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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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Convolutional Neural Network Activation Function Performance on Image Recognition of The Batak Script Muis, Abdul; Zamzami, Elviawaty Muisa; Erna Budhiarti Nababan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13192

Abstract

Deep Learning is a sub-set of Machine learning, Deep Learning is widely used to solve problems in various fields. One of the popular deep learning architectures is The Convolutional Neural Network (CNN), CNN has a layer that transforms feature extraction automatically so it is widely used in image recognition. However, CNN's performance using the tanh function is still relatively low, therefore it is necessary to select the right activation function to improve accuracy performance. This study analyzes the use of the activation function in image recognition of the Batak script. The result of this study is that the CNN model using the ReLU and eLU functions produces the highest accuracy compared to the CNN model using the tanh function. The CNN model using eLU produces the best accuracy performance in the training process, which is 99.71% with an error value of 0.0108. Meanwhile, in the testing process, the highest accuracy value is generated by the CNN Model using the ReLU function with an accuracy of 94.11%, an error value of 0.3282, a precision value of 0.9411, a recall of 0.9411, and an f1-score of 0.9416.
Web, E-Report Web-based E-Report Information System Design Harliana, Putri; Perdana, Adidtya; Farhana, Nurul Ain
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13193

Abstract

The E-Report application information system is a system that can assist and facilitate users in creating performance reports for a company. In this study, a web-based e-report system was developed and designed for digital reporting by field employees at PT. In this study, a web-based e-report system was developed and designed for digital reporting by field employees at PT. Cipto Sarana Nusantara, which specifically operates in the gas pipeline installation sector. Previously, the company generated field worker performance reports through manual systems, in the form of hard copies or sent via chat applications. This led to poorly organized reports received by administrators. To address this issue, the author developed E-Report, a web-based application that aims to facilitate field workers and administrators in submitting and collecting reports. By utilizing this system, reporting performance significantly improves in terms of reporting speed, accuracy, and ease. By utilizing this system, reporting performance significantly improves in terms of reporting speed, accuracy, and ease. By utilizing this system, reporting performance significantly improves in terms of reporting speed, accuracy, and ease. Furthermore, this system enables the generation of daily, monthly, and yearly reports more efficiently and effectively.
Improvement Master Data Management : Case Study Of The Directorate General Of The Religious Courts Of The Supreme Court Of The Republic Of Indonesia Alfiandi, Rama; Ruldeviyani, Yova
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13194

Abstract

Implementation of Master Data Management (MDM) in an organization aims to help the process of consolidating and integrating various master data sources into one separate source of truth, as well as helping to overcome data complexity that occurs in the process of synchronizing, consolidating and cleaning data from redundancy. The main obstacle at DG Badilag is that data is spread across various systems, in different formats, and is not well integrated. The aim of this research is to improve master data management at the Directorate General of Badilag using MD3M which has an impact on a more transparent, efficient and just justice system . To improve master data management, it is necessary to measure the maturity level with use Master Data Management Maturity Model (MD3M) by Spruit and Pietzka. The results of the assessment show that the MDM maturity level at the Directorate General of Badilag is 0 with an implementation level of 73% (48 out of a total of 65) of implemented capabilities. From these results, recommendations were prepared to increase the maturity level of the Directorate General of Badilag to level 2 in the three designated focus areas, namely strengthening data management in all aspects, from data structure, data quality and data protection. DG Badilag already has awareness in master data management and can increase MDM maturity to a higher level by implementing capabilities that have not been implemented and implementing activities that have not been implemented.
GridSearch and Data Splitting for Effectiveness Heart Disease Classification Putri, Rusyda Tsaniya Eka; Junta Zeniarja; Sri Winarno; Ailsa Nurina Cahyani; Ahmad Alaik Maulani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13198

Abstract

Cardiovascular disease (CVD) is a major global health issue that affects death rates significantly. This research aims to improve the early detection and diagnosis of cardiovascular illness by utilizing machine learning methods, particularly classification algorithms. According to estimates from the World Health Organization (WHO), cardiovascular disease (CVD) caused 17.9 million deaths globally in 2019, or 32% of all fatalities. The treatment and prognosis of cardiovascular illness are greatly improved by early detection and diagnosis. Classification, in particular, machine learning, has become a prominent tool for solving problems connected to heart disease. The main objective of this project is to assess how well Grid Search and various data-sharing methods classify cardiac disease. SVM, Random Forest Classifier, Logistic Regression, Naïve Bayes, Decision Tree Classifier, KNN, and XGBoost Classifier are just a few machine learning methods. The UCI heart disease dataset, which contains information from 303 heart disease patients and 165 healthy participants, is used for the evaluation. Performance parameters like recall, accuracy, precision, and F1 score are considered to evaluate the algorithms' efficacy. The investigation's expected outcomes are intended to increase doctors' ability to diagnose cardiac disease more accurately. Moreover, these results may aid in creating more complex classification models for diagnosing cardiac conditions.
Multilayer Perceptron Performance Analysis in Liver Disease Classification Pradipta, Muhammad Iqbal; Situmorang, Zakarias; Sembiring, Rahmat Widya
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13202

Abstract

Liver disease is a liver disease caused by viruses, alcohol, lifestyle and others. Someone often does not realize or is late to know liver disease so that when examined liver disease is severe, it would be better if treatment is done early by knowing the symptoms suffered. Data mining can help diagnose liver disease more easily, especially to help doctors determine whether patients suffer from liver disease or not, with symptoms almost close to liver disease. The process of diagnosing liver disease is carried out by a classification process and the result is that the patient suffers from liver or not. This research uses a data mining classification method using an artificial neural network method, namely Multilayer Perceptron. The Indian Liver Patient Dataset (ILPD) used in this study was obtained from the UCI Machine Learning Repository. The division of the data set over the training data and test data is done by Cross Validation. Performance measurement of the method uses confusion matrix. Based on the research conducted, it was found that the application of Multilayer Perceptron resulted in varying accuracy based on testing with different Fold values with the highest accuracy of 83.70% when the Fold was 7, and the lowest accuracy of 80.57% when the Fold was 3. Then the average accuracy of all Fold tests is 82.13%
Computer Vision-Based Intelligent Traffic Surveillance: Multi-Vehicle Tracking and Detection Husein, Amir Mahmud; Noflianhar Lubis, Kevi; Salim Sidabutar, Daniel; Yuanda, Yansan; Kevry; Waren, Ashwini
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13204

Abstract

The application of vehicle detection in real-time traffic surveillance systems is one of the challenging research fields with different objectives. One of the problems is the detection of many vehicles simultaneously in a video sequence sourced from CCTV cameras. In many works, the focus is only on detecting vehicle classes such as motorcycles, buses, trucks, and cars or special vehicles such as ambulances and others. In this research, we propose to apply 13 classes of vehicle types and implement YOLOv4 in the traffic surveillance task. More specifically, all classes are labeled, and then the YOLOv4 model is trained on 800 images and tested on 23 videos from three intersections in Medan City, namely Juanda Katamso Intersection, Gatot Subroto Intersection, and Uniland Intersection. Based on the test results, YOLOv4 proves successful in detecting many vehicles in frame-by-frame sequence with various types of vehicles. All vehicle detection data will be stored in the file.
Analysis of Multi-Node QoS in Shrimp Pond Monitoring System with Fog Computing Anisah; Munadi, Rizal; Away, Yuwaldi; Bahri, Al; Novandri, Andri
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13205

Abstract

Most of Indonesia’s territory consists of oceans, presenting a significant potential for developing the fisheries sector. Shrimp is among Indonesia’s flagship commodities with substantial export potential. Internationally, Indonesia holds the fourth position as the largest exporter of frozen shrimp globally. However, shrimp cultivation faces various challenges, including declining water quality due to factors such as water sources and weather, which can adversely affect harvest yields. To preempt potential failures, employing smart devices and technology in shrimp cultivation offers an effective and efficient solution for monitoring and management. This study aims to analyze water quality monitoring in ponds considering the speed of data transmission from end devices to fog using Quality of Service (QoS) parameters like delay/latency, throughput, and packet loss. Data transmission tests were conducted at data rates of 5 Mbps and 10 Mbps, with a bandwidth of 1500 Mbps. The study involves three sensors—water temperature, pH, and salinity—placed in shrimp ponds. Test results showed a decrease in throughput by 1.54% at the sensor node and 2.99% at the sink node when packet data delivery encountered barriers like obstacles. There was a 74.13% increase in latency when the delivery distance extended to 35 meters. The achievable delivery range with low latency was up to 10 meters with barriers and 25 meters without. Thus, latency and throughput values vary depending on the presence of barriers and transmission distance. Barriers tend to increase latency and decrease throughput.
Comparison PSO And IWPSO Performance In Optimizing Decision Tree Algorithm On Heart Disease Dataset Oktaviani, Inggit Dwi; Ferian Fauzi Abdulloh
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13208

Abstract

Heart disease, one of the most common and potentially fatal chronic diseases, has become a major focus in global health efforts. In this study, researchers used the decision tree algorithm on the heart disease dataset with the stages of the decision algorithm including the EDA, Split Data, and Decision tree modeling stages. Furthermore, hyperparameters use PSO and IWPSO to optimize the algorithm. The purpose of this research is to analyze the performance of Particle Swarm Optimization (PSO) and Inertia Weight Particle Swarm Optimization (IWPSO) in heart disease prediction based on relevant datasets. PSO and IWPSO were applied to the heart disease dataset, with the results showing an accuracy rate of 78% for PSO and 84% for IWPSO. These results indicate that IWPSO provides significant performance improvement compared to PSO in the context of heart disease prediction. The implications of these findings can support the development of more efficient prediction systems for early detection of heart disease, making a positive contribution to prevention efforts and further treatment of this critical health condition. In addition, the purpose of this research is to continue research in the form of C4.5 on heart disease with a result of 80.43%. In this study, IWPSO got the best accuracy of 84.23% greater than previous research. The results of this study are to provide insight that PSO and IWPSO hyperparameters can optimize decision trees in handling heart disease datasets and continue research.
Revolution in Image Data Collection: CycleGAN as a Dataset Generator Hindarto, Djarot; Handayani, Endah Tri Esti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13211

Abstract

Computer vision, deep learning, and pattern recognition are just a few fields where image data collection has become crucial. The Cycle Generative Adversarial Network has become one of the most effective instruments in the recent revolution in image data collection. This research aims to comprehend the impact of CycleGAN on the collection of image datasets. CycleGAN, a variant of the Generative Adversarial Network model, has enabled the unprecedented generation of image datasets. CycleGAN can transform images from one domain to another without manual annotation by employing adversarial learning between the generator and discriminator. This means generating image datasets quickly and efficiently for various purposes, from object recognition to data augmentation. One of the most fascinating features of CycleGAN is its capacity to alter an image's style and characteristics. Using CycleGAN to generate unique and diverse datasets assists deep learning models in overcoming visual style differences. This is a significant development in understanding how machine learning models can comprehend visual art concepts. CycleGAN's use as a data set generator has altered the landscape of image data collection. CycleGAN has opened new doors in technological innovation and data science with its proficiency in generating diverse and unique datasets. This research will investigate in greater detail how CycleGAN revolutionized the collection of image datasets and inspired previously unconceived applications.
Comparison of Hyperparameter Optimization Techniques in Hybrid CNN-LSTM Model for Heart Disease Classification Maulani, Ahmad Alaik; Winarno, Sri; Zeniarja, Junta; Putri, Rusyda Tsaniya Eka; Cahyani, Ailsa Nurina
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13219

Abstract

Heart disease, which causes the highest number of deaths worldwide, recorded about 17.9 million cases in 2019, or about 32% of total global deaths, according to the World Health Organization (WHO). The significance of early detection of heart disease drives research to develop effective diagnosis systems utilizing machine learning. The advancement of machine learning in healthcare currently primarily serves as a supporting role in the ability of clinicians or analysts to fulfill their roles, identify healthcare trends, and develop disease prediction models. Meanwhile, deep learning has experienced rapid development and has become the most popular method in recent years, one of which is detecting diseases. The main objective of this research is to optimize the hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model for classifying heart disease by comparing hyperparameter optimization using grid search and random search. Although random search requires less time in hyperparameter tuning, the classification performance results of grid search show higher accuracy. In the test, the hybrid CNN and LSTM model with grid search achieved 91.67% accuracy, 89.66% recall (sensitivity), 93.55% specificity, 92.86% precision, 91.23% f1-score, and 0.9310 AUC value. These results confirm that using a hybrid CNN and LSTM model with a grid search approach is better suited for classifying heart disease.

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