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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) METIK JURNAL
Rizal, Erian
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering

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Journal : METIK JURNAL

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Klasifikasi Penyakit Kanker Paru Menggunakan Algoritma Random Forest Berbasis Streamlit Dimas Aprilianto; Rizal, Erian
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/svz4r327

Abstract

Lung cancer is one of the leading causes of global mortality, often difficult to detect early due to its nonspecific initial symptoms. This study proposes a Machine Learning-based approach to classify lung cancer risks using the Random Forest algorithm optimized with GridSearchCV. The identified research gap is the lack of interactive web-based implementations that deliver real-time classification results with a user-friendly interface for general users. The objective of this study is to develop an accurate and efficient classification model and integrate it into a web application using Streamlit. The dataset was sourced from Kaggle, consisting of 5,000 patient records and 18 clinical and lifestyle-related features. The preprocessing steps included data cleaning, normalization, encoding, and feature recategorization. Model performance evaluation using Accuracy, Precision, Recall, and F1-Score metrics showed an accuracy of 90%. Feature importance analysis identified smoking habits, throat discomfort, and respiratory issues as dominant predictors of lung cancer. The model was then deployed into a Streamlit-based web application and tested via a User Acceptance Test (UAT) involving 50 respondents, resulting in a Mean Opinion Score (MOS) average above 84%. These findings indicate that the developed prediction system is not only technically accurate but also well-accepted by users, highlighting its potential as a practical and efficient tool for early lung cancer screening.
Analisis Preferensi Genre Film dengan Collaborative Filtering Berbasis Gemini AI Dwi Fadlullah, Ardiningrum Ikram; Rizal, Erian
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/9h881e08

Abstract

This study aims to analyze user preferences for Action, Horror, and Romance film genres in video streaming services by implementing a Gemini AI-based Collaborative Filtering algorithm. Data were obtained from 1,017 respondents through an online survey using a 1–5 Likert scale. The research stages include data cleansing, calculating genre similarity using cosine similarity, and implementing an item-based Collaborative Filtering algorithm. Furthermore, Gemini AI embedding was applied, which is the process of transforming each genre into a high-dimensional numerical vector representation to more accurately capture semantic relationships between genres. The results show that Action is the most preferred genre, while the highest similarity score between genres was found between Horror and Romance. The developed recommendation system successfully mapped genre similarities and provided relevant viewing suggestions based on other users’ preferences. The system achieved an effectiveness rate of 62.38%. These findings can serve as a foundation for developing more adaptive and personalized recommendation systems in the future.
Co-Authors Dimas Aprilianto Dwi Fadlullah, Ardiningrum Ikram Ramadhan, Muhammad Ilham