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Contact Name
Rizki Wahyudi
Contact Email
rizki.key@gmail.com
Phone
+6281329125484
Journal Mail Official
telematika@amikompurwokerto.ac.id
Editorial Address
The Telematika, with registered number ISSN 2442-4528 (online) ISSN 1979-925X (print) is a scientific journal published by Universitas Amikom Purwokerto. The journal registered in the CrossRef system with Digital Object Identifier (DOI) prefix 10.35671/telematika. The aim of this journal publication is to disseminate the conceptual thoughts or ideas and research results that have been achieved in the area of Information Technology and Computer Science. Every article that goes to the editorial staff will be selected through Initial Review processes by the Editorial Board. Then, the articles will be sent to the Mitra Bebestari/ peer reviewer and will go to the next selection by Double-Blind Preview Process. After that, the articles will be returned to the authors to revise. These processes take a month for a minimum time. In each manuscript, Mitra Bebestari/ peer reviewer will be rated from the substantial and technical aspects. The final decision of articles acceptance will be made by Editors according to Reviewers comments. Mitra Bebestari/ peer reviewer that collaboration with The Telematika is the experts in the Information Technology and Computer Science area and issues around it.
Location
Kab. banyumas,
Jawa tengah
INDONESIA
Telematika
ISSN : 1979925X     EISSN : 24424528     DOI : 10.35671/telematika
Core Subject : Education,
Jl. Letjend Pol. Soemarto No.126, Watumas, Purwanegara, Kec. Purwokerto Utara, Kabupaten Banyumas, Jawa Tengah 53127
Arjuna Subject : -
Articles 7 Documents
Search results for , issue "Vol 14, No 1: February (2021)" : 7 Documents clear
Numerical Simulation of Cholera Epidemic Model with Quarantine Trisilowati Trisilowati; Ari Andari; Muhammad Abdurrahman Rois; Mohamad Hasyim Muzaqi
Telematika Vol 14, No 1: February (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i1.1143

Abstract

Cholera is an acute diarrheal disease that spread quickly in an unsanitary environment, and one of its control measures is employing quarantine. Therefore, this research aims to construct a model for the spread of SIRQB-type (susceptibles, infective, recovered, quarantine, bacteria) infectious diseases through a nonlinear differential equation approach. Furthermore, the equilibrium points condition and their stability were investigated using the standard dynamical analysis method. The results show two points of equilibrium: the disease-free, which always exists and is unstable, and the endemic, which is stable and exists under certain conditions. Also, the simulation carried out support the analysis results, and it shows that the rate of quarantine affects the spread of the infected subpopulation.
Local Sensitivity Analysis of COVID-19 Epidemic with Quarantine and Isolation using Normalized Index Muhammad Abdurrahman Rois; Trisilowati Trisilowati; Ummu Habibah
Telematika Vol 14, No 1: February (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i1.1191

Abstract

This study discusses the sensitivity analysis of parameters, namely the COVID-19 model, by dividing the population into seven subpopulations: susceptible, exposed, symptomatic infection, asymptomatic infection, quarantine, isolation, and recovered. The solution to the ordinary differential equation for the COVID-19 model using the fourth-order Runge-Kutta numerical method explains that COVID-19 is endemic, as evidenced by the basic reproduction number (R0) of 7.5. It means 1 individual can infect 7 to 8 individuals. Then  is calculated using the next-generation matrix method. Based on the value of R0, a parameter sensitivity analysis is implemented to specify the most influential parameters in the spread of the COVID-19 outbreak. This can provide input on the selection of appropriate control measures to solve the epidemic from COVID-19. The results of the sensitivity analysis are the parameters that have the most influence on the model.
Students Grade Grouping to Optimize On-Time Graduation Predictions by Combining K-Means and C4.5 Algorithms (Case Study: University Potensi Utama) Bob Subhan Riza; Sarjon Defit
Telematika Vol 14, No 1: February (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i1.1109

Abstract

Graduating on time is the dream of every student who studies in universities. Some factors that can lead to failure in graduating on time, such as grades, though students are sometimes careless and underestimating this factor, despite knowing that problematic Grade will hinder the student from graduating on time. This research helps the study program to predict which students will graduate on time. There are 2 stages in the research, first is the process of clustering students' data using the K-Means algorithm, while the second stage predicts students' graduation using the C4.5 algorithm. Variable used are Grade, Failing Grade, Specialization, Internship, Thesis, Undergraduate Thesis 1, Undergraduate Thesis 2, and Passing Grade. Using RapidMiner and processing these data using this software can predict students that graduate on time.
An Analysis of COVID-19 using X-ray Image Segmentation based Graph Cut and Box Counting Fractal Dimension Faiz Ainur Razi
Telematika Vol 14, No 1: February (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i1.1217

Abstract

COVID-19 is a disease that spreads relatively quickly. So that many victims are infected by this virus. There are various ways to diagnose the body's infection with the coronavirus. One of them with X-ray results. Detecting COVID-19 with the help of an X-ray sometimes has problems determining the location of the lesion because it is possible because of the large amount of noise in the image. Therefore, the X-ray results will be segmented images using the graph cut algorithm to analyze normal lungs and lungs infected with COVID-19. After obtaining the segmentation results in the form of binary images, the next step is to analyze using the box-counting method's fractal dimensions. From the fractal Dimension results, normal lungs have an average dimension of 1.7890, and lungs infected with COVID-19 have an average dimension of 1.5834. Normal lungs have dimensions larger than lungs infected with the coronavirus due to the lungs' covering by lesions or abnormal conditions in body tissues. This is what causes COVID-19 patients to have complaints of difficulty breathing.
Prediction Model Grade Point Average using Backpropagation Neural Network and Multiple Linear Regression Lusiana Efrizoni; Sarjon Defit
Telematika Vol 14, No 1: February (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i1.1113

Abstract

Education in the 21st century equips students with knowledge and information and the success of achieving academic achievements during the learning process. Students' academic achievement can be seen from various aspects: the Grade Point Average. So far, efforts to predict GPA have not been made. In fact, if the student's Grade Point Average can be predicted from an early age, the study program can implement a policy to improve graduates' quality and make planning, study escort, and guidance more intensive. Based on this urgency, this study aims to produce a predictive model for the GPA of STMIK Amik Riau students in the odd semester of 2019, using the Backpropagation Neural Network algorithm and Multiple Linear Regression. Backpropagation's architectural model is 8 architectures, and 4-5-1 is the best architectural model with MSE at the time of training = 0.00099965532 and MSE during network validation = 0.0038793 with an epoch of 102 iterations and the resulting accuracy value of 95.24%. Meanwhile, the GPA prediction results, after testing using the Multiple Linear Regression algorithm, obtained an MSE value of 0. 0.27966667%, with a Multiple Correlation coefficient (R) of R = 0.9774925 and a coefficient of determination (R2) = 0.95549159. Thus the prediction of student GPA using MLR is accurate because the value of the coefficient of determination (R2) is close to 1.
Comparison of Inverse Kinematics and Forward Kinematics Methods on Walk Cycle Animation Characters Afifah Nur Aini; Ema Utami; Suwanto Raharjo
Telematika Vol 14, No 1: February (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i1.888

Abstract

The 3D animation industry is currently growing rapidly, but the process of animating 3D characters is not always fast, because it is often constrained at the animation stage due to the complexity or irregularity of the function of each rig on the 3D character object, therefore it takes a proper rig creation stage to support the animation process that is more efficient in terms of process and time. Kinematics in animation is used for reference when an object is moving. The animation uses a Kinematics approach to display natural results. This research aims to study the level of effectiveness in terms of the time span required to drive the 3D Walk cycle animation using the attached Kinematics & Advanced Kinematics methods. The animation reference used was a standard human Walk cycle with the extent for each part of the body to be animated such as head animation, hand animation, foot animation, and bed animation to complete a walking compilation of animated Walk cycle. The execution of each part is carried out by the inverse kinematics method and then proceed with the advanced kinematics method. Based on the results of the implementation in each section of the walk cycle by comparing the two methods, Inverse Kinematics is an effective method for animating the legs and the head. While the Forward Kinematics method is more effective in animating the hands, body parts, and finishing movement. The results of the comparison show that the level of time effectiveness in human character 3D animation movements using the inverse kinematics method compared to forward kinematics are 31.18% in body animation, 40.46% in foot animation, 13.94% in hand animation, 2.04% in head animation motion, and 7.61% for finishing walk cycle movement.
Image Quality Analysis of PNG Images on WhatsApp Messenger Sending Fahmi Anwar; Abdul Fadlil; Imam Riadi
Telematika Vol 14, No 1: February (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i1.1114

Abstract

Technology is growing rapidly, especially in communication with various types of information services such as internet-based messages. One of the most popular internet-based messages in Indonesia is WhatsApp Messenger. WhatsApp is a chat application that can be used on many platforms. Message sending on WhatsApp is carried out end-to-end encryption from the sender to the message recipient. The sending of messages in PNG images is secured using end-to-end encryption and compressed according to predefined rules. This study analyzes Image Compression and Alpha channel in PNG by comparing PNG images before being sent with PNG images that have gone through the sending process on WhatsApp using the test-driven development (TDD) method. The analysis results contain comparisons based on the RMSE, SSIM, PSNR, and MD5 hash values. Delivery with a gallery image attachment type using an image transparent background changes to a white image background. While those with a background other than transparent have good image quality because it has a PSNR value of more than 35 dB, and submissions with document attachment types do not experience changes in MD5 hash value and image quality.

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