Rancang Bangun Chatbot Deteksi Dini Dan Monitoring Untuk Diabetes Melitus Tipe 2 Berbasis Gaya Hidup

Ramadhan, Muhammad Thoriq Afif (2024) Rancang Bangun Chatbot Deteksi Dini Dan Monitoring Untuk Diabetes Melitus Tipe 2 Berbasis Gaya Hidup. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 5023201004-Non_Degree.pdf - Accepted Version Restricted to Repository staff only until 1 October 2026. Download (8MB) | Request a copy

Abstract

Diabetes melitus tipe 2 (DM 2) adalah masalah kesehatan global dengan prevalensi tinggi, mencapai 90%-95% dari total penderita diabetes. Diagnosis DM 2 sering terlambat karena gejala awal yang tidak terdeteksi, dan gaya hidup tidak sehat meningkatkan risiko penyakit ini. Penelitian ini bertujuan merancang chatbot berbasis WhatsApp untuk mendeteksi dini dan memonitor DM 2 menggunakan algoritma Random Forest (RF). Metode yang digunakan mencakup studi literatur, perancangan sistem chatbot, pembuatan website edukasi, dan website monitoring untuk asupan kalori, gula darah, tekanan darah, kolesterol, serta aktivitas fisik. Chatbot menggunakan arsitektur Microservice dalam Docker Container, mengintegrasikan chatbot service, website service, dan model prediksi RF. Model prediksi RF diuji dengan 1 hingga 21 Principal Components (PC), dan model RF dengan 8 PC digunakan karena peningkatan performa dengan tambahan PC tidak signifikan setelah PC ke-8. Pengujian usability sistem menggunakan System Usability Scale (SUS) dengan 17 subjek menunjukkan nilai SUS rata-rata 76,875, mengindikasikan tingkat kegunaan yang tinggi. Subjek muda menunjukkan pemahaman baik, sementara subjek lebih tua mengalami beberapa kesulitan namun tetap menunjukkan tingkat kepercayaan yang cukup tinggi. Sistem chatbot dan website monitoring diterima baik oleh berbagai kelompok usia. Implementasi chatbot diharapkan meningkatkan kesadaran pengguna tentang gaya hidup sehat dalam pencegahan DM 2 serta memfasilitasi pemantauan kesehatan yang lebih efektif. Untuk meningkatkan kualitas sistem, disarankan menambahkan referensi terkait data monitoring untuk integrasi ke dalam dataset sebagai fitur tambahan. Sistem prediksi dini DM 2 dapat dikembangkan dengan fitur voice note untuk interaksi verbal dan integrasi data kalori makanan atau minuman, serta kolaborasi dengan sistem klinik atau rumah sakit untuk monitoring pasien.
=================================================================================================================================
Type 2 diabetes mellitus (T2DM) is a global health problem with a high prevalence, accounting for 90%-95% of all diabetes cases. T2DM diagnosis is often delayed due to undetected early symptoms, and an unhealthy lifestyle increases the risk of this disease. This research aims to design a WhatsApp-based chatbot for early detection and monitoring of T2DM using the Random Forest (RF) algorithm. The methods used include literature review, chatbot system design, educational website creation, and monitoring website for calorie intake, blood sugar, blood pressure, cholesterol, and physical activity. The chatbot uses a microservice architecture in Docker Container, integrating chatbot service, website service, and RF prediction model. The RF prediction model was tested with 1 to 21 Principal Components (PC), and the RF model with 8 PCs was used because performance improvements were not significant after the 8th PC. System usability testing using the System Usability Scale (SUS) with 17 subjects showed an average SUS score of 76.875, indicating high usability. Younger subjects showed good understanding, while older subjects experienced some difficulties but still showed a fairly high level of confidence. The chatbot and monitoring website system were well received by various age groups. The implementation of the chatbot is expected to raise user awareness of a healthy lifestyle in preventing T2DM and facilitate more effective health monitoring. To improve system quality, it is recommended to add references related to monitoring data for integration into the dataset as additional features. The early detection system for T2DM can be developed with a voice note feature for verbal interaction and integration of food or drink calorie data, as well as collaboration with clinical or hospital systems for patient monitoring.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Chatbot, Diabetes Mellitus Tipe 2, Machine learning, Monitoring, Gaya Hidup, Prediksi Dini, Early detection, Lifestyle Monitoring, Type 2 Diabetes Mellitus.
Subjects:	T Technology > T Technology (General) > T174 Technological forecasting T Technology > T Technology (General) > T174.5 Technology--Risk assessment. T Technology > T Technology (General) > T58.5 Information technology. IT--Auditing T Technology > T Technology (General) > T59.7 Human-machine systems.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Biomedical Engineering > 11410-(S1) Undergraduate Thesis
Depositing User:	Muhammad Thoriq Afif Ramadhan
Date Deposited:	27 Aug 2024 02:33
Last Modified:	27 Aug 2024 02:33
URI:	http://repository.its.ac.id/id/eprint/113755

Actions (login required)

View Item