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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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Stock Price Prediction Using TCN-GAN Hybrid Model Lim Yong Teck; Angelina Pramana Thenata
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14246

Abstract

The stock market plays a vital role in national economies, offering significant profit opportunities for investors while exposing them to substantial risks due to market uncertainties. Stock prices often experience significant fluctuations, making accurate prediction a challenging task. Temporal Convolutional Network (TCN) and Generative Adversarial Network (GAN) are the deep learning method proposed for this research. The purpose of this research is to analyze how well the TCN-GAN model predicts stock prices. Previous researches show both TCN and GAN perform well on time series data. TCN excels in analyzing time-series data while GAN enhances training by generating realistic simulations. By combining the strength of both models, this approach aims to enhance stock price prediction accuracy. The proposed model uses TCN as the generator within the GAN framework and a Multilayer Perceptron (MLP) as the discriminator. TCN handles the prediction task and is trained using the GAN model. The model is trained over 500 epochs, with a learning rate of 0.0004 for the generator and 0.0001 for the discriminator. During each epoch, the generator is updated twice to enhance its performance. The resulting model achieves a MAPE score of 2.16% and an RMSE score of 814.25 on the testing dataset, demonstrating excellent performance in stock price prediction despite significant price variations.
Embedded Smart Farming System for Soil and Hydroponic Planting Media Based on The Internet of Things Rifka, Silfia; Ramiati, Ramiati; Dewi, Ratna; Khair, Ummul; Setiawan, Herry
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14255

Abstract

Smart agricultural technology by applying the internet of things (IoT) purposes to make farmers' work more efficient due to the automation system and assist farmers in monitoring the condition of their agricultural land. The focus of discussion in this research is the application of smart agriculture system technology that uses the concept of embedded systems for soil and hydroponic planting media. This system applies an automation system for water irrigation and fertilizer irrigation using four tanks, namely a water source, a water irrigation tank, a fertilizer tank, and a water circulation system in hydroponics. The system is also equipped with weather monitoring based on temperature, rainfall, and light intensity. Other parameters contained in this system are soil pH, water pH, TDS, fertilizer availability, and irrigation pump status. The monitoring system based on the Android application displays all parameters and the status of the devices used.
Leveraging Label Preprocessing for Effective End-to-End Indonesian Automatic Speech Recognition Althoff, Mohammad Noval; Affandy, Affandy; Luthfiarta, Ardytha; Satya, Mohammad Wahyu Bagus Dwi; Basiron, Halizah
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14257

Abstract

This research explores the potential of improving low-resource Automatic Speech Recognition (ASR) performance by leveraging label preprocessing techniques in conjunction with the wav2vec2-large Self-Supervised Learning (SSL) model. ASR technology plays a critical role in enhancing educational accessibility for children with disabilities in Indonesia, yet its development faces challenges due to limited labeled datasets. SSL models like wav2vec 2.0 have shown promise by learning rich speech representations from raw audio with minimal labeled data. Still, their dependence on large datasets and significant computational resources limits their application in low-resource settings. This study introduces a label preprocessing technique to address these limitations, comparing three scenarios: training without preprocessing, with the proposed preprocessing method, and with an alternative method. Using only 16 hours of labeled data, the proposed preprocessing approach achieves a Word Error Rate (WER) of 15.83%, significantly outperforming the baseline scenario (33.45% WER) and the alternative preprocessing method (19.62% WER). Further training using the proposed preprocessing technique with increased epochs reduces the WER to 14.00%. These results highlight the effectiveness of label preprocessing in reducing data dependency while enhancing model performance. The findings demonstrate the feasibility of developing robust ASR models for low-resource languages, offering a scalable solution for advancing ASR technology and improving educational accessibility, particularly for underrepresented languages.
Parameter Testing on Random Forest Algorithm for Stunting Prediction Mubarok, Ahmad Hasan; Pujiono, Pujiono; Setiawan, Dicky; Wicaksono, Duta Firdaus; Rimawati, Eti
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14264

Abstract

Stunting is a significant public health problem, especially in developing countries like Indonesia. It is often caused by chronic malnutrition in the first 1,000 days of life, which can impact a child's physical growth and cognitive development. To find risk factors and find effective solutions, data analysis was conducted by utilising machine learning to predict stunting. This research uses the Random Forest algorithm with a focus on setting parameters such as n_estimators, max_depth, and the number of features to optimise model efficiency and accuracy. Using the 2023 Indonesian Health Survey data consisting of 25,800 data, this study managed to get the highest accuracy of 91.65% by a combination of Random Forest with parameter settings n_estimators 200, max_depth 30, and Synthetic Minority Oversampling Technique (SMOTE). Despite the high accuracy results, there are limitations such as potential noise coming from synthetic data from SMOTE and the limited number of features analysed. It is hoped that this research can contribute to the field of machine learning model development that is practically used to predict stunting, and support the government's efforts to reduce the stunting prevalence rate to 14% as targeted. This model also provides strategic insights for policy makers to design more effective data-driven interventions, which can help in decision making.
Usability Evaluation of GetContact Application Using Post-Study System Usability Questionnaire and Retrospective Think Aloud Zahirah, Nabilah; Indah, Dwi Rosa; Firdaus, Mgs. Afriyan; Gumay, Naretha Kawadha Pasema; Ibrahim, Ali
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14267

Abstract

GetContact, aplikasi manajemen dan proteksi panggilan spam dengan lebih dari 700 juta unduhan di Google Play Store, masih memiliki ruang untuk peningkatan kegunaan. Studi ini bertujuan untuk memanfaatkan kegunaan GetContact berdasarkan ulasan Quora, komentar Google Play Store, dan wawancara dengan pengguna di area Palembang. Metode yang digunakan adalah Post-Study System Usability Questionnaire (PSSUQ) dan Retrospective Think Aloud (RTA). Hasil PSSUQ dari 190 responden menunjukkan tingkat kegunaan keseluruhan yang baik dengan skor 2,73. Skala Kegunaan Sistem mencatat nilai 2,60, dan Kualitas Informasi mencapai 2,80, menunjukkan kegunaan yang memuaskan. Namun, kualitas antarmuka dengan skor 2,89 masih perlu ditingkatkan. Temuan dari metode RTA juga mengidentifikasi kendala dalam fitur dan antarmuka aplikasi. Studi ini menyimpulkan bahwa meskipun tingkat kegunaan GetContact secara keseluruhan baik dan diterima oleh pengguna, pengembangan lebih dari antarmuka dan fitur masih diperlukan untuk meningkatkan kegunaan secara keseluruhan dan menciptakan pengalaman pengguna yang lebih baik.
Prediction of Organic Waste Deposits in Compost Houses using LSTM and ARIMA Algorithms Raihatuzzahra, Farah; Winarsih, Nurul Anisa Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14271

Abstract

Indonesia faces a significant waste problem and is becoming a global challenge, mainly due to inadequate food waste management. In Kendal District, the Environmental Agency struggles to optimize waste collection and predict the volume of organic waste. To address this issue, this study explores the application of predictive technology and data analysis to improve the efficiency of waste management. Two predictive models, ARIMA and Long Short-Term Memory (LSTM), were developed and compared by collecting historical data from Kendal Organic Compost House from 2020-2024 while for train and test data using data from January 2, 2023, to December 30, 2023. The ARIMA model showed better accuracy, capturing stable trends and seasonal patterns in the time series data, with an MSE of 72,799.49. Meanwhile, the LSTM model, although capable of handling non-linear and complex patterns, performed poorly with an MSE of 54,711,498,631,770.58, indicating a failure to accommodate sharp fluctuations in the data. These findings highlight the suitability of ARIMA for data with low volatility and strong seasonality, making it more reliable for short-term predictions. The results of this study are expected to assist the Kendal District Environmental Agency in planning efficient waste management strategies, optimizing compost house operations, and improving resource allocation. Future research should focus on the integration of external variables, such as weather and population dynamics, and explore hybrid models for better prediction.
Sentiment Analysis of Tokopedia App Reviews using Machine Learning and Word Embeddings Idris, Muhammad; Rifai, Ahmad; Tania, Ken Ditha
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14278

Abstract

Tokopedia, a prominent e-commerce platform in Indonesia, generates vast amounts of user feedback, offering valuable insights into customer satisfaction through sentiment analysis. However, sentiment analysis of app reviews specifically on Tokopedia reviews remains underexplored. Sentiment analysis, also known as opinion mining, categorizes user sentiments into positive or negative, offering insights into user preferences and service shortcomings. While traditional text representation techniques like TF-IDF are widely used for sentiment analysis, they lack the semantic richness provided by advanced word embeddings such as Word2Vec and FastText, which excel at capturing contextual relationships between words. These methods have shown potential to enhance the performance of machine learning models in sentiment analysis tasks. This study evaluates the performance of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naïve Bayes (NB)—combined with Word2Vec and FastText feature extraction. A dataset of 59,811 Tokopedia app reviews was scraped from the Google Play Store, preprocessed, and subjected to feature extraction using Word2Vec and FastText. SVM achieved the best performance, with an accuracy of 89.06% using FastText and 89.02% using Word2Vec. RF ranked second with accuracies of 88.07% for FastText and 88.14% for Word2Vec. NB showed the lowest performance, achieving 84.26% with Word2Vec and 83.73% with FastText. Differences in performance between Word2Vec and FastText embeddings were minimal across all algorithms, highlighting their comparable effectiveness. These results underscore SVM’s consistent superiority across various configurations for sentiment analysis.
Comparison of RNN and LSTM Algorithms Based on Fasttext Embeddings in Sentiment Analysis on the Merdeka Mengajar Platform Nugroho, Anjis Sapto; Nugroho, Kristiawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14296

Abstract

As of 2024, the Merdeka Mengajar Platform has been used by more than 3.5 million teachers across Indonesia. This number represents an increase of more than 3.85% compared to the previous academic year, which was 3.37 million. However, the utilization of this application has not yet reached the expected target number of users, so an analysis is needed to identify the factors causing this. This research uses Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to perform sentiment analysis on reviews of the Merdeka Mengajar platform. RNN and LSTM are chosen for their advantages in handling sequential data, particularly in text processing for sentiment analysis. This research aims to address the challenges in understanding the positive or negative sentiments of users on the platform. The research methodology includes important stages such as data cleaning, preprocessing, and transforming text into numerical vectors using FastText embedding. Next, RNN and LSTM models are applied to predict sentiment based on patterns in the text data. The research results show that the LSTM model is capable of capturing long-term relationships in sequential data with an expected accuracy of 93.58%. Meanwhile, the RNN model yields a lower accuracy of 91.70%. The LSTM model is more effective in classifying sentiment with high accuracy, especially in text data with complex temporal contexts. This research contributes to understanding user perceptions and feedback regarding the Merdeka Mengajar platform, which is expected to provide insights for platform developers to enhance service quality.
Analysis of Social Assistance Donor Classification at the Muhammadiyah Medan Orphanage Using SVM Helmy, Ahmad; Sitorus, Zulham; Ardya, Dwika; Hrp, Abdul Chaidir; T, Siti Isna Syahri; Sukrianto, Sukrianto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14299

Abstract

The Putra Muhammadiyah Orphanage in Medan City is a social institution that relies on donor assistance to support various social programs. The problem that occurs at the Putra Muhammadiyah Orphanage in Medan is the difficulty in identifying potential and non-potential donors who have the potential to provide sustainable social assistance contributions. This study aims to conduct a comprehensive analysis and classification of donors using the Support Vector Machine method, an effective method in machine learning to handle classification problems with SVM with high accuracy. The research data consists of donor data with several main characteristics such as the amount of donation, the frequency of donations given, and the type of assistance. The data is processed through a preprocessing stage including data normalization and data division into training and testing data. Then, the SVM model is trained to classify donors into two categories, namely Potential Donors and Non-potential Donors. Based on the data obtained from the donation bookkeeping records of the Putra Muhammadiyah Orphanage in Medan City, it can be concluded that around 55 potential donors out of 90 donors and 35 non-potential donors out of 90 donor data. From the results of the analysis and testing of the model conducted, it can be concluded that the SVM method can classify "Potential Donors" and "Non-Potential Donors" with a fairly high level of accuracy. The level of accuracy obtained reached up to 89% with a precision value of 93%, a recall value of 89% and an f1-score of 90%. With these results, this study can provide significant benefits in the management of social assistance, especially helping orphanages to determine who are potential and non-potential donors. Therefore, this study is expected to have an impact on improving the sustainability of social programs at the Putra Muhammadiyah Orphanage in Medan City.
Analyzing PEGASUS Model Performance with ROUGE on Indonesian News Summarization Kartamanah, Fatih Fauzan; Atmadja, Aldy Rialdy; Budiman, Ichsan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14303

Abstract

Text summarization technology has been rapidly advancing, playing a vital role in improving information accessibility and reducing reading time within Natural Language Processing (NLP) research. There are two primary approaches to text summarization: extractive and abstractive. Extractive methods focus on selecting key sentences or phrases directly from the source text, while abstractive summarization generates new sentences that capture the essence of the content. Abstractive summarization, although more flexible, poses greater challenges in maintaining coherence and contextual relevance due to its complexity. This study aims to enhance automated abstractive summarization for Indonesian-language online news articles by employing the PEGASUS (Pre-training with Extracted Gap-sentences Sequences for Abstractive Summarization) model, which leverages an encoder-decoder architecture optimized for summarization tasks. The dataset utilized consists of 193,883 articles from Liputan6, a prominent Indonesian news platform. The model was fine-tuned and evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric, focusing on F-1 scores for ROUGE-1, ROUGE-2, and ROUGE-L. The results demonstrated the model's ability to generate coherent and informative summaries, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.439, 0.183, and 0.406, respectively. These findings underscore the potential of the PEGASUS model in addressing the challenges of abstractive summarization for low-resource languages like Indonesian language, offering a significant contribution to summarization quality for online news content.

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