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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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Deep Learning and Imbalance Handling on Movie Review Sentiment Analysis Utami, Sri; Lhaksmana, Kemas Muslim; Sibaroni, Yuliant
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.v8i3.12770

Abstract

Before watching a movie, people usually read reviews written by movie critics or regular audiences to gain insights about the movie’s quality and discover recommended films. However, analyzing movie reviews can be challenging due to several reasons. Firstly, popular movies can receive hundreds of reviews, each comprising several paragraphs, making it time-consuming and effort-intensive to read them all. Secondly, different reviews may express varying opinions about the movie, making it difficult to draw definitive conclusions. To address these challenges, sentiment analysis using CNN and LSTM models, known for their effectiveness in classifying text in various datasets, was performed on the movie reviews. Additionally, techniques such as TF-IDF, Word2Vec, and data balancing with SMOTEN were applied to enhance the analysis. The CNN achieved an impressive sentiment analysis accuracy of 98.56%, while the LSTM achieved a close 98.53%. Moreover, both classifiers performed well in terms of the F1-score, with CNN obtaining 77.87% and LSTM achieving 78.92%. These results demonstrate the effectiveness of the sentiment analysis approach in extracting valuable insights from movie reviews and helping people make informed decisions about which movies to watch.
Trend Forecasting of the Top 3 Indonesian Bank Stocks Using the ARIMA Method Sudipa, I Gede Iwan; Riana, Roni; Putra, I Nyoman Tri Anindia; Yanti, Christina Purnama; Aristana, Made Dona Wahyu
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.v8i3.12773

Abstract

The number of investors in Indonesia increases annually. This is due to the growing popularity of investing, particularly stock investment. There are currently three largest equities in the banking industry, namely BBCA, BBRI, and BMRI. Stock prices fluctuate and form multiple patterns of price movements; therefore, investors must be able to recognize the patterns and trends of securities on the capital market in order to plan long-term investments, maximize potential profits, and reduce the risk of investment losses. In addition to knowing the trajectory of the stock market's trend, investors rely heavily on forecasting. Forecasting is necessary so that investors can anticipate future prices. The Autoregressive Integrated Moving Average (ARIMA) method is a frequently used method for forecasting time series data. In general, ARIMA is represented by the formula ARIMA (p, d, q), where p represents the Autoregressive (AR) order, d represents the difference, and q represents the Moving Average (MA) order. The trend of BBCA, BBRI, and BMRI stock data was effectively predicted using the ARIMA method. The results of this study are presented as diagrams of actual and forecasted data for the next 12 periods, as well as predictions of the optimal purchase price points for stocks. The ARIMA model of each stock also generates a low MAPE error value, with MAPE values of 4% for BBCA, 5% for BBRI, and 7% for BMRI. The MAPE value derived by each model is incorporated into the MAPE value with a high degree of precision, as it falls below 10%.
Comparison of Sentiment Analysis Methods on Topic Haram of Music In Youtube Al Fathir As, Rahmat Saudi; Utami, Ema; Dwi Hartono, Anggit
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.v8i4.12776

Abstract

Sentiment analysis on video lectures on YouTube that discuss the haram of music is an exciting topic to find out public opinion. This study aims to find what factors affect the model's accuracy in sentiment analysis, especially on video lecture content on YouTube. The data used is comment data on three video lectures that discuss the haram of music, which has been labelled into two categories: positive and negative. The data is divided into two categories, namely primary data, as many as 2099 data that have not been normalized, while secondary data has 1001 data that have been normalized. The experiment shows that the validity of the data, labelling the data, the amount of data, and preprocessing are essential points in forming a good sentiment analysis classification model because, from the test results, it was found that imbalance techniques such as SMOTE, word embedding word2Vec and FastText, and SVM and KNN classification algorithms do not provide maximum accuracy if the data used primary data. However, the data imbalance process, such as oversampling and SVM and KNN classification algorithms, will provide better model accuracy if used with secondary data. Based on the trial results, it is found that when using the SVM algorithm, primary data produces the highest accuracy at 58.35%, while secondary data is 72.23%. If using KNN, the primary data provides the highest model accuracy at 53.54%, while the secondary data has the highest accuracy at 72.81%. Based on these results, it was found that the validity of the data or data must be appropriate and related to the case raised and labelling the data must be done carefully because the most crucial is the inappropriate data in preprocessing the data must be done correctly, if data preprocessing is done in an inappropriate way then data imbalance techniques such as oversampling do not have enough influence on increasing accuracy, but if on the contrary then accuracy will increase. The selection of the right word embedding also affects accuracy. It is necessary to do many experiments to select the correct algorithm and follow the data owned because selecting the correct algorithm will provide maximum accuracy model results
Pneumonia Classification Based on Lung CT Scans Using Vgg-19 Putra, Adya Zizwan; Situmorang, D. V. M.; wahyudi, G.; giawa, J. P. K.; Tarigan, R. A.
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.v8i4.12778

Abstract

This research harnesses technology for critical health applications, specifically, pneumonia detection through medical imaging. X-ray photography allows radiologists to visualize the patient's health state, including the detection of lung infections signifying pneumonia. The study's centerpiece is the application of the VGG-19 model in classifying lung CT scan images, helping discern normal from pneumonia-indicative conditions. A comprehensive preprocessing procedure is employed, entailing pixel rescaling and data augmentation techniques. To address data imbalance, a critical issue in machine learning, we incorporate the Synthetic Minority Over-sampling Technique (SMOTE). The developed VGG-19 model demonstrates impressive performance, achieving a 94.6% accuracy rate in classifying lung CT scans. This finding underscores the potential of the VGG-19 model as a reliable tool for pneumonia detection based on lung CT scans. Such a tool could revolutionize the field, providing an efficient and accurate method for early pneumonia diagnosis, thereby allowing for timely treatment.
Classification of E-Commerce Product Descriptions with The Tf-Idf and Svm Methods Pakpahan, Dagobert; Siallagan, Veronika; Siregar, Saut
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.v8i4.12779

Abstract

The rapidly growing e-commerce sector presents a significant challenge in navigating an abundance of products. Understanding and classifying product descriptions efficiently and accurately is crucial to improving user experience and business operations. This research employed the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and Support Vector Machine (SVM) for the classification of e-commerce product descriptions into four categories: Electronics, Household Items, Books, and Clothing. The initial phase involved pre-processing of text data which incorporated text cleaning, tokenization, part-of-speech tagging, entity recognition, and conversion into a vector representation. The resulting model was trained and tested using the SVM algorithm. Our model demonstrated a high degree of accuracy, achieving 99.2% during the training phase and 95.7% in the testing phase. This model provides a valuable tool for e-commerce businesses, as it allows for accurate classification of products based on their descriptions. This could lead to improved user navigation and overall user experience on e-commerce platforms.
Implementation of Transfer Learning in CNN for Classification of Nut Type Fawwaz, Insidini; Sagala, Jimmy Deardo; Sijabat, Reivaldo Kevin Febriawan; Maringga , Novita Marissa
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.v8i4.12784

Abstract

Nut has a high nutritional value and is widely used as an ingredient in cooking and snacks. Nut is included in the group of grains and has many types. Each type of nut has different nutritional content. Some types of nuts can also cause allergies or negative reactions in certain people, so it is important to identify the type of nut to be consumed. There are many types of nut that are different from each other, but some of them are similar. This makes it difficult to distinguish between the types of nuts, so there is a need for technology that can accurately identify nut types. Transfer Learning method is used to utilize trained models and applied to nut type classification. The two CNN models used are Inception V3 and Xception. The dataset consists of 11 types of nuts consisting of 1,320 data. The data is divided into 60% for training data and 40% for validation data. Preprocessing is done to ensure the image size is consistent and clarify the focus on the data image to be tested. The training results show that the Xception model is superior to Inception V3, with an accuracy of 86.36% on the validation data, while Inception V3 only reached 74.05%. Xception is able to predict nut types more precisely.
Optimizing Gender Classification Accuracy in Facial Images Using Data Augmentation and Inception V-3 Tanjung, Juliansyah Putra; Faldi, Mhd. Rio; Sitompul, Haggai; Ridho, Muhammad; Ambarita, Jojor Putri
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.v8i4.12785

Abstract

In the digital era, facial recognition technology plays a crucial role in various applications, including gender classification. However, challenges such as variations in expressions and face positions, as well as differences in features between men and women, make this task formidable. This study aims to enhance the accuracy of gender classification using the Inception V-3 method and the Convolutional Neural Network (CNN), along with data augmentation techniques. The Inception V-3 method was chosen for its superiority in accuracy and speed. In contrast, the CNN model was selected in this study as a comparison and due to its algorithmic advantages in learning and extracting high-level features from images, including facial images, which are crucial for tasks such as gender classification. The data augmentation techniques in this study include rescaling, rotation, width and height shifts, shear range, zoom, horizontal flip, and fill method for model accuracy in gender classification with a small dataset. The study results indicate that the Inception V-3 model provides better accuracy (99.31%) in gender classification compared to the CNN model (81.31%). This conclusion underscores that the use of the Inception V-3 method with data augmentation techniques can improve the accuracy of gender classification in facial images.
Vehicle Detection and Identification Using Computer Vision Technology with the Utilization of the YOLOv8 Deep Learning Method Telaumbanua, Agustritus Pasrah Hati; Larosa, Tri Putra; Pratama, Panji Dika; Fauza, Ra'uf Harris; Husein, Amir Mahmud
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.v8i4.12787

Abstract

Vehicle identification and detection is an important part of building intelligent transportation. Various methods have been proposed in this field, but recently the YOLOv8 model has been proven to be one of the most accurate methods applied in various fields. In this study, we propose a YOLOv8 model approach for the identification and detection of 9 vehicle classes in a reprocessed image data set. The steps are carried out by adding labels to the dataset which consists of 2,042 image data for training, 204 validation images and 612 test data. From the results of the training, it produces an accuracy value of 77% with the setting of epoch = 100, batch = 8 and image size of 640. For testing, the YOLOv8 model can detect the type of vehicle on video assets recorded by vehicle activity at intersections with. However, the occlusion problem overlapping vehicle objects has a significant impact on the accuracy value, so it needs to be improved. In addition, the addition of image datasets and data augmentation processes need to be considered in the future
Analysis Indonesia’s Export Value Forecasting to G20 Countries Using Long Short-Term Memory Neural Network Method Veronica, Veronica; Silaban, Herlan; Nasution, Syafrani Putri; Indra, Evta
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.v7i3.12794

Abstract

Export is one of the most important ways for the country to generate income, which can have an impact on the country's economic stability. This research aims to forecast the value of Indonesian exports to G20 member countries. The Long Short-Term Memory method is used in this research to examine historical data on Indonesian exports from the previous 16 years. Experimental results show that the LSTM Neural Network method has the ability to predict the value of Indonesian exports to G20 member countries with a sufficient level of accuracy. The predictions generated by the model provide insight into trends and fluctuations in the value of exports in the future. The results of this study provide insight into the potential application of artificial intelligence techniques in economic and trade analysis. The results demonstrate that the LSTM model is capable of producing relatively accurate predictions, with an average score of Root Mean Square Error (RMSE) on training data is 0.10 and on testing data is 0.13, as well as graphs of prediction results demonstrating that the LSTM model can capture patterns and trends from Indonesia's export data to G20 countries. According to the prediction results, the highest export value to China is expected to be $6,100,000 in the 200th month (or in the year 2039), while the lowest export value to Mexico is expected to be $27,000 in the 135th month (or in the year 2034).
Decision Support System for Financial Aid for Underprivileged Students using the TOPSIS Method Pransiska, Apprillia Yudha; Juledi, Angga Putra; Harahap, Syaiful Zuhri
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.v8i3.12798

Abstract

The distribution of Financial Aid for Underprivileged Students is considered not to be on target due to the unmeasured selection process. So a decision-making system is needed that can select beneficiaries objectively. This research was conducted to assist the school in determining students who deserve this assistance. The purpose of this research is to build a decision-making system for selecting beneficiaries from poor students by applying the Technique for Order of Preference by Similarity to Ideal Solution method. The formulation of the problem is how to build a decision support system using the Technique for Order of Preference by Similarity to Ideal Solution method in selecting students who receive financial aid for underprivileged students. The method used goes through several stages, namely: determining alternatives and criteria, building a normalized decision matrix, building a weighted normalized decision matrix, determining positive and negative ideal solutions, determining the distance between ideal solutions, and determining preference values. There are 7 criteria used, namely social protection card recipients, total income, number of dependents, parental status, distance, class, and report card scores. The results showed that the highest preference value for each alternative was in alternative A3, with a score of 0.6665. While the lowest preference value is in alternative A20 with a score of 0.0719, From the results of the study, it was concluded that the Technique for Order of Preference by Similarity to Ideal Solution method can be used in making decisions on the selection of beneficiaries of poor students based on preference value rankings.

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