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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 54 Documents
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Search results for , issue "Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024" : 54 Documents clear
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.
Extreme Learning Machine and Multilayer Perceptron Methods for Predicting COVID-19 Yustisio, Dheva; Siswanah, Emy; Tafrikan, Mohamad
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.v8i4.14029

Abstract

The number of positive COVID-19 cases in Semarang City has increased over the last year. In anticipating and preparing proper health facilities, the government must predict the number of cases. This research applies Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP) to indicate the number of positive COVID-19 cases. These newly developed methods are part of Artificial Neural Network (ANN). The type of data used in the study is secondary data. Covid-19 patient data was taken from the Semarang City Health Office. The data on the number of positive Covid-19 cases used is data from April 9, 2020 to December 15, 2022. The prediction results of the ELM and MLP methods were then compared to determine which method was more effective in predicting the number of positive Covid-19 cases. The results of the study showed that both methods had an error of less than 10%, meaning that both methods were feasible for predicting the number of positive Covid-19 cases. However, based on the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) values, the MLP method had a smaller error rate than the ELM method. In predicting the number of COVID-19 positive cases, ELM has 93.436331% accuracy, and MLP has 97.055838% accuracy. The best method for predicting the number of COVID-19 positive cases in Semarang City is Multilayer Perceptron (MLP).
Application of the C4.5 Algorithm for Predicting Students' Learning Styles Based on Somatic, Auditory, Visual, and Intellectual Models Aminah, Siti; Yadi, Yadi
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.v8i4.14032

Abstract

Education in Indonesia has seen significant development over the past few decades, with government efforts to improve access and quality of education throughout the country. Programs such as the 12-Year Compulsory Education and curriculum revitalization have driven an increase in school participation rates. However, challenges such as the quality gap between urban and rural areas and the low competence of teachers remain key issues in achieving more equitable and high-quality education for all segments of society. This study aims to apply the C4.5 algorithm to predict students' learning styles based on the Somatic, Auditory, Visual, and Intellectual (SAVI) model. Learning styles are an important aspect of education that affects the effectiveness of learning. By understanding individual learning styles, educators can optimize teaching methods according to students' needs. In this study, student learning style data was collected and analyzed using the C4.5 algorithm, an effective decision tree method for data classification. The results of this algorithm are decision trees that categorize students into one of four learning styles based on specific features. This study shows that the C4.5 algorithm has good accuracy in predicting learning styles, with an entropy value of 1.55 and a gain of 0.156. The implementation of the results of this study is expected to help teachers develop more optimal teaching strategies in preparing learning materials according to students' learning styles.
Sentiment Analysis on KPU Performance Post-2024 Election via YouTube Comments Using BERT Sholihah, Nafiatun; Abdulloh, Ferian Fauzi; Rahardi, Majid
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.v8i4.14040

Abstract

This research aims to analyze public sentiment regarding the performance of the General Election Commission after the 2024 presidential election using the BERT (Bidirectional Encoder Representations from Transformers) model. Given the General Election Commission's crucial role in maintaining election integrity and the importance of transparency in Indonesian democracy, understanding public opinion through sentiment analysis is essential. Data was collected from YouTube comments, a platform increasingly popular for public expression. The analysis process began with data preprocessing, including case folding, text cleaning, tokenization, and stop word removal. The BERT model was then applied to classify the sentiment of the comments, with the model's performance evaluated using 10-fold cross-validation. The evaluation results showed that the first fold (k=1) achieved the best performance with an accuracy of 96%, precision of 96%, recall of 96%, and an F1-score of 96%, indicating the model's effectiveness in accurately classifying sentiment. In contrast, the ninth fold (k=9) exhibited the lowest accuracy at 86% with other metrics also lower, suggesting performance instability potentially caused by data variability. Accuracy and loss graphs confirmed that the first fold experienced consistent accuracy improvements and significant loss reduction, while the ninth fold showed performance fluctuations. This study provides valuable insights into public sentiment regarding the General Election Commission performance, with BERT demonstrating significant potential for sentiment analysis on social media platforms like YouTube.
Analysis Technique Data hiding using HPA DCO on SATA Hard Drive Ilhami, Muhammad Reyfasha; Cahyani , Niken Dwi; Jadied, Erwid Musthofa
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.v8i4.14042

Abstract

Data hiding techniques in the Host Protected Area (HPA) and Device Configuration Overlay (DCO) areas of SATA Hard Disk Drives have become a frequently used anti-forensic activity to hide data and evidence. The area is inaccessible to standard operating systems and software, making it capable of hiding data. This technique utilizes the ability of the SATA Hard Disk Drive to reconfigure the storage size so as to hide evidence. When anti-forensic data hiding Host Protected Area (HPA) and Device Configuration Overlay (DCO) activities occur, it is necessary to conduct a digital forensic investigation to find clues that are useful in solving crimes. Therefore, in this research, an assessment of data hiding techniques using Host Protected Area (HPA) and Device Configuration Overlay (DCO) on SATA Hard Disk Drives is carried out. The implementation of the HPA DCO data hiding technique on a SATA Hard Disk Drive by identifying the HPA DCO area on the SATA HDD and investigating the acquisition results on the SATA HDD is the subject of this research. It is expected that the results will provide a comprehensive overview of HPA DCO data hiding techniques on a SATA HDD as well as recommendations on how to identify and investigate SATA HDDs that have HPA DCO. This effort aims to evaluate the HPA DCO data hiding technique in various cases and provide insight into the potential use of this technique in hiding data or evidence.
Proposed Implementation uses TOGAF ADM and ArchiMate - Enterprise Architecture in Retail Industry Hengky, Ng; Dazki, Erick; Indrajit, Eko
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.v8i4.14052

Abstract

As the growth rate of the retail industry in Indonesia continues to increase, leveraging information technology (IT) to support business operations has become increasingly crucial for achieving effectiveness and efficiency. Retail companies must manage interconnected business systems, such as inventory management, supply chain, e-commerce, and customer service. Without a clear architecture, integrating these systems becomes challenging, leading to operational inefficiencies, difficulties in decision-making, and an inability to respond quickly to market trends. A comprehensive Enterprise Architecture (EA) is therefore essential for managing all core processes within a company. Implementing EA using the TOGAF (The Open Group Architecture Framework) methodology is an optimal choice, as it is widely recognized and adopted. Technology Architecture, Data Architecture, Application Architecture, and Business Architecture are the four primary domains of TOGAF. Business Architecture improves cross-departmental integration and streamlines Business Process, while Application Architecture facilitates automation and optimizes application systems for more efficient operations. Data Architecture focuses on structured data management, ensuring accurate and accessible information for decision-making. Meanwhile, technology architecture provides a flexible and adaptable technological infrastructure that responds to business changes. By implementing Enterprise Architecture (EA) through TOGAF ADM, the retail industry can streamline Business Process, integrate various systems, adopt new technologies, and optimize the supply chain more effectively. This approach not only enhances operational efficiency but also strengthens competitiveness in the retail sector by fostering innovation and providing responsive services.
Summarizer Precision Value on Tribunnews Gorontalo in the Implementation of Online Discourse Sentiment Analysis Bau, Rahmat Taufik R.L
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.v8i4.14070

Abstract

This research investigates the precision of a summarization-based sentiment analysis framework applied to online discourses, specifically from Tribunnews Gorontalo. This study aims to develop and evaluate a sentiment analysis framework that accurately parses complex meanings and nuances in online discourse. The research process begins with summarizing the content using Python, followed by tokenization and sentiment analysis using the BERT model. The precision of the sentiment analysis was meticulously measured. Results indicate that the precision analysis demonstrates that the Python-implemented model achieved a 86% precision rate when applied to ten online discourses from Tribunnews Gorontalo. This research contributes significantly to understanding public sentiments in online content, offering deeper and more accurate insights.
Performance Single Linkage and K-Medoids on Data with Outliers Allo, Caecilia Bintang Girik; B, Winda Ade Fitriya; Paranoan, Nicea Roona
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.v8i4.14072

Abstract

One way to assess the economic growth of a province is by examining its Gross Regional Domestic Product (GRDP). GRDP calculated through the production approach reflects the total value added by goods and services from various sectors within a particular region over a specified period. To determine the GRDP, 17 business sectors are considered. In 2023, the GRDP growth rate in Papua has decreased to 3.44%, down from 4.11% the previous year. To help the government improve Papua’s GRDP, an analysis is required. Clustering methods can group regencies and cities with similar characteristics. Boxplots are used to identify outliers in the data. The data contains outliers, so one method that can be used is K-Medoids. Euclidean Distance is used to calculate the distance matrix. Before calculating the distances, standardization using z-score normalization is performed to ensure that the data ranges are the same. This article aims to identify the most effective method for clustering regencies and cities in Papua using GRDP at constant price data. Both Single Linkage and K-Medoids methods are applied in this study. The DBI is used for evaluation, with lower DBI values indicating better methods. According to the DBI results, Single Linkage outperforms K-Medoids for clustering regencies and cities in Papua, with the optimal number of clusters being three. Keywords: Euclidean Distance; Davies Bouldin Index (DBI); Gross Regional Domestic Bruto; K-Medoids; Single Linkage; z-score Normalization

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