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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 52 Documents
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Search results for , issue "Vol. 9 No. 1 (2025): Research Article, January 2025" : 52 Documents clear
Median-Average Round Robin (MARR) Algorithm for Optimal CPU Task Scheduling Purnomo, Rakhmat; Putra, Tri Dharma
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13920

Abstract

Abstract: In operating systems, multitasking or multiprocessing terms are used. If more than one task operating consecutively, but the users feel that they are running simultaneously, than it is called multitasking. Round robin algorithm is a noted algorithm in multitasking. Several modifications of classical round robin algorithm have been proposed by experts. The idea behind these modifications are to get lower turnaround time and lower waiting time. The main topic’s discussion is about median-average round robin (MARR) algorithm. In this algorithm, the processes are arranged in ascending order. Then we get the median of the burst time. Afterwards, calculation of the average burst time is done. The summation of average and median, divide by two is the time quantum. So, the time quantum will be dynamic, based on each iteration of round robin. First iteration can have different time quantum compared to the second and so on. Each iteration will have one time quantum. Three analysis’s are given. Each with five processes. In the first analysis, time quantum for 1st iteration is 11 and the 2nd iteration is 4. The average turnaround time is 29. The average waiting time is 19. For the second analysis, time quantum for 1st iteration is 10 and the 2nd iteration is 8. The average turnaround time is 24.2. The average waiting time is 13.6. For the third analysis, time quantum for 1st iteration is 10 and the 2nd iteration is 9. The average turnaround time is 23.2. The average waiting time is 12.8.
Assessment Clusterization Teacher Performance with K-Means Algorithm Clustering and Agglomerative Hierarchical Clustering (AHC) Rodiatun, Rodiatun; Lestari, Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14200

Abstract

Research This aims to do clustering evaluation teacher performance with the application of the K-means clustering algorithm and agglomerative hierarchical clustering (AHC). Background study This is based on needs to increase quality teaching through analysis and evaluation and better teacher performance. The methods applied involving assessment data collection performance from teachers in the environment education local, processed using a second algorithm The results of the research show that the silhouette score value for K-means reached 0.364, while AHC produced a value 0.343. With Thus, K-means is proven more effective in grouping assessment data and teacher performance compared to AHC. The conclusion of the study This confirms the importance of implementation of the K-means algorithm to get more insight into good evaluation teacher performance. Author Ready to do repairs or revisions to the manuscript. This is in accordance with comments and suggestions from the reviewer as a condition beginning. For processing more, carry on.
Transforming Real Estate: Leveraging TOGAF ADM for Digital Optimization in Enterprise Architecture Widjaja, Herman; Indrajit, Richardus Eko; Dazki, Erick
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14219

Abstract

In this research paper, we propose an Enterprise Architecture (EA) design for PT XYZ, a middle up class real estate development company in Indonesia, leveraging the TOGAF ADM framework. The study centers on optimizing five key business processes—commercial leasing, residential sales, hotel banquet rentals, waterpark ticket sales, and parking fee collection—to enhance operational efficiency and support digital transformation. Using ArchiMate modeling for clear visualization, this architecture spans from the Preliminary Phase, Phase A Architecture Vision, Phase B Business Layer, Phase C Information System Architecture (Application Layer) to the Phase D Technology Architecture. It provides a strategic blueprint to address common challenges like data fragmentation, reliance on manual processes and human resources readiness. By implementing this EA, PT XYZ can expect improvements in scalability, flexibility, and overall agility. This approach aims to position PT XYZ as a modern, digitally-driven entity, aligning technology investments with business objectives for long-term success. Future research is recommended to explore later phases of TOGAF ADM (Phase E – Phase H) and potentially integrate additional business areas for a holistic digital transformation.
Optimizing Twitter Sentiment Analysis on Tapera Policy Using SVM and PSO Al Kaafi, alkaaf Ahmad; Suparni, Suparni; Rachmi, Hilda; Maulana, Ahmad; Nurtriani, Ririn
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14227

Abstract

This study aims to analyse the sentiment of Twitter users towards the Public Housing Savings (Tapera) policy in Indonesia using the Support Vector Machine (SVM) algorithm optimised by Particle Swarm Optimization (PSO). In recent years, social media has emerged as a primary platform for individuals to express their views and opinions on public policies. The government programme, Tapera, which was designed to increase access to housing for the public, attracted considerable attention, with a range of responses, including both positive and negative sentiments. The methodology employed in this study comprised the collection of data from Twitter, the processing of text, and the application of SVM-based classification techniques, reinforced by PSO, with the objective of enhancing the accuracy and efficiency of the model. The results demonstrated that the PSO-optimised SVM model exhibited an accuracy of 85%, accompanied by an Area Under Curve (AUC) value of 0.84 and a ROC curve that indicated the model's notable capacity for differentiating between positive and negative sentiments. These findings indicate the existence of certain sentiment patterns that can be utilised for the evaluation and improvement of Tapera policies. In conclusion, this research is expected to provide a comprehensive picture of the public response to the Tapera policy and present an analytical model that can be applied to evaluate other policies. Further research is recommended to expand data coverage and develop algorithms to achieve more accurate results.
Food and Physical Activity Tracking Application with Simple Dietary Pattern Analysis Setyadinsa, Radinal; Pendit, Ulka Chandini; Novi Trisman Hadi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14234

Abstract

This study focuses on the development of a mobile application to track food intake and physical activity while offering simple dietary pattern analysis. The primary goal was to create an intuitive tool enabling users to log meals, record physical activities, and receive actionable feedback on caloric balance. Developed using Agile methodology, the application includes user-friendly interfaces for data entry, a dashboard for visualizing caloric intake and expenditure, and feedback to enhance users’ understanding of dietary habits. Results from a one week user testing phase demonstrated high user satisfaction, with participants appreciating the app’s simplicity and clarity in presenting health-related insights. The app effectively encouraged users to engage with their dietary and activity habits, promoting informed lifestyle decisions. However, limitations such as the lack of detailed macronutrient tracking and integration with wearable devices were identified, which could improve accuracy and broaden the app's appeal. Future improvements are suggested, including the addition of macronutrient analysis, wearable device compatibility, and features like goal-setting and gamification to enhance engagement. These findings indicate that a straightforward, user-friendly health tracking app can significantly increase health awareness and support behavior change, particularly for individuals new to health monitoring. The research highlights the potential of simple digital tools to foster sustainable health improvements while addressing users’ needs effectively.
Comparison of ARIMA and GRU Methods in Predicting Cryptocurrency Price Movements Pinastawa, I Wayan Rangga; Pradana, Musthofa Galih; Setiawan, Deandra Satriyo; Izzety, Aurel
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14235

Abstract

This study compares the effectiveness of the ARIMA and GRU models in predicting Bitcoin price movements, addressing the need for reliable predictive tools amidst the high volatility of the cryptocurrency market. Previous research has highlighted the strengths of each model in financial forecasting: ARIMA for short-term, stationary data and GRU for capturing complex temporal patterns. The purpose of this study is to evaluate which model performs better in the context of Bitcoin price prediction, offering insights for investors to minimize risks and enhance decision-making in this unpredictable market. The research methodology involves applying both models to Bitcoin price data and comparing their accuracy using the Mean Absolute Percentage Error (MAPE) across various forecasting intervals. Results indicate that GRU achieves higher accuracy in long-term forecasts, while ARIMA performs optimally for shorter time frames. However, both models demonstrate limitations, especially as the prediction horizon extends, underscoring the inherent challenges of cryptocurrency price forecasting. These findings suggest that GRU may be better suited for longer investment horizons, while ARIMA remains effective for short-term predictions. The conclusions affirm the potential of using these models selectively to align with specific investment strategies in cryptocurrency markets, although further research is recommended to improve predictive accuracy under evolving market conditions.
Implementation of Case-Based E-Consultation to Handle Student’s Stress Levels Putri, Frestiany Regina; Firnawati , Artika Fristi; Andila, Shifa
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14239

Abstract

Despite the declaration of the COVID-19 pandemic's end, the effects of some cases persist in the new normal era of 2023. Several cases indicate a decline in the learning motivation of students and university students, which significantly affects aspects of understanding, creativity, productivity, and learning outcomes. University students transition from learners who, during their high school years, spent more time studying online without directly interacting with peers or teachers. One of the causes of university student dropouts is internal issues due to students' inability to adapt to the university environment. The purpose of this research is to compile cases frequently experienced by university students that cause stress and lead to the decision to discontinue their studies. This is done to find solutions and prevent similar incidents from recurring. The implementation of e-counselling helps provide solutions in the form of action recommendations on how to address student issues. We conducted the research in several stages, including data collection, literature review, modelling, model evaluation, and prototype building and testing. We obtained the solution to the collected cases from the counsellor through a focus group discussion (FGD). This research employs case-based reasoning, utilizing four reasoning processes: retrieve, reuse, revise, and retain. We chose the modified weighted average similarity function to measure the case's similarity value with the cases in the case base. Through the case-based e-counselling system, the calculation results reveal the similarity between the new case and the old cases, recommending actions that counsellors have validated as valid solutions.
Detection of Plastic Bottle Waste Using YOLO Version 5 Algorithm Yasiri, Jamilatur Rizqil; Rastri Prathivi; Susanto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14242

Abstract

Plastic bottle waste management has become one of the most pressing environmental issues, especially in countries with high plastic usage rates, such as Indonesia. This research uses the YOLOv5 (You Only Look Once version 5) algorithm to detect plastic bottle waste automatically. The YOLOv5 algorithm was chosen because it has efficient detection performance and high accuracy in small object recognition. The dataset consists of 500 images of plastic bottles obtained through cameras and internet sources. The data is processed through several stages: annotation (bounding box and labeling using Roboflow), split dataset (70% for training, 20% for testing, and 10% for validation), pre-processing (resizing images to 460x460 pixels), and augmentation (adding data variations to improve model performance). Training and evaluation of the YOLOv5 model using the precision metric of 89.8% indicates the ability of the model to accurately identify plastic bottles from the overall prediction, recall of 83.1% indicates the success of the model in detecting the majority of plastic bottles in the test data, and mean average precision (mAP) of 89.2% represents the average precision at various prediction thresholds. Test results on varied bottle image test data obtained detection accuracy between 82%-93%, indicating the model can recognize plastic bottles consistently. Sometimes, this model needs help detecting overlapping picture objects. However, this research proves the potential of the yolov5 algorithm as an automated litter detection solution that will be integrated with a system and support faster and better plastic waste management.
Stock Price Prediction Using TCN-GAN Hybrid Model Lim Yong Teck; Angelina Pramana Thenata
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14246

Abstract

The stock market plays a vital role in national economies, offering significant profit opportunities for investors while exposing them to substantial risks due to market uncertainties. Stock prices often experience significant fluctuations, making accurate prediction a challenging task. Temporal Convolutional Network (TCN) and Generative Adversarial Network (GAN) are the deep learning method proposed for this research. The purpose of this research is to analyze how well the TCN-GAN model predicts stock prices. Previous researches show both TCN and GAN perform well on time series data. TCN excels in analyzing time-series data while GAN enhances training by generating realistic simulations. By combining the strength of both models, this approach aims to enhance stock price prediction accuracy. The proposed model uses TCN as the generator within the GAN framework and a Multilayer Perceptron (MLP) as the discriminator. TCN handles the prediction task and is trained using the GAN model. The model is trained over 500 epochs, with a learning rate of 0.0004 for the generator and 0.0001 for the discriminator. During each epoch, the generator is updated twice to enhance its performance. The resulting model achieves a MAPE score of 2.16% and an RMSE score of 814.25 on the testing dataset, demonstrating excellent performance in stock price prediction despite significant price variations.
Embedded Smart Farming System for Soil and Hydroponic Planting Media Based on The Internet of Things Rifka, Silfia; Ramiati, Ramiati; Dewi, Ratna; Khair, Ummul; Setiawan, Herry
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14255

Abstract

Smart agricultural technology by applying the internet of things (IoT) purposes to make farmers' work more efficient due to the automation system and assist farmers in monitoring the condition of their agricultural land. The focus of discussion in this research is the application of smart agriculture system technology that uses the concept of embedded systems for soil and hydroponic planting media. This system applies an automation system for water irrigation and fertilizer irrigation using four tanks, namely a water source, a water irrigation tank, a fertilizer tank, and a water circulation system in hydroponics. The system is also equipped with weather monitoring based on temperature, rainfall, and light intensity. Other parameters contained in this system are soil pH, water pH, TDS, fertilizer availability, and irrigation pump status. The monitoring system based on the Android application displays all parameters and the status of the devices used.

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