cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Irpan Adiputra pardosi
Contact Email
irpan@mikroskil.ac.id
Phone
+6282251583783
Journal Mail Official
sinkron@polgan.ac.id
Editorial Address
Jl. Veteran No. 194 Pasar VI Manunggal,
Location
Kota medan,
Sumatera utara
INDONESIA
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
 2   3   4   5   6 
Search results for , issue "Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024" : 54 Documents clear
Data Visualization for Building a Cyber Attack Monitoring Dashboard Based on Honeypot I Gede Adnyana; Ayu Manik Dirgayusari; Ketut Jaya Atmaja
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.v8i4.14144

Abstract

Computer networks are essential for modern life, enabling efficient global information exchange. However, as technology advances, network security challenges grow. To enhance security, honeypots are used alongside firewalls, mimicking legitimate systems to attract hackers and analyze their attack methods. In this research, Cowrie and Dionaea honeypots are implemented. Cowrie targets brute force attacks on SSH, while Dionaea detects port scanning and denial of service (DoS) attacks. These honeypots effectively capture and log malicious activities, providing insights into attack patterns. The collected data is analyzed using the ELK Stack, which offers real-time visualization of attack trends, frequency, and methods. This analysis helps security teams quickly identify and mitigate threats. The integration of honeypots with the ELK Stack significantly enhances network defense by improving detection, analysis, and response to cyber threats. The analysis of the results shows that both honeypots effectively capture and record malicious activities entering the network, providing critical insights into the attack patterns employed by attackers. Within just minutes of deployment, the honeypots logged over 1,000 attacks, predominantly originating from botnets attempting to exploit system vulnerabilities. The captured log data is processed through the ELK Stack, allowing for real-time visualization of attack patterns, including geographic origins, attack frequency, and methods used. This enables security teams to proactively identify trends, assess risks, and implement targeted mitigation strategies more efficiently.
Complete Kernel Fisher Discriminant (CKFD) and Color Difference Histogram for Palm Disease Perangin Angin, Johanes Terang Kita; Herman, Herman; Joni, Joni
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.v8i4.14145

Abstract

Palm oil plantations play a significant role in the economy of Indonesia, supporting 16.2 million people. However, plant diseases pose a major threat to the productivity and health of palm oil crops. Early detection of these diseases is essential to prevent yield losses and mitigate damage. This study proposes the application of the Complete Kernel Fisher Discriminant (CKFD) method combined with Color Difference Histogram to classify diseases affecting oil palm fronds and leaves. The CKFD method uses a non-linear kernel transformation to improve the performance of Fisher Linear Discriminant Analysis (FLDA), while the Color Difference Histogram enhances sensitivity to color variations in different lighting conditions. Experimental results demonstrate that the CKFD method achieves superior accuracy in disease detection compared to traditional Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The proposed approach showed an average accuracy of 94.5% for detecting diseases like Curvularia sp and Cochliobolus carbonus. The combination of CKFD with Color Difference Histogram significantly reduces the impact of lighting variations on the classification results, making it a robust solution for practical deployment in palm oil plantations. This research provides an effective tool for early disease detection and management in the palm oil industry.
Optimization of Stock Forecasting in Bali Retail Businesses to Support the Digital Economy Using Weighted Moving Average (WMA) Approach Welda, Welda; Dharsika, I Gede Eka; Sarasvananda, Ida Bagus Gede
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.v8i4.14149

Abstract

The development of the digital economy provides new challenges for the retail sector, especially in stock management. Accurate stock management is a key factor in improving operational efficiency and minimizing the risk of overstock and understock. This research aims to optimize stock forecasting in retail businesses in Bali using the Weighted Moving Average (WMA) method. WMA gives greater weight to the most recent data in order to forecast future demand for goods. Sales data from 2017 to 2021 was collected and used as the basis for forecasting. The forecasting process was conducted for several products, including Dolphin and Dua Kelinci. The results show that WMA is able to provide accurate predictions, especially for products with stable demand patterns. For Dolphin products, the WMA forecast for January 2024 predicted a demand of 14.8 units, with a Mean Absolute Deviation (MAD) of 3.64. Dua Kelinci products, however, experienced more fluctuations in demand, with a forecasted January 2024 demand of 7.6 units and a MAD of 4.3. Despite some variations, WMA proved to be more accurate compared to simpler methods like Simple Moving Average (SMA). By using WMA, retailers can more efficiently manage stock, improve customer satisfaction, and reduce the risk of overstocking or understocking. This research confirms the importance of integrating advanced forecasting methods in supporting the competitiveness of the retail sector in the digital economy era.
Building Sustainable Communities: SIMARET Development for Financial Transparency with MDALC Approach Saputro, Rujianto Eko; Nanjar, Agi; safitri feriawan, Titi
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.v8i4.14150

Abstract

The increasing need for financial transparency and efficiency in community-level governance, particularly within Rukun Tetangga (RT) in Indonesia, calls for innovative solutions. This study presents the development of SIMARET, a mobile application designed to enhance the management of RT financial activities and resident participation, using the Mobile Application Development Life Cycle (MDALC) approach. The research aims to address the challenges of manual financial management, such as lack of transparency and difficulties in tracking funds and activities like neighborhood watch (Siskamling). SIMARET incorporates key features such as digital tracking of resident contributions (jimpitan), QR code-based attendance for Siskamling, and automated financial reports. The system was developed through MDALC’s structured phases: identification, design, development, testing, and deployment. Blackbox Testing and User Acceptance Testing (UAT) were conducted to ensure functionality and user satisfaction. The results show a high satisfaction rate of 97%, confirming that SIMARET simplifies financial administration and enhances community participation. The study also highlights the application’s contribution to the United Nations Sustainable Development Goals (SDG) 16 by promoting transparency and effective governance at the local level. Although SIMARET demonstrates significant potential, further research is recommended to improve its user interface design and expand its implementation in other communities.
Implementation of the Agglomerative Hierarchical Clustering Method in Ordering Hijab Products Ardyanti, Tiwy; Furqan, Mhd.
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.v8i4.14156

Abstract

The ever-evolving internet technology has an impact on various sectors, including the hijab business, where the demand for hijab products is increasing through online transactions. This research was conducted at the Kinan Hijab Store in Kota Pinang, North Sumatra, with the aim of optimizing the management of hijab product stock. The problem faced is the imbalance in the stock of hijab products, where some hijab products have excess stock that are less in demand while popular hijab products often experience a shortage of stock. To solve this problem, the Agglomerative Hierarchical Clustering method is used to group hijab products based on sales data, product type, and price. This study uses hijab sales data from May to July 2024. After the clustering process, hijab products are grouped into two categories: "Popular" and "Less Desirable". The "Popular" category includes 190 products, while the "Less Desirable" category includes 983 products. Product stock in the "Popular" category will be increased by 50% of the average sales, while stock in the "Less Desirable" category will be reduced by 25%. the effectiveness of the Agglomerative Hierarchical Clustering (AHC) method in stock planning and management by showing that it improved the inventory allocation based on customer demand patterns. The clustering method categorized hijabs into two main groups: "Popular" and "Less Preferred", based on key sales metrics such as quantity sold, price, and total sales. The implementation of the stock plan is carried out based on the sales pattern of each hijab category. Overall, the application of this method not only helps stores in understanding customer purchasing patterns but also optimizes product availability, which can ultimately increase customer satisfaction.
Clustering Analysis of Cadet Profiles Using Age, Recency, Frequency and Monetary Methods Using K-Means and K-Medoids Algorithms Nursyi, Muhamad; Sumarna, Presma Dana Scendi; Wibowo, Arief
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.v8i4.14170

Abstract

Banten Maritime Polytechnic is a new academic school established in 2019 so that the formulation of data management is still being sought to be suitable and optimal, there are many obstacles if the data is not managed properly, starting from the recruitment of prospective cadets in taking sailor competency training such as not optimal socialization. According to data from the 2021 Transportation Human Resource Development Agency, it explains that there are still few enthusiasts, especially at the Banten Maritime Polytechnic. The purpose of this study is to analyze the profile of cadets in taking sailor competency training using the age, recency, frequency and monetary methods in categorizing data and clustering with the k-means and k-medoids algorithms so that the data can be used for cadet services and related parties in the Banten Maritime Polytechnic database. This analysis can also be used for mapping in recruiting prospective cadets in taking sailor competency training so that they can see opportunities and optimize target markets. This research was conducted in 2023 based on the latest data on the 2022-2023 academic year cadet profile at the Banten Maritime Polytechnic. The results of this analysis data can be used for cadets who have not graduated and have graduated in finding work partners and channeling cadets to the shipping industry. So it is very important to manage and cluster cadet profile data in taking this sailor competency training. The use of the K-means and K-medoids algorithms helps in compiling data groupings that have large data. It works by looking at the number of small groups or groups whose numbers are represented by the variable K. To be able to group the existing data, the K-means algorithm runs iteratively from each existing data point to the K group that has been created. The results of the study are cadet profile grouping data that can be managed again for strategies and management formulations at the Banten Maritime Polytechnic, especially in increasing the recruitment of prospective cadets in taking sailor competency training.
Forward Selection as a Feature Selection Method in the SVM Kernel for Student Graduation Data Nurdin, Hafis; Carolina, Irmawati; Andharsaputri, Resti Lia; Wuryanto, Anus; Ridwansyah, Ridwansyah
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.v8i4.14172

Abstract

In the era of information technology development, accurate graduation predictions are important to improve the quality of higher education in Indonesia. This research evaluates the effectiveness of Support Vector Machine (SVM) with various kernels, including Radial Basis Function (RBF), linear, and polynomial, as well as the application of FS as an optimization method. The dataset used consists of student graduation data which includes nine independent attributes and one label. This research aims to increase the accuracy of student graduation predictions using the SVM method which is optimized through Forward Selection (FS). The SVM method is applied using 10-fold cross validation to predict on-time graduation. The results show that the combination of SVM and FS improves prediction accuracy significantly. The SVM model with an RBF kernel optimized with FS achieved the highest accuracy of 87.06% and recall of 53.68%, showing increased sensitivity in identifying student graduation cases compared to SVM without FS. Although there is a trade-off between precision and recall, the model optimized with FS shows better performance overall. This research contributes to the development of a more efficient graduation prediction method, which can help universities in planning strategies to improve academic quality. Further studies are recommended to overcome weaknesses in the recall value by using other optimization methods or combinations of other optimization algorithms
E-Homestay Application Based on Decision Support System for Optimizing Tourism Febriansyah; Siti Muntari
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.v8i4.14179

Abstract

Pagar Alam City, a growing tourist destination, has seen a steady increase in visitors each year, driving greater demand for accommodations, especially homestays. Homestays are often favored by tourists due to their affordability compared to hotels. However, many tourists face challenges in selecting a suitable homestay that meets their preferences and needs. To address this issue, this study proposes the development of a web-based Decision Support System (DSS) integrated into the e-homestay platform. The system utilizes the Simple Additive Weighting (SAW) method, chosen for its capability to assess multiple alternatives based on specific weighted criteria, including price, facilities, location, distance, and guest ratings. This approach is designed to assist tourists in identifying the optimal homestay that aligns with their preferences and budget, thereby enhancing their overall travel experience in Pagar Alam City. Moreover, the platform has the potential to promote local economic growth by supporting digital marketing of homestays, while also contributing to sustainable tourism development and management.
The Design of a Good Data Processing App Applying QR Code Nugroho, Praditya Adi; Haerani, Reni; Farida, R Dewi Mutia; Ansor, Ahmad Sofan
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.v8i4.14189

Abstract

Designing a good data processing application using QR Codes aims to increase efficiency and accuracy in data management. QR codes are a fast and easy technology for storing and scanning data, allowing users to quickly access information without entering data manually. This application is designed for various purposes, such as inventory, time and attendance management, and document tracking. The application design process includes user needs analysis, interface design, back-end development, and integration with QR code technology. The prototype method is used during the system development stage. The programming language used is a hypertext preprocessor with a MySQL database, and a framework is used to ensure that the application behaves as expected and can provide effective solutions in data processing. The results of this design are expected to significantly contribute to operational efficiency, reduce human error in data entry, and increase the speed of accessing and managing information. Apart from that, implementing QR codes in data processing applications is hoped to be widely used in various industries and business fields.
Comparative Study of XGBoost, Random Forest, and Logistic Regression Models for Predicting Customer Interest in Vehicle Insurance Airlangga, Gregorius
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.v8i4.14194

Abstract

In today’s competitive insurance market, accurately predicting customer interest in additional products, such as vehicle insurance, is crucial for optimizing marketing strategies and maximizing sales. This study presents a comparative analysis of three machine learning models such as XGBoost, RandomForest, and Logistic Regression to predict customer interest in vehicle insurance based on a dataset that includes demographic, vehicle, and policy-related features. The dataset was analyzed using five-fold cross-validation, and the performance of the models was evaluated using AUC-ROC, precision, recall, and F1-score. XGBoost demonstrated the highest recall (0.9525) and AUC-ROC (0.7854), making it the most effective model for identifying customers interested in vehicle insurance, though at the expense of lower precision (0.2585). RandomForest showed a more balanced trade-off between precision (0.3064) and recall (0.5341) but performed lower overall. Logistic Regression, while the most interpretable model, exhibited high variability in performance across different folds, with a lower average precision (0.2372). The findings of this research highlight that XGBoost is ideal for maximizing recall in high-volume campaigns, while RandomForest may be better suited for applications requiring fewer false positives. These results offer valuable insights into model selection based on business objectives and resource allocation.

Page 5 of 6 | Total Record : 54

 2   3   4   5   6 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 9 No. 4 (2025): Articles Research October 2025 Vol. 9 No. 3 (2025): Article Research July 2025 Vol. 9 No. 2 (2025): Research Articles April 2025 Vol. 9 No. 1 (2025): Research Article, January 2025 Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024 Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024 Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024 Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023 Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023 Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023 Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022 Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022 Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022 Vol. 6 No. 1 (2021): Article Research Volume 6 Issue 1: January 2021 Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021 Vol. 5 No. 2B (2021): Article Research October 2021 Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020 Vol. 5 No. 1 (2020): Article Research, October 2020 Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019 Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019 Vol. 3 No. 2 (2019): SinkrOn Volume 3 Number 2, April 2019 Vol. 3 No. 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018 Vol 3 No 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018 Vol. 2 No. 2 (2018): SinkrOn Volume 2 Nomor 2 April 2018 Vol. 2 No. 1 (2017): SinkrOn Volume 2 Nomor 1 Oktober 2017 Vol. 1 No. 2 (2017): SinkrOn Volume 1 Nomor 2 April 2017 Vol. 1 No. 1 (2016): SinkrOn Oktober Volume 1 Edisi 1 Tahun 2016 More Issue