Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Riset Informatika
Syarah Seimahura
Universitas Nusa Mandiri
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
IMPLEMENTATION OF A GAME RECOMMENDATION SYSTEM USING THE K-MEANS CLUSTERING AND CONTENT-BASED FILTERING METHODS Rianggi Silvi Anti; Nanang Ruhyana*; Syarah Seimahura; Andri Agung Riyadi
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.444

Abstract

This study focuses on developing a web-based game recommendation system using a hybrid approach, combining K-Means Clustering and Content-Based Filtering to improve the accuracy and relevance of recommendations. The dataset was taken from the RAWG API, consisting of 1,000 games with key attributes such as name, Genre, platform, rating, and age category (ESRB). The research stages included Data Preparation, exploratory analysis, attribute transformation, application of K-Means for game segmentation, and similarity calculation using Cosine Similarity. The hybrid approach was carried out by filtering recommendations based on the same cluster. The results show that the integration of the two methods produces more relevant recommendations, with UMAP and t-SNE visualizations showing clear cluster separation. The system was implemented using Django and deployed on Google Cloud Platform, resulting in an efficient, adaptive, and real-time recommendation application.
TIME SERIES FORECASTING AND CLASSIFICATION OF POTENTIAL SAFETY RISKS OF STORING RADIOACTIVE WASTE NEAR SURFACE DISPOSAL Kanita Salsabila Dwi Irmanti; Nanang Ruhyana; Syarah Seimahura; Andri Agung Riyadi
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1400.824 KB) | DOI: 10.34288/jri.v8i3.440

Abstract

Long-term radioactive waste management, especially at Near Surface Disposal (NSD) facilities, requires a predictive approach and adaptive monitoring system to anticipate risks to groundwater quality. This research aims to develop a time series model to predict groundwater level parameters including depth, pH, and tds and integrate it with a rule-based ESG risk classification system and machine learning. The method used includes the Prophet time series model for predicting groundwater parameters in the next 50 years. The prediction results are classified using rule-based classification which is then evaluated using the Random Forest algorithm. The final application was developed web-based using Streamlit. The Prophet model provided the best prediction performance for depth MAE: 0.71; MAPE: 7.41% and pH MAE: 0.21; MAPE: 4.89%, but less accurate for TDS MAE: 12.16; MAPE: 31,62%. The Random Forest model produced classification accuracy of up to 98% and was able to replicate the rule-based classification system well. The integration of these models can produce a predictive system that supports decision making in sustainable radioactive waste management.
Co-Authors Andri Agung Riyadi Kanita Salsabila Dwi Irmanti Nanang Ruhyana Nanang Ruhyana* Rianggi Silvi Anti