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All Journal International Journal of Public Health Science (IJPHS) Jurnal Penjaminan Mutu Widya Duta: Jurnal Ilmiah Ilmu Agama dan Ilmu Sosial Budaya Journal of Applied Data Sciences JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Journal of International Conference Proceedings Jurnal Penjaminan Mutu Pariwisata Budaya: Jurnal Ilmiah Pariwisata Agama dan Budaya International Journal Research on Metaverse
Warmayana, I Gede Agus Krisna
Institut Hindu Dharma Negeri Denpasar, Bali, Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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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

Abstract

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.
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

Abstract

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

Abstract

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

Abstract

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.
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

Abstract

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.
Co-Authors Anton Anton Anton, Sri Sulistyawati Armini, Ni Wayan Yusi Baso, Aidah Juliaty A Bukhari, Agussalim Dewi, Ni Made Umi Kartika Diaris, Ni Made I Ketut Mardika Ida Ayu Tary Puspa IDA AYU TARY PUSPA Kadek Ayu Erika Mardika, I Ketut Ni Putu Evie Sintya Wati Oka, Nobuto Sinarsih, Ni Ketut Sintya Wati, Ni Putu Evie Sueca, I Nyoman Yamashita, Yuichiro