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 1,196 Documents
 76   77   78   79   80 
Development of Integrated Tourism Information System PT. Yoy Manajemen Internasional Nurzaman, Fahrul; Budilaksono, Sularso; Rosadi, Ahmad; Dewi , Euis Puspita; Febrianty, Febrianty
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11226

Abstract

Yoy Manajemen Internasional is engaged in the tourism services sector. This company is a sharia-based hotel development and management company that was newly formed in October 2020. The hotel and tourism business network initially used a manual system. The tourism information system connects various entities at once, namely: hotel managers, tourist attraction operators, car rentals, restaurants, micro-enterprises supporting the tourism sector and tourists. Tourists can come from domestic or from abroad. The purpose of this research is to develop a tourism information system specifically for the sharia-based hotel industry that integrates all partners from PT Yoy Manajemen Internasional. This information system connects two interests, namely PMS (Property Management System) for the hospitality industry and YPA (YoY Personal Assistant) for mobile applications used by tourists. The system development method by applying the SDLC (System Development Life Cycle) method, consists of: planning, analysis, design, implementation, testing and training. Development of information systems with a management-to-consumer (top-down) and management-to-consumer (bottom-up) approach so that the development of information systems is expected to be more optimal. Testing this application was carried out through usability testing on both companies and SMEs assisted by PT. Yoy Management with a total of 50 respondents and 20 students of the Informatics study program. Usability testing using User-Interface and User-Experience users.
Detection and Tracking Different Type of Cars With YOLO model combination and deep sort algorithm based on computer vision of traffic controlling Hasibuan, Nisma Novita; Zarlis, Muhammad; Efendi, Syahril
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11231

Abstract

The application of CCTV cameras for traffic surveillance and monitoring is one effective solution to address urban traffic problems, as the number of vehicles that continue to increase rapidly but the area of the road remains the same will cause congestion. However, the problem in traffic surveillance and monitoring is not just focusing on vehicle detection based on category inference on video sequence data sourced from CCTV cameras alone, another important, challenging task is to combine calculations, classification and tracking of different vehicle movements in urban traffic control systems. The study expanded on previous research by breaking down the problem into different sub-tasks using the YOLOv4 approach combined with the Deep Sort algorithm for the detection and tracking of objects directly on CCTV footage of vehicle activity on the city's three-stop highway. Based on the results of YOLOv4 testing resulted in a detection accuracy rate with mAP of 87.98% where the combination of YOLOv4 with the Deep Sort algorithm can detect, track and calculate 13 types of vehicles.
Analysis of Dimensional Reduction Effect on K-Nearest Neighbor Classification Method Taufiqurrahman, Taufiqurrahman; Nababan, Erna Budhiarti; Efendi, Syahril
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11234

Abstract

Classification algorithms mostly become problematic on data with high dimensions, resulting in a decrease in classification accuracy. One method that allows classification algorithms to work faster and more effectively and improve the accuracy and performance of a classification algorithm is by dimensional reduction. In the process of classifying data with the K-Nearest Neighbor algorithm, it is possible to have features that do not have a matching value in classifying, so dimension reduction is required. In this study, the dimension reduction method used is Linear Discriminant Analysis and Principal Component Analysis and classification process using KNN, then analyzed its performance using Matrix Confusion. The datasets used in this study are Arrhythmia, ISOLET, and CNAE-9 obtained from UCI Machine Learning Repository. Based on the results, the performance of classifiers with LDA is better than with PCA on datasets with more than 100 attributes. Arrhythmia datasets can improve performance on K-NN K=3 and K=5. The best performance is obtained by LDA+K-NN K=3 which produces an accuracy value of 98.53%, the lowest performance found in K-NN without reduction with K=3. ISOLET datasets, the best performance results are also obtained by data that has been reduced with LDA, but the best performance is obtained when the classification of K-NN with K=5 and the lowest performance is found in PCA+ K-NN with a value of K=3. As for the best performance, dataset CNAE-9 is also achieved by LDA+K-NN, while the lowest performance is PCA+K-NN with the value of K=3.
Comparative Study: Preemptive Shortest Job First and Round Robin Algorithms Purnomo, Rakhmat; Putra, Tri Dharma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12525

Abstract

Abstract: Operating system is a software acting as an interface between computer hardware and user. Operating system is known as a resource manager. The main responsibility of operating system is to handle resources of computer system. Scheduling is a key concept in computer multitasking and multiprocessing operating system design by switching the CPU among process. Shortest job first (SJF) and round robin are two wellknown algorithms in CPU processing. For shortest job first, this algorithm can be preemptived. In preemptive shortest job first, when a new process coming in, the process can be interupted. Where with round robin algorithm there will be time slices, context switching, or also called quantum, between process. In this journal we wil discuss comparative study between preemptive shortest job first and round robin algorithms. Three comparative studies will be discussed to understand these two algorithms more deeply. For all comparative study, the average waiting time and average turnaround time is more for round robin algorithm. In the first comparative study, we get average waiting time 52% more. For average turnaround time, 30% more. In second comparative analysis, we get 52 % average waiting time more and we get 35 % average turnaround time more. For third comparative analysis, average waiting time we get 50% more and for average turnaround time, we get 28% more. Thus it is concluded in our comparative study for these kind of data the preemptive shortest job first is more efficient then the round robin algorithm. Keywords: comparative study, premptive shortest job first algorithm, round robin algorithm, turn around time, average waiting time, time slice
Prediction of Student Performance Based on Behavior using E-Learning During the Covid-19 Pandemic using Support Vector Machine Widarta, Agung Eka; Luthfi, Ahmad; Kusuma Dewa, Chandra
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.12857

Abstract

The COVID-19 crisis has profoundly impacted many sectors globally, including education, necessitating the shift from traditional in-person learning to independent or online learning through various digital platforms. The integrity of e-learning can be ensured by leveraging e-learning behavioral data. The objective of this research is to develop a novel data model to navigate the educational challenges of the COVID-19 era. Previous studies employed the Support Vector Machine (SVM) technique to predict student performance in an e-learning setting, yet they failed to contrast different SVM kernels and their outcomes. In contrast, this study uses SVM and compares three types of kernels: Radial, Polynomial, and Linear. The dataset used for this research was procured from X-API-Edu-Data. The SVM technique was utilized in a unique way to process the data, which comprised 17 variables and 40 observations. Notably, all 17 variables were character variables, with only four being numeric. Two variables, Raisedhands and Discussion, were selected for analysis due to their key role in effective learning and their association with student performance in an e-learning environment. The evaluation of the model was performed using the Topic variable, which represents the subjects in the dataset. The research findings revealed a marked improvement in accuracy compared to earlier studies. Among the three SVM kernels tested - Radial, Polynomial, and Linear, the Polynomial kernel demonstrated superior accuracy with a score of 0.9979. Therefore, the Polynomial model was deemed most appropriate for analyzing the Topic variable. In conclusion, the study indicates that the application of the e-learning method, specifically during the COVID-19 pandemic, proved highly effective in forecasting student performance.
Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients Pratiwi, Tika; Sunyoto , Andi; Ariatmanto , Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12912

Abstract

Music genre classification plays a pivotal role in organizing and accessing vast music collections, enhancing user experiences, and enabling efficient music recommendation systems. This study focuses on employing the K-Nearest Neighbors (KNN) algorithm in conjunction with Mel-Frequency Cepstral Coefficients (MFCCs) for accurate music genre classification. MFCCs extract essential spectral features from audio signals, which serve as robust representations of music characteristics. The proposed approach achieves a commendable classification accuracy of 80%, showcasing the effectiveness of KNN-MFCC fusion. Nevertheless, the challenge of overlapping genres, particularly rock and country, demands special attention due to their shared acoustic attributes. The inherent similarities between these genres often lead to misclassification, hampering accuracy. To address this issue, an enhanced feature engineering strategy is devised, leveraging deeper insights into the subtle nuances that differentiate rock and country music. Additionally, a refined KNN distance metric and neighbor selection mechanism are introduced to further refine classification decisions. Experimental results underscore the effectiveness of the refined approach in mitigating genre overlap issues, significantly enhancing classification accuracy for rock and country genres. This study contributes to the advancement of music genre classification techniques, offering an innovative solution for handling overlapping genres and demonstrating the potential of KNN-MFCC synergy in achieving accurate and refined genre classification.
Optimizing the Blood Donation App with Gamification Using User-Centered Design Sari, Rida Purnama; Fatudin, Arif; Saputro, Rujianto Eko; Arifudin, Dani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.12988

Abstract

In today's digital era, motivating the younger generation to participate in routine voluntary blood donations is a significant challenge in the health sector. This research aimed to develop a gamified application called Gamified Blood Donation (G-BlooD), designed using the User Centered Design methodology. This application integrates gamification into the blood donation process, with features including donor location information, available blood stock data, and individual donation history. Using gamification elements such as challenges, ranking boards, and emblems enhanced user interactivity and motivation. Evaluation of G-BlooD demonstrated its effectiveness in achieving this goal; it scored 75 (Grade B) on the System Usability Scale (SUS), indicating good usability, while an average total index calculation from all responses on the Likert scale of 84.125% underscored its success in motivating younger generations towards regular blood donations. These results suggest combining digital technology with gamification can encourage recurring voluntary blood donation among younger generations. This research opens avenues for further exploration into leveraging digital technology to address other public health concerns.
Technology Acceptance Model to Factors Customer Switching on Online Shopping Technology: Literature Review Setyoadi, Eddy Triswanto; Rahmawati, Titasari
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.12993

Abstract

Customer loyalty is a phenomenon that is the center of attention of an organization or company because it greatly influences the continuity and development of the organization. Customers are said to be loyal if they have affection for a company's products or services. So that loyal customers will express this affection by saying positive things about the company's products or services to friends, relatives and co-workers. However, if customers feel uncomfortable with a company's products and services, there is a possibility that customers will switch from loyal to disloyal. This is usually called customer switching. This research is based on a systematic review of the influence of usability and ease of use of online shopping applications as well as the factors causing customers to switch from online shopping applications to mobile retail applications. Three phases are used in this study's systematic literature review (SLR). The factors that were discovered were categorized using three main themes. Interconnected among these three elements are perceived utility, perceived ease of use, and behavioral intention to use. This study also found that when TAM is added as a new component to gauge the intention to adopt an online shopping application, "trust, ease, and information quality" are the most important factors. By carefully identifying the effects of online shopping application on business management, this research contributes theory. The findings assist online shopping application service providers in formulating sensible plans for foreseeing and enhancing clients' intentions to use online shopping applications.
The News Classification Using Bidirectional Long Short Term Memory and GloVe Sirait, Elisabet Margaret; Silalahi, Raynaldo; Tambunan, Annessa Aprilly; Amalia, Junita
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13005

Abstract

The dissemination of information and news via online media encompasses not only established news platforms but also contributions from internet users, lacking oversight. News constitutes fact-grounded insights into ongoing occurrences. This research employed Bidirectional Long- and Short-Term Memory with Hyperparameter tuning on GloVe for news classification. This research aims to optimize news categorization through hyperparameter tuning on GloVe. GloVe facilitated the transformation of words into vector matrices, exploring its efficacy in news classification with hyperparameter tuning and Bi-LSTM for text analysis. Experiments encompassed untuned and hyperparameter-tuned approaches, employing GloVe's hyperparameters using Gridsearch and manual methods. GloVe's hyperparameter tuning reveals the potential for enhancing word vector representations. Surprisingly, non-hyperparameter tuned news classification yielded superior evaluation results compared to the hyperparameter approach. The untuned experiment achieved an accuracy of 0.98, while the gridsearch method yielded 0.85 accuracy, and hyperparameter tuning generated a 0.88 precision in the -11 model. These findings underscore the nuanced interplay of hyperparameters in optimizing text classification models like GloVe.
Analysis of Factors That Affects COVID-19 Vaccination on Countries Worldwide Ferawaty, Ferawaty; Lee, Melvin; Lintong, Elisabeth; Augustinus, Daniel Cassa
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13006

Abstract

Despite the urgency of vaccination against COVID-19 worldwide, each country has different levels of vaccination rate which lead to different success rates. While several past studies have shown what factors affect a country’s vaccination rating from past epidemics, there are no correlation studies done on factors to COVID vaccination rates, with several media and institutes forming theories, with New York Times stating it’s GDP per Capita, and National Health Institute postulating literacy and other various factors, while none those two showing correlation studies of the factors as the proof. With values ranging from -1 to 1, results showed among six factors ranging from 0.51 to 0.64 for four factors showing that of six factors listed in this study, meaning they are moderately strongly related with the vaccination rate, with one having a value of 0.14, meaning it’s weakly related, another with value of -0.58, indicating strongly unrelated with vaccination rate.

Page 78 of 120 | Total Record : 1196

 76   77   78   79   80 

Filter by Year

2016 2025


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