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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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Stock Price Correlation Analysis with Twitter Sentiment Analysis Using The CNN-LSTM Method Ibnu Sina, Muhammad Noer; Setiawan, Erwin Budi
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12855

Abstract

The intricate interplay between stock prices, reflecting a company's intrinsic value, and multifaceted factors like economic conditions, corporate performance, and market sentiment, constitutes a vital research domain. Grounded in sentiment analysis, our study deciphers public opinions from vast textual data to gauge sentiment, leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. We focus on Bank Central Asia (BBCA), a prominent Indonesian banking institution, aiming to forecast stock price fluctuations by analyzing sentiment trends extracted from social media, especially Twitter. Meticulous experimentation, encompassing data segmentation, feature extraction, augmentation, and model refinement, yields significant enhancements in prediction accuracy. The CNN-LSTM model's performance improves from 73.41% to a robust 77.75% accuracy, with F1-scores rising from 73.00% to 75.42%. Importantly, strong correlations emerge between sentiment predictions and actual stock price movements, validated by Spearman correlation coefficients. Positive sentiment exhibits a substantial correlation of 0.745 with stock price changes, while negative sentiment exerts notable influence with a correlation coefficient of 0.691. In summary, our study advances the field of sentiment-driven stock price prediction, showcasing deep learning's effectiveness in extracting sentiment from social media narratives. The implications extend to understanding market dynamics and potentially integrating sentiment-aware strategies into financial decision-making. Future research directions could explore model transferability across financial contexts, real-time sentiment data integration, and interpretability techniques for enhanced practicality in sentiment-driven predictions.
Ontology-Based Food Menu Recommender System for Patients with Coronary Heart Disease Najla Nur Adila; Baizal, Z. K. A.
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12858

Abstract

Coronary heart disease is one of the leading causes of death. Knowledge of dietary patterns and proper food selection is an effort to address the risk and support coronary heart disease's healing process. Therefore, this study developed a food menu recommender system as a reference for patients with coronary heart disease. The recommender system is crucial in creating a proper dietary pattern for managing personalized meal plans. The system calculates the required nutritional needs of users. Ontology is used to represent knowledge about nutrition data and food intake. The ontology base with Semantic Web Rule Language (SWRL) enables the system to identify the most suitable foods for patients with coronary heart disease. We use SWRL rules to generate recommendation conclusions based on the existing ontology. Using this language enhances the descriptive logic capabilities, as the rules can overcome the limitations of the ontology language. Therefore, the system is built to find food menu options that match the required nutrition for patients. The nutritionist knowledge will be used to measure the system's performance compared to the recommendations made by nutritionists. From the user data sample, 150 recommended food menu data were obtained. The validation performance results obtained a precision 0.893, recall 1, and F_Score 94.3%.
User Interface Design for Baduy Ecotourism Website Using User Centered Design Method Fhalosa, Muhammad Farhan; Suwawi, Dawam Dwi Jatmiko; Riskiana, Rosa Reska
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12859

Abstract

Baduy is one of the ecotourism destinations which offers captivating natural and cultural attractions, making it a worthy place to visit. The lack of available information in the media has piqued the author's interest in designing a visually appealing website to attract tourists. To address this issue, a user interface design will be created for the Baduy Tribe Ecotourism website using the User Centered Design (UCD) method. This method focuses on users and involves four stages, specifying the context of use, specifying requirements, designing a solution, and evaluating the design. Furthermore, to assess how well users interact with a product, usability testing will be conducted using the System Usability Scale (SUS) and Single Ease Question (SEQ). The usability testing results on the created website interface obtained a SUS score of 81 and generated 3 rating components in the SEQ method, fairly easy, easy, and very easy. Therefore, it can be concluded that the usability score falls within the category of good and is acceptable to users. Through this research, the author hopes that the user interface design for the Baduy Tribe Ecotourism website will meet users' needs, providing them with easy access to valid information and a seamless experience in discovering the natural wonders of the region.
A Prototyping Model for Self-Appraisal Employee Performance Application Development in Cooperative Bagastio, Shobrun Jamil; Anisa, Gia; Suakanto, Sinung; Kusumasari, Tien Fabrianti
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12865

Abstract

Employee performance appraisal which is currently still being implemented cannot yet describe transparency. In addition, the process is still manual, which is far from effective. This study aims to develop a web-based employee performance appraisal application system that can increase the transparency of assessments in microfinance cooperatives. The focus is on using self-appraisal techniques to promote transparency in the appraisal process. Transparent assessments are critical to building trust and fairness during performance evaluations. Using the self-appraisal method, individuals can evaluate their own performance, skills, and achievements in a transparent and unbiased manner. This study investigates the process of making an employee performance appraisal system with the prototyping model method as a development method. The findings of this study contribute to existing knowledge about performance evaluation in microfinance services, particularly in relation to self-appraisal techniques, and offer practical insights for organizations wishing to increase the transparency of appraisals through web-based application systems.
Analysis of User Adoption Levels of JAKI Application Using the Government Adoption Model (GAM) Kirani, Zahra Anadya; Utomo, Rio Guntur; Fathoni, Muhammad Faris
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12868

Abstract

This study delves into an analysis of the adoption patterns within the Jakarta Today e-government application (JAKI) through the dual lenses of the Government Adoption Model (GAM) and the Structural Equation Model (SEM). Encompassing JAKI users aged 17 years and above, the research encapsulates a substantial sample size of 384 individuals. The research findings underscore the pivotal role of key factors in driving e-Government adoption within the context of JAKI. Notably, Perceived Service Response, Perceived Trust, Perceived Uncertainty, Perceived Security, and Privacy collectively wield a significant and affirmative impact on the Adoption of e-Gov. However, intriguingly, factors including Perceived Awareness, Computer-self Efficacy, Availability of Resources, Perceived Ability to Use, Perceived Compatibility, Perceived Functional Benefit, Perceived Image, Perceived Information Quality, and Multilingual Option do not exert a notable influence on the Adoption of e-Gov. These insights proffer invaluable guidance for the Jakarta City Government, facilitating an enhanced understanding of user perceptions and needs. By meticulously addressing the determinative factors that engender a favorable adoption environment, the government stands poised to elevate the efficacy and reach of its e-government service, thus fostering greater citizen engagement and interaction with the JAKI application.
Paraphrase Generation For Reading Comprehension Januarahman, Faishal; Romadhony, Ade
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12873

Abstract

Reading comprehension is an assessment that tests readers understanding of a concept from the given text. The testing process is conducted by providing questions related to the content within the context of the text. The purpose of this research is to create new question variations from existing questions, and one of the methods to achieve this is by paraphrasing questions through the task of paraphrase generation. This can help ensure that readers have fully grasped a concept of a text. This study employs a traditional approach known as the thesaurus-based approach, in which the process involves substituting synonyms using the Indonesian Thesaurus dictionary. The data used consists of a list of Indonesian language reading comprehension assessment questions ranging from elementary to high school levels. To measure the quality of the generated paraphrased questions, two evaluation processes are conducted which are automatic evaluation with the scores ranging from 0-1 and human evaluation with score ranging from 1-4. The automatic evaluation includes the BLEU-4 metric, resulting in a score of 0.044, and the ROUGE-L metric, resulting an F1-score of 0.421. As for human evaluation, the obtained relevancy score is 2.533, and the fluency score is 3.186. The results from both evaluation metrics indicate that the generated paraphrased questions exhibit diverse new word choices but tend to have slightly different meanings compared to the reference questions.
Social Media Based Film Recommender System (Twitter) on Disney+ with Hybrid Filtering Using Support Vector Machine Ramadhan, Helmi Sunjaya; Budi Setiawan, Erwin
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12876

Abstract

In the current era, the culture of watching TV shows and movies has been made easier by the presence of the internet. Now, watching movies on platforms can be done from anywhere, one of which is Disney+. At times, people find it challenging to decide which film to watch given the multitude of genres and film titles available on these platforms. One solution to this issue is a recommendation system that can suggest films based on ratings. The recommendation system to be utilized involves Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering. This is because Collaborative Filtering and Content-Based Filtering encounter issues like cold start, sparsity, and overspecialization. Thus, the objective of this study is to develop a recommendation system using Hybrid Filtering combined with Support Vector Machine (SVM). In this research, classification will be carried out using poly, linear, and RBF kernels with varying parameters. Techniques such as TF-IDF, RMSE, tuning, and data balancing with SMOTEN will be implemented to enhance accuracy during the classification process. The evaluation employed in this study utilizes the confusion matrix. Support Vector Machine, when tuned and combined with SMOTEN, achieves noteworthy results, particularly with the RBF kernel which attains a Precision score of 0.94. Recall produces a value of 0.93 with the Poly kernel, while the highest Accuracy, at 0.93, is achieved with the RBF kernel. Furthermore, the RBF kernel also demonstrates the highest F1-Score of 0.93. These findings illustrate elevated precision, recall, accuracy, and F1-Score within the context of hybrid filtering, achieved through the application of Support Vector Machine for classification and the implementation of the SMOTEN technique.
Image Augmentation for BreaKHis Medical Data using Convolutional Neural Networks Istighosah, Maie; Sunyoto, Andi; Hidayat, Tonny
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12878

Abstract

In applying Convolutional Neural Network (CNN) to computer vision tasks in the medical domain, it is necessary to have sufficient datasets to train models with high accuracy and good general ability in identifying important patterns in medical data. This overfitting is exacerbated by data imbalances, where some classes may have a smaller sample size than others, leading to biased predictive results. The purpose of this augmentation is to create variation in the training data, which in turn can help reduce overfitting and increase the ability of the model to generalize. Therefore, comparing augmentation techniques becomes essential to assess and understand the relative effectiveness of each method in addressing the challenges of overfitting and data imbalance in the medical domain. In the context of the research described, namely a comparative analysis of augmentation performance on CNN models using the ResNet101 architecture, a comparison of augmentation techniques such as Image Generator, SMOTE, and ADASYN provides insight into which technique is most suitable for improving model performance on limited medical data. By comparing these techniques' accuracy, recall, and overall performance results, research can identify the most effective and relevant techniques in addressing the challenges of complex medical datasets. This provides a valuable guide for developing better CNN models in the future and may encourage further research in developing more innovative augmentation methods suitable for the medical domain.
Comparison Analysis of C4.5 Algorithm and KNN Algorithm for Predicting Data of Non-Active Students at Prima Indonesia University Banjarnahor, Jepri; Zai , Ferman; Sirait , Janiali; Nainggolan , Dicky Wijaya; Sihombing , Nissi Grace Dian
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12879

Abstract

Education is important nowadays because universities need to improve their students' skills so they can compete in the globalization era. Education can be obtained through both formal and informal channels, and knowledge is available everywhere, especially in today's world where information tools are rapidly evolving. Inactive students are students who do not participate in a course for a maximum of two consecutive semesters. Students who are not active have the opportunity to drop out of university studies. Students who drop out of college are usually motivated by economic factors, and the cessation of the lecture process can cause inactivity and administrative costs. Therefore, this research was conducted using the C4.5 algorithm method and the K-Nearest Neighbor (KNN) algorithm to compare and predict data on inactive students at Universitas Prima Indonesia. The research continued with the data collection and data preprocessing stages, after which the data mining process was carried out to get the final results of this research. The testing process follows the process of comparing the C4.5 algorithm and the K-Nearest Neighbor (KNN) algorithm with K-fold crossing. This evaluation step is compared by considering the comparison values of the confusion matrix (precision, precision, recall). The accuracy results obtained by each algorithm provide information about the effectiveness of using these techniques in processing the specified dataset. The accuracy of the Decision Tree C4.5 algorithm is 99.12% and the K-Nearest Neighbors algorithm is 99.14%. Based on research conducted using the K-Nearest Neighbors and C4.5 algorithms to predict inactive students, the KNN algorithm is more accurate than the C4.5 algorithm.
Android-based Automatic Steak Grilling Tool Salamah, Irma; Syaniah, Yunita; Hadi, Irawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12880

Abstract

In an era of rapid technological development, technology is increasingly accessible and easily applied by humans. One of the significant developments is the Internet of Things (IoT), where physical devices such as sensors, equipment, and vehicles are equipped to communicate and interact via the Internet network. The application of IoT has expanded to various sectors, including culinary. In this regard, preparing and presenting food, especially steaks, becomes an exciting focus. There are multiple types of steaks, such as sirloin and tenderloin, and cooking involves various techniques, such as searing and grilling. However, suitability for maturity and risk during cooking is challenging for steak makers and connoisseurs. To overcome this, the application of IoT is needed in an automatic steak roaster to be a promising solution. This research is also equipped with real-time monitoring via an Android application. This aims to ensure proper doneness and consistent results in the steak cooking process. This research makes an automatic steak grill with a success rate of 83%, which shows that the tool's performance and functionality align with expectations. This tool also has an Android application to monitor and control the device remotely efficiently. This research gives confidence that this can be a solution that has been developed and provides significant benefits in roasting steaks with automatic monitoring and operation.

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