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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 24 Documents
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Search results for , issue "Vol. 5 No. 2B (2021): Article Research October 2021" : 24 Documents clear
TOPSIS Method Application in Choosing The Most-Sale POS Cashier Machine Stuffs and Tools in PT. Mahadana Wikasita Handayani, Rani Irma; Normah, Normah; Wironoto, Deni
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.10898

Abstract

PT. Mahadana Wikasita is a company engaged in the sale of machine goods and pos cashier tools. In order to face business competition there are some problems that often arise regarding the sale of goods. PT. Mahadana Wikasita is lacking in monitoring the goods sold, what items consumers need and the storage of data is less effective, so the company can not determine exactly which goods to buy. Therefore, a decision support system is needed that can help solve this problem. In this research, the decision support system used is by Techinique for Order Reference by Similarity to Ideal Solution (TOPSIS) which consists of seven stages using several criteria such as price, type, quality and customer interest. In the test results calculated using the Method For Order Preference bySimiliarity to Ideal Solution (TOPSIS) it can be concluded that the highest value is kios Pakmo mobile cashier application package with a value of 0.920, can be interpreted as the best selling item for one year.
Selection of the Best Swimming Athletes using MCDM-AHP and VIKOR Methods Akmaludin, Akmaludin; Sidik , Sidik; Iriadi, Nandang; Arfian, Andi; Surianto, Adhi Dharma
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.10998

Abstract

The process of selecting the best swimming athletes is carried out in several test stages, the first is the ability in the four basic swimming styles which often contested in international competitions and the second test is the basic physical abilities possessed by a number of swimming athletes. The tests related to the swimming style consist of breaststroke swimming, butterfly swimming, backstroke swimming and crawlstroke swimming, while the plyometrics test consists of banded knee jump, squat jump, jump to side, and dept jump, Due to the large number of selections, a test is required for every athlete. The purpose of this selection is to find the best swimming athletes who will be competed in the international swimming class event. The nine athletes of millenium aquatic swimming club that were selected previously, they are the forerunners of the selected swimming athletes and will be evaluated on a representative basis, which is the best among the nine athletes. The method used in the evaluation and selection process uses two continuous methods, namely the AHP and VIKOR methods. From the results selection assessment, it was found that the best three of the nine nominations selected, the first position selected was AT2 with an index 0.00, the second position was AT8 with an index 0.25 and the third position was AT7 with an index 0.61. Thus it can be concluded that the AHP and VIKOR methods can be used as decision support to determine optimally in the optimal selection process for swimming athletes.
Decision Support System for Millennial Generation Softskill Competency Assessment using AHP and Eliminate Promethee Method Akmaludin, Akmaludin; Sihombing, Erene Gernaria; Dewi, Linda Sari; Rinawati , Rinawati; Arisawati, Ester
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11057

Abstract

The current millennial generation has the soft skills needed to follow the trends and technology of the industrial generation 4.0. It is clear that many Millennials look more energetic and always synergistic with destructive situations and conditions. Industry 4.0 generation makes the business world switch to always using advanced technology in various sectors so that technological progress is felt faster than before, and human power is starting to be replaced by machine power, robotics, and even artificial intelligence. Thus, soft skills for the millennial generation are needed to get job opportunities in conditions where the need for human labour has begun to be eliminated in their work. The purpose of this paper is to assess the soft skills competencies possessed by the millennial generation, who are always involved with technological advances in the very fast business industry world. There are eight soft skills that the millennial generation must possess, namely critical thinking, communication, analyzing, creative and innovation, leadership, adaptation, cooperation and public speaking. The method used to select soft skills competencies for job opportunities for the millennial generation is the Analytic Hierarchical Process (AHP) method in collaboration with the Promethee elimination method. The final result of the decision support for soft-skill competency selection from 23 millennial generations, who passed the selection, was 43% (10 users) with a positive score and 57% (13 users) who experienced selection failure. This failure was due to having a negative score. Thus, the collaboration of the AHP and Promethee Elimination methods can provide optimal results for decision-making support.
Sensor lampu lalu lintas jalur kereta api untuk mengantisipasi kemacetan di persimpangan jalan Husein, Amir Mahmud; Willim, Alfredy; Nainggolan, Yandi Tumbur; Simanggungsong, Antonius Moses; Banjarnahor, Prayoga
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11074

Abstract

Traffic congestion is a problem that has long occurred in Indonesia, especially in big cities. Traffic congestion that occurs can cause various losses, one of which is time loss because it can only run at a very low speed. Then it will create a waste of energy, because going at low speed will require more fuel. Congestion is also able to increase the saturation of other road users, not only that traffic jams also have a bad impact on nature which causes air pollution. And there are many more impacts of traffic jams that can make traveling very uncomfortable. One of the locations of traffic jams often occurs on roads located around railroad crossings. Therefore, In this study, it is proposed to make a traffic light sensor adjacent to the train track to anticipate long traffic jams based on atmega8 and infrared sensors, with the stages of collecting data, recording transportation activities at the location of the jam, then designing a sensor device. The system built is to read the volume of vehicles on the road and prioritize the road with the highest volume of vehicles to get the green traffic light condition. Based on the results of the manufacture of infrared sensors and atmega8 can be tested to reduce the level of congestion at crossroads adjacent to the railroad.
Model Prediksi Dengan Machine Learning Terhadap Keberhasilan Mahasiswa Dalam Pembelajaran Online Yennimar; Manihuruk, Rohni Endetta; Br Hotang, Etis Landya
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11095

Abstract

The learning system during the Covid-19 pandemic was carried out online, online learning had a negative impact and a positive impact. The impact given can affect the success of student learning. The success of learning is the main thing that must be achieved by students. From the success of learning, it can be seen that the online learning process is going well or not. To determine the success rate of online learning, testing is carried out by applying a neural network algorithm. Neural network algorithms are used because they can solve complex problems related to prediction. This research is expected to help lecturers or campus parties to create better online learning. In this study using student grade data for Academic Year 2018/2019 and Academic Year 2019/2020, data testing using Rapidminer software and operator cross validation. In testing the Academic Year 2018/2019 and Academic Year 2019/2020 value data using 700 training cycles, 0.4 momentum, 0.2 learning rate and hidden layer 2. The level of accuracy obtained in the 2018/2019 student grade data is 95, 55% and Academic Year 2019/2020 which is 93.17%. From the test results, it was found that the accuracy rate of Academic Year 2018/2019 is higher than Academic Year 2019/2020, so the success rate in Academic Year 2018/2019 before the pandemic is better than the success rate in Academic Year 2019/2020 after the pandemic.
Data Mining using K-means method for feasibility selection of Non-cash food Assistance recipients in the Era of Covid-19 Rusdiansyah, Rusdiansyah; Supendar, Hendra; Tuslaela, Tuslaela
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11101

Abstract

All countries in the world are currently experiencing a severe economic crisis following the outbreak of the COVID-19 outbreak. In Indonesia, the Large-Scale Social Restriction (PSBB) policy is reported to have increased the number of poor people. Social assistance is a government program to improve the social welfare of the lower economic community. In carrying out the program, the central government and local governments coordinate with each other so that the program is right on target without any element of fraud. In the neighbourhood of Rukun Warga 001, Kelapa Dua Village, there are still obstacles in selecting the eligibility for social assistance recipients, namely Non-Cash Food Aid. The data on the poor are not in accordance with the actual conditions. In this study, to implementing data mining with the K-Means Algorithm. The K-Means Clustering algorithm is used to classify people who are classified as eligible to receive social assistance and those who are not entitled to receive social assistance. The data sample used is the data of Rukun Warga 001, Kelapa Dua Village. The results of this study indicate that cluster 1 with the appropriate category of receiving social assistance according to government programs in the Rukun Warga 001 neighbourhood of Kelapa Dua sub-district amounted to 13 families. And cluster 2 in the category of not eligible to receive social assistance amounted to 97 heads of families out of a total of 110 heads of families in RW 001.
Application of the C4.5 Algorithm on the Effect of Watching Youtube Videos On the Development of Early Childhood Creativity Sintawati, Ita Dewi; Widiarina, Widiarina; Mariskhana, Kartika
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11116

Abstract

Youtube media is one of the social media for communication used by the community, not out of reach of children. The rapid development of information technology, of course, this has an influence on human life. Talking about life in humans, it cannot be separated from human behavior. Some psychologists say that children tend to fully absorb what they see, and children will learn from what they see. This can trigger creativity for young children. The creativity of each individual can be seen in terms of how he makes something he thinks of because he sees an object that already exists and then he innovates it into a new form. This attracted the attention of the author to identify and describe the impact of watching YouTube videos on the development of early childhood creativity. This type of research is based on developing phenomena, how much influence is brought about by technological advances on YouTube social media in the formation of children's behavior. The process of completing the goals to be achieved in this study is to provide information about recommendations for child development with positive creativity, making it easier to determine early childhood development by using the Decision Tree Algorithm C4,5 method. The problem in this study is that early childhood imaginations are higher and will be affected by streaming video ads on YouTube. The results showed that children aged 3 and 4 years often watched, while children aged 5 and 6 years did not watch often, so the role of parents was more dominant in supervising children aged 3 and 4 years.
Bot to Monitor Student Activities On E-Learning System Based On Robotic Process Automation (RPA) Munawar, Ghifari
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11128

Abstract

Student activities in the e-learning system need to be monitored regularly by lecturers to observe their learning achievements. The monitoring process carried out is monitoring student attendance, collecting assignments, and taking quizzes. This will be a burden if it is done regularly, especially if the lecturer teaches many subjects. Robotic process automation (RPA) is a technology that uses software agents (bots) to imitate human work processes to be automated. The objectives of this research are (1) applying RPA technology as bots that can monitor student activity on the e-learning system (Moodle), and (2) measuring the time efficiency of RPA bots in processing their work. The research stages are divided into three, namely: the preparation stage, the RPA implementation stage, and the evaluation stage. The preparation stage is carried out to define the problem to be handled, the RPA implementation stage to develop bots using the UiPath platform, and the evaluation stage to compare the efficiency of work time between bots and manually (by humans). The RPA bot was developed on four work focuses, namely (1) attendance monitoring, (2) task collection monitoring, (3) quiz processing monitoring, and (4) email delivery automation. Efficiency testing was carried out on four test scenarios (FR1, FR2, FR3, FR4), where FR2 had the highest percentage of work time efficiency at 754%, and the lowest was in FR1 with a percentage of 165%, with an overall average efficiency percentage of 444%. Thus, through RPA technology, monitoring work becomes faster and saves effort.
Solo City Batik Design Security System (SiKemTi Solo) During the Pandemic Covid-19: Inggris Kusanti, Jani; Sudalyo, Ramadhian Agus Triono
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11141

Abstract

Safeguarding copyright on traditional batik works is very important to prevent duplication of Indonesian cultural products. The ease of duplicating, especially in batik designs, causes frequent violations of the design's copyright. Especially with the rise of online commerce today, it is easier for everyone to use other people's products and copy other people's products. This will not happen if there is already a system that can be used to secure the design work. For this reason, it is important to develop methods that can be used to secure traditional batik works, especially Surakarta. Based on these problems, a research was conducted on the application of image watermarking techniques that are resistant to changes generated by image processing in the form of compression. The method used to secure Surakarta batik works is compression using wavelet transformation. The aim is to develop a batik design security system method using the watermarking method. The steps taken started with taking photos of batik designs and the designer's name, photos in the form of images followed by the embedding process using the watermarking method. The watermarking method used is the DWT method. After encryption, identification is carried out to determine the level of errors that occur. The results of testing 323 batik image data in this study obtained an average mse level = 0.00000065 and an average psnr result = 188.471186102179. From the results of this study, it was found that the development of methods that can be used to secure batik products by using the watermarking method.
Bayesian Pixel Density Estimation Modeling to Detect Human Sperm Sample Image Based on Sperm Head Shape Zonyfar, Candra; Baihaqi, Kiki Ahmad
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11148

Abstract

Currently, there is a problem of the difficulty in classifying human sperm head sample images using different databases and measuring the accuracy of several different datasets. This study proposes a Bayesian Density Estimation-based model for detecting human sperm heads with four classification labels, namely, normal, tapered, pyriform, and small or amorphous. This model was applied to three kinds of datasets to detect the level of pixel density in images containing normal human sperm head samples. Experimental results and computational accuracy are also presented. As a method, this study labeled each human sperm head based on three shape descriptors using the formulas of Hu moment, Zernike moment, and Fourier descriptor. Each descriptor was also tested in the experiment. There was an increased accuracy that reached 90% after the model was applied to the three datasets. The Bayesian Density Estimation model could classify images containing human sperm head samples. The correct classification level was obtained when the human sperm head was detected by combining Bayesian + Hu moment with an accuracy rate of up to 90% which could detect normal human sperm heads. It is concluded that the proposed model can detect and classify images containing human sperm head objects. This model can increase accuracy, so it is very appropriate to be applied in the medical field

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