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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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Comparison of Activation Functions on Convolutional Neural Networks (CNN) to Identify Mung Bean Quality Karo Karo, Ichwanul Muslim; Karo Karo, Justaman Arifin; Ginting, Manan; Yunianto, Yunianto; Hariyanto, Hariyanto; Nelza, Novia; Maulidna, Maulidna
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13107

Abstract

Mung bean production levels by farmers in Indonesia are not stable. When there is a surplus, the stock of mung beans in the warehouse will accumulate, the storage factor affects the quality of mung beans. Indicators of quality mung beans can be seen from the color and size through direct observation. However, the aspect of view and assessment and the level of health of each observer is a human error in the classification of mung bean quality so that the results are less than optimal. One alternative way to identify object quality is to use deep learning algorithms. One of the popular deep learning algorithms is convolution neural network (CNN). This study aims to build a model to classify the feasibility of mung beans. The process of building the model also goes through the image preprocessing stage. In the process of building the model, there are ten setup parameters and four setup data used to produce the best model. As a result, the best CNN algorithm model performance is obtained from data setup I, with accuracy, precision, recall and F1 score above 75%. In addition, this study also analyzes Rel U and Adam activation functions on CNN algorithm on model performance in identifying mung bean quality. CNN algorithm with Adam activation function has 92% accuracy, 92.53% precision, 91.9% recall, and 92.19% F1 score. In addition, the performance of CNN algorithm with Adam activation function is superior compared to CNN algorithm with Adam activation function and previous study
Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture Karo Karo, Ichwanul Muslim; Kiswanto, Dedy; Panggabean, Suvriadi; Perdana, Adidtya
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13110

Abstract

Hair diseases are common and can be caused by a variety of factors, including genetics, stress, nutritional deficiencies, as well as exposure to sunlight and air pollution. Accurate diagnosis of hair diseases is important for proper treatment, but can be challenging due to overlapping symptoms. The development of the healthcare world has widely utilized machine learning and deep learning approaches to assist in the healthcare field. This research aims to develop hair disease classification using Convolutional neural network (CNN). The CNN-based approach is expected to help health professionals diagnose hair diseases accurately and provide targeted treatment. This research involves an experimental design with three main stages: identifying the research problem, conducting a literature review, and collecting data. The research uses a dataset of hair disease images obtained from Kaggle, which are annotated and organized based on different hair disease types. After the image data is collected, the image dataset will go through the image preprocessing stage. Experiments were conducted using hair disease image data with 15 epochs on a CNN Deep Learning model with VGG-16 architecture, and resulted in an accuracy of 94.5% and a loss rate of 18.47%, with a testing epoch time of 9 hours 48 minutes. The results of this study show that CNN with VGG-16 architecture can successfully classify 10 types of hair diseases
Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13124

Abstract

Recent computer vision and deep learning breakthroughs have improved road safety by automatically classifying traffic signs. This research uses CNNs to classify traffic signs to improve road safety. Autonomous vehicles and intelligent driver assistance systems require accurate traffic sign detection and classification. Using deep learning, we created a CNN model that can recognize and classify road traffic signs. This research uses a massive dataset of labeled traffic sign photos for training and validation. These CNN algorithms evaluate images and produce real-time predictions to assist drivers and driverless cars in understanding traffic signs. Advanced driver assistance systems, navigation systems, and driverless vehicles can use this technology to give drivers more precise information, improving their decision-making and road safety. Researcher optimized CNN model design, training, and evaluation metrics during development. The model was rigorously tested and validated for robustness and classification accuracy. The research also solves real-world driving obstacles like illumination, weather, and traffic signal obstructions. This research shows deep learning-based traffic sign classification can dramatically improve road safety. This technology can prevent accidents and enhance traffic management by accurately recognizing and interpreting traffic signs. It is also a potential step toward a safer, more efficient transportation system with several automotive and intelligent transportation applications. Road safety is a global issue, and CNN-based traffic sign classification can reduce accidents and improve driving. On filter 3, Convolutional Neural Network training accuracy reached 98.9%, while validation accuracy reached 88.23%.
Battle Models: Inception ResNet vs. Extreme Inception for Marine Fish Object Detection Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13130

Abstract

Within the domain of deep learning applied to computer vision, there exists a significant emphasis on the competition between two prominent models, namely Inception ResNet and Xception, particularly in the field of marine fish object detection. The present study conducted a comparative analysis of two advanced neural network architectures in order to assess their efficacy in the identification and localization of marine fish species in underwater images. The two models underwent a rigorous evaluation, utilizing their capabilities in feature extraction. The findings indicate a complex performance landscape, wherein Inception ResNet exhibits remarkable accuracy in identifying marine fish objects, while Xception demonstrates superior computational efficiency. The present study elucidates the inherent trade-off between precision and computational expenditure, offering valuable perspectives on the pragmatic ramifications of choosing one model over another. Furthermore, this research underscores the significance of carefully choosing a suitable model that aligns with the particular requirements of object detection applications in the context of marine fish. This study endeavors to guide professionals and scholars in marine biology and computer vision, enabling them to make well-informed choices when utilizing deep learning techniques to detect maritime fish objects in underwater settings. The research specifically focuses on the comparison between Inception ResNet and Xception models.
Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13135

Abstract

The precise identification and localization of fish entities within visual data is essential in diverse domains, such as marine biology and fisheries management, within computer vision. This study provides a thorough performance evaluation of two prominent deep learning algorithms, ConvDeconvNet and UNET, in the context of fish object detection. Both models are assessed using a dataset comprising a wide range of fish species, considering various factors, including accuracy of detection, speed of processing, and complexity of the model. The findings demonstrate that ConvDeconvNet exhibits superior performance in terms of detection accuracy, attaining a noteworthy degree of precision and recall in identifying fish entities. In contrast, the UNET model displays a notable advantage in terms of processing speed owing to its distinctive architectural design, rendering it a viable option for applications requiring real-time performance. The discourse surrounding the trade-off between accuracy and speed is examined, offering valuable perspectives for algorithm selection following specific criteria. Furthermore, this study highlights the significance of incorporating a diverse range of datasets for training and testing purposes when utilizing these models, as it significantly influences their overall performance. This study makes a valuable contribution to the continuous endeavors to improve the detection of fish objects in underwater images. It provides a thorough evaluation and comparison of ConvDeconvNet and UNET, thereby assisting researchers and practitioners in making well-informed decisions regarding selecting these models for their specific applications.
Application of the Case Based Learning (CBR) Method to Diagnose Conjunctivitis Ramadhani, Marco; Volvo Sihombing; Gomal Juni Yanris
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.v6i1.10908

Abstract

Conjunctivitis is an inflammation of the conjunctiva or the outer layer of the eye and the inner lining of the eyelids caused by micro-organisms or viruses, bacteria, fungi, chlamydia, allergies, irritation of chemicals. The problems that arise start from common symptoms that are often shown or are shown by eye diseases such as redness of the eye area and then cause pain and soreness. Diagnosis can be made by an ophthalmologist. In addition, to diagnose eye diseases, an expert system can also be used to pour expert knowledge into an expert system, thus helping diagnose eye diseases. The research objective is to analyze a desktop-based expert system program that contains the knowledge of a trusted expert/doctor who has the ability to be able to diagnose the disease from the eye disease symptoms felt by the patient quickly and precisely. The stages of research carried out in this study include literature study, data collection, system design, system creation, system testing. In addition, the authors developed using the Case-Based Reasoning (CBR) method, which is one method of building an expert system by making decisions from cases with the solution of previous cases to determine the type of conjunctivitis.
Perancangan Hand Sanitizer Otomatis Berbasis Mikrokontroler Arduino Uno Ramadian, Angga Putra; Marlinda, Linda
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.v6i1.11174

Abstract

The current COVID-19 pandemic is still very worrying and still shows no sign of ending, the development of positive confirmed cases continues to increase every day, plus there are still many people around who wash their hands by touching hand washing tools directly. This is what makes the author to make hand sanitizer automatically. An automatic hand sanitizer that is used to help reduce or minimize direct contact in preventing the transmission of COVID-19 that is currently happening. Most hand washing tools, especially hand sanitizers, are used manually by pressing. Arduino is a single board microcontroller or board that is open source or open source, so we can use it or make modifications and it is specially designed to make it easier to create objects or develop electronic devices that can interact with various sensors or controllers, the hardware has an Atmel processor. AVR. Electromagnetic light which has a wavelength longer than visible light. One of the sensors that can be used to detect objects is an infrared sensor. Infrared wavelength is between 700nm to 1mm, that means this wave is longer than light wave and shorter than radio wave. This wave can be used as a short distance transmission medium because it cannot penetrate buildings and is susceptible to other electromagnetic waves. Infrared is in the frequency of 300GHZ to 400THz.
Evaluation of Development Aspects on the Implementation of Field and Institutional Performance Dashboards at LPKA Class I Palembang Hadiwijaya, Hendra; Febrianty, Febrianty; Rezania, Rezania
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.v6i1.11215

Abstract

LPKA Class I Palembang is an institution that enforces the Integrity Zone, WBK, and WBBM. Therefore, one of the technological innovations that will support the implementation is to instill a data-based performance culture through the LPKA Class I Palembang Dashboard. The purpose of this study is to evaluate the development of the dashboard model of government institutions in this case monitoring and reporting on the performance of the fields at LPKA. This evaluation will be included in the development of the dashboard in order to provide optimal net benefits for the management of LPKA Class I Palembang. The research was conducted in 4 stages, namely: determining the research design, identifying dashboard needs, identifying indicators in the development of the Palembang Class I LPKA dashboard model, and formulating a dashboard development model. The dashboard development model was formed based on the results of literature studies and surveys conducted on prospective dashboard users at LPKA Class I Palembang. The survey was conducted on prospective dashboard users, namely structural officials, General Functional Positions, and Certain Functional Positions with a total of 102 employees. The sampling technique is purposive sampling as many as 60 people were directly involved in reporting field performance. The evaluation of the aspects of the LPKA Dashboard shows that from the aspect of presenting Data/Information there are weaknesses in the visual display that is not yet rich in graphics, the unavailability of facilities for predicting future performance conditions, and facilities for causal analysis. The results of this study state that for the aspect of collaboration between users, the benefits that have not been felt are more, namely the exchange of information between users and no conference facilities are available. Meanwhile, in the dashboard performance aspect, there is no link to the administrator.
Bahasa Inggris Erwina, Emmy; Tommy, Tommy; Mayasari, Mayasari
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.v6i1.11224

Abstract

Spelling error has become an error that is often found in this era which can be seen from the use of words that tend to follow trends or culture, especially in the younger generation. This study aims to develop and test a detection and identification model using a combination of Bigram Vector and Minimum Edit Distance Based Probabilities. Correct words from error words are obtained using candidates search and probability calculations that adopt the concept of minimum edit distance. The detection results then identified the error type into three types of errors, namely vowels, consonants and diphthongs from the error side on the tendency of the characters used as a result of phonemic rendering at the time of writing. The results of error detection and identification of error types obtained are quite good where most of the error test data can be detected and identified according to the type of error, although there are several detection errors by obtaining more than one correct word as a result of the same probability value of these words.
Customer Profilling for Precision Marketing using RFM Method, K-MEANS algorithm and Decision Tree Budilaksono, Sularso; Jupriyanto, Jupriyanto; Suwarno, M.Anno; Suwartane, I Gede Agus; Azhari, Lukman; Fauzi, Achmad; Mahpud, Mahpud; Mariana, Novita; Effendi, Maya Syafriana
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.v6i1.11225

Abstract

Precision marketing is the companys ability to offer products specifically made to customers. This decision can give the company the ability to attract customers to always buy continuously. This study presents a trend model for accurately predicting monthly supply quantities / The method used in the first stage is the RFM (Recency, Frequency, Monetary) method for selecting attributes to group customers into different groups. The output of the first stage is clustered using the K-Means Algorithm. The output of clustering is then classified using the Decision Tree and compared with the K Nearest Neighbor method. The dataset that is processed is sales data from Syifamart As-Syifa Boarding School in Subang with 351,158 rows of data. The clustering process produces 4 optimal clusters. The four clusters are then classified using the Decision Tree algorithm to determine the potential and non-potential characteristics of each customer.

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