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Two-Stage Sentiment Analysis on Indonesian Online News Using Lexicon-Based : A study case at online new unilateral layoffs at Company XYZ Vinardo; Wasito, Ito
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.v8i4.12769

The image of a supplier company is often associated with the well-known brand it supplies, and its reputation can be influenced by online news circulation. To maintain a positive image, it is crucial for the company to monitor and manage online news to rectify any false information. Failure to maintain a good company image can lead to customer order loss and even company shutdown. This paper aims to conduct a two-stage sentiment analysis on Indonesian news articles regarding unilateral layoffs by company XYZ. The first stage will analyze sentiment in the circulating news about the layoffs, while the second stage will assess sentiment after the company releases a press release to provide accurate information. The VADER lexicon-based method, utilizing the InSet and SentiStrength_ID Indonesian dictionaries, will be employed to analyze sentiment before and after the press release. This will enable us to compare sentiment and evaluate the effectiveness of the press release and the Indonesian dictionaries in analyzing sentiment in the news. The research findings indicate that the company's press release, aimed at correcting false information, had a positive impact by reducing negative sentiment and generating a more positive sentiment in the second stage. Moreover, the selection of the sentiment analysis dictionary also plays a critical role in determining the sentiment analysis results.

Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique Crosley, Nathaniel; Wasito, Ito
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.v8i4.12925

This research project aims to improve IT support efficiency at Indonesian company XYZ by using AI-based IT support ticket classification integration. This method involved collecting over 1,000 support tickets from the company's IT ticketing system, GLPI, and pre-processing the data to ensure the quality and relevance of the data for analysis. Claims data is enriched with relevant features, including textual information and categorical attributes such as urgency, impact, and requirement expertise. To improve the ticket preference matrix, AI-based language models, especially OpenAI's GPT-3, are used. These templates help to reclassify and improve the work of IT support teams. In addition, the ticket data is used to train the Random Forest classifier, allowing automatic classification of tickets based on their specific characteristics. The performance of the ticket classification system is evaluated using a variety of metrics, and the results are compared with alternative methods to assess effectiveness. of the Random Forest algorithm. This evaluation demonstrates the system's ability to correctly classify and prioritize incoming tickets. The successful implementation of this project at Company XYZ is a model for other organizations looking to optimize their IT support through AI-driven approaches. By providing simplified ticket classification and admission ticket reclassification based on AI algorithms, this research helps leverage AI technologies to improve IT support processes. Ultimately, the proposed solution benefits both support providers and users by improving efficiency, response times, and overall customer satisfaction.

Deep Learning for Exchange Rate Prediction Within Time Constrain Sumargo, Ruly; Wasito, Ito
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13633

The implementation of an open economic system in Indonesia since 1969 has significant impact to the national economic growth. The high demand and supply of goods from within the country involved in international trade demonstrate a close correlation between export and import activities with the exchange rate of the rupiah. Economic stability is measured through the stability of the rupiah exchange rate against foreign currencies. The balance between demand and supply in the global market is considered crucial for creating a stable economy. History has recorded the Indonesian economic crisis in 1998, where the exchange rate of the rupiah against the US dollar drastically raises and causing challenges to the domestic production cost. This research aiming to make predictions using data science approach based on historical (time series) data. GRU, LSTM, and RNN algorithm being assess to perform the prediction. Results show that RNN algorithms generally outperform GRU and LSTM in making the prediction, particularly with limited time series data. Although RNN is typically superior, in one instance, GRU achieved slightly higher accuracy (0.047% difference) for the CNY to IDR pair over five years. Furthermore, the research highlights the substantial impact of batch size on algorithm accuracy, considering external factors such as interest rates. These findings offer valuable insights for economic decision-making and policy formulation.

Evaluation of Cluster Models for Creating Profiles of Home Buyers Dewi, Made Dhanita Listra Prashanti; Wasito, Ito
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13888

The property industry in Indonesia is currently a dynamic and continuously evolving field, in line with rapid economic growth and urbanization. Shifts in lifestyle patterns, infrastructure development, and changes in government policies have had a significant impact on how properties are marketed in Indonesia. With a growing population and increasing purchasing power, the Indonesian property market is becoming more complex. Therefore, strategies are needed to segment consumer groups for effective marketing in the housing sector. This research will delve deeper into consumer segmentation in home selection, a technique that divides consumer diversity into distinct groups based on characteristics and behavior. By using an extensive dataset involving demographic data such as location, age, gender, occupation, and many other variables, clustering algorithms can uncover complex patterns to determine consumer segments in their home selection. The algorithms to be used for this study are K-Means clustering, the Gaussian Mixture model, and Hierarchical clustering. By using these three data clustering models, we can determine which algorithm produces the most ideal results for customer profiling. The results demonstrate that the K-Means algorithm outperforms the others in accurately identifying distinct consumer segments, hence producing customer profiles. Therefore, customer profiling can also be used by the marketing division as a tool to aid in promotions in order to better understand their target audience, hence creating a successful marketing campaign.

Fraud Detection in Mobile Phone Recharge Transactions Using K-Means and T-SNE Visualization Sakti, Irwin; Mareta, Arvin; Wasito, Ito
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.v9i1.14330

The surge in digital transactions has introduced vulnerabilities in mobile recharge systems, making them susceptible to fraudulent activities that compromise financial security and operational integrity. This study presents to address these challenges by employing a novel fraud detection framework that integrates K-Means clustering and t-Distributed Stochastic Neighbour Embedding (t-SNE) visualization. This work advances the field by integrating scalable, unsupervised learning techniques with robust visualization tools, offering a practical framework for fraud detection in mobile recharge systems. Leveraging a dataset of over 200,000 transactions, this research systematically identifies anomalies indicative of fraudulent behaviour, focusing on key transactional attributes such as processing times, geographic patterns, and error frequencies. The methodology begins with data preprocessing to ensure consistency, followed by the application of K-Means clustering to partition transactions into meaningful clusters. To enhance interpretability, t-SNE visualization is employed, enabling a clear representation of high-dimensional data and the identification of anomalous patterns. A comparative analysis with Autoencoders highlights the strengths of K-Means in terms of computational efficiency, interpretability, and clustering quality, as evidenced by higher Silhouette Scores (0.6215) and lower Davies-Bouldin Index values (0.7074). The combination of K-Means and t-SNE enables service providers to identify fraudulent activities with greater precision, offering actionable insights to mitigate financial risks. This study not only addresses the critical need for robust fraud detection systems but also lays a strong foundation for future advancements through the integration of hybrid models and enhanced feature engineering, demonstrating its adaptability to similar domains.

Evaluation of Clustering Algorithms for Identifying Shoe Characteristics Patterns at XYZ Footwear Watasendjaja, William; Chandra, Billy; Wasito, Ito
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.v9i1.14332

As the third-largest shoe-exporting country in the world, Indonesia faced a 25% decline in shoe exports in 2023 compared to the year before, both in terms of net weight and sales value. This decline in shoe exports occurred due to the increase of complexity and variety in customer orders to shoe manufacturers. These reasons require shoe manufacturers to enhance their production planning systems to become more efficient and competitive. To address this problem, this study explores the application of clustering algorithms to optimize the production planning process in shoe manufacturing companies. Using a case study at XYZ Footwear, clustering algorithms such as K-Means, Support Vector Clustering (SVC), and Deep Autoencoder were evaluated and compared to find the most effective algorithms in identifying patterns in shoe characteristics, thereby improving shoe manufacturers' production planning process. The datasets consist of the 2024 production season data, categorized into shoe categories, models, and variants, and purchase orders. The result shows that the combination of Deep Autoencoder and K-Means has better performance than just K-Means or Support Vector Clustering (SVC), achieving a silhouette score of 0.4822 and a Davies-Bouldin Index (DBI) of 0.6741. These findings highlight the effectiveness of combining deep learning (Deep Autoencoder) with clustering algorithms (K-Means) in identifying patterns in shoe characteristics.

Sistem Deteksi Kosakata Bahasa Isyarat Secara Real Time dengan Tensorflow Menggunakan Metode Convolutional Neural Network Widjaya, Vincent Surya; Wasito, Ito
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7714

Most people are not used to communicating via sign language because sign language is not a mandatory language to learn so very few people understand how to communicate using sign language. This problem for people with special needs to interact and communicate with other people, especially those who do not understand how to communicate using sign language. Convolutional Neural Network is the method that will be used in this research because this method is one of the deep learning methods that currently provides the best results in object detection. The Convolutional Neural Network method is able to imitate human abilities in the image recognition system of the human visual cortex, so that it is able to process visual information. This research used Tensorflow to develop various models as well as work related to other statistical analysis. The evaluation metrics results obtained from this research show precision of 82.99%, recall of 84.42% and f1-score of 83.68%. The value of average precision is 0.830% and average recall is 0.844%. There is also a loss value produced by this model of 0.039065. For accuracy results from real time sign language detection, the accuracy value was 94.64%.

Predicting Resale Prices using Random Forests with Fine-Tuning Hyperparameters Widjaja, Herman; Perdana, Nanda; Wasito, Ito
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103967

The accurate prediction of housing prices is essential for informed decision-making by purchasers, sellers, and policymakers in dynamic real estate markets. This study investigates the application of machine learning models—Random Forest, XGBoost, Decision Tree, and LightGBM—to predict resale flat prices in Singapore. It provides valuable insights into the use of machine learning in housing markets, particularly for datasets with similar size, complexity, and data types. The objectives are to develop predictive regression models for property prices and to analyze and compare the performance of these models. Key contributions include the development of tools to objectively estimate suitable property prices and the advancement of price prediction research through an extensive comparison of machine learning models. While previous studies have demonstrated the predictive capabilities of these models, this research focuses on the impact of hyperparameter tuning on the performance of the Random Forest model. By systematically optimizing parameters such as max_depth, n_estimators, and n_jobs, computation time was reduced by over 93% (from 865 seconds to 50 seconds) with minimal loss in accuracy. With proper hyperparameter tuning, Random Forest achieved the best performance in terms of MAE score (26.555), outperforming XGBoost (27.552), Decision Tree (28.832), and LightGBM (29.752).

Sound-Based Smart Toddler Monitoring System: AIoT Development with YAMNet on Raspberry Pi Rochadiani, Theresia Herlina; Santoso, Handri; Wasito, Ito; Sucipto, Nadya Rudie; Anggraini, Astria Febrian; Panna, Ariya
TEKNIK Vol 46, No 3 (2025): Juli 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v46i3.76484

The safety of toddlers at home is paramount for parents, but constant monitoring is difficult due to busy schedules. The limitations of camera-based monitoring solutions, namely privacy concerns and heavy processing, drive the need to develop monitoring systems that utilize sound recognition. This research aims to develop Smart Guardian, an Artificial Intelligence of Things (AIoT) system that can detect risky or emergency sound patterns from children and send real-time notifications to parents' mobile phones. The applied method includes the development of a YAMNet-based speech recognition AI model, installed on a Raspberry Pi as an edge computing device, with a microphone functioning to record environmental sounds. This system is designed to identify crucial environmental sounds such as breaking glass, explosions, screaming, water, fire alarms, smoke detectors, in addition to infant crying. The results of prototype trials under laboratory conditions indicate that the fire alarm and smoke detector classes have extremely high confidence levels (around 0.95 and 0.83). However, the glass class showed varying confidence levels (around 0.5), while cough, explosion, water, and screaming had lower confidence levels (median 0.15, 0.13, 0.25, and 0.4, respectively). The conclusion from these findings is that Smart Guardian has great potential as a privacy-focused toddler monitoring solution, although further optimization is needed to improve the speech recognition performance of events with low and varying confidence levels.

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