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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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Design of a Home Door Security System Based on NodeMCU ESP32 Using a Magnetic Reed Switch Sensor and Telegram Bot Application Ramadhani, Syahri; Putri, Dhanny Permatasari
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.v8i4.12688

Abstract

With very rapid technological advancements, it is not possible now that all activities can be carried out quickly, easily, and instantly. The Internet of Things (IoT) allows us to solve various problems by making some devices communicate with each other across the virtual world network. This study uses the prototyping method by explaining how to design a home door security system that can be controlled by a smartphone with a WiFi connection and can use a card consisting of a chip connected to a reader. The design of this tool consists of several stages, namely designing a block diagram of how the circuit works, and designing hardware (Hardware) and software (Software). From the results of system testing, it can be seen that when conducting RFID testing by tapping the card on the reader. The system is successful and the door can be opened according to the NUID card that has been registered with the program and it can fail by using a card that is not registered in the system. Which indicates the test results are working properly. The working capability of the system on the door security device is as expected and the response from the magnetic reed switch sensor as input from the notification is very good. This system using RFID functions to make it easier for users to control the door of the house with a smartphone connected to wifi so that users don't just use conventional keys.
Improved YOLOv5 with Backbone Replacement to MobileNet V3s for School Attribute Detection Nugroho, Ardanu Dhuhri; Wiga Maulana Baihaqi
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12702

Abstract

School attributes are a series of clothes and accessories that must be worn by students in the school environment. The implementation of this rule aims to create discipline in students. However, in practice, not all rules can be implemented properly because there are still students who violate these rules. One of the rules applied at school is the use of complete attributes. Currently, attribute checks in schools are done manually or through teacher supervision. However, this takes more time, is prone to errors, and is inefficient due to the large number of students being checked. This study proposes an improved YOLOv5 architecture with the replacement of the backbone to MobileNetV3s to detect school attributes. This method uses deep learning and the YOLOv5 algorithm to detect in real time the use of school attributes by students. In this study, the experimental results show that the enhanced YOLOv5 with MobileNetV3s has higher accuracy compared to the original YOLOv5. In addition, the improved model is more efficient in memory usage and weight file size. With an accuracy result of 0.912 on mAP50 and a weight size of about 90 MB and a memory usage of <7 GB, it shows the potential of replacing the backbone in this technology in overcoming attribute detection challenges in schools and can be applied in other cases. However, further research is needed to generalize these results to other problems. This research also shows that backbone replacement in YOLOv5 can affect the accuracy of the model.
Comparison of LSTM and GRU Models for Forex Prediction Pahlevi, Mohammad Rezza; Kusrini, Kusrini; Hidayat, Tonny
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.v8i4.12709

Abstract

Trading foreign currencies worth trillions of dollars takes place daily in the forex market, characterized by highly volatile movements. The forex market operates on bid and ask prices, with exchange rates determined by the principles of supply and demand. Trading involves currency pairs like EUR/USD, where the value of the Euro is compared to the US Dollar, serving as a basis for analyzing price fluctuations. Due to the volatile nature of forex, market participants must make informed decisions when buying and selling, as improper choices can result in financial losses. One approach to mitigating risk in forex trading decisions is through the use of forecasting techniques. This research study employs LSTM and GRU methods to predict forex trends, which are evaluated using various dataset divisions. The most accurate results are obtained using a dataset of 4979, split into three equal parts: 80% for training, 10% for validation, and 10% for testing. This approach yields an RMSE value of 0.054, MAPE of 0.037, and R-square of 97%
Comparison of Residual Network-50 and Convolutional Neural Network Conventional Architecture For Fruit Image Classification Dharma, Arie Satia; Sitorus, Judah Michael Parluhutan; Hatigoran, Andreas
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12721

Abstract

Classification of fruit images using machine learning technology has had a significant impact on human life by enabling accurate recognition of various fruits. With the advancements in technology, machine learning architectures have become increasingly diverse and sophisticated, providing enhanced capabilities for fruit image classification. However, previous studies have primarily focused on classifying fruits at a basic level. Therefore, there is a growing need for the development and application of Fruit Image Classification systems within the community, particularly in the field of agriculture. Such applications can play a pivotal role in leveraging technology to benefit the agricultural sector, empowering users to gain satisfaction and knowledge regarding different fruits through the utilization of these applications. In this study, we employ both a conventional Convolutional Neural Network (CNN) architecture and a Residual Network-50 for fruit image classification. To ensure robust performance evaluation, the dataset is divided into training and testing subsets, with fruits categorized into specific classes. Furthermore, identical preprocessing and optimization techniques are applied to both architectures to maintain consistency and fairness during the evaluation process. The results of our classification experiments on a dataset consisting of 17 different fruit classes reveal that the conventional CNN architecture achieves an impressive accuracy of 0.998 (99%) with a minimal loss of 0.009. On the other hand, the Residual Network-50 demonstrates a slightly lower accuracy of 0.994 (99%) but with a slightly higher loss of 0.02. Despite the higher loss, the Residual Network-50's accuracy remains comparable to that of the conventional architecture, showcasing its potential for fruit image classification. By leveraging the power of machine learning and these advanced architectures, fruit image classification systems can provide valuable insights and assistance to users. They can facilitate informed decision-making in various domains, including agriculture, food production, and consumer education.
A DESIGN UI/UX E-LEARNING ENGLISH MOBILE USING USER CENTERED DESIGN (UCD) METHOD: English Alamsyah, Dwi Rizky; Resmi, Mochzen Gito; jaelani, Irsan
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.v8i4.12727

Abstract

E-learning English Mobile application design is a Mobile-based application design needed by SMAN 2 Purwakarta for future learning purposes. With this application design SMAN 2 Purwakarta has a picture of learning applications that will facilitate students and teachers in learning and teaching activities. In this application design research, there are several methods that are widely used by previous researchers. One of the methods used in this research is the User Centered Design (UCD) method which is a new method in system development. UCD is a language that is widely applied in describing designs. The concept of UCD is the user as the center of the system development process, and the goals, the system environment are all based on the user experience. The results of this study produced a learning application prototype, namely E-learning English Mobile. After testing using the System Usability Scale (SUS). The average value obtained is 78. It can be concluded that the design of the E-Learning English Mobile application is acceptable because it meets the Acceptable category
The Performance of the Equal-Width and Equal-Frequency Discretization Methods on Data Features in Classification Process Putri, Pramaishella Ardiani Regita; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
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.v8i4.12730

Abstract

The classification process often needs help with suboptimal accuracy values, which can be attributed to various factors, including the dataset's wide range of attribute values. Discretization methods offer a solution to address these issues. This study aims to compare the effectiveness of Equal-Width and Equal-Frequency discretization methods in enhancing accuracy during the classification process using datasets with varying sizes. The research employs Naïve Bayes, Decision Tree, and Support Vector Machine as classification models, with three datasets utilized: Bandung City Traffic data (3804 records), Bandung City COVID-19 cases data (2718 records), and Bandung City Dengue Fever Disease Index data (150 records). Three experimental scenarios are executed to assess the impact of the two discretization methods on accuracy. The first scenario involves no discretization, the second employs Equal-Width, and the third applies Equal-Frequency discretization. Experimental results indicate significant accuracy improvements post-discretization. The Naïve Bayes model achieved 94% accuracy for the Traffic dataset, while the Decision Tree achieved 71% accuracy for the COVID-19 dataset and an impressive 98% for the Dengue Fever Disease dataset. These outcomes demonstrate that applying Equal-Width and Equal-Frequency discretization methods addresses the challenge of wide attribute value ranges in the classification process.
Chicken Disease Classification Based on Inception V3 Algorithm for Data Imbalance Ahsan, Muhammad Salimy; Kusrini; Dhani Ariatmanto
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12737

Abstract

In order to supply the world's protein needs, one of the most crucial industries is the poultry business. The problem that often occurs in chicken farms is disease, and this can have a significant impact on the farm. The availability of large enough amounts of data makes it possible to carry out the process of monitoring chicken diseases using deep learning technology for the classification of chicken diseases. With the availability of large enough data, the dataset has a variety of features that cause problems with data clutter. To overcome the problem of data conflict, an oversampling technique is used to increase the sample data from the minority class so that it has the same value as the other majority classes, and the Inception-V3 algorithm is used to classify chicken diseases based on fecal images. The total number of data used was 8067, which were broken down into the following four categories: Healthy, Salmonella, Coccidiosis, and Newcastle disease. Data balancing was done using oversampling to get the total data to 10500 before the evaluation process was started. The data was distributed by splitting it by 80% of the data will be used for training, 10% for data validation, and 10% for testing. The results of the test, which employed Inception V3 without oversampling, produced the highest possible score of 94.05%.
Teacher Quality Affects On Graduation Of Study Programming Data Approach There With CRISP-DM Method Harahap, Mawaddah; Hidayati, Namira; Panjaitan, Sumiati; Tambunan, Enjelyna; Sihombing, Juniati
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.v8i4.12762

Abstract

Each student's graduation is influential to the teacher in every subject that can be predicted based on the pattern of habits of the teacher who presents the subject. Web Proggramming is the subject of study that must be completed by every student. If this course is not completed, it is not allowed for the student to take other courses related to it. The custom patterns of teachers in this study were taken from 300 student respondents. An analysis is done to compare the results of questionnaire scores with the assessment of college admissions teachers. From the results of the comparison, it is possible to predict the graduation rate of students to the web programming course. The results of the experiment were that 72% of the students received highly influential predictions, 12% Influential, 7% Sufficient, 5% Influential and 4% Highly Influential.
Densenet Architecture Implementation for Organic and Non-Organic Waste Simarmata, Allwin M.; Salim, Philander; Waruwu, Netral Jaya; Jessica
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.v8i4.12765

Abstract

Garbage is the result left over from the process of daily human activities and activities which are considered no longer suitable for use, ranging from household waste to large-scale industrial waste. Therefore, the classification of waste is important because the problem of waste disposal is increasing and the way of processing is wrong. This research focuses on the classification of organic and non-organic waste using the DenseNet architecture. The dataset is processed first and each image in the dataset is resized to 128x128 pixels before being used in the model. We then trained all DenseNet types namely DenseNet121, DenseNet169, DenseNet 201, and compared their performance. Based on the test results, all DenseNet models that were trained were able to produce good accuracy, precision, recall, and F1 scores in garbage classification. In particular, our designed DenseNet121 model achieves 93.1 accuracy, 94.08% precision, 94.00% recall, 94.03% F1 score and 1min 34s training time as the best among other models. These results prove that the DenseNet architecture can be used to classify organic and non-organic waste correctly.
Two-Stage Sentiment Analysis on Indonesian Online News Using Lexicon-Based : A study case at online new unilateral layoffs at Company XYZ Vinardo; Wasito, Ito
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.v8i4.12769

Abstract

The image of a supplier company is often associated with the well-known brand it supplies, and its reputation can be influenced by online news circulation. To maintain a positive image, it is crucial for the company to monitor and manage online news to rectify any false information. Failure to maintain a good company image can lead to customer order loss and even company shutdown. This paper aims to conduct a two-stage sentiment analysis on Indonesian news articles regarding unilateral layoffs by company XYZ. The first stage will analyze sentiment in the circulating news about the layoffs, while the second stage will assess sentiment after the company releases a press release to provide accurate information. The VADER lexicon-based method, utilizing the InSet and SentiStrength_ID Indonesian dictionaries, will be employed to analyze sentiment before and after the press release. This will enable us to compare sentiment and evaluate the effectiveness of the press release and the Indonesian dictionaries in analyzing sentiment in the news. The research findings indicate that the company's press release, aimed at correcting false information, had a positive impact by reducing negative sentiment and generating a more positive sentiment in the second stage. Moreover, the selection of the sentiment analysis dictionary also plays a critical role in determining the sentiment analysis results.

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