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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
A Review on AMRR and Improved Round Robin Algorithms: Comparative Study Putra, Tri Dharma; Purnomo, Rakhmat
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.v8i4.13563

Abstract

Round Robin Algorithm is a dominant algorithm in real time system. Improved round robin and average max round robin, which is also called AMRR are two types with a breakthrough. Improved round robin is an algorithm where if the remaining burst time of the process is less than the quantum, then the running process will continue to be executed. Afterwards the next iteration will be executed as its turn. So, each iteration will have a vary of quantum. It is called a dynamic time quantum. Different with improved round robin, in AMRR, in every iteration, the quantum will be calculated. So, for every iteration, the quantum might be different, depending upon the quantum calculation of the rest burst time. The first stage of this algorithm is to calculate the average of the existing burst times. Then this average is added with the maximum existing burst time. This addition then will be divided, then we get the quantum. This calculation will be executed again after the iteration finish. Based on our analysis, with quantum 10 in these two algorithms. It is can be shown that the improved round robin is less efficient than AMRR, because its average turnaround time and average waiting time is lower. The average turnaround time is 17.25 ms for AMRR compared to 23.25 ms in improved round robin. And the average turnaround time is 9 ms for AMRR compared to 15 ms in improved round robin.
Comprehensive Study of Information Technology Strategy Components in Global ICT Companies Utilizing PESTLE and Ansoff Matrix Purawidjaja, Ratna Amalia; Chudra, Glenny; Yohannis, Alfa
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.v8i4.13953

Abstract

This research underscores the diversity and strategic significance of IT Strategy components in shaping the digital transformation and competitive edge of ICT companies. The formulation of IT Strategy documents is pivotal for industries, including ICT companies, as it ensures alignment with business goals and competitive positioning. This study conducts a comprehensive literature review of IT Strategy components within global ICT companies, specifically those specializing in telecommunication network infrastructure. Despite operating within the same sector, each company’s IT Strategy document comprises distinct components. The identified components include Auditor Report, Business Strategy, Leadership, Product/Service Line, Geographic Performance, Research & Development, Partnership & Acquisition, Summary Report, Corporate Governance, Vision & Mission, Financial Statement, Industry Trends, and Business Highlights.  These components are essential for aiding the organization’s IT Strategic Plan and the creation of the company's roadmap. Furthermore, this study identifies PESTLE analysis and the Ansoff Matrix as crucial tools in creating strategic roadmaps tailored to each company’s unique objectives and market conditions.
Model Random Forest and Support Vector Machine for Flood Classification in Indonesia Purwati, Sintia Eka; Yoga Pristyanto
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.v8i4.13973

Abstract

People, especially those living in lowland areas and along rivers. This flood phenomenon significantly affects various aspects, both in terms of economics, environment, and public safety. Flooding is a disaster that often causes problems for most people, especially those living in lowland areas and on riverbanks. This flood phenomenon significantly affects various aspects, such as the economy, environment, and community safety. This research compares the Random Forest and Support Vector Machine (SVM) methods for flood classification in Jakarta. The data used is flood data from 2016 – 2020 in Jakarta, obtained from Kaggle. Model performance evaluation is carried out using accuracy, precision, recall, and F1- Score metrics. The analysis results show that both models accurately classification floods, with Random Forest showing a more stable performance than SVM.
Comparison of Tubercolosis Detection Using CNN Models (AlexNet and ResNet) Putra, Adya Zizwan; Amir Mahmud Husein; Nicholas; Frederico Wijaya; Aribel
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.v8i4.13979

Abstract

The bacterial infection caused by Mycobacterium tubercolosis, leading to tubercolosis is a prevalent contagious disease. This bacterium commonly targets the primary respiratory organs, particularly the lungs. Tuberculosis poses a significant global health challenge and necessitates early detection for effective management. In this context, to facilitate healthcare professionals in the early detection of patients, a technology capable of accurately identifying lung conditions is required. Therefore, CNN (Convolutional Neural Network) will be employed as the algorithm for detecting lung images. The research will utilize Convolutional Neural Network models, namely AlexNet and ResNet. The study aims to compare the performance of these two models in detecting TB through the analysis of chest X-ray images. The dataset comprises X-rays from both normal patients and TB patients, totaling 4.200 data points. The training process involves dividing the data into training and validation sets, with an 80% allocation for training and 20% for validation. The evaluation results indicate that the AlexNet model demonstrates higher detection accuracy, reaching 88.33% on the validation data, while ResNet achieves 83.10%. These findings suggest that the use of CNN models, especially AlexNet, can be an effective approach to enhancing early tuberculosis detection through the interpretation of chest X-ray images, with potential implications for improving global TB management and prevention efforts.
Performance Comparison of ARIMA, LSTM, and Prophet Methods in Sales Forecasting Suryawan, I Gede Totok; Putra, I Kadek Nurcahyo; Meliana, Putu Mita; Sudipa, I Gede Iwan
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.v8i4.14057

Abstract

The development of the business world that is growing rapidly today resulted in tighter competitiveness between fellow business actors. One of the businesses that has sprung up in the market today is the bakery business. Currently, bread is one of the food needs in Indonesia that is great demand by children to the elderly, which is often used as breakfast or snack. One of the companies that produces white bread is the Bandung White Bread Factory. The number of sales at this factory continues to increase every month based on total sales data recorded since 2021. With the increasing number of sales at this factory, the factory often experiences stock shortages and cannot meet customer demand. Therefore, in this study, a model has been developed to forecast the sales of white bread using the ARIMA, LSTM, and Prophet methods. The results of the study showed that the ARIMA method (1,0,2) had the best performance compared to the LSTM and Prophet methods, because the ARIMA method (1,0,2) produced the smallest error accuracy value, namely with a MAPE value of 4.548%, an MSE value of 2248.0822, and an RMSE value of 47.4139.
Cluster Analysis of Food Social Assistance in DKI Jakarta: K-Means Approach to Identify Expenditure Patterns and Beneficiaries Suharyanti , Nining; Rusdiansyah, Rusdiansyah; Supendar, Hendra; Tuslaela, Tuslaela
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.v8i4.14095

Abstract

This study aims to evaluate the effectiveness of the K-Means algorithm in grouping social assistance recipients in DKI Jakarta based on various demographic and economic factors, such as income, number of family members, and living conditions. The main objective of this study is to optimize resource allocation in social assistance programs by identifying different recipient clusters, so that aid distribution becomes more targeted. In this study, the K-Means algorithm was used with an optimal number of clusters of 3, and produced an accuracy rate of 85%, indicating that this algorithm is effective in grouping large-scale and complex data. However, there are challenges related to the sensitivity of K-Means to outliers and data imbalances that affect the results of the analysis. The results also show that areas such as Central Jakarta and South Jakarta receive more social assistance compared to other areas such as North Jakarta and East Jakarta, reflecting differences in needs in various regions. These findings emphasize the importance of selecting the right variables, such as access to health facilities and economic conditions, in producing more accurate groupings. Overall, this study provides valuable insights into efforts to optimize the distribution of social assistance in DKI Jakarta and recommends further research to address the limitations that exist in the use of the K-Means algorithm, especially in the context of data that is imbalanced or has large variations.
Master Stockist Customer Segmentation Using RFM Model and Self-Organizing Maps Algorithm Nirwana, Ni Kadek Ayu; Dewi, Ni Putu Wahyuni; Asana, I Made Dwi Putra; Dewi, Ni Wayan Jeri Kusuma; Astari, Gusti Ayu Shinta Dwi
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.v8i4.14112

Abstract

Master Stockist PT SNS 21 Bali struggles to identify member performance based on purchasing behavior because the applicable system only records transactions and stock of goods without providing insight into customers. Customer segmentation can be carried out to identify and understand differences in customer purchasing behavior. Therefore, this study aims to determine customer segmentation using the RFM (Recency, Frequency, Monetary) model and the Self-Organizing Maps (SOM) algorithm. Segmentation development uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach. The RFM model numerically represents customer behavior through three variables, while the Self-Organizing Maps algorithm groups customers into segments with similar characteristics. In this research, the best SOM parameters are 750 iterations, learning rate 0.5, radius 0.5, and grid size 1x3, resulting in 3 clusters with a Silhouette Score of 0.647608 and a Davies-Bouldin Index of 0.536503. Cluster 1 consists of 226 new customers with low RFM values who need encouragement to be more active. Cluster 2, comprising seven members, has low recency, high frequency, and high monetary values, representing loyal customers who need to be retained. Cluster 3 consists of 239 inactive customers with high recency, low frequency, and low monetary values, requiring a reactivation strategy.
Web-Based Application Development using PHP-Native Framework on Agent of Change Integrity Zone Information System Perdana, Adidtya; Farhana, Nurul Ain; Harliana, Putri; Karo Karo, Ichwanul Muslim
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.v8i4.14118

Abstract

An Integrity Zone is an activity related to government policies and efforts to create an environment free from corruption, collusion, and nepotism (KKN). The Integrity Zone aims to encourage transparency, accountability, and clean practices in the management of a bureaucracy. To further the success of this Integrity Zone, a Agent of Change was formed who acts as a Role Model for all parties. As a role model, agents of change must be able to provide examples in attitude, behavior, and thinking. In addition, they must be able to provide creative solutions in dealing with problems in their agencies. And must be able to provide creative ideas to improve the performance of all parties. Sometimes, their ideas are not well documented. So a digital-based system is needed that can facilitate the work of these agents of change. For this reason, it is necessary to create a Web-based application for recording, reporting, and implementing change agent performance so that it is more optimal, efficient, and effective. In this study, the development of web-based applications using a framework with PHP-Native technology. Most PHP frameworks that exist today, use MVC and OOP technology but no one has utilized PHP-Native technology as a framework. This aims to facilitate the creation of applications for programmers who have not familiar with MVC or OOP concept.
Sentiment Analysis on BNI Mobile Application Review Using K- Nearest Neighbors Algorithm Nurmakhlufi, Alfin; Arsyad , Muhammad Rafi Haidar; Mulyani , Wahyu Sri; 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.v8i4.14136

Abstract

Advances in Science and Technology continue to evolve in response to the demands of modern times, particularly in various fields such as banking. The development of information technology has transformed the way transactions are conducted from traditional to digital, accessible flexibly through Mobile Banking. BNI has created the BNI Mobile Banking application to facilitate customers in their transactions. The objective of this study is to investigate how the use of BNI Mobile can influence the ease of customers in conducting transactions. The data collection method used in this study is the K-Nearest Neighbors method, focusing on user experience with the BNI Mobile Banking application
Optimization of Backpropagation Method with PSO to Improve Prediction of Land Area and Rice Productivity P.P.P.A.N.W.Fikrul Ilmi R.H.Zer; Fazli Nugraha Tambunan
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.v8i4.14142

Abstract

This research aims to optimize the Backpropagation method using Particle Swarm Optimization (PSO) optimization to improve the accuracy of prediction of harvest area and rice productivity. The results show that the best architecture for prediction of harvest area is 3-15-1, with a Mean Squared Error (MSE) value of 0.0049980 for standard Backpropagation, and 0.00092376 after being optimized with PSO. Meanwhile, for rice productivity prediction, the best architecture is also 3-15-1, with an MSE value of 0.0049998 for standard Backpropagation, and 0.000435762 after using PSO. PSO optimization significantly reduces the MSE value, which indicates that this method is more accurate than standard Backpropagation. Predictions from 2024 to 2026 show more consistent results with some provinces experiencing an increase or decrease in harvested area and rice productivity that is different from the standard Backpropagation method. Based on the prediction accuracy that reaches 100% and the lower MSE value, it can be concluded that Backpropagation with PSO optimization is a superior method. The results of this study are useful for government, farmers, researchers, and policy makers in more effective agricultural planning and better risk management

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