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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 Palm Oil Production Planning for Biodiesel Needs in North Sumatra Azura, Silva; 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.v8i3.14027

Abstract

Palm oil has become a leading product in the plantation business in Indonesia. Currently, Indonesia has become the country with the largest palm oil production capacity in the world. With this production capability, the opportunity to diversify energy made from palm oil is very possible. The analysis of this study was carried out using secondary data to find out the extent of the potential of palm oil as the main source of biodiesel raw materials in Indonesia. The results of the analysis state that with the production and expansion of oil palm land very massive, energy diversification is a relevant step and very feasible. The role of the government through the export levy tariff policy also determines palm oil consumption for the benefit of the domestic market.
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
Clustering Analysis of Socio-Economic Districts/Cities In East Java Province Using PCA And Hierarchical Clustering Methods Bhahari, Rifqi Hilal; Kusnawi, Kusnawi
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.v8i4.14078

Abstract

This study aims to analyze the socio-economic conditions of districts/cities in East Java using Principal Component Analysis (PCA) and Hierarchical Clustering. Socio-economic data for 2023 from 38 districts/cities includes the percentage of poor people, regional GDP, life expectancy, average years of schooling, per capita expenditure, and unemployment rate. PCA was used to reduce the dimensionality of the data, facilitating analysis and visualization. The reduced data was then analyzed using Hierarchical Clustering to group districts based on similar socio-economic characteristics. The clustering results were evaluated with the Silhouette Index and Davies-Bouldin Index. This study identified four main clusters with different socio-economic characteristics. The best clusters have high regional GDP, life expectancy, average years of schooling, and high per capita expenditure and low unemployment rates. The worst clusters show a high percentage of poor people and high unemployment rates. These results assist the government in designing more effective policies to improve welfare in East Java.
Analysis of Malnutrition Status in Toddlers Using the K-MEANS Algorithm Case Study in DKI Jakarta Province Sintawati, Ita Dewi; Widiarina, Widiarina; Mariskhana, Kartika
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.v8i4.14087

Abstract

Malnutrition in children is a serious health issue in various regions, including DKI Jakarta Province, which affects the physical and cognitive development of children. This research aims to classify malnutrition status in children using the K-Means algorithm, focusing on cases in DKI Jakarta. The objective is to identify patterns of malnutrition prevalence across different regions, serving as a basis for more effective interventions. The data used in this study includes the percentage of children with severely stunted, stunted, and normal nutritional status across six districts/cities in DKI Jakarta. The results of K-Means clustering show that Central Jakarta has the highest prevalence of severely stunted (10.50%) and stunted (13.01%) status, while West Jakarta has the lowest prevalence of severely stunted (4.62%) and stunted (10.22%) status. The solution offered by this research is the grouping of regions based on malnutrition prevalence, allowing for the identification of areas requiring priority intervention. The analysis results indicate that DKI Jakarta can be classified into several clusters based on malnutrition prevalence. The cluster with the highest malnutrition prevalence includes Central Jakarta, while the cluster with the lowest malnutrition prevalence includes West Jakarta and the Thousand Islands. The implementation of K-Means in this research provides an efficient approach to identifying groups of regions that need more attention in combating malnutrition in children. In conclusion, this research can serve as an important reference for policymakers in formulating more effective and efficient intervention strategies in DKI Jakarta, as well as inspire similar studies in other regions with different population characteristics

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