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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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Optimizing Facial Expression Recognition with Image Augmentation Techniques: VGG19 Approach on FERC Dataset Ilmawati, Fahma Inti; Kusrini, Kusrini; Hidayat, Tonny
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13507

Abstract

In the field of facial expression recognition (FER), the availability of balanced and representative datasets is key to success in training accurate models. However, Facial Expression Recognition Challenge (FERC) datasets often face the challenge of class imbalance, where some facial expressions have a much smaller number of samples compared to others. This issue can result in biased and unsatisfactory model performance, especially in recognizing less common facial expressions. Data augmentation techniques are becoming an important strategy as they can expand the dataset by creating new variations of existing samples, thus increasing the variety and diversity of the data. Data augmentation can be used to increase the number of samples for less common facial expression classes, thus improving the model's ability to recognize and understand diverse facial expressions. The augmentation results are then combined with balancing techniques such as SMOTE coupled with undersampling to improve model performance. In this study, VGG19 is used to support better model performance. This will provide valuable guidelines for optimizing more advanced CNN models in the future and may encourage further research in creating more innovative augmentation techniques.
C4.5 Forward Selection Based Algorithm For Class Level Classification Of Nurul Jadid Islamic Boarding School Students Irfan, Muhammad Isomul
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13514

Abstract

Pesantren is an Islamic educational institution that plays a central role in the development of education in Indonesia. Although originally established for Islamic religious education (Pendidikan Agama Islam or PAI), pesantren has evolved into an educational institution that contributes to both scholarly and community service aspects. According to the regulations set by the Ministry of Religious Affairs of the Republic of Indonesia under Number 31 of 2020, pesantren is a community-based institution that upholds the teachings of Islam rahmatan lil'alamin (Islam as a blessing for all) and the noble values of the Indonesian nation. Pesantren education is efficient because it is conducted in a boarding school setting, which shapes the character of its students or 'santri.' However, the current method of determining the grade levels of santri is often inaccurate, relying solely on the average scores of entrance exams without considering essential aspects of subjects. This leads to a decrease in students' interest in learning and delays in achieving higher levels of education. By utilizing data mining techniques, such as the C4.5 algorithm based on Forward Selection, it is possible to address this issue and enhance the accuracy of placing santri into their appropriate grade levels at the Nurul Jadid Paiton Probolinggo pesantren. This improvement can make the pesantren education system more effective in managing student learning
Classification of types Roasted Coffee Beans using Convolutional Neural Network Method Metha, Halifa Sekar; Kusrini, Kusrini; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13517

Abstract

In the current digital era, the role of technology in the agricultural industry is very necessary to increase yields which can have an impact on the productivity and welfare of farmers. Coffee is a drink that has been very popular for many years. Due to the high demand for coffee beans, this research aims to develop a system that can classify types of roasted coffee beans based on images using the Convolution Neural Network (CNN) method. Coffee bean processing is the most important stage in the coffee industry, classifying coffee beans often requires more in-depth knowledge and extensive experience regarding coffee beans. Therefore, this system can be a more effective solution. The author collects a dataset containing types of roasted coffee beans, then the Convolutional Neural Network (CNN) can analyze in the form of visual patterns each type of coffee bean. This implementation is expected to help the coffee industry identify coffee beans quickly and accurately.
SIAKAD Mobile With API Service To Improve Academic Services Husein, Amir Mahmud; Simanjuntak, Andre Juan; Sinaga, Candra Julius; Tampubolon, Mei Monica; Situmorang, Priskila Natalia C.
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13519

Abstract

Developing SIAKAD (commonly called SIAM, Student Academic Information System) Mobile using API Service to improve academic services for students is the goal of researchers doing so because it supports the implementation of education to create better information distribution services for everyone who wants access to it. And this also has an impact on academic performance, it is easier to organize lecturer attendance schedules, value recapitulation, and so on. Conventional SIAKAD (SIAM) which can be accessed via a computer or laptop has limited accessibility and practicality, which can hinder students from accessing academic information flexibly. Therefore, after researchers have examined and paid attention to several systems that can be implemented to assist users in accessing them, the development of SIAKAD in the form of a mobile application is a solution to increasing accessibility and ease of access to academic information. The API service is used as a communication bridge between the SIAKAD mobile application and the backend system. Through this, the Mobile Application can communicate (send requests and receive responses from the backend system) quickly and efficiently. But to shorten the application development time we use the SCRUM method and for the business process model, we use BPMN to create, design and design this application. The results of this study the authors see a compare of the time that can increase after using Mobile in access SIAKAD (SIAM).
Diabetes Disease Detection Classification Using Light Gradient Boosting (LightGBM) With Hyperparameter Tuning Ramadanti, Elisa; Aprilya Dinathi, Devi; christianskaditya; Chandranegara, Didih Rizki
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13530

Abstract

Diabetes is a condition caused by an imbalance between the need for insulin in the body and insufficient insulin production by the pancreas, causing an increase in blood sugar concentration. This study aims to find the best classification performance on diabetes datasets with the LightGBM method. The dataset used consists of 768 rows and 9 columns, with target values of 0 and 1. In this study, resampling is applied to overcome data imbalance using SMOTE and perform hyperparameter optimization. Model evaluation is performed using confusion matrix and various metrics such as accuracy, recall, precision and f1-score. This research conducted several tests. In hyperparameter optimization tests using GridSearchCV and RandomSearchCV, the LightGBM method showed good performance. In tests that apply data resampling, the LightGBM method achieves the highest accuracy, namely the LightGBM method with GridSearchCV optimization with the highest accuracy reaching 84%, while LightGBM with RandomSearchCV optimization reaches 82% accuracy.
GOVERNANCE EVALUATION ELECTRONIC SECURITY SYSTEM (ESS) (Case Study: ABC Central Bank) Aziz, RZ Abdul; Ikhsanudin, Anas; Hasibuan, M Said
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13540

Abstract

As we know, the role of the security system has a very important role for a state institution to provide security and comfort in carrying out its functions, such as the ABC central bank. A good security system is a security system that is supported by a reliable electronic security system and is composed of several components such as a CCTV monitoring system, Access Control System (ACS), Security Alarm System (SAS), and Fire Alarm System (FAS). This system is very necessary to provide support for the duties of these state institutions to protect devices, data and electronic infrastructure from potential threats and security risks. The main functions of electronic security systems include prevention, detection, response to incidents, and recovery after disturbances/disasters. For this reason, efforts are needed to provide an evaluation of the system maturity level and information security management as a form of risk management to maintain the continuity of system use. This research uses the INDEKS KAMI 4.1 to map ESS governance maturity and the OCTAVE Allegro method to analyze information security management. From the analysis carried out, it has been concluded that the ESS implementation has been operated well in accordance with the security system requirements and has reached a good level of governance maturity. Information security management analysis carried out using the OCTAVE Allegro method has succeeded in identifying information security management with the result that information security management has been implemented well. This is proven by the existence of indicators, namely CCTV recording data, log systems as information assets that have been managed and distributed according to authority
K-Medoids Algorithm to Clustering COVID-19 Patients with Various Age Levels at Hospitals in Yogyakarta Province Insani, Pratamasari Noor; Darmawan, Endang; Sugiyarto, Sugiyarto
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13551

Abstract

COVID-19 causes a wide spectrum of symptoms, such as mild upper respiratory infection or life-threatening sepsis. From 20.2% of cases of COVID-19 progressed to severe disease with a mortality rate of 3.1% where 60%-90% of patients with comorbidities were hospitalized. The purpose of this study was to find out that cluster analysis using K-Medoids can distinguish COVID-19 patients at various age levels which analytical method has sensitivity and specificity values in analyzing clustering in COVID-19 patients. This study uses a cohort retrospective design conducted at five hospitals in Yogyakarta Province. The study used patient medical record data from March 2020 – September 2021 with a total of 916 patient data that met the inclusion criteria. Cluster analysis will be carried out using Google Colaboratory with the Python programming language. The clustering results are divided into 2 cluster groups where cluster 1 consists of 558 patients and cluster 2 consists of 358 patients with various age levels. The test resulted in 2 clusters with a DBI value of 5,191631. The results of statistical tests showed that there was a significant relationship (p-value = 0,023) between age, recovery rate, and patient mortality. From the test results, it can be seen that ages 50 to 59 years are suspected of COVID-19
Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods Damarsiwi, Dyah Kartika; Pambudi, Elindra Ambar; Fitriani, Maulida Ayu; Wibowo, Feri
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13556

Abstract

Face detection is a process by a computer system that can find and identify human faces in digital images or videos. One of the main challenges faced in the face detection process is the complex background. Complex backgrounds, such as many color combinations in the image, can interfere with the detection process. To overcome this challenge, this research uses a combination of two methods: Scale Invariant Feature Transform (SIFT) and Haar Cascade Classifier. Scale Invariant Feature Transform (SIFT) is a method used in image processing to identify and describe unique features in an image. The SIFT method looks for keypoint descriptors in images that can be used as a reference in comparing different images. After the keypoint descriptor is found with SIFT, the Haar Cascade Classifier method is used to detect faces in the image. Haar Cascade Classifier is a practical algorithm for object detection in images. After facial features are extracted with these two methods, the results are compared with the K-Nearest Neighbor (KNN) approach. This research involves the introduction of 28 color images with complex backgrounds. The results of combining these two methods produce an accuracy of 81.75%. This shows that combining these two methods effectively overcomes complex background challenges in face detection.
Extraction of Shape and Texture Features of Dermoscopy Image for Skin Cancer Identification Aldi, Febri; Sumijan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13557

Abstract

Skin diseases are increasing and becoming a very serious problem. Skin cancer in general there are 2, namely melanoma and non-melanoma. Cases that are often encountered are in non-melanoma types. A critical factor in the treatment of skin cancer is early diagnosis. Doctors usually use the biopsy method to detect skin cancer. Computer-based technology provides convenient, cheaper, and faster diagnosis of skin cancer symptoms. This study aims to identify the type of skin cancer. The data used in the study were 6 types of skin cancer, namely Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus image, Pigmented Benign Keratosis image, or Vascular Lesion, with a total of 60 dermoscopy images obtained from the Kaggle site. Dermoscopy image processing begins with a pre-processing process, which converts RGB images to LAB. After that, segmentation is carried out to separate objects from the background. The method of extracting shape and texture features is used to obtain the characteristics of dermoscopy images. As many as 2 types of shape features, namely eccentricity and metric, and 4 types of texture features, namely contrast, correlation, energy, and homogeneity. The result of this study is that it can identify the type of skin cancer based on image features that have been extracted using a program from the Matlab application. The technique of extracting shape and texture features is proven to work well in identifying the type of skin cancer. In the future it is expected to use more data, and add color features in identifying dermoscopy images.
Comparison of Naïve Bayes and SVM in Sentiment Analysis of Product Reviews on Marketplaces Nurul Zalza Bilal Jannah; Kusnawi, Kusnawi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13559

Abstract

At this time more and more people are switching to shopping online in existing marketplaces such as Shopee. Marketplaces provide various advantages and disadvantages to customers such as lower costs and goods sent not according to orders. Product reviews from customers greatly affect the sales level of business people so that sentiment analysis is carried out. The importance of conducting sentiment analysis of product reviews in the marketplace is to add an overview of how the product is received by users. This research uses Naïve Bayes and SVM algorithms for sentiment analysis of beauty care product review datasets obtained from Shopee scraping results. This research implements k fold cross validation for data splitting process of 10 folds. The Naïve Bayes algorithm obtained the highest accuracy value of 85.53% on fold 2 and the lowest accuracy value of 77.16% on fold 3. While the SVM algorithm obtained the highest accuracy value of 88.58% on fold 2 and the lowest accuracy value of 82.99% on fold 7. With this it is stated that SVM can work better for sentiment analysis of beauty care product reviews on the Shopee marketplace because it gets a higher average accuracy value of 86.14% compared to the Naïve Bayes algorithm.

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