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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 1,196 Documents
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Evaluation of Clustering Algorithms for Identifying Shoe Characteristics Patterns at XYZ Footwear Watasendjaja, William; Chandra, Billy; Wasito, Ito
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.v9i1.14332

Abstract

As the third-largest shoe-exporting country in the world, Indonesia faced a 25% decline in shoe exports in 2023 compared to the year before, both in terms of net weight and sales value. This decline in shoe exports occurred due to the increase of complexity and variety in customer orders to shoe manufacturers. These reasons require shoe manufacturers to enhance their production planning systems to become more efficient and competitive. To address this problem, this study explores the application of clustering algorithms to optimize the production planning process in shoe manufacturing companies. Using a case study at XYZ Footwear, clustering algorithms such as K-Means, Support Vector Clustering (SVC), and Deep Autoencoder were evaluated and compared to find the most effective algorithms in identifying patterns in shoe characteristics, thereby improving shoe manufacturers' production planning process. The datasets consist of the 2024 production season data, categorized into shoe categories, models, and variants, and purchase orders. The result shows that the combination of Deep Autoencoder and K-Means has better performance than just K-Means or Support Vector Clustering (SVC), achieving a silhouette score of 0.4822 and a Davies-Bouldin Index (DBI) of 0.6741. These findings highlight the effectiveness of combining deep learning (Deep Autoencoder) with clustering algorithms (K-Means) in identifying patterns in shoe characteristics.
Thyroid Disease Prediction Using Random Forest with KNNImputer for Missing Values Pratama, Raffy Nicandra Putra; Winarno, Sri; Wijaya, Tan Nicholas Octavian
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.v9i1.14334

Abstract

Thyroid disease is a health dysfunction that requires immediate and accurate diagnosis. This research seeks to design a classification model based on the Random Forest algorithm to detect the type of thyroid disease utilizing data from the UCI Repository. In the data processing stage, KNNImputer is used to handle missing data by calculating the average value of the nearest neighbors based on Euclidean distance, thus ensuring better data quality for model training. The developed model was evaluated utilizing the confusion matrix, which showed an accuracy of 98%, with precision, recall, and F1 score values ​​reached 98% based on weighted avg.These results corroborate that the proposed model is highly reliable in detecting various types of thyroid diseases, such as Negative, Hypothyroid, and Hyperthyroid. This research makes an important contribution to the application of data mining technology for medical diagnosis, while proving that optimal data processing through methods such as KNN Imputer can significantly improve model performance.
KNN Approach to Evaluating the Feasibility of Using Scientific Publications as Final Projects Abror, Dzulchan; Nasyuha, Asyahri Hadi; Chung, Meng-Yun; Perangin-angin, Moch. Iswan
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.v9i1.14370

Abstract

This study aims to explore the feasibility of using scientific publications as a substitute for traditional final assignments in higher education by applying the K-Nearest Neighbors (K-NN) algorithm. Traditional final assessments, such as theses, are widely used in evaluating students, but with the increasing availability of peer-reviewed scientific publications, there is potential to use them as a more dynamic and relevant assessment tool. This study uses a dataset containing scientific publications and theses, with features such as research quality, relevance, methodology, and clarity. This study applies the K-NN algorithm to classify these materials and determine whether scientific publications can serve as an effective substitute. The results show that the K-NN algorithm, using k=4, achieved 95% accuracy, successfully distinguishing between scientific publications and theses. However, some misclassifications occurred, indicating areas for improvement, such as incorporating additional features such as citation counts or peer-review scores. These findings suggest that scientific publications, if properly classified, can indeed replace traditional final assignments, encouraging critical thinking and engagement with current research. Future research should refine the feature set and explore other machine learning models to improve accuracy. The practical implications of this research are the potential to develop more innovative and relevant approaches to assessment in higher education, which are more aligned with modern educational practice.
Fuzzy C-Means Algorithm for Grouping Students Based on Preferences and Academic Potential Urva, Gellysa; Desriyati, Welly
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.v9i1.14369

Abstract

Personalized education is increasingly becoming a necessity in the modern era to ensure that students get a learning experience that is relevant to their interests and academic potential. This study aims to group students into three main clusters, namely Science, Arts, and Business, using the Fuzzy C-Means (FCM) algorithm. The FCM algorithm was chosen because of its flexibility in handling multidimensional data and allows students to have degrees of membership in more than one cluster, reflecting the multidisciplinary nature of their preferences. The research dataset consists of data on students' interests in fields of study (Science, Arts, Business) and academic grades in related subjects. The clustering results show that: The Business cluster includes 59 students (46.9%), reflecting the dominance of interests in economics, global trend analysis, and business organization activities. Artcluster consists of 39 students (30.0%), who show a preference for visual arts, art portfolio development, and involvement in community design. Science cluster has 30 students (23.1%) with interests in biology, science experiments, and biotechnology. Evaluation using Davies Bouldin Index (DBI) yields a value of 0.78, indicating good cluster quality. In addition, manual validation from teachers shows that more than 85% of students in each cluster fit the grouping based on direct observation. This study makes a significant contribution to the development of data-driven academic recommendation systems, enabling educational institutions to design learning programs that are more adaptive, relevant, and in accordance with student needs.
MCDM-AHP and CODAS Collaboration Techniques for Selection of Expert Education Personnel Suriyanto , Adhi Dharma; Akmaludin, Akmaludin; Widianto, Kudiantoro
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14182

Abstract

Educational progress is largely determined by human resources who have the best qualifications, with the ability of human resources to provide hope for educational development for a creative and potential future. The ability of human resources to create various variants of knowledge that can be developed to enlighten the progress of thinking through education to create expert education personnel. The aim of this research is to provide techniques to guarantee the quality of the selection process for expert education personnel for the competitive progress of mastering educational technology who are able to independently increase the creative and potential thinking of graduates. To achieve this, of course, strict collaboration techniques are needed in the selection process to obtain expert education personnel. The method proposed in this research is MCDM-AHP in collaboration with CODAS. These two methods can collaborate in providing guarantees for an optimal selection process for education personnel through eight selected assessment criteria and twelve alternatives. From the results obtained, the highest priority was obtained by ALT10 with a weight of 0.229. This gain goes through the stages of normalizing criteria and alternatives with the optimization results of both. With the research results that have been described in detail, the collaboration of the MCDM-AHP and CODAS methods can be used as a measuring tool for optimal assessment of the acquisition of decision support results and can be used as a comparison with other methods for measuring the level of optimization of results.
AHP-SWARA Implementation Method for Evaluation and Selection Employee Promotion Akmaludin, Akmaludin; Suriyanto, Adhi Dharma; Widianto, Kudiantoro
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14319

Abstract

The evaluation process of employee selection is very important for organizations that want to carry out quality leadership, the purpose of this study is to objectively prove the results of the selection of job promotions that have been evaluated continuously every time leadership occurs. The evaluation results of the leadership selection process become routine, so that the results of leadership promotions can provide improvisation to organizations that are increasingly advancing towards future leadership targets. The proposed method for the evaluation and selection process uses the Analytic Hierarchy Process (AHP) and specifically Stepwise Weight Assessment Ratio Analysis (SWARA). Both of these methods utilize expert intervention in providing input in providing assessments of multi-criteria and alternatives. So that the priority of the criteria is carried out by an index process similar to that owned by the two methods, thus providing more optimal results for decision-making support. The assessment of the results requires seven criteria and twenty-four alternatives. The results obtained require two index processes for both criteria and alternatives. The first rank is determined by the weight of the calculation results of the seven criteria and alternative assessments from experts. The first rank of twenty-six employees was given to K20 with a weight of 0.932 and followed by K2 with a weight of 0.08. Thus, job promotion can be developed with a double index that can provide optimal results in supporting job promotion decision making
Research on Sobel Edge Detection Algorithm of Grayscale Images to Analyse Car Number Plate Putra, Tri Dharma; Purnomo, Rakhmat
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14538

Abstract

Image processing is a very important subject to be discussed in computer science. Many applications of image processing are already in the field. Image processing techniques are applied in color and grayscale images. The application of image processing are ranging for military, medical and many other applications. One most important thing to analyse image and enhance its quality is doing edge detection. Edge detection in image is a well known approach to be used to detect discontinuity in grayscale image. Edge detection functions to identify edge line in images. Sobel algorithm is one of most known algorithm, others are prewitt, canny, homogeneity algorithms. Image can be made sharper and will enhance its quality. To detect number plate of cars, an edge detection algorithm needs to be applied. In number plate, to recognize the cars number plate, the image should be clear and clean from dirt. Sometimes we can not recognize the plate number if it is too blur or has many dirt. So in its application we need a strong edge detection algorithm to recognize car number plate easily. In this journal, five car’s images are presented. Each with the original image, grayscale image and the image after edge detected by sobel algorithm. It is concluded that this algorithm is quiet good in the implementation. But in the result, there are poor quality image also. For PSNR of images after edge detected, their values are between 19 and 20 dB, which are not good.
Optimizing Software Development Through Flow Metrics Analysis in the Scaled Agile Framework (SAFe) Akbar, Achmad Fathurrazi; Indrajit, Eko; Makmur, Amelia; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14545

Abstract

To meet the ever-changing market demands, more efficient strategies are required due to the complexity of today's software development. Because it can combine Agile concepts with organizational structures to manage large-scale projects involving multiple teams and departments, the Scaled Agile Framework (SAFe) has been widely adopted. This research investigates how Flow Metrics from the SAFe framework can be used as a tool to improve productivity, efficiency and alignment of the software development process. This research examines how measurements such as flow velocity, flow efficiency, flow time, and flow load can be used to pinpoint bottlenecks, streamline processes, and improve the value delivered to clients. This research uses a qualitative methodology to examine the use of Flow Metrics in two interdependent Program Increments (PIs) by combining interviews with Agile practitioners and a literature survey. The analysis highlights how the Continuous Delivery (CD) Pipeline, backlog synchronization, and program increment planning—three critical components of SAFe—interact with each other. By highlighting the importance of metrics-based performance evaluation, collaborative planning, and continuous improvement, the findings of this research are intended to offer a useful foundation for businesses looking to implement SAFe for large-scale software development. This research advances a more comprehensive understanding of how SAFe and Flow Metrics can facilitate increasingly complex software development while guaranteeing adaptability to changing business needs.
Optimizing Marketplace Registration Page Design with Predictive Heatmap Analysis Bagaskoro, Galih; Eko Saputro, Rujianto; Shouni Barkah, Azhari; Nanjar, Agi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14547

Abstract

Optimizing marketplace registration pages is crucial for improving user experience and conversion rates. This study evaluates the design of registration pages for four leading Indonesian marketplaces Tokopedia, Shopee, Blibli, and Lazada—using Predictive Heatmaps from UX Pilot alongside Heuristic Evaluation and Gestalt Principles. The analysis identifies key usability issues, such as distractions from branding elements, inconsistent visual hierarchy, and a lack of real-time validation and feedback mechanisms. Findings indicate that while branding elements effectively capture user attention, they often divert focus from essential features, a trend observed not only in these marketplaces but also in broader UI design contexts. such as Call-to-Action (CTA) buttons and registration forms. Shopee and Lazada successfully utilize high-contrast CTA buttons to direct user interaction, whereas Tokopedia and Blibli suffer from visual distractions caused by mascots and unnecessary decorative elements. Heatmap results also reveal inconsistent grouping of interface components, reducing page efficiency. To enhance user experience and conversion rates, recommendations include improving CTA button visibility through contrasting colors and strategic placement, minimizing decorative distractions, and implementing real-time validation and feedback. The application of Gestalt Principles further aids in optimizing interface organization by grouping related elements more effectively. This study underscores the importance of a structured design approach incorporating heuristic and predictive analytics to enhance the usability of online registration pages. Future research may explore the impact of interactive elements and A/B testing in refining registration interfaces.
Mobile Learning Application on Two-Dimensional Figure Material for Children with Intellectual Disabilities Pausi, Ahmad; Rayhan Noerfikri, Mohamad; Tullah, Rahmat; Ferawati, Ferawati
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14566

Abstract

Mobile learning apps are widely acknowledged for their effectiveness in enhancing learning results. This study aims to develop and validate an mobile learning app for computer two dimensional figure. Using the userfriendly App figma platform known for visual programming, it integrates interactive modules and multimedia for diverse learning styles. The study adopted a Research and Development approach following the ADDIE model (analysis, design, development, implementation, and evaluation). The research was conducted at SKh YKDW 02 Tangerang and involved 6 students. The outcomes pertaining to validation experts percentage scores are as follows: The aspect of media and design received a score percentage of 91,25%, affirming its very valid. Students responses the average percentage for the four assessment aspects clarity of material, motivation, interest, and easy of use navigation reached 91,10%, placing it in the very good category. The development of this mobile learning application for two dimensional figure material for children with intellectual disabilities material demonstrates significant potential as an innovative educational tool.

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