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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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Information Security Evaluation of Data Centre Architecture Using COBIT 5 Nurbojatmiko, Nurbojatmiko; Irahman, Muhammad Shidqa; Nashikha, Ainun; Ramadhan, Raihan Lail
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.v9i1.13224

Abstract

Pusat Teknologi Informasi dan Pangkalan Data (Pustipanda) UIN Jakarta is an institution in charge of managing all information systems and data management for UIN Jakarta. However, security issues are still one of the problems faced by Pustipanda today, such as data leaks, and websites that are often problematic. This research aims to assess the level of information security at the UIN Jakarta Pustipanda data centre using the COBIT 5 framework. Information security is very important in supporting organizational operations, especially facing cyber threats in the data centre environment. The research approach included document analysis, observation, and interviews with stakeholders at Pustipanda UIN Jakarta. Identification of information security weaknesses, assessment of compliance with security standards, and design of appropriate solutions are the subject of the research. It is hoped that the results will provide a comprehensive picture of information security in the data centre as well as concrete recommendations for improvement. The results of the research include an understanding of the status of information security at Pustipanda UIN Jakarta, as well as guidelines for improving information security in accordance with COBIT 5 principles. These efforts aim to reduce risk and protect the integrity, confidentiality, and availability of data in the data centre environment
Development of a Web-Based Alumni Information System at Universitas Hindu Indonesia Sanjaya, Kadek Oky; Jaya, I Kadek Noppi Adi; Arta, I Made Esa Juana; Maharani, Ni Made Sintha
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.v9i1.13227

Abstract

The development of an Alumni Information System based on a Website is an effective solution in managing information and data regarding an institution's alumni. Issues related to non-systemic and manual information dissemination, as well as challenges in gathering alumni data, are expected to be resolved by this system. It is anticipated that this system will facilitate alumni in connecting and interacting. The aim of this research is to develop an effective and efficient alumni information system to enhance alumni engagement and participation in institutional activities. The research follows a waterfall model involving various stages, starting from needs analysis, design, implementation, testing, to maintenance. The developed alumni information system includes features such as alumni profiles, current news and information, job vacancies, and alumni activities. This system is implemented in the form of a website using the CodeIgniter framework. Testing results using black box testing indicate that this system effectively manages various data and information crucial for alumni. Alumni using this system can easily access and update their profile information, as well as connect with fellow alumni and the institution. For future research, it is hoped that a more flexible information system can be developed, perhaps in the form of a mobile-based application.
Enhancing Supervised Learning through Empirical Enrichment Using Style Transfer Generative Datasets Hindarto, Djarot
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.v9i1.13229

Abstract

An innovative strategy for improving supervised learning by utilizing empirically enriched datasets through the application of generative style transfer techniques. Within the realm of artificial intelligence, supervised learning has emerged as a significant domain. However, the challenge of acquiring datasets that are both representative and diverse persists. To tackle this issue, this research integrates the notion of style transfer to broaden the range of data accessible for supervised learning models. This method employs the style transfer process to generate diverse style variations within the existing data. Incorporating various image variations enhances the dataset and enables the model to gain a deeper comprehension of the image's content. Experiments were performed utilizing a conventional dataset that was enhanced using a style transfer technique and subsequently inputted into a supervised learning model. The results demonstrate substantial enhancements in model performance, particularly in terms of its ability to generalize to new test data. This confirms the efficacy of this approach in enhancing the quality of supervised learning. These findings emphasize the significant potential of employing style transfer in dataset enrichment to improve and intensify model comprehension in managed learning scenarios, as well as its implications in the advancement of artificial intelligence technologies that are more flexible and capable of adjusting to various visual scenarios.
Forecasting Airline Passenger Growth: Comparative Study LSTM VS Prophet VS Neural Prophet Afarini, Nihayah; Hindarto, Djarot
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.v9i1.13237

Abstract

To conduct an exhaustive examination of airline passenger growth prediction methods, this study compares the performance of three distinct strategies: LSTM, Prophet, and Neural Prophet. To forecast passenger volumes accurately, the aviation industry needs robust prediction models due to rising demand. This research evaluates the performance of LSTM, Prophet, and Neural Prophet models in passenger growth forecasting by utilizing historical airline passenger data. A comprehensive examination of these methodologies is conducted via a rigorous comparative analysis, encompassing prediction accuracy, computational efficiency, and adaptability to ever-changing passenger traffic trends. The research methodology consists of various approaches for preprocessing time series data, engineering features, and training models. The findings elucidate the merits and drawbacks of each method, furnishing knowledge regarding their capacity to capture intricate patterns, fluctuations in passenger behavior across seasons, and abrupt shifts. The results of this study enhance comprehension regarding the relative efficacy of LSTM, Prophet, and Neural Prophet in prognosticating the expansion of airline passenger numbers. As a result, professionals and scholars can gain valuable guidance in determining which methodologies are most suitable for precise predictions of forthcoming passenger demand. This comparative study serves as a significant point of reference for enhancing aviation prediction models to optimize the industry's resource allocation, operational planning, and strategic decision-making.
Stunting Disease Classification Using Multi-Layer Perceptron Algorithm with GridSearchCV Cahyani, Indah Ardhia; Ashuri, Putri Intan; Aditya, Christian Sri Kusuma
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.v9i1.13245

Abstract

Stunting is a growth and development disorder caused by malnutrition characterized by a child's height less than the standard deviation set by WHO. In 2022, stunting cases in Indonesia are considered a high prevalence rate, reaching 21.6%. There are several factors that can cause stunting in children, namely maternal and antenatal care factors, home environment factors, breastfeeding practices, and feeding factors during toddlerhood. There are several impacts that occur when children are stunted, namely increased risk of child mortality, susceptibility to illness, impaired brain development, physical disorders and metabolic disorders.   Currently, deep learning has been widely used for disease classification and prediction, one of the deep learning methods is Multi-Layer Perceptron (MLP). The purpose of this research is to classify stunting disease using a deep learning method, namely MLP. The dataset used consists of 8 attributes, namely gender, age, birth weight, birth length, body weight, body length, breastfeeding and stunting with a total of 10,000 records. The encoding process is carried out to convert categorical data into numeric attributes of gender, breastfeeding, and stunting.  This research produces a higher accuracy value than previous research which used the C4.5 algorithm with an accuracy of 61.82%, whereas in this study using MLP which was integrated with the GridSearchCV hyperparameter it obtained an accuracy of 82.37%. This proves that the MLP method is successful in classifying stunting compared to previous research algorithms.
Determining The Optimal Number of K-Means Clusters Using The Calinski Harabasz Index and Krzanowski and Lai Index Methods for Groupsing Flood Prone Areas In North Sumatra Syahputri, Ziana; Sutarman; Machrani Adi Putri Siregar
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.v9i1.13246

Abstract

The k-means algorithm is a partitional clustering method. K-Means has several advantages, including being easy to implement, having a high level of convergence and producing denser clusters. Meanwhile, the drawback is that it is difficult to determine the optimal number of clusters. The K-Means method will be used to solve problems in areas prone to flood disasters in North Sumatra. This research aims to find the optimal number of clusters with the Calinski Harabasz Index and Krzanowski And Lai Index based on the Cluster Tightness Measure (CTM) value. There are eleven variables used in this research. Based on the research results, it was concluded that the CTM CH result of 0.376 was smaller than the CTM KL of 0.7843. So it can be said that determining the optimal number of clusters using CH with k = 6 is better than KL with k = 2.
Model Performance Evaluation: VGG19 and Dense201 for Fresh Meat Detection Hindarto, Djarot
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.v9i1.13247

Abstract

To guarantee consumer safety and meet quality expectations, accurate detection of meat quality is a critical component of the food industry. The objective of this research endeavor is to assess and contrast the fresh meat detection capabilities of two distinct artificial neural network architectures, denoted as Dense201 and VGG19. Automated systems that can identify vital qualities in fresh meat, including color, texture, and cleanliness, have become feasible due to the development of image processing technology. For this reason, however, there are still few direct comparisons between various architectures of artificial neural networks, particularly VGG19 and Dense201. Comparing and contrasting the performance of both models in identifying the quality of meat from visual images, this study attempts to fill this void. Utilizing a vast dataset containing a variety of fresh meats exhibiting substantial visible variations constituted the research methodology. The assessment was conducted by examining the efficacy of both models in determining the quality of meat using established performance metrics, including accuracy, precision, recall, and F1-score. Regarding the detection of fresh meat, it is anticipated that the findings of this study will offer a comprehensive understanding of the benefits and drawbacks associated with every artificial neural network architecture. Contributing to a greater comprehension of the application of precise and efficient meat detection technology, this study also furnishes the food industry with a foundation for determining which model best meets the requirements of meat quality detection on a larger production scale.
EYE-R : Augmented Reality as Mobile Based Helper Application for Colorblinds Irfansyah, Naufal Herma; Kurniawan, Rahadian
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.v9i1.13254

Abstract

The ability to identify color is a basic ability for a human to live easily. Unfortunately not all humans have normal sight, there are some humans who have an eye disorder called color blindness. Color blindness is an eye disorder that affects color perception of the affected person in everyday life, therefore the person that has color blindness needs a device to help them to identify some colors. In the medical world, the usage of Augmented Reality is still limited for education for medical practitioners, so the application of AR as a tool to help patients is still considered to be minimal. Augmented Reality as a Mobile Based Colorblind Helper Research aims to find out how effective a color detection system using AR as a tool to help the colorblinds identify color in an application called “EYE-R”. The research method employed in the research and development of this application is the Waterfall method that involves the stages of Requirements, Design, Programming, Testing, and Implementation. The main feature of “EYE-R” is chosen using a survey which is Real Time Color Detection which is developed using the Unity engine and implemented using Android. The research results show that the majority of users can operate the Real Time Color Detection system accurately. The user satisfaction results recorded using USE Questionnaire shows that the EYE-R Real Time Color Detection system really helps users’ daily lives and can be used very well.
Optimizing Automotive Manufacturing Systems through TOGAF Modelling Afarah, Sabrina Fajrul; Hindarto, Djarot; Wahyuddin, Mohammad Iwan
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.v9i1.13256

Abstract

The objective of this research is to examine the viability of implementing the Open Group Architecture Framework to enhance the efficiency and performance of automotive manufacturing systems. The automotive industry remains confronted with challenges pertaining to the enhancement of manufacturing processes, the reduction of product development time, and the adjustment to swift technological progressions. The primary obstacles encountered in implementing process innovation, the complexity of the IT infrastructure, and the absence of system integration constitute the most significant challenges. The primary aim of this study is to present a resolution through the application of the TOGAF framework. By implementing this strategy, system synchronization will be enhanced, IT infrastructure will be simplified, and process innovation will be able to respond to market fluctuations more rapidly. The existing business processes are streamlined and consistent with the strategic progress of vehicle manufacturing firms. Nonetheless, business processes involving architectural applications continue to diverge from market demands and fail to align with evolving business requirements. In the context of automotive manufacturing, the TOGAF modeling methodology will be applied to analyze the data architecture, application architecture, strategic elements, and information technology infrastructure. Advise stakeholders in the automotive industry, facilitating the integration of TOGAF principles into endeavors to redesign systems. This will reduce the attainment of innovation, adaptability, and efficiency, all of which are critical for sustaining competitiveness in a dynamic marketplace. By applying TOGAF principles to the automotive manufacturing system, Enterprise Architecture can support ever more complex business requirements.
Building the Future of the Apparel Industry: The Digital Revolution in Enterprise Architecture Hindarto, Djarot
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.v9i1.13260

Abstract

Using qualitative methodology, this study investigates the effects that the digital revolution in corporate architecture has had on the apparel industry. In this article, digital technologies, like AI, big data analytics, and the Internet of Things, are the main points of emphasis. They have revolutionized business and operational practices, as well as marketing strategies in the sector. According to the findings of this study, the implementation of advanced technologies significantly contributes to the enhancement of operational efficiency, the introduction of innovative products, and the enhancement of the competitiveness of businesses. The research also highlights the impact that digital transformation has had on sustainability and personalization in the clothing production industry. It demonstrates that adopting an enterprise architecture that is aligned with digital technologies not only increases operational efficiency but also strengthens innovative and competitive capacity. Furthermore, this research acknowledges the significance of ethically responsible and transparent business practices in this digital era, as well as taking into consideration the effects that digital transformation has on society and the environment. The findings of this study provide industry stakeholders with a strategic perspective that can be utilized in the formulation of adaptive business strategies, the exploitation of opportunities, and the facing of challenges in the ever-changing business environment that is associated with the digital era

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