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
 95   96   97   98   99 
Transliteration of Latin Letters to Bali Characters Based on Unicode for Mobile Devices using Finite State Automata and Levenshtein Distance Sandhiyasa, I Made Subrata; Pande, Ni Kadek Nita Noviani; Fibriyanthini, Luh
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.v8i3.13856

Abstract

The preservation of Balinese script writing has been pursued by the local government with the issuance of the Bali Province Regional Regulation Number 1 of 2018 concerning Balinese Language, Script, and Literature. However, the use of Balinese script in daily life is declining, especially among generation Z, most of whom find Balinese language difficult to use and have never been taught it. Technological advances, particularly smartphone technology, can play an important role in shaping generation Z's habits, values, and social interaction patterns. This research uses the Finite State Automata (FSA) method to convert Latin letters to the Balinese script Unicode standard, following the Balinese script writing rules. FSA governs transliteration behavior by using the working principles of State, Event, and Action. Besides transliterating sentences typed by users, the application produced by this research also displays Balinese script words related to the words typed by users using the Levenshtein Distance method. The ‘related words’ feature allows users to know more about Balinese script than just the typed word. From the test results conducted through two different test cases, the first test case tested the application's ability to transliterate words/sentences typed by users without selecting words from the application's suggestions. The results showed that of the 50 words tested, 39 were correctly transliterated. The second test case tested the app's ability when the user selects a word from the suggestions given by the app. The result shows that out of 50 words tested, 43 transliteration data are correct, with the total accuracy of both test cases being 82%.
Enterprise Architecture Implementation Scholastic Learning Zone Literacy Improvement St. Kristoforus 2 High-School Yudianto, Tri; Indrajit, Eko; Makmur, Amelia; Dazki, Erick
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.v8i3.13859

Abstract

This research aims to explore the transformation of Enterprise Architecture in the implementation of a Scholastic Learning Zone for the improvement of learners' literacy at Santo Kristoforus 2 High School. The background of this research is based on the importance of literacy as the primary foundation in learning, as well as the need to integrate innovative educational technology. The main objective of the research is to understand how the implementation of Enterprise Architecture can support the implementation of the Scholastic Learning Zone effectively and efficiently. The research method used is a qualitative approach with a case study at Santo Kristoforus 2 High School, involving in-depth interviews with educators, direct observation, and analysis of related documents. The results showed that the implementation of Enterprise Architecture significantly contributed to improving the structure and process of education, thus supporting the improvement of learners' literacy. Key findings include improved accessibility of learning resources, school administration efficiency, and increased learner engagement in the learning process. Additionally, the implementation facilitated the creation of eBooks for learning materials, further enhancing literacy by providing students with readily accessible and interactive content. The conclusion of this study shows that the transformation of Enterprise Architecture in the implementation of the Scholastic Learning Zone not only improves literacy but also strengthens the education system. Further research is recommended to test this model in different educational contexts to extend the validity of the findings.
Implementation of the C4.5 and Naive Bayes Algorithms to Predict Student Graduation Lianah, Lianah; Harahap, Syaiful Zuhri; Irmayati, Irmayati
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.v8i3.13860

Abstract

This research aims to determine student graduation using two data mining methods, namely the Naive Bayes Classifier and the C4.5 Algorithm. Research stages include data analysis, data pre-processing, model design in data mining, classification results, method evaluation, and evaluation results. This research uses student data consisting of training data and testing data to evaluate the performance of the two methods in predicting student graduation based on attributes such as attendance scores, behavior scores, Final Semester Examination (UAS) scores, and report card scores. The classification results show significant differences between the two methods. The Naive Bayes Classifier produces predictions that 37 students pass and 17 students do not pass, while the C4.5 Algorithm predicts that 30 students pass and 24 students do not pass. This difference in results indicates that there are differences in the approaches of the two methods to student graduation data, with the Naive Bayes Classifier tending to provide more positive predictions than the C4.5 Algorithm. Evaluation of the performance of the method shows that the Naive Bayes Classifier has an accuracy rate of 100%, which is a perfect result, while the C4.5 Algorithm has an accuracy rate of 89%. This significant difference in evaluation results confirms that the Naive Bayes Classifier is superior in classifying student graduation compared to the C4.5 Algorithm in the context of this research. These findings can help in making decisions regarding student graduation evaluations in the future.
Implementation Of Technology Towards The Merdeka Curricullum Doing Diagnostic Assessment For Student with Autism Spectrum Disorder In Preschool Level Suryadi, Yeanny; Yus, Anita; Milfayetti, Sri
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.v8i4.13862

Abstract

This research aims to developing an effective and applicable diagnostic assessment instrument that has been prepared based on the requirements of competency standards for graduates at the preschool institute. The instrument functions is to separate mild and moderate levels of the autism spectrum, for students with learning disabilities resembling autism spectrum symptoms in early childhood. This research used R and D methode from Borg and Gall with result is this application product containing a 23-item questionnaires that has been validated by material, language and media experts. Subject of this research is teachers of preschool institutions, and the objek is the instrument of diagnostic assessment wich researcher build. The practicality test results of this instrument have a percentage level of 92.52% in the 'very practical' category, with a validity test level of 84%, on the Likert scale showing the instrument is 'very feasible'.
Application of Neural Network Method to Determine Public Satisfaction Level on Pertalite Fuel Rahmadani, Fitri; Masrizal, Masrizal; Irmayanti, Irmayanti
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.v8i3.13869

Abstract

This research aims to analyze public interest in Pertalite fuel using the Data Mining method, specifically using the Neural Network method. The stages in this research include Data Analysis, Data Preprocessing, Designing Classification Models in Data Mining, Classification Results in Data Mining, Designing Evaluation Models in Data Mining, and Evaluation Results on Data Mining. The classification results show that of the total of 105 community data analyzed, 97 community data showed interest in Pertalite fuel, while only 8 community data showed no interest. The accuracy results obtained were 100%, indicating that the Neural Network method is very suitable and effective in classifying people's interest in Pertalite fuel. The Data Analysis process was carried out to understand and analyze the characteristics of data regarding public interest in Pertalite fuel. Data preprocessing is carried out to clean, transform and integrate data so that it is ready for the classification process. Next, the Designing Classification Models in Data Mining process is carried out to design a classification model using the Neural Network method. Classification Results in Data Mining produces information that the majority of people have an interest in Pertalite fuel. Designing Evaluation Models in Data Mining is carried out to design classification evaluation models, which then produce Evaluation Results on Data Mining which show an accuracy level of 100%. Thus, this research shows that the Neural Network method is very effective in classifying people's interest in Pertalite fuel.
Edge Computing Architecture Sensor-based Flood Monitoring System: Design and Implementation Hindarto, Djarot
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.v8i3.13874

Abstract

The purpose of this research is to develop and execute a system for monitoring floods using sensors and edge computing architecture. The goal is to make flood detection and prediction more accurate and faster. The growing frequency and severity of flood disasters in different parts of the world has prompted the necessity for a better system to track these events. The primary goal of this study is to design a system that can reduce network load and latency by processing sensor data locally at edge devices before sending it to the cloud. To detect and anticipate flood events, the research method incorporates several environmental sensors that measure things like soil moisture, water level, and rainfall. These readings are subsequently processed by edge nodes using machine learning algorithms. Compared to more conventional methods that depend only on cloud computing, the results demonstrate that the system can deliver quicker and more accurate predictions. Results showed a detection and prediction accuracy of 98.95% for floods. Edge computing also succeeded in drastically cutting down on bandwidth consumption and communication latency. This research concludes that edge computing architecture based on sensors can effectively monitor floods and has excellent potential for use in many different areas prone to flooding. Improving the prediction algorithm and investigating its potential integration with a more thorough early warning system should be the focus of future research.
An Enterprise Architecture in the Construction Management Software using the Business Model Canvas Susatyo, R Wahyu Indra; Indrajit, Eko; Dazki, Erick
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.v8i3.13877

Abstract

Indonesia's construction industry has boomed over the past decade, acting as a powerful engine for the nation's economic growth. However, this success story comes with a growing list of challenges. Construction projects are becoming increasingly intricate, demanding not only efficiency and quality but also enhanced safety and a minimized environmental footprint. To tackle these complexities, the industry is undergoing a crucial transformation towards "smart construction." This approach leverages the power of information and communication technology (ICT) throughout the entire project lifecycle, from planning and design to execution and maintenance. By integrating ICT tools like Building Information Modeling (BIM) and cloud-based project management platforms, smart construction streamlines processes, minimizes errors, and optimizes resource allocation, ultimately leading to improved efficiency, enhanced worker safety, and a reduction in environmental impact. A key pillar of smart construction lies in the integration of enterprise architecture (EA) within construction management software. EA provides a structured framework for aligning IT systems with construction businesses' specific goals and objectives. This ensures that software development and management are adaptable to evolving industry needs and foster continuous innovation. This research delves into the application of EA within the construction management software industry, specifically focusing on the TOGAF Architecture Development Method (ADM). By exploring EA through this established framework, the research aims to shed light on how the construction industry in Indonesia can leverage technology to its fullest potential. This will ensure continued growth within the sector and contribute to a lasting positive impact on the nation's economy.
Analytical Study Forecasting Students Using Random Forest and Linear Regression Algorithms Nurdin, Muhammad; Fauziah, Fauziah
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.v8i4.13886

Abstract

Forecasting new student admissions essential for higher education institutions as it helps them plan for staffing and budgetary needs. Accurate predictions are difficult due to factors like economic conditions, government policies, and University competition. This study aims to analysis forecasting at Nasional university using Random Forest and Linear Regression algorithms. By examining historical admission data, the research seeks to identify key factors influencing the number of accepted students. Methodology involves collecting data from past admissions and applying both Random Forest and Linear Regression to compare their performance. Preliminary results, based on parameters such as application form purchases from 2015 to 2023, form prices, accreditation, and leading study programs, suggest that Random Forest offers more stable and realistic predictions. Analysis for MAE, MSE, RMSE, MAPE, MAD suggests that Linear Regression is more accurate for this data. predicts closer to actual values with lower overall errors. This makes Linear Regression preferable as it provides more reliable predictions with less deviation compared to Random Forest. Looking at admissions forecasts for the next 5 years, Random Forest predicts a steady decrease from 4224 in 2024 to 4129 in 2028. In contrast, Linear Regression suggests a stable trend with slight annual dips, going from 4954 in 2024 to 4941 in 2028. Therefore, Linear Regression is a more stable and realistic choice compared to Random Forest for this forecasting task in this research.
Evaluation of Cluster Models for Creating Profiles of Home Buyers Dewi, Made Dhanita Listra Prashanti; Wasito, Ito
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.v8i4.13888

Abstract

The property industry in Indonesia is currently a dynamic and continuously evolving field, in line with rapid economic growth and urbanization. Shifts in lifestyle patterns, infrastructure development, and changes in government policies have had a significant impact on how properties are marketed in Indonesia. With a growing population and increasing purchasing power, the Indonesian property market is becoming more complex. Therefore, strategies are needed to segment consumer groups for effective marketing in the housing sector. This research will delve deeper into consumer segmentation in home selection, a technique that divides consumer diversity into distinct groups based on characteristics and behavior. By using an extensive dataset involving demographic data such as location, age, gender, occupation, and many other variables, clustering algorithms can uncover complex patterns to determine consumer segments in their home selection. The algorithms to be used for this study are K-Means clustering, the Gaussian Mixture model, and Hierarchical clustering. By using these three data clustering models, we can determine which algorithm produces the most ideal results for customer profiling. The results demonstrate that the K-Means algorithm outperforms the others in accurately identifying distinct consumer segments, hence producing customer profiles. Therefore, customer profiling can also be used by the marketing division as a tool to aid in promotions in order to better understand their target audience, hence creating a successful marketing campaign.
K-Means and Naive Bayes Algorithms for Evaluation of Education Personnel Performance Based on SPMI Standards Wahyudi, Eko; Wijaya, Rian Farta; Khairul , Khairul
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.v8i3.13890

Abstract

This research compares the K-Means and Naive Bayes algorithms in evaluating the performance of educational staff based on SPMI standards at STMIK Triguna Dharma. The main objective is to identify the effectiveness of the two algorithms in grouping performance evaluation data and determine the advantages and disadvantages of each method. Primary data was obtained through surveys and interviews, while secondary data came from institutional archives. The K-Means algorithm shows 100% accuracy with the ability to group educational staff into very good, good, quite good, poor and poor performance categories. Meanwhile, the Naive Bayes algorithm shows 91% accuracy, with 100% precision results for the "good" and "fairly good" categories. These results indicate that K-Means is more effective in grouping educational staff based on performance evaluation compared to Naive Bayes. This research makes a significant contribution in the field of evaluating the performance of educational staff and offers insights for a more effective implementation of SPMI in higher education.

Page 97 of 120 | Total Record : 1196

 95   96   97   98   99 

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