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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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Mathematical Modelling In Logistics Transportation Problems with The Direct Search Rahman, Silvi Anggraini; Mawengkang, Herman; Sutarman, Sutarman
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12601

Abstract

Migration of rural communities to cities increases logistics activities in urban areas to meet customer needs because there is a close relationship between economic expansion and usage. Daily fluctuating demand for logistics, uncertain driving times and insufficient parking spaces are some of the factors that link the crisis in urban logistics in urban areas, which directly affects operational costs, the environment and its success or failure. The related steps of modeling optimization have a major impact in making complex transportation and logistics systems competitive with each other. This paper proposes a model optimization to solve transportation problems mathematically. The integer programming model would be suitable for the problems that have been described. the author uses direct search to complete the model.
Classification of Tea Leaf Diseases Based on ResNet-50 and Inception V3 Trihardianingsih, Liana; Sunyoto, Andi; Tonny Hidayat
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12604

Abstract

Technological advances have made a major contribution to controlling plant diseases. One method for resolving issues with plant disease identification is the use of deep learning for digital image processing. Tea leaf disease is a plant disease that requires fast and effective control. So, in this study, we adopted the Convolutional Neural Network (CNN) architectures, namely ResNet-50 and Inception V3, to classify six types of diseases that attack leaves. The amount of data used was 5867, which were divided into six classes, namely healthy leaf, algal spot, brown blight, gray blight, helopeltis, and red spot. The process of distributing the data involves randomly splitting it into three portions, with an allocation of 80% for training, 10% for validation, and 10% for testing. The process of classification is carried out by adjusting the use of batch sizes in the training process to maximizehyperparameters. The batch sizes used are 16, 32, and 64. Using three different batch size scenarios for each model, it shows that ResNet-50 has better performance on batch size 32 with an accuracy value of 97.44%, while Inception V3 has the best performance on batch size 64 with an accuracy of 97.62%..
Classification of Public Sentiment on Fuel Price Increases Using CNN Maharani, Anak Agung Istri Arinta; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12609

Abstract

The government's policy of changing fuel prices is carried out every year. The public gave responses to this policy categorized as positive, negative, or neutral sentiments. The community's response was conveyed through tweets on the Twitter application. Based on the public's response to the policy, sentiment classification can be done using data mining classification techniques. Some research has been carried out on classification techniques using deep learning and machine learning methods. In general, deep learning methods get better results, and this research will be approached using the CNN method. The system stages start from crawling data, labeling, and preprocessing, which consists of cleaning, case folding, tokenization, normalization, removing stopwords and stemming, classification using CNN, and evaluation using 10-Cross Validation. The dataset used is 17.270. The results show that the developed classification system is relatively high, with the highest accuracy of 87%, 93% recall, 93% precision, and 90% F1 score. An in-depth analysis of the classification results and an understanding of sentiment toward rising fuel prices can also provide valuable insights.
Analysis Content Type and Emotion of the Presidential Election Users Tweets using Agglomerative Hierarchical Clustering Sujadi, Cika Carissa; Sibaroni, Yuliant; Ihsan, Aditya Firman
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12616

Abstract

Over the past few years, social media has become essential for getting up-to-date information and interacting online. During presidential elections in Indonesia, Twitter has grown as a crucial platform for expressing opinions and sharing information. This study focuses on analyzing the content types and emotions of tweets related to Anies Baswedan, one of the presidential candidates. The results show a variety of discussions, including support, criticism, and discussion of policies for the 2024 presidential candidate. Clustering enables meaningful information extraction from vast Twitter data. Data were clustered using Agglomerative Hierarchical Clustering, which resulted in the identification of 10 clusters. With 4 clusters containing opinion content and 6 clusters containing information content. In addition, 6 clusters reflect excitement, 3 reflect expectations, and 1 reflect doubt. This research provides insights into the Twitter conversation around the 2024 presidential election, providing an understanding of content and emotions expressed by users.
Design of Batak Toba Script Recognition System Using Convolutional Neural Network Algorithm Steven Willian; Rochadiani, Theresia Herlina; Thamrin Sofian
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12617

Abstract

Indonesia is one of the countries with diversity and abundant cultural wealth, one of which is the Batak Toba script as one of the wealth originating from the Batak tribe. However, the existence of the Batak Toba script is decreasing along with the rapid development of the times, due to the lack of interest of the younger generation and public awareness in preserving the Batak Toba script. From these problems, the author conducted research to create a model of introducing the Batak Toba script, as an effort to preserve the Batak Toba script which is one of Indonesia's cultural wealth. The purpose of this research is to create a Batak Toba script recognition model using a digital handwriting dataset, and has an output in the form of visual text and with audio pronunciation of each script. The method used in this research is the Convolutional Neural Network algorithm combined with RMSprop optimizer. Convolutional Neural Network is an algorithm that is one of the deep learning methods that has good performance on image data. The results of this study incised a recognition model with a relatively high level of accuracy, which is equal to 99,54% which was tested on the Batak Toba script dataset in the form of digital handwriting. Through this research, the model using the Convolutional Neural Network algorithm used in this research is able to produce good results for recognizing the Batak Toba script in the form of handwriting.
Distribution Route Optimization Using Nearest Neighbor Algorithm and Clarke and Wright Savings Pratiwi, Miftah; Lubis, Riri Syafitri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12622

Abstract

in the process of this research Problems that occur is an irregular delivery of goods that takes a lot of time and delivery distance, so the distribution delivery process is not optimal. Therefore, researchers will conduct research on how to determine the best distribution route in optimizing delivery distance and time. The purpose of this study is to find distribution routes that are more optimal by using the nearest neighbor and Clarke and Wright savings and do a comparison with the company's current route This. The company's current mileage route is 319.9 km while using the nearest neighbor algorithm, the total distance traveled so far is obtained 240.9 and experienced mileage savings of 79 km or 25.22% and with the Clarke and Wright savings algorithm, the distance traveled is obtained 239.2 km and experienced mileage savings of 80.7 km or 25.22 %. Therefore, the Clarke and Wright Savings algorithm is more
Finite Element Model of Rock Obstruction on Overtopping at the Coastline Marpaung, Rony Genevent; Tulus, Tulus; Mardiningsih, Mardiningsih
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12630

Abstract

Wave overtopping is a common phenomenon that occurs during extreme sea conditions, where water waves travel over the surface of an open structure towards the sea and pass over its crest. To prevent flooding and coastal erosion, rock structures are often constructed as wave barriers along the shore. These barriers serve as a solution to mitigate wave overtopping. One of the key factors influencing overtopping is the arrival of continuous and sufficiently high-water waves that can pass through the top of coastal defense structures. Several phase settlement methods have been developed and applied to analyze wave overtopping using the Navier-Stokes (NS) equation. By employing the finite element method, numerical solutions and simulations are sought by inputting specific parameter values. This process aims to validate the accuracy of the resulting mathematical model. To accomplish this, a program is developed based on the discretization of the model, enabling a system analysis approach. The obtained results exhibit minimal error values, thereby demonstrating optimal outcomes in terms of rock placement. The entire fluid mechanics system analysis is simulated using the COMSOL Multiphysics 5.6 program, which provides a comprehensive platform for studying and evaluating the performance of the wave barrier system
Best Word2vec Architecture in Sentiment Classification of Fuel Price Increase Using CNN-BiLSTM Aqilla, Livia Naura; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12639

Abstract

The policy of increasing fuel prices has been carried out frequently in recent years, due to the instability of international price fluctuations. This study uses sentiment analysis to examine fuel price increases and their impact on public sentiment. Sentiment analysis is a data processing method to obtain information about an issue by recognizing and extracting emotions or opinions from existing texts. The method used is Word2vec Continuous Bag of Words (CBOW) and Skip-gram. Testing uses different vector dimensions in each architecture and uses a CNN-BiLSTM deep learning hybrid which performs better on sizable datasets for sentiment categorization. The results showed that the CBOW model with 300 vector dimensions produced the best performance with 87% accuracy, 87% recall, 89% precision and 88% F1 score.
Analysis of Community Satisfaction Levels using the Neural Network Method in Data Mining Hasibuan, Sabdi Albi; Sihombing, Volvo; Nasution, Fitri Aini
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12634

Abstract

Data mining is a process that is carried out to extract data into information. There are several models that can be done in data mining, such as classification, association, clustering, regression. But in this study will be carried out using a classification model. Research conducted on the level of public satisfaction for shopping on the Lazada application. This study aims to determine the level of public satisfaction on the Lazada application. This research was also conducted because the goods sold on the Lazada application are quite cheap and when compared to the original price there is a considerable difference. Therefore, research was conducted on the level of community satisfaction on the Lazada application. This research will be conducted on data mining with a classification model and using the neural network method. The results obtained from the data mining process using 100 community data, the results obtained are 81 community data (representation obtained by 81%) of people who are satisfied shopping on the lazada application and by 19 (representation obtained by 19%) people who are not satisfied shop on the Lazada app. From these results, many people are satisfied with shopping on the Lazada app. So from the results of this classification it can be concluded that the goods sold on the Lazada application are good goods.
Performance Analyze of Fog Computing Against Topology Using YAFS Fog Simulator Adiansyah, Naufal Rafi; Karimah, Siti Amatullah; Mugitama, Satria Akbar
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12659

Abstract

This research focuses on the analysis of fog computing performance on mesh, star, and ring topologies using the YAFS Fog Simulator. The reason YAFS (Yet Another Fog Simulator) was chosen was based on the consideration that this fog computing simulator, among other things, was designed to analyze topology and load balancing as well as include processing time for data transfer between devices into the fog layer. In addition, YAFS has a better level of time processing accuracy than other fog simulators. There are three test scenarios with additional load which includes 4, 8, and 12 fog nodes in each topology. Each scenario also has an additional load which includes 4, 8, and 12 devices in the form of sensors and actuators, respectively. The experimental results from the three scenarios show that the greater the load from the fog node and equipment, the longer the processing time will be. In addition, the results of the three scenarios also show that the mesh topology has the best time processing accuracy among the three tested topologies.

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