MEDSTANCE : RANCANG BANGUN MEDICAL ASSISTANCE KONSUMSI OBAT BERBASIS INTERNET OF THINGS

Nurul Anisa Hanabiyah, . (2024) MEDSTANCE : RANCANG BANGUN MEDICAL ASSISTANCE KONSUMSI OBAT BERBASIS INTERNET OF THINGS. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

[img] Text
ABSTRAK.pdf

Download (164kB)
[img] Text
AWAL.pdf

Download (1MB)
[img] Text
BAB 1.pdf
Restricted to Repository UPNVJ Only

Download (473kB)
[img] Text
BAB 2.pdf
Restricted to Repository UPNVJ Only

Download (467kB)
[img] Text
BAB 3.pdf
Restricted to Repository UPNVJ Only

Download (759kB)
[img] Text
BAB 4.pdf
Restricted to Repository UPNVJ Only

Download (1MB)
[img] Text
BAB 5.pdf

Download (230kB)
[img] Text
DAFTAR PUSTAKA.pdf

Download (246kB)
[img] Text
RIWAYAT HIDUP.pdf
Restricted to Repository UPNVJ Only

Download (134kB)
[img] Text
LAMPIRAN.pdf
Restricted to Repository UPNVJ Only

Download (836kB)
[img] Text
HASIL PLAGIARISME.pdf
Restricted to Repository staff only

Download (10MB)
[img] Text
ARTIKEL KI .pdf
Restricted to Repository staff only

Download (297kB)

Abstract

In Indonesia, cardiovascular diseases, cancer, diabetes, and other chronic illnesses are leading causes of death. Patients with chronic conditions require continuous medical care, including regular medication intake to effectively manage their conditions. However, a major issue faced is non-adherence to medication, where approximately 50% of patients do not follow their prescribed schedules, and 20-30% do not take medication at all. Factors such as misunderstanding of treatment and difficulty in understanding medication instructions are primary reasons for non-adherence. To address these challenges, this research developed "MedStance," a medical assistance system based on the Internet of Things (IoT). MedStance consists of a medication reminder system and a medication dispenser system. Research findings indicate that MedStance has been successful in several key aspects, including achieving an 99.72% accuracy rate in reminder validation, efficient recording of medication intake times into the Firebase database with a 95% accuracy rate, accurate detection of medication positions at 92%, and successful medication retrieval from compartments at a rate of 82%. This system is expected to significantly assist users in simplifying medication intake and enhancing adherence to prescribed medication schedules.

Item Type: Thesis (Skripsi)
Additional Information: [No. Panggil : 2010314003] [Pembimbing 1 : Achmad Zuchriadi P.] [Penguji 1 : Henry B. H. Sitorus] [Penguji 2 : Silvia Anggraeni]
Uncontrolled Keywords: Medicine, Reminder, Dispenser, ESP32.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Fakultas Teknik > Program Studi Teknik Elektro (S1)
Depositing User: NURUL ANISA HANABIYAH
Date Deposited: 03 Sep 2024 04:12
Last Modified: 03 Sep 2024 04:12
URI: http://repository.upnvj.ac.id/id/eprint/31412

Actions (login required)

View Item View Item