Article Per Year (5 Year)
p-Index From 2020 - 2025
1.116
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Tourism Economics and Policy JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Erawati, Kadek Nonik
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Public Health Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
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
Retrospective of IKN Nusantara Investment; Study of Apple Investment Curtailment: Technostructure of Indonesia and Vietnam HEPTARIZA, Anita; DARMAWAN, Andreas James; SIHWINARTI, Dwi; RAMAYU, I Made Satrya; ERAWATI, Kadek Nonik
Journal of Tourism Economics and Policy Vol. 4 No. 4 (2024): Journal of Tourism Economics and Policy (October - December 2024)
Publisher : PT Keberlanjutan Strategis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38142/jtep.v4i4.1147

Abstract

In the government's efforts to collect investment in IKN (National Capital City) Nusantara in Indonesia, technostructure; or advanced technological infrastructure is one of the crucial factors in attracting investment from global technology companies, but Indonesia still faces significant challenges in technostructure readiness compared to countries such as Vietnam, reflecting on the investment selection of technology company Apple. This study aims to analyze the current state of Indonesia's technostructure, compare it with Vietnam, and formulate what strategic steps are needed by the Indonesian government to develop a better technostructure in the next three decades. Using a descriptive qualitative method, this study collects purposive sampling-based data through literature studies, documentation, and other supporting data to provide an in-depth understanding of the technostructure conditions of the two countries. The discussion includes a detailed analysis of the state of Indonesia's technostructure and the challenges it faces. Then the study continues by comparing the technostructure readiness of Indonesia and Vietnam to highlight the strengths and weaknesses of each country, and the final stage details the strategic steps that the Indonesian government must take in the next three decades to achieve competitive technostructure readiness in Indonesia. This study concludes that it is important for Indonesia to accelerate the development of technostructure to increase the attractiveness of global technology investment, especially with open management.
Decision Tree for Bitcoin Price Prediction Based on Market Factors Wardani, Ni Wayan; Nugraha, Putu Gede Surya Cipta; Erawati, Kadek Nonik
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 2 (2024): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.199

Abstract

The volatile nature of Bitcoin poses significant challenges for accurate price prediction, which is critical for informed decision-making by investors and policymakers. This study explores the application of decision tree algorithms to predict Bitcoin prices using a dataset comprising historical data on Bitcoin prices, market capitalization, and trading volumes. The research emphasizes feature engineering techniques, including derived metrics such as rolling averages and volatility indices, and integrates ensemble methods like Random Forest and Gradient Boosting to enhance predictive performance. The decision tree model achieved an accuracy of 53%, demonstrating its capability to capture general trends in Bitcoin price movements, particularly during high volatility periods. The study highlights the importance of key features such as the Relative Strength Index (RSI) and Moving Averages (MA14) while identifying limitations in predicting price decreases. Recommendations for future research include integrating external data sources, such as sentiment analysis and macroeconomic indicators, and exploring advanced modeling techniques to improve robustness and accuracy. This research contributes to the growing field of cryptocurrency price prediction by providing interpretable and actionable insights into market dynamics. The findings offer valuable tools for analysts and investors navigating the complexities of the cryptocurrency market.
LSTM Neural Network for Predicting Tourist Arrivals to Bali Erawati, Kadek Nonik; Sugiartawan, Putu; Ardriani, Ni Nengah Dita; Hartama, I Dewa Agung Bayu Mega; Frasetya, I Gusti Ngurah Hendra; Mahendra, I Gede Orka
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.211

Abstract

Tourism is a key pillar of Bali’s economy, contributing significantly to employment, cultural preservation, and income generation. Accurate forecasting of tourist arrivals is crucial for sustainable growth and resource optimization. This study applies Long Short-Term Memory (LSTM) neural networks to predict tourist arrivals in Bali, leveraging historical data and external factors such as global economic indicators, flight frequencies, cultural events, and environmental conditions. LSTM’s ability to model complex temporal dependencies and non-linear relationships offers significant advantages over traditional methods like ARIMA, especially in handling seasonal patterns and irregularities. The model was trained on a robust dataset, preprocessed to address missing values, outliers, and variability. Performance evaluation metrics, including RMSE, demonstrate high predictive accuracy during stable periods but highlight limitations in handling anomalies such as the COVID-19 pandemic. To address these challenges, recommendations include integrating additional external variables, employing hybrid models, and conducting scenario-based sensitivity analyses to enhance adaptability and robustness. The results highlight the practical utility of AI-driven forecasting tools in tourism management, providing actionable insights for policymakers and stakeholders to optimize planning, mitigate risks, and support sustainable development. This research contributes to the growing field of AI applications in tourism, promoting resilience and competitiveness in an increasingly dynamic global 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.
Rancang Bangun Sistem Informasi Akuntansi Pada Perusahaan Jasa Reparasi Erawati, kadek nonik; Suryana, I Gede Putu Eka
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 4 No 2 (2021): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.154

Abstract

An industry that is engaged in repair is an industry that is engaged in goods and special services, namely serving hardware, IT Consulting, network installation, and CCTV. The problem that occurs is accounting difficulties in managing transaction data because accounting is required to recap transaction information taken from different Microsoft Excel sheets or files. Not only that, but the manual process in making accounting reports also takes time so there can be delays in making accounting reports and errors when structuring accounting reports. Based on the above background, the formulation of the problem in the research is How to Design and Build an Accounting Data System in an Industry that is engaged in repairs, on the other hand, the research objective to be achieved in the reporting process is being able to design and build an industrial accounting information system. The result of this research is to create a system that can digest customer information, supplier information, account information, universal daily information, transaction information, user information, and accounting reports. This system is expected to help accountants in making accounting reports to identify the state of industry accounting
Co-Authors Ardriani, Ni Nengah Dita Darmawan, James Frasetya, I Gusti Ngurah Hendra Hartama, I Dewa Agung Bayu Mega HEPTARIZA, ANITA I Gede Made Yudi Antara Kumara, I Dewa Made Putra Mahendra, I Gede Orka Putu Gede Surya Cipta Nugraha PUTU SUGIARTAWAN RAMAYU, I Made Satrya Santiago, Gede Agus Sihwinarti, Dwi Suryana, I Gede Putu Eka Wardani, Ni Wayan Yastawil, Jamiin Al Yastawil