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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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Analisis Perbandingan SAW dan TOPSIS pada Sistem Pendukung Keputusan Karyawan Terbaik Firdonsyah, Arizona; Warsito, Budi; Wibowo, Adi
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11475

Abstract

The decision-making process has many assessment criteria needed as the basis for its assessment. A large number of problems regarding the length of time required in the decision-making process require decision-makers to find solutions. Decision Support System is one option that can be developed by decision makers because it can help improve efficiency and accuracy in the decision-making process. The process of developing decision support requires certain calculation methods as part of the processing. The methods that are quite widely used to build a decision support system include the Simple Additive Weighting (SAW) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. This research aims to analyze the accuracy of the cases raised as solutions to decision-making problems. A dynamic decision support system has been successfully created to design dynamics in the calculation of the SAW method and the TOPSIS method. The system is evaluated and analyzed for its accuracy level based on manual calculations. The results obtained are the SAW system has an accuracy value of 65% and the TOPSIS system is 100%. Furthermore, the calculation of the accuracy value of the SAW and TOPSIS methods in order to find out the best method to use by taking parameters in the form of the same value results generated from the calculations of the two methods. The results obtained are the accuracy value of the SAW method of 40% and the TOPSIS method of 100% based on testing using 60 employee data and 8 criteria used.
Comparison of Drug Type Classification Performance Using KNN Algorithm Aldi, Febri; Nozomi, Irohito; Soeheri, Soeheri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11487

Abstract

The error of decommissioning is a serious problem that is often faced in medicine. In the face of these problems, information technology has a very important role. One of the information technologies that can be used is to use the machine learning classification algorithm K-Nearest Neighbor KNN. KNN is a type of machine learning algorithm that can be applied to problems with classification and regression prediction. The classification of types of drugs for patients greatly affects the health of the patient. The patient data is processed and transformed to numbers, which are then divided into training data and test data from 90:10, 80:20, 70:30 and using the Cross Validation model. KNN works through the nearest neighboring value with a value of k = 3 calculated by the calculation of Euclidean Distance, and then evaluated using the Confusion Matrix. The performance of the KNN algorithm resulted in the highest Accuracy value of 98.33%, a Precision value of 98.8%, a Recall value of 96.2%, and an F-measure value of 97.48%. The performance is obtained from the sharing of training data and 90:10 test data. The data share results in high performance compared to other data shares, including using the Cross Validation model. And the lower the k value, the higher the value of the resulting performance. The results show that the performance of the KNN algorithm is working well.
Deteksi Malaria Berbasis Citra Mikroskop Menggunakan Metode Convolutional Neural Network Muttaqin, Muhammad; Untoro, Meida Cahyo; Algifari, Muhammad Habib; Faisal, Amir
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11488

Abstract

Malaria is a tropical disease that infects human red blood cells caused by infection with the plasmodium parasite. Plasmodium parasites spread to humans through female Anopheles mosquitoes and can reproduce in human blood cells. Malaria is a health problem that is at risk of causing other health problems such as anemia and even death. The current gold standard for malaria diagnosis is laboratory diagnosis by microscopic examination to find the malaria parasite through the blood cells of the patient. However, the diagnosis of malaria through microscopic observation of blood cells has the potential to take a long time, because the plasmodium parasite has a very small size. The malaria detection system using the Convolutional Neural Network (CNN) method is designed to detect malaria in human blood cells. CNN is a machine learning method designed to classify objects in an image. The system was built in three stages of development, namely the development of a CNN model for malaria detection, software development and hardware development. The hardware components used in the system include Raspberry pi, Raspberry Pi camera module, and LCD. The results of the malaria detection test using the CNN model gave an accuracy of 98.76% which was tested on blood cell images from a microscope
Explanatory Data Analysis to Evaluate Keyword Searches for Educational Videos on YouTube with a Machine Learning Approach Mambang, Mambang; Hidayat, Ahmad; Wahyudi, Johan; Marleny, Finki Dona
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11502

Abstract

One of the most important parts of data science is the process of explanatory data analysis. This study aims to analyze learning videos on YouTube using search keywords such as learning biology, chemistry, physics, computers, mathematics, management, accounting, citizenship, history, and culture. The method used is the explanatory data analysis technique with a Machine Learning approach. The dataset used in this study uses learning video search keywords found on the YouTube digital platform. After doing a thorough analysis of all existing variables, we found that in the context of searching for learning video keywords on YouTube, the viewing variable has a heatmap correlation of 0.97 on the likes variable, 0.97 on the subscribers variable, -0.15 on the duration variable and 0.95 on the comment variable. The duration variable negatively correlates with all variables based on the analysis using a correlation heatmap using the seaborn library. Our analysis found that the number of learning videos with the search keyword Mathematics had the highest number of views among other variables. Further research can use existing variables or also add variables and add search keywords on YouTube. The data analysis approach can also be done using SPSS, R and also a Machine Learning approach with different libraries.
Api Service Infrastructure Using Kubernetes And Terraform Based On Microservices Ngoorder.Id Azwar Riza Habibi
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11522

Abstract

The growing number of ngorder.id service users causes traffic to Api Ngorder to be higher, so a new infrastructure is needed in order to maintain Api Ngorder uptime during high traffic and also maintain service stability. In the implementation process, Api scripts that are currently running on a monolith cluster will be divided into several categories and will be split into several kubernetes clusters. To support autoscale, a Horizontal Pod Autoscaler was added, and to route traffic it would use the Api Gateway from Amazon Web Service. In this infrastructure test, it is done by testing the logic script function using Katalon Studio and testing at the infrastructure level by doing a crash test in the form of failing to deploy and terminating the pod, as well as performing a stress test to test autoscaling in the cluster, the entire test can be run by performing a stress test on the php service pods. by setting the autoscaler parameter Memory Utilization Percentage 125%, 150% and 250%, proving that the HorizontalPodAutoscaler (HPA) as an autoscaler handler can function according to the targets and parameters that have been determined.
Case Study: Improved Round Robin Algorithm Putra, Tri Dharma; Purnomo, Rakhmat
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11530

Abstract

In this journal, discussion is given to analyse the improved round robin algorithm more thoroughly. Round robin algorithm plays a significant role to be used in embedded systems. Round robin algorithm usually applied in real-time systems. Here, three case studies are given, and also the analysis of each case study. Comparisons are given about the average turn around time and average waiting time, also number of context switching between the three case studies. Improved round robin algorithm, is a modification from the generic round robin algorithm. In improved round robin algorithm if the remaining burst time is less than the time slice that is allocated, then the currently running process is continue to be executed. Then finish the currently running process from ready queue and execute the next ready queue. Three case studies are given with three different time quantum, which are 3, 4, and 5 ms. The result of this case study analysis is that, the efficiency of the quantum 5 ms is the most effective one. There is an increase of 50% context switching from quantum 3 to quantum 5. And for average turn around time we get 13.13% reduction in efficiency. While in average waiting time we get reduction 12.08% efficiency.
Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network Method Jatmika, Sunu; Saputra, Danang Eka
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11540

Abstract

Indonesia is an agricultural country where most of the population grows rice and most farmers cannot detect early if there is a pest attack on rice plants . This research discuss about deep learning implementation to classify or identify diseases in rice leaves using mobile application. This system will make users easily to diagnose diseases by displaying diagnostic results in the form of the name of the disease along with its taxonomy, disease description and drug recommendations for disease solutions. There are four classes of leaves used in this research, including healthy leaves, leaf blight, brown spot and potassium deficiency. The design of the model uses two approaches, one of them are modeling convolutional neural network from the scratch and modeling with transfer learning using inception v3 architecture. Both models will go through training process to produce a model that is ready to be used for classification. In application testing, a comparison is made between two models. From the tests that have been carried out, it is concluded that the system with model made using transfer learning approach, produce good accuracy with an accuracy of 90%. Meanwhile the System with the other model gain an accuracy of 62%. So when the data used in research are extremely low, it is best to use transfer learning as an approach to design a mode.
Supervised Model untuk Sentiment Analysis berdasarkan Cluster Hotel Review menggunakan Rapidminer Juliadi, Revin Novian; Puspitarani, Yan
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11564

Abstract

Customer feedback in the modern era like today is mostly presented in the form of digital reviews, including customer feedback at an inn or hotel, customer feedback is very valuable data where from this data the management can find out, identify and analyze the customer experience and what they need. With customer feedback in the form of digital reviews, it will allow a lot of data that can be obtained by hotel management and will provide many benefits if the data is processed correctly. To take advantage of large text review data, a combination of data mining and natural language processing techniques was chosen to process text in depth and efficiently. Text mining in the form of creating an opinion mining model using the Naïve Bayes classification algorithm is applied to find information and measure the main sentiments expressed in the reviewed text dataset, then the application of K-Means text grouping aims to group texts and get information about the main topics discussed from the content of the review dataset text in each group . By applying the constructed sentiment analysis model, approximately 90.90% accuracy results were obtained in reading texts and measuring sentiments related to hotel customer feedback data.
Penerapan Algoritma Huffman dan Unary Codes untuk Kompresi File Teks Wijaya, Bayu Angga; Siboro, Sarwando; Brutu, Mahendra; Lase, Yelita Kristiani
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11567

Abstract

Technique in carrying out data compression is an important point in technological developments. With compression in data in the form of text can include many uses, including for data transfer, copying and for backing up data. From its uses, this aspect is important for data security. There are many compression techniques on the data, including using huffman algorithms and unary code. One of its applications will be implemented on a text data that is widely used by digital actors in storing important data. The data must not be known by unauthorized parties in accessing the data. Therefore, huffman algorithms and unary code can solve this problem. By compressing the selected data also encrypts it as an extra security. The Huffman algorithm is a lossless compression algorithm or a technique that does not change the original data, by converting the unit of data content into bits. So this algorithm is widely used in the compression process. The Unary Codes algorithm is also a lossless compression technique that is generally used by combining several modification techniques. In this unary codes algorithm, each symbol in the string will be searched for its frequency. Then sorted from the last order (descending). The use of these two text data compression techniques results in a file size that is smaller than the original but can be returned to the original data
Combination Grouping Techniques and Association Rules For Marketing Analysis based Customer Segmentation Husein, Amir Mahmud; Dodi Setiawan; Andika Rahmad Kolose Sumangunsong; Andreas Simatupang; Shela Aura Yasmin
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11571

Abstract

Changes in people's transaction behavior using the internet resulted in the exponential growth of e-commerce. With the growth of digital shopping transactions, it is difficult to predict customer segments and patterns using traditional mathematical models. Timely identification of emerging trends from large volumes of data plays a major role in business processes and decision making. This is different from previous research works that apply the RFM model based on K-Means Clustering to find potential customers as an ingredient in determining marketing targets. In this study, a clustering technique approach is proposed to classify customer data which is evaluated using the Davies Bouldin, Calinski Harabasz and Silhouette methods to determine the optimal number of clusters, then the results are used in the Apriori algorithm to find patterns of goods that are often purchased together. Based on the test results on the K-Means Clustering, Spectral Clustering, and Gaussian Mixture Model techniques produced 5 clusters with 76% more accurate the K-Means Clustering method than the other two methods so that it was determined as a method in the RMF model, then the results of customer grouping were used on the Apriori algorithm to find patterns of concurrent product purchases by customers that are expected to be useful in future marketing management.

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