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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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LSB-2 Steganography with Brotli Compression and base64 Encoding for Improving Data Embedding Capacity Satriyawibawa, Muhammad Yiko; Andono, Pulung Nurtantio; Soong, Lim Way; Kiat, Ng Poh
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13264

Abstract

Steganography functions as a technique for embedding messages or data in various forms of media, such as images, audio, video, or text, with the aim of avoiding detection by unauthorized parties. Steganography techniques can be used as a solution to hide and protect data. In this research, steganography will be carried out using images as the transmission object. This research was conducted to offer a modification of the Least Significant Bit (LSB) steganography technique using the LSB-2 method with Brotli compression and base64 encoding. Modification and use of Brotli compression and base64 coding aims to increase the message capacity that can be embedded in a transmission object while maintaining the quality of the transmission object. Experiments using small data and big data. The experimental results will be presented in tabular form by comparing the original image with the steganographically processed image using metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) as a comparison. The experiments carried out resulted in an increase in image capacity by reducing capacity usage with an average of 47.13% for small data and an average of 71.34%. The big data experiment resulted in an increase in the PSNR value of around 3.49%, accompanied by a decrease in the average MSE value of 33.85%, and a constant SSIM value of 0.9999, thus proving that the proposed method was successful in increasing image capacity and improving stego-image quality. when embedding big data.
Problems in The Adoption of Agile-Scrum Software Development Process in Small Organization: A Systematic Literature Review Putrianasari, Rahmawati; Budiardjo, Eko Kuswardono; Mahatma, Kodrat; Raharjo, Teguh
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.v9i1.13271

Abstract

Agile methods are becoming increasingly popular in modern corporate strategies, which represents a paradigm change in project management techniques. The concept of pragmatic agility has become essential for enterprises to manage the complexities of ever-changing contexts. However, some organizations—especially small ones with limited resources—face unforeseen difficulties while implementing Agile-Scrum software development. In order to clarify the challenges small businesses, encounter throughout this adoption process, this study combines ideas from fifteen studies into a thorough and systematic analysis of the literature. The issues that have been discovered may be categorized into four primary areas: technology, people, process, and organization, and agile techniques. Organizations are able to anticipate obstacles by using a comprehensive understanding provided by the methodical examination and classification of situations. This proactive approach is essential to preventing unfavorable outcomes, as those seen in the past when implementation errors were made worse by culture problems, insufficient support from upper management, and waning consumer cooperation. This research provides small firms with a navigational aid by synthesizing lessons from the literature, enabling them to plan an Agile-Scrum adoption process that is more smoothly executed. Organizations may enhance their preparation, protect themselves from frequent traps, and ultimately maximize the transformative potential of Agile techniques in their developmental undertakings by adopting these insights.
Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting Olajuwon, Sayyid Muh. Raziq; Kusrini, Kusrini; Kusnawi , Kusnawi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13275

Abstract

This research aims to uncover the sentiment of Twitter users regarding the polemics surrounding the 2023 Qatar World Cup using a text-based sentiment analysis approach. The research methodology involves collecting data from Twitter posts, encompassing discussions, opinions, and responses related to the Qatar World Cup 2023. The TF-IDF weighting is applied to identify significant keywords in each post, while the K-Nearest Neighbor algorithm is employed to classify sentiments as positive, negative, or neutral. The findings reveal a comprehensive picture of how the public perceives the Qatar World Cup 2023 on the Twitter platform. The results not only cover positive and negative aspects of online discussions but also identify trends and patterns of sentiment that emerge during specific periods.The application of these methods provides valuable insights into understanding the dynamics of public opinion related to international sports events through the lens of social media. The results of the analysis demonstrate that a majority of Twitter users express positive sentiments towards the Qatar World Cup 2023, highlighting excitement and anticipation. However, some negative sentiments also arise, primarily related to controversies and concerns about the event. The research further identifies temporal variations in sentiment, reflecting changing public perceptions over time.This research contributes to the development of sentiment analysis methods by using a combination of TF-IDF weighting and the K-Nearest Neighbor algorithm to delve into Twitter users' perspectives. Consequently, the findings have practical applicability for further research and implementation in managing the social impact and public perception of major sporting events like the World Cup. .
Prediction of Student Entrepreneurship Future Work based on Entrepreneurship Course using the Naïve Bayes Classifier Model Hasan, Hanapi; Yulastri, Asmar; Ganefri; Tansa Trisna Astono Putri; Marta, Rizkayeni
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.v9i1.13293

Abstract

Entrepreneurs are critical to a country's economic progress and job creation. Few people felt schools have much to offer with business a generation ago. Students are expected to be an entrepreneur as the outcome of the course. The goal of this study is building a model to predict students' future employment, particularly in the field of entrepreneurship, using big data analysis and data mining. Various educational institutions can use data mining methodologies to identify hidden patterns in data contained in databases. The feature selection technique was utilised in this study to select and assess the significance of each element. The model was built using the final parameters determined by the feature selection technique (Correlation Based Feature Selection). Using the 10-fold cross validations for training and testing dataset distribution, the Naïve Bayes classifier was used to forecast the students' future of work. The dataset for the study was gathered from a student's performance report at Universitas Negeri Medan's engineering department. The effectiveness of using feature selection algorithms was compared to the effectiveness of not using feature selection algorithms, and the results are discussed. According to the findings of this study, the accuracy of Naïve Bayes with Correlation Based Feature Selection is 87.4%, which is higher than the model that did not use any feature selection. It was also discovered that the overall accuracy of the Correlation Based Feature Selection and Naïve Bayes Classifier models appears to be higher than that of the other treatments.
Analysis of Goods Stock Using the Apriori Algorithm to Aid Goods Purchase Decision Making Azis, Nur; Sucipto, Purwo Agus; Herwanto, Agus; Munthe, Era Sari; Irwanto, Dola
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.v9i1.13335

Abstract

After the covid-19 pandemic outbreak and the high uncertainty index during the covid-19 pandemic. The business world is experiencing a huge impact in addition to the sluggish interest of buyers is also limited in its movement. On this occasion, the researcher intends to provide an overview that can help business people, especially in purchasing goods that are useful for filling the stock of goods in the warehouse. To get maximum results and minimum error rate. Researchers use the Apriori Algorithm in analyzing stock items and use the Tanagra version 1.4 application. Research data used the sales history of the past 1 year here the data used is between May 2022 and April 2023. With a total itemset of 375. But after applying the Golden Rule (threshold), there are only 10 products with sales reaching 1623 items. This research produces a final ordered association based on the minimum support and minimum confidence that has been determined, namely 12 rules with a combination of 2 itemsets with a confidence value of 100%.
Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process Kushartanto, Ahmad Ilham; Fauziah; Aldisa , Rima Tamara
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13349

Abstract

Skin, as the outermost layer of the body, is often in contact with bacteria, germs and viruses because of its most external position. According to statistics from the 2009 Indonesian Health Profile, skin illness is the third most common ailment seen in outpatient settings across the country's hospitals. Therefore, maintaining healthy skin is important because it protects the body's internal organs from injury and attack by pathogens. The development of image classification, such as the classification of skin diseases, has become a focus in the health sector. This research analyses the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in web-based skin disease classification and overcomes the problem of imbalanced training data. With data augmentation and preprocess, this research improves data generalization and compares performance metrics such as Recall, Accuracy, and F1 Score. The results show that the average accuracy of CNN is 83.8%, while SVM reaches 81%. Although both models have high metrics for the normal class, other more complicated classes can only be handled by CNN with a value of more than 0.9. Apart from that, the CNN method also provides a higher Confidence Score than SVM, as well as a faster execution time. In conclusion, the CNN method is superior and recommended for skin disease classification based on web applications based on various performance test results.
Simulation and Modelling of Pre-emptive Priority CPU Scheduling Algorithm Putra, Tri Dharma; Purnomo, Rakhmat
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.v9i3.13352

Abstract

A model is a representation of an idea, thing or object in a simplified form. Model contains information about a system that is created with the aim of studying the actual system. Simulation is an imitation of system using a computer model. In this journal simulation is done by using OS-SIM, an operating system simulator. Process scheduling in an important part in operating systems. Several scheduling algorithms exist in the field. Shortest job first, round robin, first come first serve, priority and all of their variants. In this journal discussion about pre-emptive priority scheduling algorithm is presented thoroughly. Pre-emptive priority scheduling algorithm is an algorithm based on priority. The higher the number of the priority, the higher the priority. Five processes are available and given. Each with burst time, priority and different arrival times. Simulation and modelling with OS-SIM are discussed to understand this algorithm more easily. Some statistics numbers in the system are calculated automatically by the OS-SIM. Some screen shot pictures of the simulator are given to describe the model. It is concluded that for these processes the average turnaround time is 42/5 = 8.4 ms and for average waiting time is 28/5=5.6 ms and the total burst time is 14 ms.
Depression Detection of Users in Social Media X using IndoBERTweet Fadhel, Muhammad; Maharani, Warih
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.13354

Abstract

According to the Ministry of Home Affairs, the population of Indonesia stands at 273 million, Indonesia has approximately 167 million active subscribers to virtual entertainment platforms, including YouTube, Facebook, Instagram, and Twitter. The use of online entertainment is huge, particularly on Twitter, and has been associated with mental health implications, such as depression. This research objective is to do a comprehensive study about the IndoBertweet deep learning framework to investigate the prevalence of depression in social media, focusing on Twitter. Utilizing the DASS-42, the research estimates depression levels based on user interactions and reactions to tweets. The results of this research showed that the IndoBERTweet method achieved an accuracy rate of 82% in detecting depression using Twitter data. This research highlights the importance of intervention strategies to support the mental health of social media users, emphasizing the importance of proactive measures in addressing mental well-being issues in the digital space.
Development of Android-Based Smart System for Gingivitis Diagnosis Using Certainty Factor Hadistio, Ryan Rinaldi; Simamora, Windi Saputri; Muis, Abdul
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.v9i1.13361

Abstract

Gingivitis is a gum disease that causes bleeding, swelling, redness, discharge, changes in normal contours, and although health authorities take this seriously, sometimes some patients consider it normal. This study aims to educate the public about the importance of understanding the condition of their bodies, especially the most vulnerable teeth. Lack of time to consult an expert leads to this disease being neglected. Therefore, it is necessary to develop a consultation application in the form of an expert system. The built system adopts the deterministic factor method. The certainty factor works by reading the entire data submitted by the expert and giving the result as a percentage of confidence that the patient has gingivitis. The experts used in this system are dental experts. Data obtained from direct experts and consultations resulted in new knowledge in the form of the percentage of trust patients suffering from gingivitis. The data collected are symptoms and solutions obtained from experts. This research provides a new service for patients suffering from gingivitis without the need to see a specialist directly. Based on the testing data provided to the patient and based on the patient's condition at that time, the test results of the system reached a confidence level of 98.74%. So that the results of consultation are obtained in the form of information about the disease and the solutions needed.
Effect of Epoch Value on the Performance of the RNN-LSTM Algorithm in Classifying Lazada App Review Sentiments Putra, Maswan Pratama; Yuliant Sibaroni
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13368

Abstract

In today's development, the process of buying and selling transactions between sellers and buyers is so developed. not only done directly but can also be done online or can be called e-commerce. Which is where the development of technology is so fast that it indirectly encourages entrepreneurs to develop through e-commerce. Lazada is one of the online stores in Indonesia that has many users and Lazada makes it easy to shop without the need to come to the place or directly. However, purchasing goods using e-commerce has problems regarding the quality of the goods you want to buy, therefore purchasing goods can be seen through reviews of each one you want to buy. Sentiment analysis is carried out using the Recurrent Neural Network (RNN) method with Long Short Term Memory (LSTM). And using the Epoch value as a parameter in processing validation data and test data to produce the best accuracy value

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