Pengembangan Lanjutan Backend Pulse Wise – Aplikasi Mobile untuk Memonitor Pasien Gagal Jantung Berbasis Machine Learning dan Smartwatch

Valentino, Darrell (2026) Pengembangan Lanjutan Backend Pulse Wise – Aplikasi Mobile untuk Memonitor Pasien Gagal Jantung Berbasis Machine Learning dan Smartwatch. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pulse Wise merupakan ekosistem digital yang dikembangkan untuk mendukung pemantauan pasien gagal jantung melalui pencatatan kesehatan harian, pengelolaan pengobatan, data biometrik, layanan analitik, dan akses informasi bagi dokter. Penelitian terdahulu telah menghasilkan aplikasi pasien, model machine learning untuk prediksi dan rekomendasi, serta dashboard dokter, tetapi komponen tersebut dikembangkan pada lingkungan dan jalur layanan yang berbeda sehingga pengelolaan data dan integrasi antarlayanan belum dilakukan melalui satu backend yang konsisten. Tugas akhir ini bertujuan mengembangkan backend terpusat yang mampu mengelola data dan mengintegrasikan berbagai layanan dalam satu ekosistem. Pengembangan dilakukan melalui analisis kebutuhan, perancangan arsitektur dan basis data, implementasi modul, integrasi layanan, deployment, dan pengujian. Backend dibangun sebagai antarmuka pemrograman aplikasi berbasis Representational State Transfer (REST API) menggunakan Node.js dan Express.js dengan dukungan PostgreSQL, Prisma, dan Redis. Sistem menyediakan autentikasi, diari kesehatan, data biometrik, pengobatan, relasi dokter-pasien, dashboard dokter, Content Management System (CMS) edukasi, serta integrasi Heart Failure Monitoring System (HFMS). Seluruh 248 pengujian otomatis berhasil dijalankan tanpa kegagalan. Pengujian API melalui Newman juga menyelesaikan 88 request dan 24 assertion tanpa kegagalan. Selain itu, pengujian end-to-end HFMS berhasil memverifikasi alur pemeriksaan kesiapan data, prediksi, rekomendasi, dan akses melalui jalur pasien maupun dokter. Hasil tersebut menunjukkan bahwa backend Pulse Wise telah dikembangkan sebagai layanan terpusat yang dapat dijalankan pada lingkungan produksi.
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Pulse Wise is a digital ecosystem designed to support the monitoring of patients with heart failure through daily health records, medication management, biometric data, analytical services, and information access for clinicians. Previous studies produced a patient application, machine learning models for prediction and recommendation, and a clinician dashboard. However, these components were developed in separate environments and service flows, leaving data management and service integration without a consistent backend layer. This study aims to develop a centralized backend capable of managing data and integrating multiple services within a unified ecosystem. The development process comprised requirements analysis, architecture and database design, module implementation, service integration, deployment, and testing. The backend was implemented as a REST API using Node.js and Express.js, supported by PostgreSQL, Prisma, and Redis. The system provides authentication, health diary, biometrics, medication management, doctor-patient relationship management, clinician dashboard services, an educational content management system, and machine learning integration. API testing with Newman completed 88 requests and 24 assertions without failure, while end-to-end Heart Failure Monitoring System (HFMS) testing successfully verified readiness checks, payload construction, prediction, recommendation, and access through patient and clinician flows. These results indicate that the Pulse Wise backend was successfully developed as a centralized service that can operate in a production environment.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	backend, gagal jantung, machine learning, REST API, heart failure
Subjects:	Q Science > QA Mathematics > QA76.76.A63 Application program interfaces
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Darrell Valentino
Date Deposited:	29 Jul 2026 03:35
Last Modified:	29 Jul 2026 03:35
URI:	http://repository.its.ac.id/id/eprint/138984

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