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Gede Iwan Sudipa
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Galaksi
Published by Sraddha Panca Widya Nusantara Foundation
ISSN : -     EISSN : 30482399     DOI : https://doi.org/10.70103/galaksi.v1i1.1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering
Jurnal Galaksi : Global Knowledge, Artificial Intelligence and Information System provides an international forum for researchers and professionals to share their knowledge and report new advances on all topics related to knowledge systems and advanced information systems. This archival journal publishes state-of-the-art research reports on emerging topics in Global Knowledge of Artificial Intelligence and Information System, reviews of important techniques in related areas, and application papers of interest to a general readership. The journal focuses on knowledge systems and advanced information systems, including their theoretical foundations, infrastructure and enabling technologies. We solicit submissions of original research, and experience and vision papers that address this theme. Suggested topics include, but are not limited to, the following areas: Knowledge and information processing: theory, techniques and systems Knowledge and data engineering Artificial intelligent Decision support Active and dynamic systems Data sharing and warehousing Temporal and spatial database processing Intelligent information retrieval Machine learning Learning and adaptation Knowledge discovery, data mining and big data analytic Computer vision and pattern recognition Modelling and object orientation Software re-engineering Co-operativeness, interoperability and software re-usability Human-computer interaction Hypertext, hypermedia and multimedia Data and knowledge visualization Immersive Technology Underlying computational techniques Soft computing (including neural nets, fuzzy logic, probabilistic reasoning, and rough set theory) Evolutionary computing Hybrid computing Uncertainty management Agent architectures and systems (including multi-agent scenarios) Internet of Things Platforms High performance computing systems Dstributed intelligent systems Mobile systems Control system Embedded System Application to specific problem domains Biomedical systems Geographical systems Software information systems Emerging applications (such as Internet technologies and digital libraries)
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 22 Documents
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Data Visualization of Higher Education Participation Rates in Indonesia Provinces Joko Saputro; Kanika Saini; Hany Maria Valentine
Jurnal Galaksi Vol. 1 No. 2 (2024): Galaksi - August 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i2.20

Abstract

The objective of this study is to analyze the disparity in higher education enrollment in Indonesia using data visualization produced by the Exploratory Data Analysis (EDA) technique. This graphic specifically examines the Gross Participation Rate of higher education among provinces, expenditure quintiles, and genders for the period of 2019 to 2023. The data was gathered from Statistics Indonesia and presented using Google Data Studio to offer a clearer and more comprehensible representation of the disparity. The findings indicate notable variations in Gross Participation Rate across provinces, with DI Yogyakarta continuously exhibiting the highest Gross Participation Rate and Bangka Belitung Islands displaying the lowest Gross Participation Rate over the duration of the study. Furthermore, Gross Participation Rate exhibits greater values within the quintile 5, which represents the group with the highest expenditure. Additionally,  Gross Enrollment Rate is also higher in females when compared to males. The resulting visualization can serve as a potent instrument for policy makers to comprehend and tackle disparities in access to higher education in Indonesia.
Optimising Double Exponential Smoothing for Sales Forecasting Using The Golden Section Method Kadek Dian Pradnyani; I Made Subrata Sandhiyasa; I Made Agus Oka Gunawan
Jurnal Galaksi Vol. 1 No. 2 (2024): Galaksi - August 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i2.21

Abstract

To achieve maximum profits and a satisfying impression on consumers, companies are required to have the right strategy in selling their products. In determining the right strategy, it requires the availability of accurate information that can be analyzed to determine a sales strategy so that it can increase the number of sales and generate large profits, namely by forecasting. In the Double Exponential Smoothing method, the problem that arises is determining the optimum α parameter value to provide the smallest size of forecasting error, which is sought using the trial and error method, so it requires quite a lot of time. To overcome this problem, a non-linear optimization algorithm using the Golden Section algorithm is used. The Golden Section algorithm is an algorithm that uses the principle of reducing the boundary area α which might produce a minimum objective function value. It is hoped that this forecasting design will be able to provide information that will help the company take decisions or steps in providing stock of goods for sale so that there will be no overstock in the warehouse and can increase Dewaayu Shop's profits.  based on the test results, the value of  MAPE value is obtained of 21.59579369% and RMSE value of 2.42465034.
Multi Criteria Decision Making Approach in Determining the Best Online Streaming Platform for Alpha Generation Ahmad Jurnaidi Wahidin; Yoga Listi Prambodo; Asruddin Asruddin
Jurnal Galaksi Vol. 1 No. 2 (2024): Galaksi - August 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i2.22

Abstract

This research aims to determine the best online streaming platform for Generation Alpha using a Multi-Criteria Decision Making (MCDM) approach that combines the Analytical Hierarchy Process (AHP) and Weighted Product (WP) methods. The criteria evaluated include free nature, flexibility, ease of use, content diversity, and privacy security. Through the AHP model, the weights of the criteria were determined and used in the WP method to rank the alternatives. The results show that YouTube is the best platform with the highest preference value, followed by Netflix and Disney+ Hotstar. The combination of AHP and WP methods allows for more objective and scalable decision-making, providing relevant recommendations for users and developers of streaming platforms. The model also has the potential to be applied in other multi-criteria evaluation contexts.
Augmented Reality Portal Using Markerless Method at the Ergendang Cave Tourist Attraction T Ahyuza Rahmadina; Tantri Hidayati Sinaga; Nurjamiyah Nurjamiyah
Jurnal Galaksi Vol. 1 No. 2 (2024): Galaksi - August 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i2.23

Abstract

For some visitors, natural tourism represents an attractive form of tourism in itself. One of the most notable natural attractions in North Sumatra is Ergendang Cave, situated in Penungkiren, Sinembah Tanjung Muda (STM) Hilir, Deli Serdang Regency. The promotional media for Ergendang Cave still exhibits deficiencies that impede its ability to attract local tourists. The utilisation of augmented reality (AR) technology has the potential to enhance the efficacy of promotional media by facilitating the creation of portal applications that integrate the virtual and tangible realms through markerless methodologies. This Android-based application has been constructed using the Unity 3D platform and the C# (sharp) programming language, in accordance with the prototype model SDLC system development method. This study presents information on the surrounding situation of Ergendang Cave tourism with 360° panoramic photos through a portal that aims to help attract tourists. The portal provides users with the ability to virtually visit the location in real time, effectively bridging the gap between the user's physical location and the virtual representation of Goa Ergendang Tourist Attractions. This could be particularly beneficial during the ongoing pandemic, as it offers a unique opportunity for tourists to virtually experience the attractions without the need for physical travel.
Accuracy Improvement of Convolutional Neural Network with Ghost Weight Normalization for Pneumonia Classification Baihaqi, Galih Restu; Shalsadilla, Shafatyra Reditha; Argaputri, Maulida Khairunisa
Jurnal Galaksi Vol. 1 No. 3 (2024): Galaksi - Desember 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i3.35

Abstract

Pneumonia is a critical respiratory condition that requires accurate and timely diagnosis to ensure effective treatment. In this study, we propose the integration of Ghost Weight Normalization (GWN) into a Convolutional Neural Network (CNN) to enhance the accuracy and performance of pneumonia detection. The dataset used was derived from the Kaggle repository, comprising 5,856 chest X-ray images divided into two classes: Normal and Pneumonia. The CNN + GWN model demonstrated improved classification metrics with an accuracy, precision, recall, and F1-score of 95%, outperforming the CNN-Based model, which achieved 92%. While the CNN + GWN model required slightly longer training time and more epochs to achieve its best performance, the trade-off resulted in more robust and reliable predictions. The enhanced performance is attributed to the ability of GWN to normalize weights effectively, providing diverse normalization variations and improving training stability. These results underscore the potential of the CNN + GWN model for reliable pneumonia detection and highlight its capability to address the limitations of conventional CNN architectures.
Implementation of Recurrent Neural Network Gated Recurrent Unit (GRU) Model for Predicting Top-Tier Bitcoin Adamu, Yusuf Aliyu
Jurnal Galaksi Vol. 1 No. 3 (2024): Galaksi - Desember 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i3.41

Abstract

Cryptocurrency investments are becoming increasingly popular due to their potential as digital assets, though high price volatility poses significant challenges for investment decision-making. This study employs the Gated Recurrent Unit (GRU) model to forecast the closing prices of five prominent cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Binance Coin (BNB), and Dogecoin (DOGE), using historical data from Yahoo Finance spanning 2019 to 2024. The model's performance was assessed using evaluation metrics, including Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The results demonstrate that BNB achieved the best performance, with a MAPE of 2.38% and RMSE of 17.03, followed by ETH and XRP, which recorded MAPEs of 2.51% and 2.64%, respectively. BTC exhibited the highest RMSE at 2280.73, highlighting its significant price volatility, while DOGE had the lowest RMSE at 0.01, despite recording the highest MAPE at 4.11%. Forecasts for the next six periods indicate that BTC and ETH are likely to experience gradual price increases, XRP and BNB are expected to stabilize, and DOGE will remain relatively stable with low volatility. The study concludes that the GRU model is effective for cryptocurrency price forecasting, but integrating it with fundamental and technical analysis could further enhance accuracy and support more informed investment decisions.
Advanced Long Short-Term Memory (LSTM) Models for Forecasting Indonesian Stock Prices Santosa, Firman; Oktafanda, Ego; Setiawan, Hendrik; Latif, Abdul
Jurnal Galaksi Vol. 1 No. 3 (2024): Galaksi - Desember 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i3.42

Abstract

The Indonesian stock market is a key indicator of national economic dynamics. Blue-chip stocks, including Bank Central Asia (BBCA), Bank Rakyat Indonesia (BBRI), and Bank Mandiri (BMRI), hold significant influence due to their liquidity and impact on the market index. However, their price volatility, driven by global economic conditions, monetary policies, and market sentiment, poses challenges for accurate forecasting. This study employs the Long Short-Term Memory (LSTM) model to address these challenges. LSTM, a deep learning technique, effectively handles time series data by capturing long-term dependencies and complex price patterns. Using historical stock data from 2019 to 2024, the model was trained and optimized. Evaluation metrics, including Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE), were used to assess performance. BBCA stocks achieved the best results, with a MAPE of 0.0099 and RMSE of 128.02.The findings demonstrate LSTM's robustness in forecasting stock price trends, providing investors with valuable tools for informed decision-making. This research advances predictive analytics in financial markets, particularly in emerging economies like Indonesia, and highlights LSTM’s potential to improve accuracy in volatile environments.
Time Facial Expression Recognition Using Optimized CNN Models for Behavioral and Emotional Analysis Wirawan, Komang Diva Andi; Putra, I Nyoman Tri Anindia
Jurnal Galaksi Vol. 1 No. 3 (2024): Galaksi - Desember 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i3.43

Abstract

Facial expression recognition is a significant field in human-computer interaction, aiming to analyze emotions such as happiness, sadness, anger, and fear. This study develops a facial expression detection system using Convolutional Neural Networks (CNN) to address challenges like lighting variations and facial angles. The research begins with collecting and preprocessing datasets, including FER-2013, to normalize, augment, and label images for seven emotion classes. The CNN model is designed with convolutional, pooling, and fully connected layers, optimized using ReLU activation, Adam optimizer, and categorical crossentropy loss function. Training is conducted on 80% of the dataset, with 20% for validation, achieving a validation accuracy of 91.7%. System performance is evaluated using precision, recall, F1-score, and real-time testing integrated with cameras, achieving an average detection accuracy of 90%. Results demonstrate the system's robustness in detecting emotions under varying conditions, highlighting its potential for applications in security, education, and emotional therapy. Future research recommends incorporating larger datasets and advanced transfer learning methods to improve system efficiency and accuracy.
Towards Improved Heart Disease Detection: Evaluating Naïve Bayes and K-Nearest Neighbors in Medical Data Classification Angelyn, Mariane Cetty; Iswara, Ida Bagus Ary Indra; Putra, Desak Made Dwi Utami; Sastaparamitha, Ni Nyoman Ayu J.
Jurnal Galaksi Vol. 1 No. 3 (2024): Galaksi - Desember 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i3.45

Abstract

The application of machine learning in healthcare is increasingly critical for improving diagnostic accuracy and timely treatment. This study explores the classification of heart disease using Naïve Bayes and K-Nearest Neighbors (KNN), focusing on evaluating their effectiveness through a comparative analysis. The research addresses the challenge of identifying an optimal method for heart disease classification, emphasizing the need for reliable algorithms. Using a dataset from Kaggle with detailed preprocessing, we implement Naïve Bayes and KNN to assess classification performance. The study introduces a comparative perspective on classification accuracy, precision, recall, and F1-score, revealing the strengths and limitations of each method. The results highlight the superior performance of Naïve Bayes with an accuracy of 88%, offering novel insights for data-driven healthcare decisions.
Blockchain Technology: A Review Study on Improving Efficiency and Transparency in Agricultural Supply Chains Mwewa, Timothy; Lungu, Gilbert; Turyasingura, Benson; Umer, Yusuf; Chavula, Petros
Jurnal Galaksi Vol. 1 No. 3 (2024): Galaksi - Desember 2024
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v1i3.46

Abstract

The agricultural sector is vital to economic development and food security in Sub-Saharan Africa (SSA). However, persistent challenges in agricultural supply chains, such as inefficiencies, lack of transparency, and limited traceability, contribute to high post-harvest losses, unfair pricing for farmers, and reduced consumer trust. Blockchain technology, with its decentralized and transparent ledger system, offers a promising solution to these issues. This review explores blockchain’s potential to improve supply chain efficiency and transparency in SSA. By integrating blockchain with smart contracts, IoT devices, and real-time data sharing, stakeholders can enhance traceability, automate processes, and reduce transaction costs. Blockchain-based platforms provide direct market access for farmers, ensuring fair pricing and minimizing intermediary influence. Furthermore, blockchain’s immutable nature guarantees data credibility, fostering consumer trust and compliance with quality standards. Despite its potential, blockchain adoption in SSA faces challenges, including high costs, inadequate infrastructure, limited technical expertise, and low awareness of its benefits. Addressing these barriers requires affordable, scalable solutions and supportive policy frameworks. This review highlights blockchain’s transformative role in resolving inefficiencies and improving transparency in SSA’s agricultural supply chains. Collaborative efforts among governments, private stakeholders, and international organizations are crucial to fostering adoption, driving economic growth, and enhancing food security.

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