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
Articles
313 Documents
<![CDATA[Design of Artificial Neural Networks to Recognize Fingerprint Patterns ]]>
<![CDATA[Frinto Tambunan]]>;
<![CDATA[Yudi Y]]>;
<![CDATA[Muhammad Fauzi]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v3i1.34
<![CDATA[Image or pattern recognition system is one of the branches in computer science, this system can help the processing of fingerprint patterns, especially in the banking, police and users of other institutions who really feel the importance of using fingerprints. Several stages in fingerprint pattern image recognition are through the process of scanning, then the resulting digital fingerprint image is converted to a certain value, among others, the threshold process, the division of images, and representation of input values. The training process is carried out using two treatments: the first with a different level of understanding and the second training with different unit numbers, the best training is obtained with a level of understanding of 0.3 and the number of hidden units 10 by producing a short training time and relatively small errors. Fingerprint pattern recognition is done by two trials, based on 1 number of training patterns and 5 number of training patterns. From the research data, the ability of the system to recognize output patterns is greater if the number of training patterns increases, with a number of 1 training patterns, the system is able to recognize 50% external patterns while the 5 system training patterns are able to recognize 70% output patterns.]]>
<![CDATA[Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra ]]>
<![CDATA[Mhd Ali Hanafiah]]>;
<![CDATA[Anjar Wanto]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 3, No 2 (2020): May ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v3i2.66
<![CDATA[The poverty line is useful as an economic tool that can be used to measure the poor and consider socio-economic reforms, such as welfare programs and unemployment insurance to reduce poverty. Therefore, this study aims to classify poverty lines according to regencies/cities in North Sumatra Province, so that it is known which districts/cities have high or low poverty lines. The grouping algorithm used is K-Means data mining. By using this algorithm, the data will be grouped into several parts, where the process of implementing K-Means data mining uses Rapid Miner. The data used is the poverty line data by district/city (rupiah/capita/month) in the province of North Sumatra in 2017-2019. Data sourced from the North Sumatra Central Statistics Agency. The grouping is divided into 3 clusters: high category poverty line, medium category poverty line, and the low category poverty line. The results for the high category consisted of 5 districts/cities, the medium category consisted of 18 districts/cities and the medium category consisted of 10 districts/cities. This can provide input and information for the North Sumatra government to further maximize efforts to overcome the poverty line in the area.]]>
<![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[Evacuation Planning for Disaster Management by Using The Relaxation Based Algorithm and Route Choice Model ]]>
<![CDATA[Dedy Hartama]]>;
<![CDATA[Agus Perdana Windarto]]>;
<![CDATA[Anjar Wanto]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 2, No 1 (2018): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v2i1.14
<![CDATA[Research in the field of disaster management is done by utilizing information and communication technology. Where disaster management is discussed is about evacuation planning issues. The evacuation stage is a very crucial stage in the disaster evacuation process. There have been many methods and algorithms submitted for the evacuation planning process, but no one has directly addressed evacuation planning on dynamic issues concerning time-varying and volume-dependent. This research will use the Relaxation-Based Algorithm combined with the Route Choice Model to produce evacuation models that can be applied to dynamic issues related to time-varying and volume-dependent because some types of disaster will result in damage as time and evacuation paths are volume-dependent so as to adjust to the change in the number of people evacuated. Disaster data that will be used in this research is sourced from Disaster Information Management System sourced from DesInventar. The results of this study are expected to produce an evacuation planning model that can be applied to dynamic problems that take into account the time-varying and volume-dependent aspects.]]>
<![CDATA[Implementation of AHP and WASPAS (Weighted Aggregated Sum Product Assessment) Methods in Ranking Teacher Performance ]]>
<![CDATA[Mesran Mesran]]>;
<![CDATA[Suginam Suginam]]>;
<![CDATA[Dito Putro Utomo]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 3, No 2 (2020): May ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v3i2.43
<![CDATA[The performance of a teacher is an outcome that is demanded by the School Principal in awarding a teacher who is at a formal level of education. In producing effective decisions on performance ratings, it is, of course, necessary to use a computer-based information system, this is known as the Decision Support System. In order for the resulting decisions to be better, using methods that can process existing data so that the method used is able to produce a final report in the form of a decision. At SD IT Al-Munadi Marelan-Medan, so far not using computer applications to help decision-makers to rank teacher performance. Although all this time the needs of the school principal are reports on the performance of teachers in schools. In the research that the authors conducted using a combination of AHP and WASPAS methods which are expected to improve the results of decisions on teacher performance ranking.]]>
<![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[Analysis of Weight Product (WP) Algorithms in the best Go Car Driver Recommendations at PT. Maranatha Putri Bersaudara ]]>
<![CDATA[Roni Kurniawan]]>;
<![CDATA[Agus Perdana Windarto]]>;
<![CDATA[M Fauzan]]>;
<![CDATA[Solikhun Solikhun]]>;
<![CDATA[Irfan Sudahri Damanik]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 3, No 1 (2019): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v3i1.28
<![CDATA[This study aims to rank the best Go Car Driver. The problem arises because of the inaccuracy in giving value to the driver which results in the decision being given incorrectly so that the assessment tends to be subjective. This research was conducted at PT. Maranatha Putri Bersaudara. Sources of data obtained by observing, interviewing. The settlement method used is a decision support system with the Weight Producted method. The assessment criteria used are Performance (C1), Number of orders (C2), Rating (C3), Attitude (C4), Rating (C5) and Appearance (C6) where the alternatives used are 4 samples. The results obtained using the Weighted Product method are Alternative1 and Alternative4 which are recommended as the best go car driver with the assessment results of 0.0307 and 0.0272. It is expected that research results can be input to the relevant parties in recommending the best go car driver so as to minimize subjective judgment.]]>
<![CDATA[GRDP Growth Rate Clustering in Surabaya City uses the K-Means Algorithm ]]>
<![CDATA[Nur Ahlina Febriyati]]>;
<![CDATA[Achmad Daengs GS]]>;
<![CDATA[Anjar Wanto]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 3, No 2 (2020): May ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v3i2.60
<![CDATA[Gross Regional Domestic Product (GRDP) is an indicator used to measure economic performance in a period. GRDP is the amount of added value generated by all business units in a particular area. It can also be said to be the sum of the value of the final goods and services produced by all economic units. Therefore, this study aims to cluster the GRDP Growth Rate according to business fields in the city of Surabaya, so that it is known which sectors have high or low growth. The clustering algorithm used is K-Means. By using this method, the data will b,e grouped into several clusters, where the implementation of the K-Means Clustering process uses the Rapid Miner tools. The data used is the GRDP Growth Rate in Surabaya City by Business Field, 2010-2019 (Percent). The data is divided into 3 clusters: high, medium, and low. The results obtained are nine categories/sectors with high clusters, 5 categories / sectors with medium clusters, and three categories,s / sectors with low clusters. This can be input and information for the Surabaya City government to further maximize efforts to increase the GRDP Growth Rate in the area.]]>
<![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[Comparative Analysis of Pathfinding Algorithms A *, Dijkstra, and BFS on Maze Runner Game ]]>
<![CDATA[Silvester Dian Handy Permana]]>;
<![CDATA[Ketut Bayu Yogha Bintoro]]>;
<![CDATA[Budi Arifitama]]>;
<![CDATA[Ade Syahputra]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 1, No 2 (2018): May ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v1i2.7
<![CDATA[Maze Runner game is a game that requires pathfinding algorithm to get to the destination with the shortest path. This algorithm is used in an NPC that will move from start node to destination node. However, the use of incorrect algorithms can affect the length of the computing process to find the shortest path. The longer the computing process, the longer the players have to wait. This study compared pathfinding algorithms A *, Dijkstra, and Breadth First Search (BFS) in the Maze Runner game. Comparison process of these algorithms was conducted by replacing the algorithm in the game by measuring the process time, the length of the path, and the numbers of block played in the existing computing process. The results of this study recommend which algorithm is suitable to be applied in Maze Runner Game.]]>
