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JISA (Jurnal Informatika dan Sains)
Published by Universitas Trilogi
ISSN : 27763234     EISSN : 26148404     DOI : https://doi.org/10.31326/jisa
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JISA (Jurnal Informatika dan Sains) is an electronic publication media which publishes research articles in the field of Informatics and Sciences, which encompasses software engineering, Multimedia, Networking, and soft computing. Journal published by Program Studi Teknik Informatika Universitas Trilogi aims to give knowledge that can be used as a reference for researchers and can be useful for society. Accredited “SINTA 4” by The Ministry of Research-Technology and Higher Education Republic of Indonesia, Free of Charge (Submission,Publishing). JISA (Jurnal Informatika dan Sains) is scheduled for publication in June and December (2 issue a year) This Journal accepts research articles in these following fields: Software Engineering: Web Development, Mobile Apps Development, Database Management System Multimedia: Augmented Reality, Virtual Reality, Game Development Networking: Cloud Computing, Internet of Things, Wireless Sensor Network, Mobile Computing Soft Computing: Data Mining, Data Warehouse, Data Science, Artificial Intelligence, Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 158 Documents
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Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification Yoga Religia; Donny Maulana
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.925

Abstract

Airline companies need to provide satisfactory service quality so that people do not switch to using other airlines. The way that can be used to determine customer satisfaction is to use data mining techniques. Currently, the website www.kaggle.com has provided Airline Passenger Satisfaction data consisting of 22 attributes, 1 label and 25976 instances which are included in the supervised learning data category. Based on several previous studies, the Naïve Bayes algorithm can provide better classification performance than other classification algorithms. Several studies also state that the use of Naive Bayes can be optimized using Genetic Algorithm (GA) to obtain better performance. The use of Genetic Algorithm for Nave Bayes optimization in classifying Airline Passenger Satisfaction data requires further research to ensure the performance of the given classification. This study aims to compare the use of the Naive Bayes algorithm for the classification of Airline Passenger Satisfaction with and without GA optimization. The data validation process used in this study is to use split validation to divide the dataset into 95% training data and 5% testing data. The test results show that the use of GA on Naive Bayes can improve the classification performance of Airline Passenger Satisfaction data in terms of accuracy and recall with an accuracy value of 85.99% and a recall of 87.91%.
DDoS ATTACK MITIGATION WITH INTRUSION DETECTION SYSTEM (IDS) USING TELEGRAM BOTS Mohammad Taufan Asri Zaen; Ahmad Tantoni; Maulana Ashari
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1043

Abstract

 In the current IS/IT era, service to consumers is an absolute must to be prepared to survive in business competition. Physical and logical attacks with the aim of disrupting information technology services for individuals/agencies/companies or reducing the performance of IS/IT used. The development of IoT in the industrial revolution 4.0, which is all online, is a challenge in itself, from a negative point of view, all of them are able to carry out attacks on ISP servers, often carried out by hackers. DDoS (Distributed Denial of Service) attacks are the most common attacks. The development of software for DDoS attacks is very much on the internet, including UDP Unicorn software to attack very easily and can be done by anyone. Software for real-time monitoring of DDoS attacks, one of which is the Telegram bot. Telegram is a messaging system centered on security and confidentiality, while bots are computer programs that do certain jobs automatically. Telegram bot is free, lightweight and multiplatform. In the case study, this research contains 10 access points to the internet that will be mitigated from DDoS attacks. In this study, it was found that DDoS attacks caused traffic to become very high/congested by fulfilling upload traffic so that legitimate traffic users could not access the internet, connection to the internet was slow, the traffic was also unnatural, making it unable to connect to wireless devices and making Mikrotik  login page becomes unable to appear. The purpose of this study is to mitigate DDoS attacks with the help of telegram bots so as to facilitate the notification of DDoS attacks in the event of an attack so that it is fast to deal with and find the perpetrators of the attack. The conclusion of this study is that DDoS attacks using UDP unicorn software resulted in a traffic spike of 53.5 Mbps on the upload traffic side, causing traffic for legitimate/authenticated users to slow down. By using telegram bots to know DDoS attacks occur in real time with a success rate of attack detection up to 100% notifications on telegram bots. Mitigation of DDoS attacks takes steps to track users using the torch feature on the routerboard interface menu, trace internet connection lines using wired or wireless transmission media, and ensure always monitoring the proxy interface from winbox. 
Climate Prediction Using RNN LSTM to Estimate Agricultural Products Based on Koppen Classification Novia Andini; Wiranto Herry Utomo
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.911

Abstract

The yield of an agricultural process is very important and influential, where the harvest is used as a support for human life both as food and a source of income. Many factors can influence the success of agriculture, such as the climate that is going on around in the surrounding area. The wrong prediction in determining the future climate will cause crop failure due to incompatibility with the type of plant. In this era, many technologies have been able to predict climate, one of which is technology machine learning that has many types and techniques, which machine learning technology has been widely used in predicting many things. This study aims to predict the climate in an area which is intended to determine crop yields based on the Koppen classification, and also the prediction based on several parameters such as temperature, humidity, duration of sun exposure and rainfall. And the results of this study is have a loss of 0.006 and with the MAPE value as an indicator of the percentage error and as an indicator for determining the accuracy of the prediction results, which is 3.29%, which means that it is included in the very accurate category in predicting climate to estimate agricultural yields.
Assessment of Employee Using Simple Multi-Attribute Technique Exploiting Rank (SMARTER) and Behaviorally Anchor Rating Scale (BARS) Method Heni Sulastri
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.944

Abstract

Lecturers' active role as the spearhead of higher education has an essential role in improving higher education quality and sustainability. Therefore, assessing work behaviour is needed to measure how lecturers participate in achieving the vision and mission, quality improvement, and service guarantee to students and complementary documentation. This condition became the basis of research. They are implementing decision support systems with Simple Multi-Attribute Rating Technique Exploiting Ranges (SMARTER) and Graphic Rating Scale (GRS) to measure a lecturer's behaviour by using multiple criteria. With the SMARTER method and  Behaviorally Anchor Rating Scale (BARS). By applying the impermeable BARS method, the work behaviour assessment process results in ease and accuracy that is more in line with the employees' behaviour being assessed. With the SMARTER approach, an assessment of employee work behaviour is produced, with 90% of alternatives used. The results are Good.
Application of Information Gain to Select Attributes in Improving Naïve Bayes Accuracy in Predicting Customer's Payment Capability Herfandi Herfandi; Mohammad Taufan Asri Zaen; Yuliadi Yuliadi; M. Julkarnain; Fahri Hamdani
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1044

Abstract

The customer is the main factor in the running of PT. XYZ. A good understanding of customers is very important for predicting the capability of customers to pay. The implementation of credit collectibility is used to determine the quality of customer credit, one of which is the customer's capability to pay interest and principal on time. While manually, it is very difficult to accurately predict the capability of customer credit payments. Data mining techniques with the Naïve Bayes algorithm were chosen to classify customers to be able to find patterns, analyze and predict, because they have good performance, are efficient, and simple. The Naïve Bayes algorithm has a weakness in terms of sensitivity to many attributes, so the accuracy is low. Based on the problem stated, his study will apply the Information Gain method to select the most influential attribute on the label in order to increase the accuracy of the Naïve Bayes algorithm. This research produces a new dataset with seven attributes: TENOR, SALARY, DOWN PAYMENT, INSTALLMENT, APPROVAL, OTR CLASS, AGE with Labels: Status and Id: Id number based on the Information Gain method. The dataset comparison process with 995 data records showed an increase in accuracy, precision, and AUC using the new dataset compared to the old dataset, but in the t-Test test with an alpha value = 0.05 there is a difference but not significant. In the evaluation process, performance experienced a significant increase in the use of new datasets with the following percentages of performance improvement: accuracy = 8%, precision = 18.42%, recall = 17.65% and AUC= 0.057%. The results of this study obtained AUC of 0.876, accuracy of 87.88%, precision of 61.90%, and recall of 76.47%, and classified into good classification. 
Features Selection based on Enhanced KNN to Predict Raw Material Needs on PT. SANM Siti Aisyah Naili Mutia; Tjong Wan Sen
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.912

Abstract

Raw material inventory must be able to meet production needs. So it is necessary to plan / predict raw material needs in the following month to determine the raw material inventory. Currently PT. SANM uses a manual counting method, the expenditure of raw materials for six months, then deducts the current raw material inventory. As a result, there are raw materials that are over order or lacking, which causes production to be constrained. The manual calculation method is not effective enough to meet the raw material inventory. In this research, the researcher proposes an algorithm which is contained in Data Mining, that is Enhanced KNN using GWO to predict raw material needs. Because GWO and Enhanced KNN algorithms give the results are easy to understand, have good accuracy compared to other machine learning methods, can cover the trapped problem from KNN traditional and capable of improving the accuracy using feature selection method. The method used in this study is to compare Enhanced KNN with and without GWO that gives a significant increase in the accuracy value by 16.5%, from 44.6% to 61.1%.
Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications Yoga Religia Religia; Gatot Tri Pranoto; I Made Suwancita
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.946

Abstract

The selection of prospective customers who apply for credit in the banking world is a very important thing to be considered by the marketing department in order to avoid non-performing loans. The website www.kaggle.com currently provides South German Credit data in the form of supervised learning data. The use of data mining techniques makes it possible to find hidden patterns contained in large data sets, one of which is using classification modeling. This study aims to compare the classification of South German Credit data using the Naïve Bayes algorithm and compare the classification of South German Credit data using the Naïve Bayes algorithm with particle swarm optimization (PSO). The test was carried out using a confusion matrix to determine the accuracy, precision and recall values of the research model. Based on the test, it is known that PSO is able to increase the accuracy and recall of Nave Bayes, but PSO has not been able to increase the precision value of Nave Bayes. The test results show that PSO optimization gives Naïve Bayes an increase in the value of accuracy by 0.46%, and gives Naïve Bayes an increase in recall value by 3.02%. 
The Design of a Monitoring Application System for The Production of Foam Products Using the UML And Waterfall Methods Henny Yulianti; Gatot Tri Pranoto
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1045

Abstract

The development of information technology, which is followed by a higher level of competition in the foam product industry, encouraging companies to manage their company's resources properly and to plan effective, systematic and mature activities within the company. As a company with a variety of products, the most dominant problem is in the productivity process. Production is the most important part of a manufacturing company, where in carrying out its production activities this company produces based on orders from customers (Job Orders). And the problems that often occur are planning revisions in the midst of production and changing production schedules between groups (lines), delays in production planning in terms of prioritizing planning, and still being done manually in making daily reports. By implementing monitoring, which is the supervision and control of an activity where measurements and evaluations are completed repeatedly from time to time, monitoring is carried out for the purposes of the company and to maintain ongoing management. Monitoring will provide information about the status and trend of production activities towards the company's goals. The solution to this production problem is to build a web-based foam product production monitoring system application using the Waterfall method which is integrated with UML the method used is use case diagrams, activity diagrams, sequence diagrams, class diagrams and component diagrams and software development with PHP and MySQL technology. With Black box testing, it is proven that the design of this foam production monitoring system application can assist the company's foam product production activities in fulfilling customer orders and accurate reports so that it becomes effective and efficient. in improving the productivity and performance of the company.
Combining Super Resolution Algorithm (Gaussian Denoising and Kernel Blurring) and Compare with Camera Super Resolution Muhamad Ghofur; Tjong Wan Sen
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.914

Abstract

This problem addresses the problem of low-resolution image (noisy) that will proof later by PSNR number. The best way to improve this low-resolution problem is by utilizing Super Resolution (SR) algorithm methodology. SR algorithm methodology refers to the process of obtaining higher-resolution images from several lower-resolution ones, that is resolution enhancement. The quality improvement is caused by fractional-pixel displacements between images. SR allows overcoming the limitations of the imaging system (resolving limit of the sensors) without the need for additional hardware. This research aims to find the best SR algorithm in form of stand-alone algorithm or combine algorithm by comparing with the latest SR algorithm (Camera SR) from the previous research made by Chang Chen et al in 2019. Furthermore, we confidence this research will become the future guideline for anyone who want to improve the limitation of their low-resolution camera or vision sensor by implementing those SR algorithms.
Water Quality Monitoring System with Parameter of pH, Temperature, Turbidity, and Salinity Based on Internet of Things Yazi Adityas; Muchromi Ahmad; Moh Khamim; Khalis Sofi; Sasmitoh Rahmad Riady
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.965

Abstract

This research aims to monitor the quality of water used for aquariums. The physical parameters used are water pH, water temperature, water turbidity, and water salinity. Using a pH sensor, temperature sensor, turbidity sensor, and salinity conductivity sensor with Arduino as the controller. The prototype method used in this research, starting from the formulation, research, building stages to testing and evaluating the results of the research. The working process of the system is when the system is activated, the sensors will detect and capture the amount of value contained in the water, then the data from the sensor is sent to a database in the cloud using an ethernet shield that is connected to the media router as a liaison for the internet network then displayed on the website dashboard in the form of graphs and monitoring record tables in real time. The sensors function to detect water quality, where quality standards have been set in this system, namely temperature standards of 27-30°C, pH standards of 7.0-8.0, turbidity standards of 2.5-5 ntu, and salinity of 20-28 ppt. If the sensor detects non-compliance with water quality standards, the buzzer in this system will sound. From the results of system testing, sensors can detect water quality in real time within 5-10 seconds. Based on the research results, this water quality monitoring system is effective to help ensure the quality of the water in the aquarium so that it always meets the standards.

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