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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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A CNN Model for ODOL Truck Detection Arifuddin, Nurul Afifah; Gusti, Kharisma Wiati; Amalia, Rifka Dwi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13780

Abstract

This study developed a Convolutional Neural Network (CNN) model as one of artificial intelligence method to detect trucks experiencing over-dimension and over-loading (ODOL). The primary goal of this research is to enhance the efficiency of truck monitoring, reduce road infrastructure damage, and support the sustainability of transportation using artificial intelligence approaches. The model was trained using a dataset consisting of ODOL and non-ODOL truck images, and successfully achieved a testing accuracy of 94.23%. The confusion matrix analysis demonstrated the model's ability to classify trucks with high precision.  Additional testing on truck images not included in the training or testing dataset showed the model's potential for good generalization.
Integration of AHP and Modified VIKOR Method to Select the Optimum Destination Route Simbolon, Miranda Melania Nathasia; Gultom, Parapat; Rosmaini, Elly
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13784

Abstract

One common approach to rating options is group decision making using many criteria. Here, we use the same criteria to evaluate each option. Sometimes, decision makers are faced with some situations where they have to choose from a set of alternatives that have several different criteria. Thus, the decision maker cannot use a common method. Therefore, in this research, a modification to a method is carried out. To address the issue of developing alternate routes to Medan City's historical tourism attractions, the AHP and VIKOR approaches have been suggested. When considering options with both specific and broad requirements, this study adapts the VIKOR technique to find a workable solution. In order to demonstrate the suggested model's use and evaluate the efficacy of this approach change, this study offers numerical examples based on case studies. The findings demonstrate that the revised approach is both practical and efficient.
Comparison of Genetic Algorithm and Particle Swarm Optimization in Determining the Solution of Nonlinear System of Equations Mindasari, Eva; Sawaluddin; Gultom, Parapat
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13785

Abstract

Nonlinear systems of equations often appear in various fields of science and engineering, but their analytical solutions are difficult to find, so numerical methods are needed to solve them. Optimization algorithms are very effective in finding solutions to nonlinear systems of equations especially when traditional analytical and numerical methods are difficult to apply. Two popular optimization methods used for this purpose are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This study aims to compare the effectiveness of GA and PSO in finding solutions to nonlinear systems of equations. The criteria used for comparison include accuracy and speed of convergence. This research uses several examples of nonsmooth nonlinear systems of equations for experimentation and comparison. The results provide insight into when and how to effectively use these two algorithms to solve nonlinear systems of equations as well as their potential combinations
Inventory Information System Using Fifo And Holt Winters Multiplicative Methods Firmansyah, Verdi Tri; Anik Vega Vitianingsih; Syahadiyanti, Litafira; Pamudi; Alda Raharja
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13791

Abstract

In the era of society 5.0, information technology plays an important role in daily life, company operations, and management. Medium-scale stores such as Nisrina Mart require an inventory control process to determine the number of products to be restocked quickly and accurately. However, what happened was the opposite, shop owners experienced a lot of losses because the inventory control process carried out manually had the potential to experience inaccurate data and material losses. Based on these problems, this research tries to propose the development of an inventory information system for reporting incoming and outgoing goods using the First In First Out (FIFO) method. Stock forecasts based on previous sales data are generated to project future stock needs using the Holt-Winters Multiplicative trend moment method. The software development model uses a waterfall which includes requirements, design, implementation, testing, and maintenance. The test results of the Holt Winter multiplication method show a prediction error rate of 0.27. Meanwhile, the level of accuracy in predicting goods sales is 73%. The implementation of this information system is expected to provide convenience in stock monitoring, reduce prediction errors, and increase the accuracy of product data analysis reports to support more effective and efficient management decision-making.
Classification of Breast Cancer with Transfer Learning on Convolutional Neural Network Models Wijaya, Bayu Angga; Hulu, Mesrawati; Resel, Resel; Halawa, Nestina; Tarigan, Angki Angkota
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13792

Abstract

Breast cancer is a serious medical condition and a leading cause of death among women. Early and accurate diagnosis is crucial for improving patient outcomes. This study explores the use of Convolutional Neural Networks (CNNs) with Transfer Learning using DenseNet121 and ResNet50 models to enhance breast cancer classification via mammography. Transfer Learning enables CNN models to leverage knowledge learned from larger datasets such as ImageNet to improve performance on specific breast cancer datasets. The dataset comprised medical images with three breast variations: benign, malignant, and normal, totaling 531 data points. Data was split with a 70% training and 30% validation ratio. Two CNN models, AlexNet and ResNet50, were evaluated to compare their performance in classifying these breast cancer types. The experimental results show that AlexNet achieved a training accuracy of 98.01%, while ResNet50 achieved 64.07%. AlexNet demonstrated superior performance in identifying complex patterns in mammography images, resulting in more accurate classification of different breast cancer types. These findings highlight the potential of deep learning applications to support more precise and effective medical diagnostics for breast cancer. This research contributes significantly to the development of AI technologies in healthcare aimed at improving early detection of breast cancer. The implications of this study could expand our understanding of Transfer Learning applications in medical contexts, driving further advancements in this field to enhance patient care and prognosis
Analysis of COVID-19 Virus Spread in Jakarta Using Multiple Linear Regression Muhtar, Na'il Muta'aly; Gunawan, Putu Harry
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13796

Abstract

COVID-19, first identified in Wuhan, China in December 2019, quickly spread worldwide and was declared a pandemic by WHO in March 2020. Indonesia reported its first case on March 2, 2020, and the pandemic has had a significant impact on the country's economic, social, and health sectors. This study aims to predict the death rate due to COVID-19 in Jakarta using multiple linear regression method. The dataset collected from Andra Farm - Go Green website includes COVID-19 cases recorded in all sub-districts in Jakarta on November 1, 2023. Pre-processing was performed to improve the quality and accuracy of the model. The method used was multiple linear regression. The analysis results show that variables such as total travel and discarded trip have a significant influence in predicting the number of positive cases. The study found that lowering the correlation threshold for selecting independent variables reduced the mean squared error (MSE) and improved model performance, highlighting the importance of variable selection in developing accurate predictive models. These findings provide important insights for the government in making informed decisions regarding post-pandemic healthcare. This research underscores the value of robust data processing and variable selection techniques in enhancing predictive accuracy for public health planning.
Deep Learning Approach for Traffic Congestion Sound Classification using Circular Neural Networks Muthi, Muhammad Ariq; Gunawan , Putu Harry
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13798

Abstract

Traffic congestion has become one of the main problems that occur in big cities around the world. Traffic congestion also has a negative impact if not handled seriously. Traffic congestion occurs because there is a buildup of vehicle volume that exceeds the capacity of the road. The efficiency and quality of living in cities can be negatively impacted by traffic congestion, which can also result in higher fuel consumption, pollution, and delays. There needs to be a method that can overcome and identify this. Therefore, by classifying sounds, this research aims to reduce traffic congestion. The author uses deep learning with the Convolutional Neural Network (CNN) method as the algorithm model. The model employs Mel-Frequency Cepstral Coefficients (MFCC) as the primary feature extraction technique to capture the essential characteristics of the audio signals. This research is expected to be able to classify traffic congestion sounds with good accuracy, so it can be used as a solution to overcome traffic congestion. Experiments were conducted using a training dataset, and for testing, the road sound dataset has been collected at traffic light intersections. To evaluate the proposed method, the implementation showed promising results, achieving an accuracy of 97.62% on the training data and 88.19% on the test data in classifying traffic congestion sounds.
Sales Trend Analysis With Machine Learning Linear Regression Algorithm Method Sipahutar, Alwidahyani; Munthe , Ibnu Rasyid; Juledi, Angga Putra
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13809

Abstract

The development of online business in Indonesia is now very rapid, with the process being done by ordering goods through resellers or distributors using one of the social media. Item purchases are made based on product information, prices, discounts and inventory quantities using a decision model. In the sales process, Toko Serbu Aek Batu usually releases several different items to be offered to the market at different prices, but not all items are in high demand. Multiple linear regression is an analysis that describes the relationship between dependent variables and factors that affect more than one independent variable. The purpose of this study is to analyze sales trends using a linear regression method using rapidminer. The results of this study are prediction calculations using manual calculations with rapidminer the same results, predicting the price desired by buyers using a linear regression algorithm with the original price is not much different and rapidminer is very accurate to be used in predicting sales trends at the price desired by customers, so that sellers can pay more attention to things that are very influential in the sales process.
Comparison Of Exponesial Smoothing With Linear Regression Predicting Amount Of Goods Sales Panggabean, Erwin; Sinaga, Anita Sindar Ros Maryana; Sagala, Jijon Raphita; Ramadhan, Alya Sophia; Josua, Alpon
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13811

Abstract

A trading business is a business that operates in the sales sector with the aim of obtaining maximum profits through sales activities. To be able to sell efficiently, a prediction system is needed, so that there is no excess or shortage of inventory and the sales process can run smoothly. Human limitations in solving prediction problems without using tools that apply prediction methods are one of the obstacles in finding the right prediction value. Therefore, we need a prediction system that can help find accurate and fast values. So the problem formulation is how to design and build a sales prediction system using exponential smoothing and linear regression methods, then compare the two and find out which method is the best, both of which use periodic data prediction models. The data collection method used is secondary data from previous research and journals, as well as combining library study methods, namely information obtained from books, references and scientific works related to predictions. The tool used to build applications is MS-Visual Studio 2010 and WEB based system
ChatBot-based Bus Ticket Booking Prototype Using WhatsApp Febriansyah , Bagas; Abdillah, Leon A.
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13812

Abstract

Customer relationship management (CRM) is the most critical part of any business's operations. To deploy CRM at PO Harapan Jaya, a ticket ordering system is required to make things easy for customers. Researchers constructed a chatbot prototype using the WhatsApp app with the goal of making it easier and more efficient for PO Harapan Jaya customers to buy bus tickets. The eXtreme Programming (XP) approach is a strategy for creating a ticket-booking chatbot prototype using WhatsApp. In developing the WhatsApp chatbot prototype for this research, a CRM system is proposed that can collect and manage information entered by customers, ticket purchase history, and produce proof of ticket reservations via WhatsApp chat, which will then be shown to the PO Harapan Jaya admin to obtain bus tickets ordered by customers. Researchers conducted a black box test on the prototype. This study intends to demonstrate how deploying chatbots in the WhatsApp application may speed up the bus ticket purchase process, increase customer service quality, and assist PO Harapan Jaya in optimizing CRM tactics. Based on the extensive programming processes, including planning, design, coding, and testing, it is possible to determine that the final WhatsApp chatbot will work properly and may be used by users to buy PO Harapan Jaya bus tickets. Customers may book tickets, examine departure schedules, and contact the PO Harapan Jaya admin if they have any issues when ordering tickets.

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