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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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Development of a Web-Based Dashboard System for Monitoring Study Programme Accreditation Instruments Gunawan, Agus; Gunadi, I Gede Aris; Dewi, Luh Joni Erawati
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.v8i3.12799

Abstract

Accreditation is a form of assessment or evaluation of the quality of education, both at the level of study programme and higher education institutions, conducted by independent organizations or bodies external to the universities. BAN-PT is one such external agency responsible for accrediting higher education institutions in Indonesia. However, many universities encounter challenges during the accreditation process, such as the absence of a monitoring system and data integration in the preparation of some documents, which are essential for accreditation. To address this issue, researchers have recognized the need to design and build a system that can aid in the preparation and monitoring of accreditation forms. The system was developed using the Extreme Programming model, which adopts the agile concept as the core of application development. The Extreme Programming model consists of four key activities, namely system planning, system design, code writing, and system testing. Based on the black box testing results, the developed system was found to operate as intended. Based on the benchmark results for each aspect of the UEQ, Attractiveness has a value of 1.69 with the good category, Perspicuity has a value of 1.57 with the above average category, Efficiency has a value of 2.11 with the excellent category, Dependability has a value of 1.68 with the good category, Stimulation has a value of 1.29 with the above average category, and Novelty has a value of 0.86 with the above average category.
White Blood Cell Detection Using Yolov8 Integration with DETR to Improve Accuracy Nugraha, Shinta Jitny Ayu; Erfianto, Bayu
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.v8i3.12811

Abstract

One of the body's most crucial blood cell kinds is the white blood cell. White blood cells, called leukocytes, are crucial for the body's defence mechanism and against hazardous foreign substances, tumour cells, and infectious bacteria. This paper suggests a computer-based automated system for detecting white blood cells using the YOLOV8 transformer and white blood cell analysis in digital images of blood cells. The Generate process uses Yolov8. In Generate, this will produce image processing in the form of annotation results on each type of white blood cell and dataset with COCO format. The DETR Model training conducted in this study is to increase the accuracy value of the white output of the blood cell picture formation. Test results using recall, precision, f1 score and object detection values. In the lymphocyte and basophil datasets, the number of white blood cell images used is only 10 images. Following the results of training from yolov8 using Roboflow, the results were increased relatively high, with an average increase of 0.68 in all five images of white blood cells. This test also gets an average improvement in detection results from Yolo to DETR, getting a fairly significant result of 68%, which is because YOLO cannot handle undetected objects (which are not in the training dataset; furthermore, DETR can handle multiple objects in a single image. Typically, detecting traditional objects such as YOLO requires repeatedly multiple object detection with a fixed batch size
Android-based Marketplace Application for Surakarta Local Products Baronio, Nodas Constantine; Prasetyo, Sri Yulianto Joko
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.v8i3.12812

Abstract

During the Covid-19 pandemic, many companies suffered losses, leading to a reduction in employees and adversely affecting the community's economy. A significant portion of the local economy depends on the sale of locally produced goods. Moreover, the rapid advancement of information technology has intensified business competition, further impacting the development of local products. Unfortunately, the potential of information technology, particularly Marketplace platforms, to boost the economy and facilitate transactions between customers and sellers has been overlooked. To address these issues, this research aims to develop a Marketplace application to assist the community in selling and promoting their locally made products. The research involves several stages, including analysis, design, simulation prototyping, implementation, monitoring, and management. The result of this study is an Android-based Marketplace application designed to support the sale of local products.
Performance Comparison between Signature Cryptography: A Case Study on SNAP Indonesia Ramadhoni, Moehammad; Santoso, Handri
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.v8i4.12819

Abstract

SNAP (Standar Nasional OPEN API Pembayaran) was submitted by several sub-working groups formed jointly by ASPI and the Bank of Indonesia for encouraging digital transformation in the banking industry. In the document Pedoman Tata Kelola (Bank of Indonesia, n.d.), there is the use cryptographic algorithms that are used as validation for third parties to use the Open API. The algorithms used in the document are HMAC and RSA. The third party will send the signature in the API header along with the sent API payload. The signature describes the body payload, the endpoint URL that was called by the third party, and the time when the API call was made, so the signature will change all the time. However, there are other algorithms that can be used as a form of validation, such as ECC and ZK-SNARK. In this journal, the performance of the four cryptographic algorithms is compared. The performance we compare is overall speed when creating the signature and verifying it. The result is that HMAC is the most efficient algorithm, but for financial data, it is better to use ECC which uses asymmetric keys and is faster than RSA contained in the SNAP document, especially when 256 bits security level that ECC could be 10 times faster then RSA.
The Sentiment Analysis of BBCA Stock Price on Twitter Data Using LSTM and Genetic Algorithm Optimization Setiawan, Rizki Tri; Setiawan , Erwin Budi
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.v8i4.12825

Abstract

In today's business world, there is significant development and emergence of various and diverse innovations. Therefore, every company needs to develop itself in various ways, one of which is going public. This involves a company selling a percentage of its value to the public in order to facilitate its growth in every aspect required. However, it is not easy for issuers to attract investors to invest their capital because each investor has different criteria in terms of investment unit value. Essentially, the stock price depends on the strengths and weaknesses of the company. Hence, in order to expand the market and manage customer relationships, information is needed as a decision support. One of the sources of information that can be used is Twitter, which includes positive, neutral, and negative opinions. This study employs the LSTM classification method and word embedding using GloVe, followed by Genetic Algorithm optimization, which is used to predict sentiment in tweets related to the BBCA stock. The model is built with classification using Long Short-Term Memory to determine the level of accuracy produced. Then, the word embedding method using GloVe is used, and the obtained results with the GloVe-LSTM method yield an overall accuracy score of 71%. Furthermore, the optimization method using Genetic Algorithm is applied to enhance the previous method, GloVe-LSTM, resulting in an accuracy of 87% with the best individual values of 111,170, 0.398, 93, etc., and the best fitness score of 0.8724.
Comparative Analysis of CNN and CNN-SVM Methods For Classification Types of Human Skin Disease Anggriandi, Dendi; Utami, Ema; Ariatmanto, Dhani
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.v8i4.12831

Abstract

Cancer is one of the leading causes of death worldwide, with skin cancer ranking fifth. The skin, as the outermost organ of the body, is susceptible to various diseases, and accurate diagnosis is crucial for effective treatment. However, limited access to dermatologists and expensive skin biopsies poses challenges in achieving efficient diagnosis. Therefore, it is important to develop a system that can assist in efficiently classifying skin diseases to overcome these limitations. In the field of skin disease classification, Machine Learning and Deep Learning methods, especially Convolutional Neural Network (CNN), have demonstrated high accuracy in medical image classification. CNN's advantage lies in its ability to automatically and deeply extract features from skin images. The combination of CNN and Support Vector Machine (SVM) offers an interesting approach, with CNN used for feature extraction and SVM as the classification algorithm. This research compares two classification methods: CNN with MobileNet architecture and CNN-SVM with various kernel types to classify human skin diseases. The dataset consists of seven classes of skin diseases with a total of 21.000 images. The results of the CNN classification show an accuracy of 93.47%, with high precision, recall, and F1-score, at 93.55%, 93.74%, and 93.62%, respectively. Meanwhile, the CNN-SVM model with "poly," "rbf," "linear," and "sigmoid" kernels exhibits varied performances. Overall, the CNN-SVM model performs lower than the CNN model. The findings offer insights for medical image analysis and skin disease classification research. Researchers can enhance CNN-SVM model performance with varied kernel types and techniques for complex feature representations.
Development of Expert System Application to Detect Chicken Disease using the Forward Chaining Method Ramadan, Ahmad; Harahap, Syaiful Zuhri; Muti’ah, Rahma
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.v8i3.12843

Abstract

Chicken is the most widely kept and consumed poultry in Indonesia. Due to the large population of poultry, a variety of diseases have also emerged, from minor diseases to diseases that can kill chickens and infect humans. As a result of these diseases, there are implications for the losses suffered by chicken farmers. Most farmers find it very difficult to identify chicken diseases due to their lack of knowledge. On the other hand, expecting treatment from a veterinarian or expert is very limited and expensive. Therefore, a system is needed that can easily help chicken farmers detect diseases in their pet chickens. This research aims to build an expert system to detect chicken diseases by applying the forward chaining method. The expert system is implemented as a web-based application. The research stages start with identifying problems, collecting data, creating a knowledge base and production rules, building applications, and getting results. The results showed that the forward chaining method provides convenience in detecting chicken diseases. This is proven by only selecting the symptoms of the disease that appear, and then the application will provide conclusions regarding what type of disease is being suffered by chickens. In addition, this application also provides information related to ways of handling and control that can be done to overcome the chicken disease. Hopefully, the results of this research can facilitate chicken farmers in identifying and handling diseases effectively and efficiently.
Fingerprint Identification for Attendance Using Euclidean Distance and Manhattan Distance Putra, Adya Zizwan; Yek, Sallyana; Kwok, Shane Christian; Tarigan, Elovani; Sego, William Frans
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.v8i4.12844

Abstract

Attendance is an action to confirm that someone is present at the office, school, or event. The use of attendance in an agency or company is really important as it can improve the level of discipline and productivity. However, the traditional way of doing attendance is considered less effective, less secure, and more difficult to organize. Therefore, a modern attendance system that utilizes fingerprints can be the right solution, especially because every fingerprint is unique. In this research, we focus on designing a fingerprint identification system for attendance purposes by using two distance measure methods, namely Euclidean Distance and Manhattan Distance. The dataset used in the research contains 111 fingerprint images with 90 images for training the designed fingerprint identification system and the remaining 21 images for testing the system. Each fingerprint image has undergone image pre-processing stage before being used. We compare Euclidean Distance and Manhattan Distance based on their performances in identifying fingerprint. From the test results, the fingerprint identification accuracy obtained using Euclidean Distance is 76.19%, while the accuracy obtained using Manhattan Distance is 71.43%. In general, both algorithms succeed in providing the correct identification results. This proves that Euclidean Distance and Manhattan Distance can be utilized for fingerprint identification purposes.
Sentiment Classification of Fuel Price Rise in Economic Aspects Using Lexicon and SVM Method Alfauzan, Muhammad Fikri; Sibaroni, Yuliant; Fitriyani
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.v8i4.12851

Abstract

After being hit by COVID-19 for a long time around the world which resulted in the paralysis of all countries, especially the economic aspects of all countries that dropped dramatically, the world was again shocked by the conflict between Russia and Ukraine which resulted in an increase in world oil prices including in Indonesia, many people complained and opposed the government's policy of increasing fuel prices because fuel affects various aspects, including economic aspects. Based on these problems, researchers use sentiment analysis methods that aim to find out people's opinions on issues that are being discussed throughout Indonesia and this research focuses on comparing the SVM algorithm with TF-IDF feature extraction then using K-Fold Cross Validation after that it is compared with the Lexicon Inset dictionary, in this case the model with Lexicon Inset which contains weighting on each word. In this study, it was found that the dataset model using the SVM algorithm with TF-IDF feature extraction and then using K-Fold Cross Validation obtained an average accuracy of 0.85 using the SVM algorithm. While the model using the automatic labeling dataset using the Indonesian sentiment Lexicon (Lexicon Inset) obtained an average accuracy of 0.68. Classification using SVM with TF-IDF feature extraction is superior to using Lexicon Inset.
Implementation of Stock Goods Data Mining Using the Apriori Algorithm Sinaga, Bosker; Marpaung, Meman; Tarigan, Ita Roseni Br; Tania, Keke
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.v8i3.12852

Abstract

Stock inventory in the pharmacy should be well recorded. This is to provide the best service to customers/buyers. Buyers who come with empty results or the drug they want to buy will not feel disappointed, especially if they have made several purchases. This research implements data mining of drug stock inventory, where in the research carried out there are many empty and excess inventory items, resulting in less than optimal service. The research method, namely the survey research method, is a research method that is carried out using surveys or data collection through research respondents. The algorithm used in analyzing the data is the Apriori algorithm. The results of this study are the results of association rules based on predetermined parameters, namely a minimum support of 25% and a minimum confidence of 60%. The rule that is formed is that if consumers buy amoxicillin, they will buy mefenamic acid with a support value of 37% and a confidence of 183%.

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