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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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Expert System for Diagnosing Learning Disorders in Children Using the Dempster-Shafer Theory Approach Nugraheni, Murien; Nuraini, Rini; Tonggiroh, Mursalim; Nurhayati, Siti
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.v8i4.12960

Abstract

Learning disorders can occur in children where a child experiences difficulty mastering important skills such as reading, writing, or arithmetic. Learning disorders can have an emotional impact on children, such as low self-confidence, anxiety, or frustration. Therefore, it is important for parents and educators to recognize the signs of learning disorders so that appropriate intervention can be given. The aim of this research is to develop an expert system that can diagnose learning disorders in children using the Dempster-Shafer Theory algorithm to make it easier to diagnose and produce the right diagnosis. The Dempster-Shafer Theory approach has the ability to provide probability values in evidence based on the level of belief and reasoning in accordance with logic and then combine it with information from certain events. This research produces an expert system built on a website that can diagnose based on symptoms and display diagnosis results, definitions of types of learning disorders, and treatment options. The accuracy test results show a value of 92%, which means that the system built using the Dempster-Shafer Theory approach is able to diagnose learning disorders in children well.
Ambon Banana Maturity Classification Based On Convolutional Neural Network (CNN) Nisa, Yuha Aulia; Sari, Christy Atika; Rachmawanto, Eko Hari; Mohd Yaacob, Noorayisahbe
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.v8i4.12961

Abstract

The banana (Musa paradical), is an excellent fruit produced nationally and high in vitamins. In Indonesia, banana production is at a higher level than other fruit products. However, one of them is the issue with bananas' post-harvest, which arises when they are produced in huge quantities on a large scale or by an industry that sorts bananas. So far, the determination of the maturity level of bananas is done by relying on visual analysis limited to the color of the skin by the human eye. However, this identification approach has several drawbacks. First, this method requires significant effort in the banana sorting process. In addition, the perception of the fruit's maturity level can vary, because humans can experience fatigue and lack of consistency in judgment. In addition, human judgment is also influenced by subjective factors that can affect the final result. Considering this problem, developed a system to classify the ripeness level of Ambon bananas. This system utilizes image enhancement features to increase contrast, which is implemented using a Convolutional Neural Network (CNN). The classification process is carried out through image processing using MATLAB R2022a software, which forms the basis of a classification system with 4 classes which include 486 images of unripe Ambon bananas, 235 images of half-ripe Ambon bananas, 309 images of perfectly ripe Ambon bananas, 184 images of rotten Ambon bananas. The dataset analyzed in this study totaled 1214 data divided into 1093 training data and 121 test data. The CNN method is used in this data classification, and the results show an accuracy rate of 95.87%.
Prediction of the Human Development Index for Equitable Development in West Sumatra Province Using the C4.5 Algorithm Sirait, Weri; Azizah, Nur
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.v8i4.12968

Abstract

Unequal development in Indonesia can be seen from the Human Development Index. The Human Development Index is a tool used to measure the attainment of the quality of life of a region or country and as material for economic policy on quality of life. It contains components of health level, education level and welfare level. In 2022, West Sumatra Province achieved the 9th highest Human Development Index in Indonesia, namely 73.26, with this figure the West Sumatra Province Human Development Index is above the national average. However, there are still regencies/cities in West Sumatra Province that have achievements below the national average. This factor causes the development conditions in West Sumatra Province to be uneven. Uneven human development conditions will make it difficult for the government to improve Human Resources (HR). In this research, the C45 Data Mining Algorithm was implemented to predict the Regency/City Human Development Index in West Sumatra Province. As is the method of the Central Bureau of Statistics in measuring the Human Development Index, the variables used from the Human Development Index indicators are Life Expectancy, Years of School Expectation, Average Length of Schooling, and Per Capita Expenditures. The Central Statistics Agency data used in this research covers all regencies/cities in West Sumatra during the period 2018-2022. Range levels are grouped into three groups, namely, low, medium, and high. Based on testing using RapidMiner software with the Cross Validation operator, an accuracy value of 86.61% was obtained.
Prediction of Student Graduation with the Neural Network Method Based on Particle Swarm Optimization Nurdin, Hafis; Sartini, Sartini; Sumarna, Sumarna; Maulana, Yana Iqbal; Riyanto, Verry
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.v8i4.12973

Abstract

In private universities in Indonesia, student graduation is something that is worth paying attention to, because it will be an aspect of the success of the university. Universities certainly have data on students who graduated, where student graduation data is very important to be taken into consideration by private universities, however with a lot of data it will make it difficult for private universities to find information from this data. Other researchers have previously carried out student graduation data with the same data by examining student graduation data using other methods. So we need a data mining algorithm that has never been tested on student graduation data. The method used is the neural network method with an optimization method, namely the particle swarm optimization method, to test the data, which will later produce information that is very useful for universities. After testing the student graduation data and getting accuracy results using the neural network method of 84.55% and after being optimized using the particle swarm optimization method, the accuracy results were optimal with a value of 86.94%. These results can be used by private universities to predict that students will graduate on time before they take their final semester so that the student graduation rate will be high.
Detection And Classification of Citrus Diseases Based on A Combination of Features Using the Densenet-169 Model Firdaus, M. Haikal; 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.v8i4.12974

Abstract

This research is motivated by the urgent need to improve the capability of detecting diseases in citrus plants, which play a crucial role in maintaining agricultural sector productivity. Diseases such as blackspot, canker, and greening can have a serious impact on harvest yields and overall plant health. Therefore, this research aims to enhance the accuracy in classifying diseases in citrus plants by applying a Deep Learning approach. In this study, we chose to adopt the DenseNet-169 architecture and conducted experiments with two different scenarios: one using original features and the other using a combination of features. This method was employed to classify four different classes, namely blackspot, canker, greening, and healthy plants, using an LDI dataset consisting of 3,000 images. This dataset was divided into three parts, namely training, testing, and validation sets. The experimental results indicate that the DenseNet-169 model with the use of feature combination achieved the highest accuracy rate at 96.66%, whereas the model using only original features achieved 91.33%. This significant improvement of 5.33% in accuracy provides strong evidence that the feature combination approach has a highly meaningful positive impact on the model's ability to identify and classify diseases in citrus plants. These findings confirm that the use of feature combinations is a highly effective strategy in improving the model's performance in disease classification tasks in citrus plants.
Digital Transformation in University: Enterprise Architecture and Blockchain Technology Iswahyudi, Iswahyudi; Hindarto, Djarot; Indrajit, R. Eko
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.v8i4.12977

Abstract

Implementing digital transformation in higher education is now required for effective adaptation to rapid technological advances. Utilizing Enterprise Architecture (EA) with blockchain technology is a recommended strategic approach for implementing digital transformation. The college first ascertains the University's digital transformation requirements and goals, which include data management, security, operational efficiency, and transparency. In addition, formulating a strategic plan to determine the optimal integration of blockchain technology within the college's architectural framework, including the judicious selection of the most suitable blockchain platform, is essential. In addition, developing a company's architecture must prioritize seamless integration with existing systems while upholding data security and consistency principles. Consideration should be given to the significance of training and awareness building among faculty and students. In addition, the implementation process must be conducted in phases with consistent monitoring and evaluation. The success of this project is contingent upon the formation of partnerships and collaborations with blockchain technology companies, as well as a thorough understanding of the applicable regulatory framework. By adopting this methodology, the University can increase operational efficiency, bolster data security measures, and improve the educational experience. This research aims to increase efficiency, data security, transparency, and educational innovation in universities using Blockchain Technology and University Enterprise Architecture.
Blockchain-Based Academic Identity and Transcript Management in University Enterprise Architecture 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.v8i4.12978

Abstract

The present research examines the implementation of Blockchain-based Identity and Academic Transcript Management in a university's enterprise architecture. This research is motivated by the increasing demand for secure, transparent, and efficient student identity management and the need to store easily verifiable academic transcripts. Blockchain technology has been spotlighted because it provides security and data integrity solutions. This research aims to determine if integrating Blockchain into the University's Enterprise Architecture can improve the management of student identities and academic transcripts by reducing the risk of forgery and facilitating more dependable access for interested parties. Increased security and efficiency in managing student data are the practical implications of this research, which can help universities reduce the risk of data loss and increase stakeholder trust. This research method includes surveying various universities that have adopted Blockchain technology in their academic identity and transcript management. In addition, we will assess its technical implementation, evaluate its effect on efficiency, and conduct interviews with university personnel involved in the implementation process. This study's anticipated outcome is providing universities planning to adopt Blockchain technology for Enterprise Architecture with actionable guidance. This research will identify the benefits, challenges, and best practices of integrating Blockchain in academic identity and transcript management and lay the groundwork for further improvement of educational services in university settings.
PyTorch Deep Learning for Food Image Classification with Food Dataset Iswahyudi, Iswahyudi; Hindarto, Djarot; 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.v8i4.12987

Abstract

Classification of food images is crucial in today's increasingly digitally connected world. In the rapidly evolving mobile applications and social media era, the demand for an automated system that can recognize food types from an image is intensifying. This study employs deep learning and the PyTorch framework to develop a dependable and efficient solution for classifying food images. This research is motivated by the growing complexity of food introduction challenges. The primary challenge is improving the accuracy of food type recognition and overcoming variations in the visual presentation of food, such as lighting, shooting angles, and proportional and textural differences. Convolutional Neural Networks (CNN) are effective for image classification and are incorporated into the methods utilized. In addition, we employ ResNet101 transfer learning techniques to capitalize on the knowledge of trained models for large image datasets. The primary objective of this study is to develop a food image classification model that is accurate, training-efficient, and capable of accurately recognizing various types of food. In testing and evaluation, the developed model could realize multiple types of food with satisfactory accuracy. The accuracy of training reached 99.35%, while the accuracy of testing reached 94.65%. This study also reveals how Resnet101 transfer learning is utilized by deep learning technology.
Identification of 10 Regional Indonesian Languages Using Machine Learning Nugraha, Azhar Baihaqi; Ade Romadhony
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.v8i4.12989

Abstract

Language Identification plays a pivotal role in deciphering the rich tapestry of Indonesia's diverse regional languages, encompassing a wide spectrum of scripts, and spoken forms. Language Identification, an integral component of Natural Language Processing, is frequently addressed through Text Classification. In this study, we embark on the task of identifying 10 Indonesian languages, leveraging the NusaX dataset, with the overarching objective of contextual language determination. To achieve this, we harness a diverse array of machine learning techniques, including Support Vector Machine, Naïve Bayes Classifier, Decision Tree, Rocchio Classification, Logistic Regression, and Random Forest. We complement these methods with two distinct feature extraction approaches: N-gram and TF-IDF. This comprehensive approach enables us to construct robust models for language identification. Our findings unveil the strong efficacy of these models in discerning Indonesian languages, with the Naïve Bayes Classifier emerging as the frontrunner, achieving an impressive accuracy rate of 99.2% with TF-IDF and an even more remarkable 99.4% with N-Gram. To gain deeper insights, we delve into error analysis, revealing that misclassifications often stem from shared words across different languages. This research is underpinned by the necessity for a robust language identification model, underscoring its critical role within the complex linguistic landscape of Indonesian regional languages. These results hold great promise for applications in automated language processing and understanding within this diverse and multifaceted linguistic context.
Comparison Of The C.45 And Naive Bayes Algorithms To Predict Diabetes Alam, Alam; Alana, Divi Adiffia Freza; Juliane, Christina
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.v8i4.12998

Abstract

Diabetes mellitus is an urgent global health problem and has a major impact on people around the world. This disease is characterized by high levels of sugar (glucose) in the blood due to disturbances in the production or use of the hormone insulin by the body. This study aims to carry out accurate early detection of diabetics so that they can be treated as soon as possible to reduce the risk of death and to compare the two algorithms that have the best level of accuracy. The algorithms used in this study are the C4.5 and Naïve Bayes Decision Tree Algorithms. The results of the experiments carried out in this study the Decision Tree Algorithm C4.5 and Naïve Bayes can be used in modeling the early detection of diabetes. The highest average accuracy results were obtained at 90.835% using the Decision Tree C4.5 Algorithm. As for the Naïve Bayes Algorithm, an average accuracy rate of 90.745% is obtained. The pruning process was carried out using the Decision Tree Algorithm C4.5, the accuracy performance increased to 91.30%. There were 18 patterns or rules for the early detection of diabetics from the built model. The determination of attributes, the number of attribute dimensions, and the number of samples greatly affect the performance of the model built.

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