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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal Penjaminan Mutu
Santiago, Gede Agus
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Computer Science & IT Control & Systems Engineering Education Other

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PENGEMBANGAN E-MODUL BERBASIS ANIMASI 2D PADA MATAKULIAH DASAR PERPAJAKAN UNTUK MENINGKATKAN KREATIFITAS MAHASISWA Ayu Anom, I Gusti; Dita Ardriani, Ni Nengah; Santiago, Gede Agus
Jurnal Penjaminan Mutu Vol 10 No 01 (2024)
Publisher : UHN IGB Sugriwa Denpasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25078/jpm.v10i01.3650

Abstract

Pemanfaatan teknologi dalam pendidikan menjadi fokus utama peningkatan kualitas pembelajaran di era digital. Pendekatan yang menarik adalah pengembangan e-modul berbasis animasi 2D untuk mata kuliah pengantar perpajakan. Penelitian ini bertujuan untuk meningkatkan kreativitas siswa melalui e-modul interaktif dan menarik yang muncul dari animasi 2D. Metode pengembangan e-modul melibatkan beberapa tahapan, antara lain analisis kebutuhan, desain konten, dan pengembangan animasi 2D, serta evaluasi dan revisi. Animasi 2D menyampaikan konsep perpajakan secara visual dan dinamis sehingga memudahkan siswa dalam memahami dan meningkatkan daya tarik pembelajaran. Penelitian dilakukan dengan menerapkan e-modul pada proses belajar siswa. Hasil evaluasi menunjukkan bahwa e-modul berbasis animasi 2D secara signifikan meningkatkan kreativitas siswa dalam memahami materi perpajakan. Interaksi yang diperoleh dari e-modul juga memfasilitasi pembelajaran aktif dan partisipatif. Pengembangan e-modul berbasis animasi 2D pada mata kuliah pengantar perpajakan untuk meningkatkan pemahaman konsep perpajakan dan memperkaya pengalaman belajar mahasiswa melalui pendekatan yang inovatif dan menarik. Penelitian ini menekankan pentingnya mengintegrasikan teknologi dalam pendidikan untuk menciptakan lingkungan belajar yang inspiratif dan dinamis bagi siswa.
Multivariat Predict Sales Data Using the Recurrent Neural Network (RNN) Method Ardriani, Ni Nengah Dita; Yastawil, Jamiin Al Yastawil; Erawati, Kadek Nonik; Yudi Antara, I Gede Made; Santiago, Gede Agus
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90165

Abstract

Sales is an activity or business selling a product or service. In this study, I took a case study on Kaggle. Sales problems at the company cause inventory to be very high or vice versa, causing a loss of sales because there are no items to sell. Inventory that is too high results in increased costs due to existing resources being inefficient. In the opposite condition, it will cause a product vacancy in the market. Using the Recurrent Neural Network (RNN) Algorithm, this study predicts sales. The data used is sales data in 2020 with the parameter Number of sales per day in the last four months. The results obtained through testing several training scenarios and testing the implementation of the algorithm, in this case, is the highest accuracy value of 96.92% in the network architecture of three input neuron layers, three hidden layer neurons, one output, division of training, and test data 70: 30, learning value rate of 0.9 and a maximum of 9000000 epochs
Object Detection Based on You Look Only Once Version 8 for Real-Time Applications Santiago, Gede Agus; Sugiartawan, Putu; Ardriani, Ni Nengah Dita
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.94843

Abstract

This research focus to involves human detection in crowded situations, especially in the lecturer's room. The lecturer's room is very vulnerable because it can be accessed by anyone with only one entry and exit to the lecturer's room, so it would be perfect to place this Yolo camera in front of the lecturer's room so that incoming and outgoing activities can be monitored during work days on campus. The main challenge is how the system can distinguish individuals in dense crowds and identify their relative locations to each other. In this context, it is necessary to find a solution that can overcome the uncertainty of recognizing individuals in a group and accurately understand the location and distance between them. One proposed solution is to use the YOLO algorithm on video recordings to detect human objects in the lecturer's room during working hours. This research introduces the YOLOv8 model, a real-time detection system with high speed and accuracy in detecting and classifying objects in video recordings. YOLOv8 can accurately detect object movement, making it an efficient real-time framework for dealing with complex objects. This research experiment involved using eight different smartphone devices to collect datasets. Using various smartphone devices aims to test object detection performance under various shooting conditions, including variations in image quality, lighting, shooting angle, and camera resolution. The research results show that using multiple smartphone devices in dataset collection can improve the robustness and accuracy of object detection models. By integrating datasets from various sources and shooting conditions, the YOLOv8 model was successfully trained to better recognize objects in different situations, even in campus environments that often have challenges such as weather variations and lighting fluctuations. The test results show an accuracy rate of 93.33% in human object detection
Adaptive Operator and Scaling Factor Selection in Differential Evolution using Parametrized Reinforcement Learning Santiyuda, Kadek Gemilang; Sugiartawan, Putu; Santiago, Gede Agus; Ardriani, Ni Nengah Dita; Kafiyanna, Moch Ilham Nur
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 3 (2025): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.206

Abstract

Mutation strategy selection along with parameter settings are well known challenges in enhancing the performance of differential evolution (DE). In this paper, we propose to solve these problems as a parametrized action Markov decision process. A multi-pass deep Q-network (MP-DQN) is used as the reinforcement learning method in the parametrized action space. The architecture of MP-DQN comprises an actor network and a Q-network, both trained offline. The networks’ weights are trained based on the samples of states, actions and rewards collected on every DE iterations. We use 99 features to describe a state of DE and experiment on 4 reward definitions. A benchmark study is carried out with functions from CEC2005 to compare the performance of the proposed method to baseline DE methods without any parameter control, with random scaling factor, and to other DEs with adaptive operator selection methods, as well as to the two winners of CEC2005. The results show that DE with MP-DQN parameter control performs better than the baseline DE methods and obtains competitive results compared to the other methods.
Deep Learning Approach for USD to IDR Forecasting with LSTM Ardriani, Ni Nengah Dita; Sugiartawan, Putu; Santiago, Gede Agus; Darma Wandika, I Made Pranadata; Wiwahana Prasetya, I Made Irfan
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.210

Abstract

This Research explores the use of Long Short-Term Memory (LSTM) networks for forecasting the USD to IDR exchange rate, with the goal of improving prediction accuracy in the volatile foreign exchange market. By leveraging historical data, including daily exchange rates and trading volume, the LSTM model captures long-term dependencies and patterns within the time series data. The results show that the LSTM model effectively predicts general trends and medium-term fluctuations, demonstrating its capacity to follow market dynamics. However, the model struggles with extreme volatility and sudden market shifts, particularly during unforeseen geopolitical or economic events. This limitation highlights the need for further enhancement through the incorporation of additional features, such as macroeconomic indicators, sentiment analysis, and real-time news data. Furthermore, the study suggests the potential benefits of combining LSTM with other machine learning techniques to create hybrid models that can better handle short-term fluctuations and extreme events. In conclusion, while LSTM shows promise for exchange rate forecasting, its performance can be improved by refining model parameters, incorporating diverse data sources, and exploring hybrid approaches. This research provides valuable insights for traders, investors, and policymakers seeking to make more informed decisions in the foreign exchange market.
Using Neural Networks for USD to IDR Exchange Rate Prediction Santiago, Gede Agus; Sugiartawan, Putu; Erawati, Kadek Nonik; Mahendra, I Gede Orka; Kumara, I Dewa Made Putra; Frasetya, I Gusti Ngurah Hendra
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 1 (2025): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.212

Abstract

Predicting the USD to IDR exchange rate is critical for financial markets, international trade, and economic policy. This research employs neural networks to model the complex and non-linear patterns inherent in time-series data. The methodology involves collecting historical daily exchange rate data, preprocessing to handle missing values, normalizing features, and transforming the data into a format suitable for modeling. The neural network architectures utilized include Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Model evaluation metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), indicate the neural networks’ effectiveness in capturing general trends with high accuracy, despite challenges during periods of high market volatility. Comparative analysis with traditional methods, such as ARIMA, highlights the superior ability of neural networks to manage non-linear relationships and long-term dependencies. This study provides valuable insights into developing advanced tools for exchange rate prediction, leveraging the power of machine learning. The results demonstrate the potential of neural networks in financial forecasting, with opportunities for improvement through integrating additional external factors and optimizing model architectures.
Co-Authors Ardriani, Ni Nengah Dita Ayu Anom, I Gusti Darma Wandika, I Made Pranadata Dita Ardriani, Ni Nengah Erawati, Kadek Nonik Frasetya, I Gusti Ngurah Hendra I Gede Made Yudi Antara Kafiyanna, Moch Ilham Nur Kumara, I Dewa Made Putra Mahendra, I Gede Orka PUTU SUGIARTAWAN Santiyuda, Kadek Gemilang Wiwahana Prasetya, I Made Irfan Yastawil, Jamiin Al Yastawil