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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 730 Documents
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Development of District Civil Service Applications Fadhli Ranuharja; Ambiyar; Yose Indarta; Agariadne Dwinggo Samala; Ika Parma Dewi
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11377

Abstract

This study aims to provide the needs for institutions such as urban villages to provide services, information to residents, making it easier to socialize activities and assist in the administration of correspondence permits from the urban village. The new system needs to be implemented in Tanjung Ayun Sakti Village as a solution to overcome obstacles in accessing information and services in Tanjung Ayun Sakti Village. The application of a population service information system can have a fairly good impact and be beneficial for all interested parties. The development of this system uses the PHP programming language, CodeIgniter framework, XAMPP as a database server for simulation on localhost and the SDLC (Software Development Life Cycle) method. From the results of the development of this information system, the validity test was carried out by experts, then the practicalists are very good. aims to assist the population service process in the form of sending a permit online.
Heavy-loaded Vehicles Detection Model Testing using Synthetic Dataset Daniel Avian Karjadi; Bayu Yasa Wedha; Handri Santoso
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11378

Abstract

Currently, many roads in Indonesia are damaged. This is due to the presence of large vehicles and large loads that often pass. The more omissions are carried out, the more damaged and severe the road is. The central government and local governments often carry out road repairs, but this problem is often a problem. Damaged roads are indeed many factors, one of which is the road load. The road load is caused by the number of vehicles that carry more than the specified capacity. There are many methods used to monitor roads for road damage. The weighing post is a means used by the government in conducting surveillance. This research is not a proposal to monitor the road, but this is only to create a model for the purpose of detecting heavily or lightly loaded vehicles. This research is to classify using Convolutional Neural Network (CNN) with pre-trained Resnet50. The model generated from the Convolutional Neural Network training process reaches above 90%. Generate Image deep learning algorithms such as the Generative Adversarial Network currently generate a lot of synthetic images. The testing dataset that will be used is generated from style transfer. The model is tested using a testing dataset from the generated style transfer. Style transfer is a method of generating images by combining image content with image styles. The model is pretty good at around 92% for training and 88% for testing, can it detect image style transfer? The Convolutional Neural Network model is said to be good if it is able to recognize the image correctly, considering that the accuracy of the model is very good. One of the reasons why the training model is good but still makes errors during testing, then the image dataset is overfitting
Compare VGG19, ResNet50, Inception-V3 for Review Food Rating Andrew Andrew; Handri Santoso
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11383

Abstract

The food industry is undergoing a phase of very good improvement, where business actors are experiencing very rapid growth. Creative ideas are many and creative on several social media. When an online business is growing rapidly, many managers in the food sector market their products through online media. So it is quite easy for customers to place orders via mobile. Especially during the COVID-19 pandemic, where a ban on gatherings has become a government recommendation for many food business actors to sell online. Since then, almost all food industry players have made their sales online. There are many advantages of doing business online. The food served is in the form of pictures that attract market visitors so that it can create its own charm. Food is just a click away to order, and the order comes. No need to queue and everything has been delivered to the ordered goods. After the ordered goods arrive, the customer reviews the food or drink. Because customer reviews are the result of customer ratings. The result of the review is one of the sentiment analyses, which in this study is in the form of a review of the images available on the display marketplace. The method used is Convolutional Neural Network. The dataset will be extracted features and classifications. The research will do a comparison using VGG19, ResNet50, and Inception-V3. Where the accuracy of VGG19 = 96.86; Resnet50 : 97.29; Inception_v3 : 97.57.
CycleGAN and SRGAN to Enrich the Dataset Budi Priswanto; Handri Santoso
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2022): Articles Research Volume 7 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11384

Abstract

When developments in the field of computer science are growing rapidly. For example, the development of image or video predictions for various fields has been widely applied to assist further processes. The field of computer vision has created many ideas about processing using deep learning algorithms. Sometimes the problem with using deep learning or machine learning is in the availability of the dataset or the unavailability of the dataset. Various methods are used to add to or enrich the dataset. One way is to add an image dataset by creating a synthetic image. One of the well-known algorithms is Generative Adversarial Networks as an algorithm for generating synthetic images. Currently, there are many variations of the GAN to around 500 variants. This research is to utilize the Cycle GAN architecture in order to enrich the dataset. By doing GAN as a synthetic image generator. This is very important in procuring image datasets, for training and testing models of Deep Learning algorithms such as Convolutional Neural Networks. In addition, the use of synthetic images produces a deep learning model to avoid overfitting. One of the causes of the overfitting problem is the lack of datasets. There are many ways to add image datasets, by cropping, continuously rotating 90 degrees, 180 degrees. The reason for using Cycle Generative Adversarial Networks is because this method is not as complicated as other GANs, but also not as simple. Cycle GAN synthetic images are processed with Super Resolution GAN, which aims to clarify image quality. So that it produces a different image and good image quality.   
Internet of Things-based Agricultural Land Monitoring Andrew Andrew; Haryono Haryono
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11416

Abstract

Agriculture is an industrial sector that produces raw materials such as rice, corn, and agricultural products. In the current era, there should be no problem if there is a food shortage because society, industry, and education do not make a real contribution to supporting the agricultural industry. The state also needs good agricultural land, so that the state can fulfill the needs of its people. Without good agriculture, a country will not be able to meet the needs of its people. Modern society today is not or is rarely concerned with agriculture. Agriculture is carried out only by providing fertilizer, water, and land, paying attention to the quality of the agricultural land. One of the problems of declining agricultural production is crop failure. One of the reasons island for agriculture. Soil is the most important part of the world of agriculture. If the land is not cultivated then the land is difficult to become an ideal place for agriculture. The Internet of Things can be used as a solution to problems by tilling the soil and monitoring soil conditions. In conditions in the dry season, soil moisture needs to be done by water. In the rainy season, the land should not be flooded, let alone submerged and flooded. In order to maintain the balance of moisture and waterlogged soil, the Internet of Things is a solution for monitoring and managing agricultural land. Internet of Things is a device that can communicate with each other from one device to another, such as sensors and actuators. Good land cultivation makes agricultural land fertile. Agricultural land processing is maximized by adding a monitoring system for agricultural land using a micro-controller Arduino Uno, NodeMCU ESP8266, several sensors, and integrated devices. The purpose of this research is to make a prototype that is useful for monitoring agricultural land
Learning Fuzzy Neural Networks by Using Improved Conjugate Gradient Techniques Hisham M. Khudhur; Khalil K. Abbo
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11442

Abstract

One of the optimal approaches for learning a Takagi Sugeno-based fuzzy neural network model is the conjugate gradient method proposed in this research. For the PRP and the LS approaches, a novel algorithm based on the Liu-Storey (LS) approach is created to overcome the slow convergence. The developed method becomes descent and convergence by assuming some hypothesis. The numerical results show that the developed method for classifying data is more efficient than the other methods, as shown in Table (2), where the new method outperforms the others in terms of average training time, average training accuracy, average test accuracy, average training MSE, and average test MSE.
Analisis Regresi Linier Berganda Pada Faktor yang Berpengaruh Terhadap Motivasi Kerja Karyawan Gracia Theofani; Eko Sediyono
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11453

Abstract

This study aims to analyze the factors that influence employee motivation at PT XYZ. The analysis was carried out using four variables, namely Facilities, Work Environtment, Salary and Jobdesk. In collecting data, this study used primary and secondary data. Primary data were collected by distributing questionnaires to employees at PT XYZ using the Voluntary Sampling Technique, questionnaires were distributed to employees with a willingness to participate in the research. From the questionnaire obtained 71 data as primary data. Secondary data used results of previous studies and data from PT XYZ which was given to assist the research. Furthermore, data processing is done by using multiple linear regression algorithm. The data is processed and analyzed using the RStudio software. Parameter significance test and classical assumption test performed with RStudio. From this research, it is concluded that the variables that have the most influence on employee motivation at PT XYZ are Facilities and Jobdesk, where these variables have a positive effect on employee motivation at PT XYZ. The results of the analysis show that an increase in facilities will increase work motivation by 20,659% and jobdesk will increase work motivation by 27,901%. This research is expected to be the company's decision in its efforts to increase employee motivation.
Arduino Implementation for Development Digital Capacitance Meters as Laboratory Measurement Devices Denny Hardiyanto; Prabakti Endramawan; Ridho Nur Taufiqul Manan; Dyah Anggun Sartika
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11456

Abstract

Electronics Practicum in the Laboratory is a routine activity carried out to support student skills. Capacitors are one of the components that are often used in practice. Capacitors are one of the passive electronic components that have a magnitude value in the form of capacitance in Farad units. The capacitance value indicates the capacitor's ability to store electric charge. However, the value contained on the capacitor label is not necessarily the actual value because the capacitor has a tolerance range. Of course, this is very influential in the measurement and performance of electronic circuits that use capacitors. In addition, another factor that supports this research is that the available measuring instruments, such as the multimeter, are not yet equipped with capacitance measurements. Capacitance meters available in the market are still analog. The purpose of this study is to design a device that can measure the capacitance value of capacitors as a measurement device in a digital laboratory, namely the Digital Capacitance Meter. This device is made using Arduino Uno as a microprocessor for data processing. The method used is to apply the process of charging and discharging the capacitor. In this case, Arduino Uno activates a timer to measure the time required to charge and discharge the capacitor so that the Time Constant value is obtained. By using the formula T = 0.693RC, the capacitance value can be obtained. In testing using 3 different capacitors and 10 times testing on each capacitor, the accuracy of the device is 97.76% and a relative error of 2.24%.
Superior Class to Improve Student Achievement Using the K-Means Algorithm Yopi Hendro Syahputra; Juniar Hutagalung
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11458

Abstract

The accumulation of new student data every year makes searching and processing data difficult, including selecting superior class students according to their talents and abilities. Therefore, the application of the K-Means Clustering data mining method is carried out to support decisions in grouping superior classes. The report card values ​​for each class were used as parameters with a data sample of 80 students and 3 clusters were taken which then resulted in the selection and distribution of superior classes. The purpose of the study was to classify students in the superior class so that they could improve student achievement at SMK Raksana 2 Medan. Results Based on the calculation of the variable distance at the initial centroid with a sample of 80 students and the third iteration, the WCV value is 360.9745 and the BCV value is 7.3575 with a ratio value of 0.0203. Each cluster, namely: Cluster 1 has 43 students including the superior class category. Cluster 2 has 18 students and Cluster 3 has 19 students. Clusters 2 and 3 are included in the regular class category with a total of 37 students. The web-based K-Means application can provide information and solutions needed by schools to classify and determine superior classes so that they can improve student achievement in schools. These results can be used by the school to analyze student achievement and can assist teachers in forming superior classes so as to motivate students to study harder.
Application of Hot Fit Model to Analyze Information Technology Ams (Academic Management System) Elmayati Elmayati; Bunga Intan; Deni Nurdiansyah; Aprilsa Milenia; Yogi Kelpin
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2022): Article Research Volume 7 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11462

Abstract

Bina Insan University has applied a computer-based information system. This system is named AMS (Academic Management System). The application of AMS (Academic Management System) at this time is still experiencing various obstacles and obstacles at the level of user acceptance. This study aims to analyze the results of the evaluation of the success factors for implementing AMS (Academic Management System) using the Hot-Fit Model (Human Organization Technology – Net benefits). This model was chosen because this model can provide an explanation and provide an evaluation of the factors that influence the implementation of a system at the University of Bina Insan Lubuklinggau in terms of Human (Human), Organization (Organization), Technology (Technology), and Net benefits. In addition, the success of implementing AMS (Academic Management System) at the University of Bina Insan Lubklinggau, is also influenced by the support and encouragement from universities to AMS (Academic Management System) users, as well as the availability of adequate facilities within the Bina Insan Lubuklinggau University to use AMS (Academic Management Systems). From the analysis that has been carried out on 80 respondents who have filled out the research questionnaire, the results show that to test the validity of the variables (Human), Organization (Organization), Technology (Technology), and Net benefit, it shows that each question measured on all variables is valid, which indicated by Corrected Item – Total Correlation or (rcount) the entire score of Corrected Item – Total Correlation or (rcount) greater than rtable of 0.220, and for the F test results obtained a value of F = 13.334 with a significance of 0.000. meaning that the variables of human, organization and technology together have a significant effect on net-benefit (Y).

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