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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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Analysis of Indonesian Netizen Sentiment on Platform X Regarding the Arrival of Refugees in Indonesia Using the Multinominal Naive Bayes Method Joefitra Zaqy, Muhammad; Marlina, Leni; Wijaya, Rian Farta
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.v8i3.13940

Abstract

This research aims to analyze the sentiments of Indonesian netizens regarding the arrival of Rohingya refugees in Indonesia using the Multinomial Naive Bayes method. Sentiment analysis was carried out on comments obtained from platform X. The data collection technique used the crawling method to extract comments from platform X users regarding the issue of the arrival of Rohingya refugees. The tool used for crawling is Google Collab. The data analysis process includes sentiment labeling, data preprocessing (case folding, stopword removal, tokenizing, stemming), and classification using the Multinomial Naive Bayes method. The research results show that the majority of Indonesian netizens' sentiments regarding the arrival of Rohingya refugees in Indonesia are negative, with a percentage of 81%. Positive sentiment reached 8%, while neutral sentiment was 11%. The Multinomial Naive Bayes method produces an accuracy of 82.5% in classifying netizen sentiment. The tools used to process the data are the Orange Data Mining application version 3.36.2 It is hoped that this research can contribute to the development of computer science, especially in the fields of Text Mining, Natural Language Processing, Machine Learning and Artificial Intelligence (AI). It is also hoped that this research will provide benefits to parties related to handling the Rohingya refugee problem in Indonesia, such as the government, humanitarian organizations, mass media, academics, the general public, and other researchers.
Implementation of Zero Trust Security in MSME Enterprise Architecture: Challenges and Solutions Rahman, Abdul; Indrajit, Eko; Unggul, Akhmad; Dazki, Erick
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.v8i3.13949

Abstract

This research examines the implementation of Zero Trust Security in Enterprise Architecture in Micro, Small, and Medium Enterprises to improve cybersecurity. The background of this research focuses on the increasing cyber threats faced by MSMEs and their limitations in adopting advanced security systems. The purpose of this study is to evaluate the effectiveness of Zero Trust Security in protecting MSME data and information systems from internal and external threats, as well as identifying challenges and solutions in its implementation. The research method used is a case study on several MSMEs with a qualitative and quantitative approach involving in-depth interviews, surveys, and secondary data analysis. The results showed that the implementation of ZTS significantly improved information system security in MSMEs, with a 45% reduction in security incidents after ZTS adoption. In addition, ZTS was also shown to increase cybersecurity awareness among MSME employees. The main challenges identified include the need for adequate training, changes in organizational culture, and budget limitations. To overcome these challenges, this study recommends the adoption of continuous training strategies, increased cybersecurity awareness, and the utilization of affordable yet effective security solutions. The conclusion of this study confirms that Zero Trust Security is an effective and efficient approach to improving the cybersecurity of MSMEs. However, further research is recommended to explore the application of Zero Trust Security in various other industry contexts and to develop more affordable solutions for MSMEs with limited resources.
A Comparative Study of Alternative Automatic Labeling Using AI Assistant Julianto, Indri Tri; Kurniadi, Dede; Balilo Jr, Benedicto B.; Rohman, Fauza
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13950

Abstract

The development of AI assistants has become increasingly sophisticated, as evidenced by their growing adoption in assisting humans with various tasks. In particular, AI assistants have demonstrated potential in the field of sentiment analysis, where they can automate the labeling of text data. Traditionally, this labeling process has been performed manually by humans or using tools like the VADER Lexicon. This study is imperative to evaluate the performance of AI Assistants in sentiment labeling, as compared to traditional human-based labeling and the application of the VADER sentiment analysis algorithm. The methodology involves comparing the labeling results of Gemini and You AI with those of human labeling and VADER. Performance is evaluated using the Naive Bayes and K- Nearest Neighbour algorithms, and K-Fold Cross Validation is employed for evaluation. The results indicate that the performance of both AI assistants can closely approximate the performance of human labeling. Gemini's best accuracy is achieved with the k-NN algorithm at 53.71%, while You AI's best accuracy is achieved with the Naive Bayes algorithm at 48.30%. These results are close to the accuracy of human labeling (61.12%) using the k-NN algorithm and VADER (54.29%) using the Naive Bayes algorithm. This suggests that AI assistants can serve as an alternative for text data labeling, as the differences in performance are not statistically significant.
Comparison Of Machine Learning Algorithms On Stunting Detection For 'Centing' Mobile Application To Prevent Stunting Sabilillah, Ferris Tita; Sari, Christy Atika; Abiyyi, Ryandhika Bintang; Susanto, Ajib
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13967

Abstract

Stunting is a growth disorder caused by chronic undernutrition, with long-term impacts on child health and development. In Indonesia, the prevalence of stunting reached 31.8% in children under five years old in 2018, indicating an urgent need for effective interventions. In an effort to address this issue, we developed a mobile application called Centing (Cegah Stunting) that utilizes machine learning for early detection and prevention of stunting. In this study, we compare the performance of four machine learning algorithms Logistic Regression, Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP) in detecting children's nutritional status based on a dataset from Kaggle with 121 thousand data and four main features: age, gender, height, and nutritional status. The experimental results show that SVM with RBF kernel and CNN achieved the highest accuracy of 98%, while Logistic Regression and MLP achieved 76% and 97% accuracy respectively. SVM with RBF kernel was chosen as the best model due to its high accuracy and efficiency in computation time. These findings suggest that the Centing application, with the implementation of SVM RBF, has significant potential in early detection and prevention of stunting, and makes an important contribution to improving child health in Indonesia.
Study of Public Sentiment Towards Beauty Products Using A Machine Learning Approach: Random Forest Analysis On Social Media Tresya Noviania Pasaribu; Tanjung, Juliansyah Putra; Dosma Hutauruk; Endang Sapriana Hutagalung; Saputra Silitonga
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.v8i3.13969

Abstract

In this digitalized era, the development of technology and the internet has brought significant changes in various aspects of life, including the way we shop. The trend of online shopping is increasingly prevalent and favored by the public, not least for cosmetic products. This research uses a quantitative approach to analyze public opinion or sentiment towards beauty products, especially beauty products. The data used in this research comes from online platforms. This research uses a beauty product dataset obtained from Kaggle. This research uses the Random Forest algorithm to analyze the data and produce findings, This algorithm is one of the advanced tools in Machine Learning that is focused on sorting data into the right categories, which in this context is used to classify public sentiment towards beauty products into categories such as positive, negative or neutral. Random Forest achieved a very high accuracy rate of 94.68% in the evaluation. However, it should be noted that the positive class has a low recall (25%) and a low F1-score (40%), indicating that the model may struggle to detect positive sentiment towards beauty products beauty products. In general, the model did well in classifying neutral and negative sentiments. Sentiment analysis shows that the majority of public sentiment towards beauty products is neutral, with a significant amount of negative and positive sentiment. It is evident that user opinions are informative or descriptive without conveying strong positive or negative emotions.
An IT Governance Analysis in Interior Contracting Industry: A COBIT 2019 Approach Susatyo, R Wahyu Indra; Indrajit, Eko; Dazki, Erick
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13978

Abstract

The very rapid development of technology is currently having an impact on every industry, which must adapt by carrying out technological transformation to survive and have added value for customers. Many businesses, including interior contractors, use a variety of hardware and software, as well as information systems, to streamline their business processes. Under these conditions, the importance of strong IT governance to ensure that the implementation of IT investments continues to provide great benefits for the company's progress has been considered a top priority. This research explores how IT governance functions in this industry using COBIT 2019, a leading evaluation framework. The main areas of COBIT 2019 will be used to assess a company's IT capabilities. This study focused on an interior contractor company in Serpong, Indonesia, which was already using enterprise resource planning (ERP) and project management software. The analysis identified 12 out of 40 domains that need improvement to achieve certain target levels. These agreed targets aim to improve IT capabilities, such as reducing dependence on external vendors for system development and creating clear standards for managing technological change. Despite these recommendations, further investigation revealed a gap between the desired and current conditions. This research proposes solutions to bridge this gap, including achieving greater IT system independence and establishing clear guidelines for navigating technological advances.
Application of the Arima Method to Prediction Maximum Rainfall at Central Java Climatological Station Ruslana, Zauyik Nana; Prihatin, Rudi Setyo; Sulistiyowati, Sulistiyowati; Nugroho, Kristiawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13984

Abstract

The existence of extreme weather that is difficult to predict results in frequent hydrometeorological disasters. ARIMA is a prediction method that can capture trend patterns, seasonal cycles, and random fluctuations that are often found in patterned data. Although many samples of rain data collection points are needed to produce denser data, one point can be considered to represent an area that is not too large, such as Semarang City. This method is quite accurate for short-term forecasts, with the results of monthly maximum rainfall forecasts in 2023 showing varying MAPE values. For the 12-month forecast, prediction results range from fair to very accurate. The 7-month forecast also shows decent to very accurate results. However, the 5-month forecast shows less accurate results. This shows that ARIMA can be a useful method in forecasting monthly maximum rainfall, especially during the dry season. The application of ARIMA in Semarang City can help in planning hydrometeorological disaster mitigation, considering that the Semarang City area often experiences extreme weather that is difficult to predict. Thus, the use of ARIMA can provide significant benefits in preparing for and reducing the impact of hydrometeorological disasters in the region. In addition, with more accurate forecasts, the government and society can take preventative steps earlier, such as better water management, creating an adequate drainage system, and increasing public awareness of the threat of disasters. Therefore, this research emphasizes the importance of using reliable prediction methods such as ARIMA to improve preparedness in dealing with hydrometeorological disasters.
Retail Marketing Strategy Optimization: Customer Segmentation with Artificial Intelligence Integration and K-Means Clustering Putri, Yuliarni; Aldo, Dasril; Ilham, Wanda
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14000

Abstract

This study aims to optimize retail marketing strategies through customer segmentation using the K-Means clustering method and RFM (Recency, Frequency, Monetary) analysis. By utilizing transaction data from a large retail company, customers are categorized into six segments: VIP Customers, Loyal Customers, Potential Loyalists, New Customers, At-Risk Customers, and Dormant Customers. This segmentation allows for the implementation of more targeted marketing strategies for each customer group. For example, VIP Customers who represent 3.0% of total customers are very active with significant spending, so they deserve exclusive offers and premium services. Loyal Customers, which account for 7.0% of total customers, show high transaction frequency and loyalty, suitable for loyalty programs and recurring discounts. Potential Loyalists, which comprise 15.0%, show the potential for increased loyalty through retention campaigns. New customers representing 16.3% need a brand recognition and promotion strategy to increase their initial engagement. At-Risk Customers covering 30.7% indicated a decrease in transaction activity and required intervention to prevent churn, while Dormant Customers covering 28.1% required a strong reactivation strategy. The clustering evaluation showed an average Silhouette score of 0.3115, which indicates that the clusters that are formed are quite well defined, although there is still room for improvement. This research provides valuable insights to develop more effective and efficient marketing strategies, as well as increase customer satisfaction and loyalty.
Performance Comparison of KNN and CNN in Classifying Balinese Gangsa Instrument Tones Yusadara, I Gede Putra Mas; Dewi, Ni Made Rai Masita; Budaya, I Gede Bintang Arya
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14019

Abstract

Balinese traditional music, particularly the Gamelan Gangsa, represents a unique aspect of Indonesia’s cultural heritage. Despite its cultural significance, the study and teaching of this instrument face challenges, particularly in tone standardization and the availability of effective learning tools. This research addresses these challenges by exploring the application of Artificial Intelligence (AI) technologies specifically K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN) in the identification and classification of Gamelan Gangsa tones. The study involved the creation of a dataset comprising audio recordings of the instrument, followed by the development and evaluation of KNN and CNN models. The results indicate that KNN, with an accuracy of 90%, outperformed CNN, which achieved an accuracy of 85%. The findings suggest that KNN is particularly effective in distinguishing subtle tonal differences, making it a valuable tool for supporting traditional music education. This research not only contributes to the technical understanding of Gamelan Gangsa’s acoustic characteristics but also underscores the potential of AI in cultural preservation. The development of AI-based tone identification systems can facilitate the teaching and learning of traditional music, ensuring its transmission to future generations. The study serves as a foundation for further exploration into the integration of AI technologies with cultural heritage, demonstrating how modern innovations can enhance the appreciation and understanding of traditional arts.
Optimization of Dimsum Production Profits Using the Branch and Bound Method Andini, Qonita Putri; Husein, Ismail
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.v8i3.14026

Abstract

The dimsum industry in Indonesia is currently experiencing very significant development, because many businesses offer processed dimsum products for convenient consumption. The characteristics of dimsum are varied and suitable to be served as a snack. This has created an increasing number of dimsum enthusiasts, seen from the emergence of restaurants serving dimsum menus originating from China. The aim of this research is to determine the maximum profit achieved in making dimsum using the Branch and Bound technique. Using the branch and bound method because it is a mathematical model which is a development of a linear program, where all decision variables must be integers, this method limits the optimal solution to a whole by creating an upper and lower branch for each solution which has a fractional value. to be a round value so that each restriction will produce a new branch. Based on the research results, it can be concluded that the optimal production level using the Branch And Bound method is IDR 19,054,950 per month. When compared with the profits before using the Branch and Bound method, the profits obtained were IDR 18,800,000. This shows that by using the Branch and Bound method, the profit of the Mikaila Bakery cake shop increases by IDR. 1.3% or around Rp. 254,950 per month. Sensitivity analysis shows that profits will remain at optimal conditions if changes in the objective function coefficients are less than or equal to the objective function coefficients in the initial model.

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