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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Compiler PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat International Journal of Informatics and Computation Jurnal Informatika dan Rekayasa Perangkat Lunak Prosiding Seminar Nasional Multidisiplin Ilmu Respati Makara Journal of Science
Sugeng Winardi
Fakultas Sains Dan Teknologi, Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Sentiment Analysis of ChatGPT Tweets Using Transformer Algorithms Sugeng Winardi; Mohammad Diqi; Arum Kurnia Sulistyowati; Jelina Imlabla
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.8632

Abstract

This study explores the application of the Transformer model in sentiment analysis of tweets generated by ChatGPT. We used a Kaggle dataset consisting of 217,623 instances labeled as "Good", "Bad", and "Neutral". The Transformer model demonstrated high accuracy (90%) in classifying sentiments, particularly predicting "Bad" tweets. However, it showed slightly lower performance for the "Good" and "Neutral" categories, indicating areas for future research and model refinement. Our findings contribute to the growing body of evidence supporting deep learning methods in sentiment analysis and underscore the potential of AI models like Transformers in handling complex natural language processing tasks. This study broadens the scope for AI applications in social media sentiment analysis.
PENINGKATAN UNJUK KERJA HIDROLISIS ENZIMATIK JERAMI PADI MENGGUNAKAN CAMPURAN SELULASE KASAR DARI Trichoderma reesei DAN Aspergillus niger Anwar, Nadiem; Widjaja, Arief; Winardi, Sugeng
Makara Journal of Science Vol. 14, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Increasing the Performance of Enzymatic Hydrolysis of Rice Straw Using Mixed Crude Cellulases from Trichoderma reesei and Aspergillus niger. The objective of this work is to compare the effectiveness of mixed crude enzyme cellulase from T. reesei and A. niger with commercial enzyme from A. niger, and to investigate effect of enzyme to substrate ratio to performance of enzymatic hydrolysis of rice straw. The commercial enzyme from Fluka Biochemica was used, and crude enzyme were prepared by solid fermentation with simple media. Before hydrolized, the rice straw was grinded and sieved and then heated at 85 o C with 2% sodium hydroxide for six hours. Hydrolysis was conducted in 300 mL beaker flask equipped with mechanical stirrer. Samples were analyzed by dinitrosalicylic acid method and measured by spectrophotometer. Both of commercial and mixed crude enzyme show that, the higher enzyme to substrate ratio was higher the glucose concentration obtained. However, ratio of glucose obtained to enzyme used become smaller. The mixture of crude enzyme from T. reesesi dan A. niger that produced in this work was two fold more effective to hydrolyze rice straw than using cellulase enzyme of A. niger from Fluka Biochemika
Effective Stock Prediction Model Using MACD Method Hamzah; Sugeng Winardi
International Journal of Informatics and Computation Vol. 4 No. 2 (2022): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v4i2.51

Abstract

Stock market predictions help investors to optimize benefits in the financial markets. Various papers have proposed different techniques in stock market forecasting, but no model can provide accurate predictions. In this study, we discuss how to predict stock prices using a MACD (Moving Average Convergence/Divergence Oscillator) method. We collect the dataset, preprocess it, extract features, evaluate the model, and then deploy the MACD method to develop a stock price prediction model. In this study, we collect several features, including date, open, high, low, close, and volume, to conduct the training and testing process. The results of the experiments reveal good accuracy and a low error rate. As a result, it has the potential to be a promising solution for dealing with accurate and dynamic prices. Based on the experimental result, our proposed model can obtain a transaction profit rate of 40.00% and an average profit per transaction of 1.42%.
Robust Stock Price Prediction using Gated Recurrent Unit (GRU) Hamzah; Sugeng Winardi; Poly Endrayanto Eko Chrismawan; Rainbow Tambunan
International Journal of Informatics and Computation Vol. 5 No. 1 (2023): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v5i1.56

Abstract

Forecasting the direction of price movement of the stock market could yield significant profits. Traders use technical analysis, which is the study of price by scrutinizing past prices, to forecast the future price of the nickel stock price. Therefore, in this study, we propose Gated Recurrent Units (GRU) to predict nickel stock price trends. This research aims to produce an accurate nickel stock price trend prediction model. The research method utilized historical data on nickel stock prices from Yahoo Finance. The research results show that the model developed accurately predicted nickel stock price trends. From the RMSE, MAE, and MSE analysis results, the RMSE value was 0.0123, the MAE value was 0.0089, and the MSE value was 0.0002 on the test data.
Pelatihan Penggunaan LMS untuk Peningkatan Kualitas Layanan Perkuliahan di Fakultas Sains dan Teknologi, Universitas Respati Yogyakarta: Training on Using LMS to Improve the Quality of Lecture Services at the Faculty of Science and Technology, Universitas Respati Yogyakarta Ordiyasa, I Wayan; Sugiarto, Raden Bagus Nurhadi Wijaya; Winardi, Sugeng; Meliala, Dyan Avando; Utari, Evrita Lusiana; Sahal, Ahmad
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 2 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i2.8500

Abstract

Training on the Use of Learning Management Systems (LMS) is essential for enhancing the quality of academic services in an era of increasingly adopting technology. The integration of LMS with conventional methods, known as blended learning, which combines distance learning, regular classes, and LMS, results in a more effective and efficient learning process. With the shift towards digital learning, LMS use becomes crucial for improving the efficiency, accessibility, and quality of academic services. Through e-learning, students not only listen to lectures but also actively observe, perform, demonstrate, and more. Teaching materials can be virtualized in various formats to create more engaging and dynamic content, motivating students to delve deeper into the learning process. This training aims to equip educators and administrative staff with knowledge of LMS features and potential, enabling them to maximize its use for content delivery, facilitating teacher-student interaction, and enhancing course management and evaluation. The training methods include presentations on basic LMS concepts, demonstrations of key features, and hands-on practice sessions that allow participants to actively engage in the learning process. Additionally, interaction between participants and facilitators is enhanced through discussions and Q&A sessions, ensuring deep understanding and practical skills in LMS usage to improve academic service quality. Consequently, this training is expected to provide a solid foundation for educational institutions to meet challenges and leverage the opportunities offered by the digital era in providing quality academic services.
Optimising Bcrypt Parameters: Finding the Optimal Number of Rounds for Enhanced Security and Performance Listiawan, Indra; Zaidir, Zaidir; Winardi, Sugeng; Diqi, Mohammad
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.2111

Abstract

Recent advancements in the field of information security have underscored the imperative to fine-tune Bcrypt parameters, particularly focusing on the optimal number of rounds as the objective of research. The method of research is a Brute Force Search method to find the optimal value of bcrypt rounds. The primary focal point of optimization lies in the number of Bcrypt rounds due to its direct impact on security levels. Elevating the number of rounds serves to fortify the security of the Bcrypt algorithm, rendering it more resilient against brute-force attacks. The execution of the Bcrypt rounds in the experimental method mirrors real-world scenarios, specifically in the evaluation of Bcrypt parameters with a focus on entropy assessment of the hash. The selection of the number of rounds should consider the specific needs of the system, where security takes precedence or faster performance is a crucial factor.
Rubber Leaf Image Classification Using Artificial Intelligence Methods as an Effort to Improve Plantation Production Results Buyung, Irawadi; Utari, Evrita Lusiana; Mustiadi, Ikhwan; Winardi, Sugeng; Ariyanto, Ipan; Listyalina, Latifah
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13587

Abstract

Purpose: Rubber is one of the plantation commodities that contributes positively to the trade surplus in the agricultural sector. Seeing the positive trend in global rubber consumption and production, demand is expected to continue increasing in the future. To enhance rubber productivity, rubber processing technology can be used to make it more efficient, thus increasing the amount of latex extracted from the sap and reducing waste materialDesign/methodology/approach: One technology that can be developed to increase the productivity efficiency of rubber plants is by using Artificial Intelligence. This technology is expected to be implemented in the rubber plantation sector, specifically in the automatic recognition of rubber leaves.Findings/result: The measurement and performance analysis of the rubber leaf image classification algorithm based on Artificial Intelligence has also been evaluated, showing near-perfect accuracy on training data (99.86%) and very good performance on validation data (97.43%), with a very low validation loss (0.0873), indicating that the model has learned well by the last epochOriginality/value/state of the art: The population in this study consists of image data from various tree leaves, including 10 types of rubber leaves and non-rubber leaves 
Co-Authors Anwar, Nadiem Ariyanto, Ipan Arum Kurnia Sulistyowati Birowo, Adianto Evrita Lusiana Utari, Evrita Lusiana Hamzah Hamzah Hamzah Ikhwan Mustiadi Irawadi Buyung, Irawadi Jelina Imlabla Listiawan, Indra Listyalina, Latifah Marsongko, Yudi Marzuki, Abdul F. Meliala, Dyan Avando Mohammad Diqi Ordiyasa, I Wayan Poly Endrayanto Eko Chrismawan Rainbow Tambunan Sahal, Ahmad Sjarief Widjaja Sugiarto, Raden Bagus Nurhadi Wijaya Zaidir