination and Choice Translation Reality (ELECTRE) (Case Study: United States of Drumband Indonesia North Sumatra Region) ]]>
<![CDATA[S Sunaryo]]>;
<![CDATA[Natalia Silalahi]]>;
<![CDATA[M Mesran]]>;
<![CDATA[Surya Darma Nasution]]>;
<![CDATA[Guidio Leonarde Ginting]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.93
<![CDATA[Unity Drumband Indonesia (PDBI) Region of North Sumatra is an organizational body engaged in sports groups, which involves players in various musical instruments. In selecting players, PDBI North Sumatra through the committee to conduct strict selection, through a judging system with several stages of a rigorous process. From the selection of participants, compare each criterion one by one to get the best athletes. Implementation of decision support systems is expected to provide the right solution and can assist the jury in generating a decision. Thus the process of selecting athletes can produce a decision directly without a long time. So that accelerate the selection process of drum band athletes who will strengthen the team of North Sumatra. The Elimination and Choice Translation Reality (ELECTRE) method is required in decision support systems for the selection of drum band athletes who will strengthen the North Sumatra team.]]>
<![CDATA[Simple Additive Weighting on The Selection of Candidate Students in The Job Skills Training Program ]]>
<![CDATA[R Rayendra]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.74
<![CDATA[The Job Skills Training Program (JST) is an education and training service program that is oriented towards the development of job skills in accordance with industrial needs, given to students so that they have competence in certain skill areas as evidenced by a certificate of competence to work and be absorbed in the business and industry. Course institutions that receive assistance from this program often experience problems in the selection of potential candidates because the number of applicants is large enough to meet the same criteria while students are limited. The Simple Additive Weighting (SAW) method is one of the decision support methods used in this study to obtain alternative candidate students who match the criteria. From the results of calculations using the SAW Method, it is concluded that the SAW Method can be used to support decision making in the selection of JST Program candidates that produce the highest-ranking scores.]]>
<![CDATA[Decision Making In Determining CV. Sinar Siantar With The TOPSIS Technique ]]>
<![CDATA[Indra Riyana Rahadjeng]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.87
<![CDATA[This research aims to build a decision support system in ranking the routes that are passed by public transportation. Pematangsiantar (often abbreviated as Siantar only) is one of the cities in North Sumatra Province, and the second-largest city in the province after Medan. Due to the strategic location of Pematangsiantar, the Trans Sumatra toll road was crossed. The city has an area of 79.97 km2 and a population of 240,787 people (2010). Public transportation is one of the transportation used by the community together and pays the fare. So far, public transportation is part of urban transportation which has an important role in people's lives. However, this problem is still hampered by problems of the route that must be traversed by public transportation, especially Sinar Siantar. Decision support systems are interactive systems that support decisions in decision interpretation with alternatives obtained from the tabulation of data, information and model planning. In this research, the method used is Technique For Order Preference By Similarity To Ideal Solution (TOPSIS). The TOPSIS method is based on the concept that the best alternative chosen has not only the shortest distance from the positive ideal solution but also has the farthest distance from the negative ideal solution. The TOPSIS method has several advantages, including simple and easy to understand concepts, efficient computing, and the ability to measure the relative performance of alternatives in simple mathematical decision making. Based on the calculation, the value of the V3 process, which has the highest value is obtained, so that this route is the best alternative through the CV. Sinar Siantar is the alternative route to A3, namely Jalan Jawa, because the alternative criteria best meet all the other alternatives. This decision support system is expected to assist in determining public transport routes to become more efficient so that the result is the determination of public transport routes.]]>
<![CDATA[The Geographic Information System of Gas Station (SPBU) Location In Musirawas, Lubuklinggau, and North Musirawas Based on Mobile Web ]]>
<![CDATA[Joni Karman]]>;
<![CDATA[Oskar Al Amin]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 4, No 1 (2020): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v4i1.68
<![CDATA[Making a Geographical Information System for the Location of Public Fuel Filling Stations (SPBU) in Musirawas, Lubuklinggau, and North Musirawas based on Mobile Web, the community, still has difficulty in finding the location for gas stations, especially people outside the region who also do not know the mileage so that they have the potential to run out of fuel and the community also does not know the types of fuel available at the gas station so that you have the potential to get lost when looking for a gas station location. This research uses data collection methods, by observing and recording directly at the research site (observation), conducting a direct question and answer with the resource (interview) and documentation by reading existing books and literature. The results of the geographic information system design for the location of gas station stations (SPBU) in Musirawas, Lubuklinggau, and North Musirawas based on Web Mobile was made by using the PHP programming language and MySQL database and making program listings using the Adobe Dreamweaver application. The system created by this user can select the closest gas station menu directly by pressing the button where the nearest gas station is located so that the closest gas station location where you are will appear. Information on gas stations can also be known with information which is located according to the location of the gas stations chosen, and users can provide feedback in the form of comments or suggestions for improvements to the gas stations.]]>
