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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 1,196 Documents
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Implementation of the Bayes Method for diagnosing tuberculosis Nina Sari; Volvo Sihombing; Deci Irmayani
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10903

Abstract

Tuberculosis is an infection caused by acid-resistant bacilli or an infectious disease that can attack anyone through the air is also a dangerous infectious disease besides it is also a chronic or chronic disease that can strike between the ages of 15-35 years. The purpose of this study is to help prevent tuberculosis by implementing an expert system using the Bayes method. The method used in this research includes identifying problems faced in the medical world for treating tuberculosis, analyzing the problem, then formulating the problem and applying an expert system with the Bayes method to solve the problems that are obtained, the next stage is designing an application as needed, testing the application with the aim of knowing the success rate of the system. The implementation of the Bayes method in diagnosing tuberculosis is found. The result is that the calculation process using the Bayes method is based on the symptoms experienced by the patient. It can be seen that the patient is "most likely" to have pulmonary tuberculosis with a confidence value of 0.64 or 64%. From the results of the research conducted, it can be concluded that in diagnosing Tuberculosis by using the Bayes method expert system, it can help medical parties handle cases more quickly in terms of recognizing the symptoms of Tuberculosis so that people quickly know the disease they are experiencing.
Implementation of the Bayes theorem method for identifying diseases of children under five Ramadhani, Macro; Sihombing, Volvo; Masrizal, Masrizal
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10907

Abstract

Disease is very susceptible to occur in children under five because the immune system in children under five has not been fully developed. Lack of knowledge about the diseases of children under five and the symptoms they experience makes parents fearful. The lack of knowledge of children's diseases from experts can result in delayed treatment. Problems that occur can be overcome by utilizing artificial intelligence technology. One of the artificial intelligence technologies is an expert system. Information needs very quickly from an expert to deal with problems or diseases of children under five that are expected by parents or society. So that is what drives the development of a software application, namely an expert system for the identification of diseases of children under five. An expert system for the identification of toddlers' diseases is made as a tool to diagnose diseases experienced by toddlers by using the symptoms experienced by toddlers as a tool to detect diseases experienced by children under five. The system can identify 5 types of disease with 23 symptoms of disease. This expert system uses the development method of problem identification, system design, implementation and testing. Inference in this expert system uses the Bayes theorem method. This system is built with Visual Basic and Microsoft Access as the database. The results of consulting tests with this system show that the system is able to determine the disease along with the initial treatment and treatment solutions that must be carried out, based on the symptoms previously selected by the user.
Deteksi Plagiarisme Skripsi Mahasiswa menggunakan Metode Cossine Similarity Oppi Anda Resta; Aditya, Addin; Febry Eka Purwiantono
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10909

Abstract

The main requirement for graduation from students is to make a final scientific paper. One of the factors determining the quality of a student's scientific work is the uniqueness and innovation of the work. This research aims to apply data mining methods to detect similarities in titles, abstracts, or topics of students' final scientific papers so that plagiarism does not occur. In this research, the cosine similarity method is combined with the preprocessing method and TF-IDF to calculate the level of similarity between the title and the abstract of a student's final scientific paper, then the results will be displayed and compared with the existing final project repository based on the threshold value to make a decision whether scientific work can be accepted or rejected. Based on the test data and training data that has been applied to the TF-IDF method, it shows that the percentage level of similarity between the training data document and the test data document is 8%. This shows that the student thesis is still classified as unique and does not contain plagiarism content. The findings of this study can help the university in managing the administration of student theses so that plagiarism does not occur. Furthermore, it is necessary to study further adding methods to increase the accuracy of system performance so that when the process is run the system will work faster and optimally.
Segmentation of Mango Fruit Image Using Fuzzy C-Means Marlinda, Linda; Fatchan, Muhamad; Widiyawati , Widiyawati; Aziz, Faruq; Indrarti, Wahyu
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10933

Abstract

Mango contains about 20 vitamins and minerals such as iron, copper, potassium, phosphorus, zinc, and calcium. The freshness of the ripe mango will taste sweet. The level of ripeness of the mango fruit can be seen from the texture of the skin and skin color. Ripe mangoes have a bright, fragrant color and a smooth skin texture. The problem found in mango segmentation is that the image of the mango fruit is influenced by several factors, such as noise and environmental objects. In measuring the maturity of mangoes traditionally, it can be seen from image analysis based on skin color. The mango peel segmentation process is needed so that the classification or pattern recognition process can be carried out better. The segmented mango image will read the feature extraction value of an object that has been separated from the background. The procedure on the image that has been analyzed will analyze the pattern recognition process. In this process, the segmented image is divided into several parts according to the desired object acquisition. Clustering is a technique for segmenting images by grouping data according to class and partitioning the data into mango datasets. This study uses the Fuzzy C Means method to produce optimal results in determining the clustering-based image segmentation. The final result of Fuzzy C-based mango segmentation processing means that the available feature extraction value or equal to the maximum number of iterations (MaxIter) is 31 iterations, error (x) = 0.00000001, and the image computation testing time is 2444.913636
Grape disease detection using dual channel Convolution Neural Network method Harahap, Mawaddah; Angelina, Valencia; Juliani, Fenny; Celvin; Evander, Oscar
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10939

Abstract

Grapes are one type of fruit that is usually used to make grape juice, jelly, grapes, grape seed oil and raisins, or to be eaten directly. So far, checking for disease in grapes is still done manually, by checking the leaves of the grapes by experts. This method certainly takes a long time considering the extent of the vineyards that must be evaluated. To solve this problem, it is necessary to apply a method of detecting grape disease, so that it can help the common people to detect grape disease. This research will use the Dual-Channel Convolutional Neural Network method. The process of detecting grape disease using the DCCNN method will begin with the extraction of the leaves from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to extract the features, color, and texture of the extracted leaves. The result is the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method. However, more datasets will cause the execution process to take longer. Changes in the angle and frequency values in the Gabor method at the time of testing will reduce the accuracy of the test results. The conclusion of this study are the DCCNN method can be used to detect the type of leaf disease in grapes and the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method.
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.

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