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
394 Documents
<![CDATA[The Determination of Shortest Path Using Genetics Algorithm Assisted Matlab ]]>
<![CDATA[Hendra Cipta]]>;
<![CDATA[Rina Widyasari]]>
<![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.v3i2.64
<![CDATA[The problem is taking the shortest path for a road driver is an interesting thing. This paper explains how to find the shortest path using a genetic algorithm to achieve the best solution. Then it shows how to implement the genetic algorithm using the MATLAB program. For example, it is given a connected directed graph containing 20 vertices and 41 arcs where these vertices are assumed to be cities.]]>
<![CDATA[Software Usability Measurement Inventory for Student Information Academic System at Politeknik Negeri Media Kreatif ]]>
<![CDATA[Yuyun Khairunisa]]>;
<![CDATA[Sari Setyaning Tyas]]>;
<![CDATA[Adnan Purwanto]]>;
<![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.v4i1.96
<![CDATA[Academic information system is a system that is urgently needed by universities to manage academic business processes. State Polytechnic of Creative Media implements SIAM as Student Information Academic System. SIAM being developed has the advantage of being able to convey more detailed information. In addition, SIAM has implemented the SSO (Single Sign On) system where this technology is in great demand, especially in very large and heterogeneous networks. In this research Software Usability Measurement Inventory (SUMI) methods used to conduct usability test. SUMI is a rigorously tested and proven method of measuring software quality from the end user's point of view. The analysis results from SUMI will be used to improve the development of the academic system life cycle.]]>
<![CDATA[ANN: Model of Back-Propagation Architecture on the Logs Production by Type of Wood ]]>
<![CDATA[Muhammad Noor Hasan Siregar]]>
<![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.v1i2.12
<![CDATA[Indonesia is rich in forest products. The production forest is a forest area that can be utilized for the community, such as logs, rattan, and some forest plants that have high economic value. This research aims to make the best architectural model by using artificial neural network. The method used is backpropagation algorithm. With this model it will continue to predict the output of logs. Data are sourced from BPS-Statistics Indonesia. Based on the results of research results of logs production using backpropogation method, obtained the result of 3 model architecture (18-18-1, 18-25-1 and 18-18-25- 1) that model of architecture 18- 25-1 is the best model with 72% accuracy, MSE: 0.0221670942 and epochs: 660.]]>
<![CDATA[Design and Build Daily Android-Based Financial Applications ]]>
<![CDATA[Susi Susilowati]]>;
<![CDATA[Abdul Rohmat Sigit]]>
<![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.v3i2.46
<![CDATA[The rapid development of information and communication technology is directly proportional to the community's need for fast and accurate information, especially the development of technology on mobile phones (cellphones) that present smartphones with various sophisticated features. Smartphones are becoming a necessity for society today not only as a communication device, but also as a source of information. In general, the community carries out daily financial management in the traditional way by taking notes using a writing instrument, where the paper media as its documentation and the help of a calculating tool is a calculator to calculate financial income and expenditure. Constraints that occur are that the public needs stationery to do every recording of transactions that are carried out every day. So it requires quite a lot of money, the time required will take a long time to record financial transactions that are carried out every day by the process of writing on paper media, in addition to that the public is also often confused by financial records that are lost or damaged due to paper documents. This Mobile Based Daily Financial Registration Application aims to help its users in financial management using a mobile device, so that users can expect to be able to manage their finances optimally, both income and expenditure. As a result of the application, the public can enter the daily money in and daily money out data along with their information and then the data will be displayed on the front page of the application menu, in addition to the data that has been entered can also be edited and deleted as needed and accompanied by making features money entry data reports and money out data in the form of graphs and pdfs.]]>
<![CDATA[The Implementation of Web-GIS in Developing Tourism Object in Langkat Regency with Location Based Service Method ]]>
<![CDATA[Surya Hendra Putra]]>;
<![CDATA[Evan Afri]]>
<![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.v4i1.77
<![CDATA[This research aims to help out the Culture and Tourism Office of Langkat Regency in presenting Langkat Regency tourism objects widely across the globe through the web. Therefore, it can be trusted to be able to increase the number of both domestic and foreign tourists to visit Langkat Regency. This research shows a digital map on the web using the Google Maps API. With the belief that with there is an app that can clearly and completely displaying the name, photo, description and location of tourist objects which are taken from the database contained in the Google DBMS, it can increase the prospect of the number of tourists coming to Langkat Regency. Given that Langkat Regency has excellent tourism potential. One of them is the Bukit Lawang area which has captivity for orangutans, then which are endangered and endangered by elephant captivity. With web-GIS using the Location-Based Service (LBS) method by utilizing Google Maps API technology and the CodeIgniter Framework. With LBS, the coordinates of tourist locations in the form of latitude and longitude derived from the Google Maps application can be easily entered in the webGIS code structure.]]>
<![CDATA[Improving Adaptive Learning Rate With Backpropogation on Retail Rice Price Prediction in Traditional Markets ]]>
<![CDATA[Erwin Binsar Hamonangan Ompusunggu]]>;
<![CDATA[Solikhun Solikhun]]>;
<![CDATA[Iin Parlina]]>;
<![CDATA[Sumarno Sumarno]]>;
<![CDATA[Indra Gunawan]]>
<![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.v3i1.26
<![CDATA[Rice is the most important staple food and carbohydrate food in the world especially people in Indonesia. This study aims to predict the retail price of rice in traditional markets using backpropogation by improvising Adaptive Learning Rate to increase the value of accuracy. Data sources were obtained from the Central Statistics Agency (BPS) in 33 provinces in Indonesia for the retail price of rice in the traditional market (Rupiah / kg) for the past 6 years (2011-2016). The results of the study state that the improvised learning rate uses 2 models: 2-10-1 and 2-15-1 (LR= 0,1; 0,5; 0,9) that the best architectural models are 4-15-1 (LR= 0.9) with an accuracy of 82%, Training MSE 0,000999936, Testing MSE 0.016051433 and Epoch 20515. The results of this study are expected to provide input to the government in providing input on predictions of retail rice prices that have an impact on the stability of rice prices in Indonesia.]]>
<![CDATA[Tourism Destinations Popularity Rating In Malang Raya using Naive Bayes Classifier and Selection Sort Based on Twitter Word Polarity ]]>
<![CDATA[Yunifa Miftachul Arif]]>;
<![CDATA[Mochammad Wahyu Firmansyah]]>;
<![CDATA[Roro Inda Melani]]>;
<![CDATA[S Supriyono]]>
<![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.v3i2.58
<![CDATA[The development of tourism today has been supported by advances in information technology that can make it easier for everyone to get information about tourist attractions. Technology plays an essential role in improving the tourism industry sector. During the tour, tourists usually share moments by uploading photos or making a status on social media related to their experience visiting a tourist site. Malang, which has various types of tourism, makes it a tourist destination. However, the number of tours in Malang makes tourists confused to choose the trip to be visited. Because of this, we need a system that can provide information in the form of popular tourist rankings. In this research, a system that can determine the ranking of tourist attractions in Malang Raya was made. The data used comes from social media user tweets on Twitter using the keyword name of tourist attractions in Malang. The Naive Bayes Classifier method is used to help tweet classification, and the Selection Sort method is used to help the ranking process of tourist attractions. The final results obtained in the Batu City tourism ranking resulted in an accuracy of 86.3%, while in the tourism rating the artificial type of Batu City produced an accuracy of 100%. The difference in accuracy occurs because there are the same positive values at several tourist attractions, so the Selection Sort method cannot work. Because of this, further research is needed for ranking methods that can rank with the same positive value to produce a better ranking of tourist attractions.]]>
<![CDATA[Sentiment Analysis of Covid-19 As A Social Media Pandemic ]]>
<![CDATA[Cahyo Prianto]]>;
<![CDATA[Nisa Hanum Harani]]>
<![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.v4i1.90
<![CDATA[A large amount of information about Covid-19 that spreads quickly can lead to a perception of opinion and sentiment for those who read it. This research studies how text networking is formed, sentiment analysis and topics modelling that is widely discussed related to the Covid-19 theme. The text networking analysis was carried out on data taken from 4 different times, namely on 26 March, 29 March, 28 June and 23 July 2020 giving the result that the largest edge, nodes and modularity were in the conversation data on July 23, 2020. Sentiment analysis shows how the public responds to the Covid-19 pandemic. Sentiment analysis from tweet data in March 2020 showed 51% as positive sentiment and 49% as negative sentiment, with an accuracy rate of 0.7586, specificity 0.6667, prevalence 0.5862. Then tweet data in June 2020 showed 59% as negative sentiment and 41% as positive sentiment, with an accuracy rate of 0.6486, specificity 0.6111, prevalence 0.5135. Analysis of topic modelling has succeeded in collecting words related to certain topics, such as the data on March 26, 2020, representing talks related to the topic of "doing activities from home", "health", and "government policy". The data on March 29, 2020, represent talks related to the topic of "activities from home", "expression of feelings", "new habits". The data on June 28, 2020, represent talks related to the topic of "health protocol", "social assistance", "health". And on July 23, 2020 data represents talks related to the topic of "data security", "fine policy", and "policy".]]>
<![CDATA[Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting) ]]>
<![CDATA[Sandy Putra Siregar]]>;
<![CDATA[Anjar Wanto]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 1, No 1 (2017): November ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v1i1.4
<![CDATA[Artificial Neural Networks are a computational paradigm formed based on the neural structure of intelligent organisms to gain better knowledge. Artificial neural networks are often used for various computing purposes. One of them is for prediction (forecasting) data. The type of artificial neural network that is often used for prediction is the artificial neural network backpropagation because the backpropagation algorithm is able to learn from previous data and recognize the data pattern. So from this pattern backpropagation able to analyze and predict what will happen in the future. In this study, the data to be predicted is Human Development Index data from 2011 to 2015. Data sourced from the Central Bureau of Statistics of North Sumatra. This research uses 5 architectural models: 3-8-1, 3-18-1, 3-28-1, 3-16-1 and 3-48-1. From the 5 models of this architecture, the best accuracy is obtained from the architectural model 3-48-1 with 100% accuracy rate, with the epoch of 5480 iterations and MSE 0.0006386600 with error level 0.001 to 0.05. Thus, backpropagation algorithm using 3-48-1 model is good enough when used for data prediction.]]>
<![CDATA[Architectural Model of Backpropagation ANN for Prediction of Population-Based on Sub-Districts in Pematangsiantar City ]]>
<![CDATA[Marseba Situmorang]]>;
<![CDATA[Anjar Wanto]]>;
<![CDATA[Zulaini Masruro Nasution]]>
<![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.v3i1.39
<![CDATA[A population is a group of individuals who occupy or live in a place or area that interacts with one another. Because the population has a very important role in an area, it is important to make predictions to find out how much the level of increase or descent of the population in an area, especially in Pematangsiantar. Therefore this research was conducted. This study uses population data in 8 Sub-Districts in Pematangsiantar. Data was taken from the Central Statistics Agency (BPS) of Pematangsiantar city in 2011-2017. The method used is the Artificial Neural Network (ANN) Backpropagation. These data will be processed into 2 parts namely training data and Testing data. This research will use 5 architectural models namely, 3-25-1, 3-30-1, 3-45-1, 3-54-1 and 3-68-1. From these 5 architectural models, after analysis, models 3-45-1 were chosen as the best models with epoch 553 values, MSE training 0,0001108768, MSE testing 0.0012355953 and an accuracy rate of 88%. The results of this paper are expected to be widely useful, especially for academics as further research material, especially those related to population in Pematangsiantar, because this research is still limited to discussing the level of accuracy, not prediction results.]]>
