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All Journal Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Algoritma
Fatdha, Eiva
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Computer Science & IT

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SISTEM REKOMENDASI VIDEO GAME BERBASIS USIA SEBAGAI ALAT PENGAWASAN ORANG TUA DI PLATFORM STEAM MENGGUNAKAN CONTENT-BASED FILTERING Oktavianda, Oktavianda; Efrizoni, Lusiana; Fatdha, Eiva; Asnal, Hadi
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/2wj54451

Abstract

Video recreations are a prevalent shape of amusement, particularly among children. In any case, numerous parents in Indonesia still need understanding of age appraisals for video recreations, driving to less viable supervision. This could uncover children to unseemly substance. This think about points to create an age-based video amusement suggestion framework utilizing the Content-Based Filtering strategy on the Steam dataset. The framework is planned to help guardians in selecting recreations suitable for their children. Evaluation results show the model performs very well, achieving a precision of 0.98 and a recall of 1.00. Additionally, the model records a Mean Absolute Error (MAE) of 0.469236, Mean Squared Error (MSE) of 6.440935, and Root Mean Squared Error (RMSE) of 2.537900. These findings highlight how well the system filters and suggests age-appropriate video games, assisting parents in better monitoring their kids' gaming habits.
Optimasi Algoritma Knn Menggunakan Smote Untuk Prediksi Stroke Khairi, Zuriatul; Yanti, Rini; Fitri, Triyani Arita; Fatdha, Eiva
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2474

Abstract

Stroke is a disease with a high mortality and disability rate, especially in Indonesia. Early detection of stroke risk is important to prevent serious consequences. This study examines the distribution of stroke cases based on age groups and evaluates the performance of the K-Nearest Neighbors (KNN) algorithm on imbalanced data and after applying the Synthetic Minority Oversampling Technique (SMOTE). The analysis uses two data division scenarios: 80:20 and 70:30 between training and test data. The results show that the risk of stroke increases with age. No cases were found in the 20–30 age group, cases began to appear in the 30–40 age group, and increased sharply above the age of 50. KNN without SMOTE had an accuracy of 95% (80:20) and 94% (70:30), but low recall, 0.04 and f1-score 0.07 (80:20), and recall 0.03 and f1-score 0.05 (70:30). After SMOTE, recall increased to 0.36 and f1-score 0.21 (80:20), and recall 0.28 and f1-score 0.17 (70:30). Accuracy decreased to 86% in both ratios, but recall and f1-score increased, indicating that the model was more sensitive to stroke cases. Overall, SMOTE effectively reduces majority class bias and helps the model recognize overlooked stroke patterns. However, sensitivity still needs to be improved through parameter tuning, selection of relevant features, or alternative algorithms to enhance prediction reliability.
Co-Authors Efrizoni, Lusiana Hadi Asnal, Hadi Oktavianda, Oktavianda Rini Yanti, Rini Triyani Arita Fitri Zuriatul Khairi