Garuda - Garba Rujukan Digital

Implementing Blockchain in Media Management Systems to Ensure Content Authenticity and Copyright Protection Warmayana, I Gede Agus Krisna
Journal of Social Science Utilizing Technology Vol. 2 No. 3 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i3.1171

Background. Blockchain technology has gained widespread attention recently due to its potential to improve transparency, security, and efficiency in various sectors. In the media industry, content authenticity and copyright protection issues are often significant challenges. Blockchain technology offers potential solutions to ensure the authenticity of content and protect copyrights through a decentralized and immutable system. Purpose. This study aims to explore the implementation of blockchain technology in media management systems to ensure content authenticity and copyright protection. This research focuses on how blockchain can create a transparent and secure record of every transaction and change related to media content. Method. This research uses a qualitative approach with a case study method. The data was collected through in-depth interviews with blockchain technology experts, media professionals, and copyright lawyers. Analysis of documents and literature was also carried out to support the findings. Thematic analysis techniques identify key themes and patterns in the collected data. Results. The results show that implementing blockchain technology in media management systems can significantly improve content authenticity and copyright protection. Blockchain allows the creation of transparent records of ownership and changes in content, which are difficult to forge. This helps trace the content’s origin and ensures that copyrights are respected and protected. Conclusion. The research concludes that blockchain technology has great potential to address content authenticity and copyright protection challenges in the media industry. Blockchain implementation can create a more transparent and secure media management system, increasing trust between content creators, distributors, and consumers. The research recommends further development and trial of blockchain implementation in various media contexts to explore the full potential of this technology.

The benefit of rebon shrimp-based supplementary feeding on serum albumin level in children who have undergone stunting Anton, Sri Sulistyawati; Bukhari, Agussalim; Baso, Aidah Juliaty A; Erika, Kadek Ayu; Anton, Anton; Warmayana, I Gede Agus Krisna
International Journal of Public Health Science (IJPHS) Vol 13, No 2: June 2024
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v13i2.23654

Stunting is still an unresolved global health problem caused by inadequate nutritional intake, significantly affecting a person’s future development. Rebon-shrimp is high protein and inexpensive local food, but still underutilized. This quasi-experimental study aimed to determine the effect of supplementary feeding from Rebon-shrimp on serum albumin levels in stunting children aged 24-60 months. The intervention group (n=44) received rebon shrimp-based supplementary food for 90 days, while the control group (n=44) received a placebo. Measurement of serum albumin was carried out by the ELISA method using blood samples. The results showed a statistical difference (p<0.001) in serum albumin levels in the intervention group, while the control group did not differ statistically (p=0.363). The intervention group experienced an increase in albumin levels of 15.55 g/L, while the control group tended to experience a decrease in serum albumin levels of -1.92 g/L. There was no significant difference in serum albumin levels before the intervention in the two groups (p=0.180). Still, after the administration of rebon products, there was a significant difference in serum albumin levels between the two groups (p<0.001). Supplementary food made from rebon shrimp was beneficial for increasing the serum albumin level of stunting children.

Sistem Politik Berkeadilan Pancasila: Upaya Nyata Peningkatan Ketahanan Hukum Nasional: Pancasila Just Political System: Real Efforts to Increase National Legal Resilience Andrew Shandy Utama; Lubis; Arief Fahmi Lubis; Jana Milia; I Gede Agus Krisna Warmayana
Jurnal Kolaboratif Sains Vol. 7 No. 7: July 2024
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v7i7.5781

Penelitian ini bertujuan untuk menganalisis sistem politik berkeadilan berdasarkan Pancasila dan upaya peningkatan ketahanan hukum nasional di Indonesia. Menggunakan metode literatur review, penelitian ini mengumpulkan dan menganalisis berbagai sumber literatur yang relevan, termasuk undang-undang dan kebijakan pemerintah. Temuan penelitian menunjukkan bahwa penerapan nilai-nilai Pancasila dalam sistem politik sangat penting untuk mencapai keadilan sosial, dengan sila kedua yang menekankan hak asasi manusia dan sila kelima yang menekankan keadilan sosial bagi seluruh rakyat. Sistem politik berkeadilan harus berlandaskan pada prinsip-prinsip demokrasi, transparansi, dan akuntabilitas, yang diatur dalam berbagai peraturan hukum, seperti UU No. 7 Tahun 2017 tentang Pemilihan Umum dan UU No. 30 Tahun 2014 tentang Administrasi Pemerintahan. Peningkatan ketahanan hukum nasional memerlukan reformasi yang mencakup peningkatan kapasitas aparat penegak hukum dan penguatan pengawasan serta akuntabilitas, sesuai dengan UU No. 15 Tahun 2004. Penelitian ini juga menekankan pentingnya pendidikan hukum berbasis Pancasila dan partisipasi masyarakat dalam memperkuat ketahanan hukum nasional. Secara keseluruhan, penelitian ini menyimpulkan bahwa penerapan nilai-nilai Pancasila dan berbagai peraturan hukum yang relevan merupakan langkah kunci dalam menciptakan sistem politik yang adil dan berkelanjutan di Indonesia.

Implementing Blockchain in Media Management Systems to Ensure Content Authenticity and Copyright Protection Warmayana, I Gede Agus Krisna
Journal of Social Science Utilizing Technology Vol. 2 No. 3 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i3.1171

Background. Blockchain technology has gained widespread attention recently due to its potential to improve transparency, security, and efficiency in various sectors. In the media industry, content authenticity and copyright protection issues are often significant challenges. Blockchain technology offers potential solutions to ensure the authenticity of content and protect copyrights through a decentralized and immutable system. Purpose. This study aims to explore the implementation of blockchain technology in media management systems to ensure content authenticity and copyright protection. This research focuses on how blockchain can create a transparent and secure record of every transaction and change related to media content. Method. This research uses a qualitative approach with a case study method. The data was collected through in-depth interviews with blockchain technology experts, media professionals, and copyright lawyers. Analysis of documents and literature was also carried out to support the findings. Thematic analysis techniques identify key themes and patterns in the collected data. Results. The results show that implementing blockchain technology in media management systems can significantly improve content authenticity and copyright protection. Blockchain allows the creation of transparent records of ownership and changes in content, which are difficult to forge. This helps trace the content’s origin and ensures that copyrights are respected and protected. Conclusion. The research concludes that blockchain technology has great potential to address content authenticity and copyright protection challenges in the media industry. Blockchain implementation can create a more transparent and secure media management system, increasing trust between content creators, distributors, and consumers. The research recommends further development and trial of blockchain implementation in various media contexts to explore the full potential of this technology.

Decentralized Materials Data Management using Blockchain, Non-Fungible Tokens, and Interplanetary File System in Web3 Warmayana, I Gede Agus Krisna; Yamashita, Yuichiro; Oka, Nobuto
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.380

In materials science, utilizing globally distributed data is essential for advancing materials design through technologies such as materials informatics. Achieving this requires secure, transparent, and efficient methods for managing and sharing materials data. This study explores the potential of blockchain, smart contracts, Non-Fungible Tokens (NFTs), and the InterPlanetary File System (IPFS) within the Web3 framework for managing and sharing materials data. We developed and tested a prototype data management system using a thermophysical properties dataset. This system facilitates NFT minting, data storage on IPFS, and secure, traceable ownership transfer of NFTs, enhancing traceability, transparency, and security in data sharing. Additionally, decentralized systems employing blockchain technology, smart contracts, NFTs, and IPFS effectively address vulnerabilities associated with single points of failure common in traditional centralized systems. This study offers valuable insights for future materials design, demonstrating the efficacy of blockchain and related technologies in managing and sharing materials data.

Improving Prostate Cancer Classification with Random Forest Techniques Warmayana, I Gede Agus Krisna
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.195

Prostate cancer is a leading cause of cancer-related mortality among men worldwide, necessitating accurate and efficient classification methods for improved diagnosis and treatment planning. This research explores the application of Random Forest algorithms to classify prostate cancer cases using a dataset comprising 100 samples with features such as radius, texture, perimeter, area, smoothness, compactness, symmetry, and fractal dimension. The study emphasizes the integration of preprocessing, feature selection, model training, and evaluation to enhance classification performance. The model achieved a classification accuracy of 75%, with a high recall of 88% for malignant cases, demonstrating its potential in identifying high-risk patients. However, the model exhibited challenges in predicting benign cases due to class imbalance, as reflected in the low precision (33%) for this minority class. Addressing these limitations, techniques such as data balancing, advanced hyperparameter tuning, and enhanced feature engineering are suggested. This study provides valuable insights into key predictors of prostate cancer and highlights the potential of Random Forest techniques as a robust tool for clinical decision-making. Future work should focus on integrating additional clinical and genomic data to further improve classification accuracy and interpretability.

Effect of Traditional Massage Stimulation on Interleukin 6 (IL-6) Serum Level on Stunted Children Sueca, I Nyoman; Anton, Sri Sulistyawati; Dewi, Ni Made Umi Kartika; Diaris, Ni Made; Warmayana, I Gede Agus Krisna; Sinarsih, Ni Ketut; Armini, Ni Wayan Yusi
Journal of International Conference Proceedings Vol 7, No 2 (2024): 2024 ICSM Thailand & AIC Proceeding
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v7i2.3844

Failure to thrive or stunting is a major complication of chronic inflammation and recurrent infections in children. An uncontrolled inflammatory response is associated with stunting syndrome. Mediators that play a role include IL-6. This study aims to determine the effect of traditional massage stimulation on IL-6 serum levels in stunted children aged 12 – 60 months. This study is a quasi-experimental design involving 21 stunted children who received the 15-minute massage treatment three times a week for four weeks. Examination of IL-6 serum levels was carried out using the ELISA method using the Human IL-6 ELISA Kit RAB 0306-1KT Sigma-Aldrich. The serum IL-6 levels before the intervention (60,234pg/ml) had a higher mean value than serum IL-6 after intervention (21,261pg/ml). The paired t-test showed a significant difference in the children's serum IL-6 values before and after the massage intervention (p0.000). It was concluded that traditional massage stimulation reduces Interleukin 6 (IL-6) serum levels in stunted children.

Predictive Analysis for Optimizing Targeted Marketing Campaigns in Bike-Sharing Systems Using Decision Trees, Random Forests, and Neural Networks Warmayana, I Gede Agus Krisna; Yamashita, Yuichiro; Oka, Nobuta
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.29

This research explores the use of machine learning models to predict bike rental demand and optimize targeted marketing campaigns in bike-sharing systems. Utilizing the day.csv and hour.csv datasets, which provide daily and hourly bike rental data, we implemented Decision Tree Regressor, Random Forest Regressor, and Neural Networks (MLPRegressor) to forecast demand. The Random Forest model outperformed the others, achieving an RMSE of 709.08 and an MAE of 469.99 for daily predictions, while the Neural Network demonstrated potential for hourly forecasts. Our analysis revealed significant trends, including increased demand during summer months and peak usage times on weekday mornings and evenings, highlighting the importance of temporal and weather-related factors in predicting bike rental demand. The study's predictive insights allow bike-sharing companies to enhance operational efficiency by optimizing bike allocation during peak periods and reducing idle capacity during off-peak times. Furthermore, the ability to predict demand accurately enables the development of data-driven marketing strategies, such as launching promotions during high-demand periods and targeting specific user groups based on rental patterns. Despite the promising results, challenges such as data preprocessing complexities and computational resource constraints were encountered. Additionally, the study's scope was limited by the available data, suggesting the need for future research to incorporate additional data sources, like real-time traffic conditions and social events, and to explore more advanced machine learning techniques to further improve prediction accuracy. In conclusion, this research underscores the value of predictive analytics in optimizing bike-sharing systems and marketing strategies, contributing to more efficient and user-centric urban mobility solutions.

Predicting FIFA Ultimate Team Player Market Prices: A Regression-Based Analysis Using XGBoost Algorithms from FIFA 16-21 Dataset Warmayana, I Gede Agus Krisna; Yamashita, Yuichiro; Oka, Nobuto
International Journal Research on Metaverse Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i2.25

This study investigates the use of XGBoost, a machine learning algorithm, for predicting player prices in FIFA Ultimate Team (FUT) from FIFA 16 to FIFA 21. Virtual economies in gaming, particularly in FUT, have grown substantially, with in-game asset prices influenced by a variety of factors such as player attributes, performance metrics, and market dynamics. The objective of this research is to enhance the accuracy of price predictions in FUT through advanced machine learning techniques. The dataset comprises historical player data, including attributes such as rating, skills, and in-game statistics. XGBoost was employed due to its ability to handle large, complex datasets and capture non-linear relationships effectively. The model achieved an R-squared value of 0.8911, indicating that it explains 89% of the variance in player prices, while the RMSE value of 30368.85 reveals the model's precision in estimating prices. Feature importance analysis showed that attributes such as WorkRate and Rating significantly influenced price predictions. Compared to traditional methods like linear regression, XGBoost provided superior accuracy and computational efficiency, making it a valuable tool for understanding player price dynamics in virtual gaming markets. The findings suggest that accurate price predictions can improve gaming strategies for players and provide valuable insights for game developers in optimizing virtual economies. This research also highlights the potential for further exploration using advanced machine learning algorithms to predict price fluctuations in gaming environments.

Title

Found 19 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 19 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 19 Documents
Search