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[Implementation of Backpropagation Artificial Neural Networks to Predict Palm Oil Price Fresh Fruit Bunches ]]>
<![CDATA[Edi Ismanto]]>;
<![CDATA[Noverta Effendi]]>;
<![CDATA[Eka Pandu Cynthia]]>
<![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.v2i1.17
<![CDATA[Riau Province is one of the regions known for its plantation products, especially in the oil palm sector, so that Riau Province and regional districts focus on oil palm plants as the main commodity of plantations in Riau. Based on data from the Central Bureau of Statistics (BPS) of Riau Province, the annual production of oil palm plantations, especially smallholder plantations in Riau province has always increased. So is the demand for world CPO. But sometimes the selling price of oil palm fresh fruit bunches (FFB) for smallholder plantations always changes due to many influential factors. With the Artificial Neural Network approach, the Backpropagation algorithm we conduct training and testing of the time series variables that affect the data, namely data on the area of oil palm plantations in Riau Province; Total palm oil production in Riau Province; Palm Oil Productivity in Riau Province; Palm Oil Exports in Riau Province and Average World CPO Prices. Then price predictions will be made in the future. Based on the results of the training and testing, the best Artificial Neural Network (ANN) architecture model was obtained with 9 input layers, 5 hidden layers and 1 output layer. The output of RMSE 0000699 error value and accuracy percentage is 99.97% so that it can make price predictions according to the given target value.]]>
<![CDATA[Analysis of determining customer priority complaints by using Analytic Hierarchy Process (AHP) techniques in PDAM Tirtauli Pematangsiantar ]]>
<![CDATA[Eka Desriani Aritonang]]>;
<![CDATA[Agus Perdana Windarto]]>;
<![CDATA[Wida Prima Mustika]]>
<![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.v3i2.50
<![CDATA[The purpose of this study is to analyze the priority determination of customer complaints by using ranking techniques in decision support systems. Data sources were obtained from PDAM Tirtauli Pematangsiantar by conducting interviews and direct observation. Data obtained directly at PDAM Tirtauli Pematangsiantar. The technique used is the AHP (Analytical Hierarchy Process) method. The assessment criteria used are: Dead Air (K1), Pipeline Damage (K2), Customer Damage Pipeline (K3) and Damage Meter (K4). The results of the study were obtained from 4 criteria, obtained "Dead Water" (K1) with a value (0.502) as the first rank, and "Service pipe damage" (K2) as the second rank.]]>
<![CDATA[Real Time Unit Monitoring Information Systems Using The Waterfall Method PT. Andhana Kirana Yasa Jakarta ]]>
<![CDATA[Imam Budiawan]]>;
<![CDATA[Muhammad Nasrulloh]]>;
<![CDATA[I Ispandi]]>
<![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.v4i1.82
<![CDATA[PT. Andhana Kirana Yasa is a company engaged in car accessories installation services. The speed in the production process and the quality of products produced become the main value to compete with competitors. But to achieve the desired quality there are still problems that are experienced, first reject the unit of production and second, there is a cripple unit (no part), which is the unit in the production process. Still, the stock parts are exhausted in the warehouse so that accessories cannot be installed. The existing problems if not handled quickly and precisely cause a decrease in productivity. The installation service company has been given a maximum target lead time of 1 unit (car) for 3 hours. To overcome this problem, a monitoring application system/program is needed, which monitors the unit's production conditions directly so that when an obstacle occurs, it can be effectively dealt with. This application program is created using the waterfall method, which has the manufacturing stages starting from Requirement Analysis, System Design, Implementation, Integration and Testing, Operation and Maintenance. The programming languages used are HTML, PHP and JavaScript, MySQL as a database, and Sublime Text Editor as an editor. The purpose of making the application program is to monitor the unit production directly against problems that occur and produce data that is used to evaluate employee performance.]]>
<![CDATA[Android-Based Core Fiber Optic Management Information System Design In PT. Telkom Kebumen ]]>
<![CDATA[Ari Waluyo]]>;
<![CDATA[Ega Latif Permana]]>
<![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.v3i1.31
<![CDATA[The study aims to design the information system of fiber optic core management based on Android at PT.Telkom Kebumen. This study used qualitative method with a descriptive approach. Data collection techniques are carried out by observation and literature study that related to research. The software development method used waterfall. The problem of the research result are data processing of core management still using Ms.Excel so that the data received by technicians is not accurate because the data changes and data additions are slowly, and to looking for data is too difficult because the data that saved on Ms.Excel is too much. There are suggestions that submitted, designing a new information system that can help the job so that can be more effective and efficient. By utilizing the advanced android technology which owned by all technicians.]]>
<![CDATA[Prototype of Temperature Control For Cold Chamber Using Fuzzy Based on Microcontroller ]]>
<![CDATA[Rakhmat Kurniawan. R]]>
<![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.v3i2.63
<![CDATA[Cold Chamber is a small cold room in a cooling system as a storage place for cold air before the air is flowed to the object to be cooled. A cooling system will be greatly influenced by temperature lowering sources. To be able to provide optimal results in a cooling system, it requires a cold chamber whose temperature can be controlled automatically. To be able to control the temperature, a tool is needed. So that the tool can control the temperature, the tool needs a sensor that can read the temperature in the cold chamber, namely DS18B20. This sensor works by reading the temperature of the environment and then sending it to the microcontroller. To control the temperature, the microcontroller will send commands to the fan which functions as a sender of cold air to the cold chamber, and a pump which functions to drain water on the hot side of the cooling element. The cold air source in this tool comes from Peltier TEC1-12706. The reading result and the desired temperature setting will be displayed on the LCD. If the actual temperature is greater than the desired temperature, the fan will blow more cold air into the cold chamber and the pump will flow water faster. If the actual temperature is less than the desired temperature, the fan will stop blowing and the pump will slow down the flow of water. With this tool, it will be easier to adjust the temperature in a cooling system.]]>
<![CDATA[Analysis of Backpropagation Algorithm Using the Traingda Function for Export Prediction in East Java ]]>
<![CDATA[Nur Ahlina Febriyati]]>;
<![CDATA[Achmad Daengs GS]]>
<![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.v4i1.95
<![CDATA[Exports are significant for a country's economic development, especially in regions that carry out export activities because not all countries and regions have the same natural and human resources. Therefore this study aims to predict the level export of oil, gas, and others in the province of East Java and understand the forecast for the number of exports in the coming year. This is important to provide information to the East Java provincial government so that it can make policies so that the export value can be increased, at least so that the export value remains stable. The prediction algorithm used is the Backpropagation Neural Network algorithm using the Gradient Descent training function with Adaptive Learning rate. The research data is data on the Export of oil, gas, and others in East Java Province from 2008 to 2019. The prediction process analysis uses 3 network architecture models, namely: 5-10-1, 5-15-1, and 5-20-1. Based on the analysis results, the 5-10-1 model is the best compared to the other two models with an accuracy rate of more than 90% and MSE testing 0.0012454304, which means that this model is good for predicting the export of oil, gas, and others.]]>
<![CDATA[Cloud Computing Implementation with Docker Engine Swarm Mode for Data Availability Infrastructure of Rice Plants ]]>
<![CDATA[Oktalia Juwita]]>;
<![CDATA[Diksy Media Firmansyah]]>
<![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.v1i2.10
<![CDATA[Data of rice plant is important in Indonesia because rice is the staple food for Indonesian people. Rice plant data can be formatted into web service, so anyone can access the information from anywhere by using internet. But, high numbers of request are the problem for web server apps. One of the solution is by distributing request into some server. In this paper, we will compare failed request and time per request in conventional server and clustered server with docker swarm. Server apps in clustered server shows lower value of failed request than conventional server in our experiment. With two containers, number of failed request obtain 0.78% lower than conventional server in 25.000 requests, and 0.69% lower than conventional server in 50.000 requests.]]>
<![CDATA[Application of the Simple Multi Attribute Rating Technique (SMART) Method on the Selection of Anti Mosquito Lotion based on the Consumer ]]>
<![CDATA[Indra Riyana Rahadjeng]]>
<![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.v3i2.45
<![CDATA[The purpose of this study is to select the best anti-mosquito lotion based on consumers by utilizing decision support system techniques. The method used is the Simple Multi Attribute Rating Technique (SMART). The research data were obtained by observing, interviewing and administering random questionnaires to 150 respondents. The location of the study was conducted in Pondok Cina, Beji District, Depok City, West Java. The results of the questionnaire will be processed first by using Microsoft Excel before using the SMART method in completion. From the questionnaire distribution, several assessment criteria were obtained in the selection of anti-mosquito lotions, namely: Product Safety (C1), Price (C2), Product Quality (C3), Halal Label (C4), Product Health. While the alternatives used in the selection of anti-mosquito lotions based on observations, interviews and questionnaires are: Soffel (A1), Lavenda (A2), Caladine (A3), Autan (A4) and Neem Lotion (A5). the results of the calculation of the SMART method obtained alternative Autan (A4) as the best alternative for anti-mosquito lotion with a value (48.31). The results of the election can be used as information for users of anti-mosquito lotions.]]>
<![CDATA[Promotion Media Recommendations on The Acceptance of New Students In Private Educations With The Simple Additive Weighting Method ]]>
<![CDATA[R. Fanry Siahaan]]>;
<![CDATA[Liber Simbolon]]>
<![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.v4i1.76
<![CDATA[Ensuring the existence of private universities (PTS) is the number of new students who enrol in each new academic year. One of the ways that private universities STMIK Pelita Nusantara to attract new student candidates is through promotion. Promotion is a communication activity carried out to introduce something to the public and simultaneously influence the wider community to buy and use the product. The purpose of this study was to determine which promotional media was more effective which had an impact on the number of new student enrollments at the private tertiary education institution STMIK Pelita Nusantara using the SAW method as a measuring instrument. The steps taken are: determining the value of the criteria for each alternative, determining the weight, normalizing the matrix, and normalizing the decision matrix to a scale that is compared to all the alternative ratings. The result of the research is the promotion mix with the alternative Worth of Mouth promotion as the best media with a value of 1.]]>
<![CDATA[Implementation of Data Mining using the Clustering Method (Case: Region of the Actors of Theft Crime by Province) ]]>
<![CDATA[Frinto Tambunan]]>
<![CDATA[ IJISTECH (International Journal of Information System and Technology) Vol 2, No 2 (2019): May ]]>
Publisher : <![CDATA[Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa ]]>
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DOI: 10.30645/ijistech.v2i2.25
<![CDATA[Theft is a behavior that causes harm to victims who are targeted and cause casualties. This study aims to classify areas of theft crimes based on provision by using data mining techniques. Data was obtained from the Indonesian statistical center (Badan Pusat Statistik) consisting of 34 provinces. The grouping technique used is K-Means. Clusters are divided into 3 namely: C1: areas with high crime rates of theft, C2: areas with crime rates of ordinary theft and C3: areas with low theft crime rates. Data processing is done using the help of RapidMiner software. The results of the k-means analysis obtained 17 provinces in Indonesia have the highest theft crime rate (C1), namely: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Lampung, DKI Jakarta, West Java, Central Java, East Java, Banten, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, South Sulawesi and Papua. The results of the study concluded that more than 50% of regions in Indonesia still had high rates of crime of theft.]]>
