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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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Implementation of the K-Means Method for Clustering Regency/City in North Sumatra based on Poverty Indicators Wardani, Syafira Eka; Harahap, Syaiful Zuhri; Muti’ah, Rahma
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.v8i3.13720

Abstract

Poverty has many negative effects on people's lives, such as difficulty meeting basic needs, limited access to adequate health and education services, and limited economic opportunities. North Sumatra faces significant poverty problems as one of the largest provinces in Indonesia. This requires special attention and a thorough investigation. Reducing poverty is a very important issue for the government of North Sumatra Province. Poverty-alleviation strategies can no longer be applied uniformly. Instead, it is necessary to consider all the factors that cause poverty in each region. This means that the approach that must be given to each regency or city based on its poverty level must be adjusted. To overcome this problem, clustering must be carried out to identify areas with different levels of welfare. The aim of this research is to cluster regencies and cities in North Sumatra Province using the K-means method based on poverty indicator variables. This research only uses three poverty indicators: gross regional domestic product, human development index, and unemployment rate. The optimal number of clusters is determined based on the results of the silhouette coefficient. The research method begins with dataset collection, exploratory data analysis, data preprocessing, and k-means clustering. The value k = 6 produces a silhouette coefficient of 0.4135. This research produced six regency/city clusters. Cluster 1 consists of 11 regencies and 1 city; cluster 2 consists of 1 regency and 2 cities; cluster 3 consists of 4 regencies; cluster 4 consists of 7 regencies; cluster 5 consists of 4 cities; and cluster 6 consists of 2 regencies and 1 city. The variables gross regional domestic product, human development index, and unemployment rate have a big influence on the cluster results. This will enable the government to adopt policies to tackle poverty quickly and effectively.
Sentiment Analysis of Public Responses on Social Media to Satire Joke Using Naive Bayes and KNN Putra Selian, Rasyid Ihsan; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Febrian Rusdi, Jack
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.v8i3.13721

Abstract

This study examines the use of Satire Joke as a humorous communication style in conveying criticism of the government through social media. Satire Joke is often used to depict the government's inability to address important social issues, such as slow bureaucratic processes and unfulfilled political promises. The aim of this research is to analyze public sentiment towards Satire Joke expressed on the YouTube social media platform. The methods used in this study are Naïve Bayes and K-Nearest Neighbors (KNN) due to their effectiveness in data classification. The results of this study are expected to help gain an understanding of social issues for the community and public knowledge. This research is also expected to contribute to the development of sentiment analysis methods in the future. The analysis results show that 400 data have neutral sentiment, 850 data have negative sentiment, and 947 data have positive sentiment. Based on testing, both Naive Bayes and KNN methods show good performance. The Naive Bayes method achieved the best accuracy of 90.29%, while the KNN method achieved an accuracy of 60.75%.
Ontology-based Food Menu Recommender System for Pregnant Women Using SWRL Rules Fadillah, Ichsan Alam; Baizal, Z. K. 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.v8i3.13722

Abstract

Pregnancy is a crucial period in a woman's life because her body must prepare and support the growth and development of the fetus. During pregnancy nutritional needs will increase. Lack of nutritional intake during pregnancy can cause serious health problems, one of which is anemia. However, excess nutrition during pregnancy also has a negative impact on pregnant women. Therefore, a recommender system is required to provide food menu recommendations according to the daily nutritional needs of pregnant women. Currently, there has been a lot of research on ontology-based food recommender systems that can provide food recommendations to users, but there is no research that specifically provides food menu recommendations that suit the needs of pregnant women. Therefore, in this research, we propose an ontology-based food menu recommender system using SWRL (Semantic Web Rule Language) rules for pregnant women. In this food menu recommender system, ontology is used to represent food knowledge and its nutritional content, and SWRL rules are used to reason logical rules in the ontology to determine the appropriate food menu for pregnant women. This recommender system also considers diseases and allergies that pregnant women have so that it can provide food menu recommendations that are more suitable for users. From 15 data samples from pregnant women, the system provides 75 food menu recommendations for pregnant women. Based on the validation results that have been carried out, the precision value is 0.986, the recall is 1, and the F1-score is 0.992.
Implementation Transfer Learning on Convolutional Neural Network for Tubercolosis Classification Putra, Adya Zizwan; Prayugo, Reynaldi; Siregar, Rizki Mudrika Alfanda; Syabani, Rizky; Simarmata, Allwin
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.v8i3.13723

Abstract

Tuberculosis (TB) is an infectious disease that can have serious effects on the lungs and is among the top 10 causes of death worldwide. This disease is caused by the transmission of Mycobacterium tuberculosis bacteria through the air when coughing or sneezing. Without treatment, pulmonary tuberculosis can result in permanent lung damage and can be life-threatening. Accurate and early diagnosis is crucial for effective treatment and control of the disease.The challenge lies in the accurate classification of tuberculosis from lung images, which is essential for timely diagnosis and treatment. Traditional diagnostic methods can be time-consuming and sometimes lack precision. To address this issue, this research aims to achieve high accuracy in classifying tuberculosis using the Convolutional Neural Network (CNN) algorithm through transfer learning methods. By utilizing visual images of tuberculosis-affected and normal lungs, we propose a solution that leverages advanced deep learning techniques to enhance diagnostic accuracy. This approach not only expedites the diagnostic process but also improves the reliability of tuberculosis detection, ultimately contributing to better patient outcomes and more effective disease management. The dataset applied consists of two labels: tuberculosis and normal. This dataset contains 4200 lung images of individuals with tuberculosis and normal lungs. By applying the transfer learning method, Transfer learning is a machine learning method where a pre-trained model is used as the starting point for a new, related task. it was found that the ResNet50 model achieved the highest accuracy at 99%, followed by InceptionV3 at 97%, and lastly, DenseNet121 at 91%.
Robust Regression on Simple Housing Environmental Performance Measurement Hrp, Hafizah Miranda Putri; Nababan, Esther Sorta Mauli; Tulus
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.v8i2.13725

Abstract

Robust regression in residential environmental performance measurement can provide significant benefits. This method can help identify and overcome uncertainties in measurement results so that decision making related to environmental protection and management can be done more accurately. This type of research is quantitative with a descriptive approach. The results of this study indicate that the results of the M-estimate robust regression estimation on simple housing environmental performance obtained the M-estimate robust regression equation, namely ŷ = 15.562+0.476X1-0.453X2+0.222X3-0.427X4. Based on the p-value test results, it shows that population attributes, house attributes, energy consumption volume and utilities and services have a significant effect on environmental performance. The results of the hypothesis testing of the M-estimation robust regression method on the environmental performance of simple housing obtained that the variables of population attributes and energy consumption volume have a positive and significant effect on environmental performance while the variables of house attributes and utilities and services have a negative and significant effect on environmental performance.
Analyzing Public Sentiment Towards BSI Service Disruptions Through X: Naïve Bayes Algorithm Yudhistira, Yudhistira; Talita, Aini Suri
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.v8i3.13729

Abstract

Disruptions to banking services can negatively affect customer trust and happiness, thus affecting the bank's reputation in the eyes of the public. Analysis of sentiment expressed on social media is very important because it can provide a direct picture of individual perceptions and responses in real time. This research aims to analyze public sentiment towards disruptions in Bank Syariah Indonesia (BSI) services through social media using the Naive Bayes algorithm. Through this analysis, the research seeks to understand the pattern of public responses and perceptions of BSI disruptions and evaluate the performance of the Naive Bayes algorithm in classifying sentiment on related tweet data. The data used came from specific social media platforms, where sentiment analysis was conducted by categorizing the data into positive, negative, and neutral categories. The research findings show that the sentiment analysis of the community towards BSI service disruptions through X social media platforms shows a diverse pattern of responses and perceptions. This finding recorded 525 data points with negative sentiment, 325 data points with neutral sentiment, and 141 data points with positive sentiment. The research also compared the performance of the Naive Bayes algorithm with the Google Cloud Natural Language API, which showed an accuracy rate of 81.03%. This research provides valuable insights for Bank Syariah Indonesia in understanding public perception of BSI services on social media.
Decision Support System for Selecting Online Teaching Methods Using the Fuzzy MCDM Algorithm Marsono; Nasyuha, Asyahri Hadi; Syahra, Yohanni; Mariami, Ita
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.v8i3.13731

Abstract

The global pandemic that has hit the world recently has forced educational institutions to adopt online teaching methods. However, choosing an effective online teaching method is a major challenge. This research develops a Decision Support System (DSS) that uses the Fuzzy Multi-Criteria Decision Making (FMCDM) Algorithm to select the best online teaching method. This system is designed to assist decision making in educational institutions by considering various criteria such as learning effectiveness, technology affordability, ease of use, and user satisfaction. This research uses data collection methods that involve surveys from lecturers and students to obtain their preferences and experiences with various online teaching platforms. The data collected is then processed using the FMCDM model to evaluate and rank teaching methods based on predetermined criteria. Fuzzy systems are used to overcome uncertainty and subjectivity in criteria assessment. The results of this research show that the system developed is able to effectively assess and rank various online teaching methods. From the analysis carried out, interactive teaching methods using videos and real-time quizzes received the highest ranking based on predetermined criteria. This suggests that the combination of engaging visual content and high interactivity is highly valued in online teaching contexts
Combination of Lexical Resources and Support Vector Machine for Film Sentiment Analysis Agustina, Putri; Putri, Raissa Amanda
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.v8i3.13733

Abstract

Text data generated by internet users holds potentially valuable information that can be researched for new insights. One strategy for obtaining information from a text data set is to classify text into predetermined categories based on existing data. Text classification is an aspect of Text Mining. One of the popular approaches in Text Mining uses the Support Vector Machine (SVM) classification algorithm, which aims to classify text and separate data into different classes. However, in some cases, SVM classification algorithms may face difficulties in understanding the context of the text properly due to unclear wording, varying sentence structures, or a lack of understanding of interpretation. To address this problem, applying SVM classification using lexical resources can be an effective solution. In this research framework, the first step is to obtain data, which in this case is a film review dataset taken from the kaggle.com site. After obtaining the data, the next step is preprocessing. The results of the preprocessing are then divided into 80:20 percentages. The 80% training data is used to search for the form of polarization, and this training data lexicon is used for training the SVM model. Based on the modeling results, the overall model accuracy is around 85%, calculated using the confusion matrix. The precision value, which shows the proportion of correct positive predictions, reached 88%. The precision for negative predictions reached 80%, and for neutral predictions, it reached 0%. These results show that the Lexicon+SVM model has good performance, with an accuracy of 85%.
Student Organization Website with E-Voting Feature by Using Student Card Verification Concept Design Paskalis, Yohanes Maria Jonathan Glenn; Bachri, Karel Octavianus
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.v8i3.13734

Abstract

Student organizations hold an election to decide their next head and vice head every year. The best voting method for student organizations is to use an independent website with a voting system. The voting system can use students’ identity card and their student email as base for verification. OCR and face detection can be used for extracting all the needed information to validate the student card and verify it with the corresponding student email input. Other than the voting system, the website can be used to promote the student organization itself. The website was built using Nuxt for its front-end, Firebase for its back-end, and Cloud Vision API for its OCR and face detection module. There is a Lighthouse test, a stress test for the voting system, and a test to determine the optimal file size for the voting system. The results are a website that has an average Lighthouse score of 97.58. The stress test, which used a script that does submission repeatedly, results suggest that the voting system can handle up to 2000 voters at the same time. The optimal file size determined by the authors to be 500KB as the result of its test. The conclusions are a great performing website with a voting system can be built using Nuxt and Firebase, the voting system can be improved by adding another step of verification, and it’s best to use and image with a file size above 250KB when using Cloud Vision API for optimal results
Indonesians Perception on the South China Sea Dispute: Support Vector Machine and Naïve Bayes Approach Hafizha, Adinda Aulia; Nidatya, Nurfarah
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.v8i3.13735

Abstract

In recent years, relations between Indonesia and China have become increasingly cordial. However, a potential source of tension is emerging in the form of a heightened dispute in the South China Sea. The government of Indonesia is considered an ally, however there has been a long-standing negative opinion among Indonesians regarding China, which has influenced the way both the general public and the political elite have perceived the relations between Indonesia and China. This research has two objectives. The first is to examine Indonesian perceptions regarding the South China Sea conflict. The second is to compare the performance of Support Vector Machine (SVM) and Multinomial Naïve Bayes as a method of sentiment analysis. Using 7.051 Indonesian-language posts from social media X as a dataset, the result shows that a significant portion of Indonesians view the dispute negatively, fearing potential escalation and threats to national security. Despite these concerns, there is reason to believe that Indonesia can play a proactive role in resolving the conflict through ASEAN and UNCLOS frameworks. Meanwhile, SVM has been demonstrated to be an effective method for handling sentiment analysis data, achieving an accuracy of 87.95%. This work contributes to the field of sentiment analysis by highlighting social media as a valuable platform and by demonstrating the effectiveness of SVM. Furthermore, the study offers new insights for the field of international relations by analyzing the South China Sea dispute through a machine learning lens, which may lead to the development of novel perspectives.

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