SKRIPSI DIGITAL
Rancang Bangun Sistem Rekomendasi Obat Berdasarkan Gejala Pengguna Pada Apotek Sehat Berbasis Web Menggunakan Machine Learning = Design and Development of a Web-Based Medication Recommendation System for Sehat Pharmacy Based on User Symptoms Using Machine Learning
A web-based medication recommendation system is intended to serve as an initial source of information about medications and is not a substitute for diagnosis or consultation with healthcare professionals. The system was developed to help the public obtain initial information about medications appropriate for mild symptoms. This study aims to design and develop a medication recommendation system for Apotek Sehat that assists users in obtaining medication recommendations based on symptoms submitted through a chatbot. The methods used include Natural Language Processing (NLP) to process user inputs and classify symptoms, as well as a Large Language Model (LLM) to support the generation of chatbot responses. The system was developed using the Waterfall method, while the classification models were evaluated by comparing Logistic Regression, Support Vector Machine, MLP Neural Network, and Naive Bayes using Accuracy, Precision, Recall, and F1-Score metrics. The evaluation results show that Logistic Regression achieved the best performance with an accuracy of 92.5% and was selected as the main model for the medication recommendation system. The developed system can provide medication information and recommendations based on user symptoms and assist users in accessing medication information digitally.
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Karya Umum 
Filsafat 
Agama 
Ilmu-ilmu Sosial 
Bahasa 
Ilmu-ilmu Murni 
Ilmu-ilmu Terapan 
Kesenian, Hiburan, dan Olahraga 
Kesusastraan 
Geografi dan Sejarah