Sistem Rekomendasi Musik Penenang Untuk Ibu Penderita Postpartum Depression Menggunakan Metode Crnn

Nabila, Putri Aliya (2022) Sistem Rekomendasi Musik Penenang Untuk Ibu Penderita Postpartum Depression Menggunakan Metode Crnn. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 05211840000074-Undergraduate_Thesis.pdf Restricted to Repository staff only Download (5MB) | Request a copy

Abstract

Masa nifas merupakan periode setelah persalinan yang rentan bagi ibu untuk mengalami berbagai gangguan kesehatan mental, salah satunya adalah postpartum depression (PPD). Postpartum depression dapat memberikan dampak buruk bagi kesehatan fisik dan psikologis ibu, serta berdampak negatif pada perkembangan bayi. Intervensi non-farmakologis seperti terapi musik telah terbukti efektif dalam mengurangi gejala depresi. Musik penenang dapat menurunkan kecemasan, memperbaiki kualitas tidur, dan meningkatkan suasana hati ibu. Namun, pemilihan musik yang tepat sesuai dengan kebutuhan emosional ibu merupakan tantangan tersendiri. Penelitian ini bertujuan untuk membangun sistem rekomendasi musik penenang yang dipersonalisasi bagi ibu penderita postpartum depression dengan menggunakan metode Convolutional Recurrent Neural Network (CRNN). Metode CRNN dipilih karena kemampuannya dalam mengekstraksi fitur spasial dan temporal dari sinyal audio musik. Data yang digunakan dalam penelitian ini mencakup dataset lagu-lagu penenang dan profil emosional ibu. Sistem ini bekerja dengan menganalisis karakteristik audio musik dan menyesuaikannya dengan preferensi serta kondisi psikologis ibu. Hasil pengujian menunjukkan bahwa model CRNN mampu mengklasifikasikan dan merekomendasikan musik dengan akurasi yang baik. Sistem rekomendasi yang dikembangkan diharapkan dapat menjadi alat bantu bagi ibu penderita postpartum depression dalam mengelola kesehatan mental mereka secara mandiri dan efektif.
==============================================================================================================================
The postpartum period is a vulnerable time for mothers to experience various mental health disorders, one of which is postpartum depression (PPD). Postpartum depression can have detrimental effects on a mother's physical and psychological health, as well as negatively impact the baby's development. Non-pharmacological interventions such as music therapy have been proven effective in reducing depressive symptoms. Calming music can reduce anxiety, improve sleep quality, and enhance a mother's mood. However, selecting the appropriate music according to a mother's emotional needs is a challenge. This study aims to build a personalized calming music recommendation system for mothers with postpartum depression using the Convolutional Recurrent Neural Network (CRNN) method. The CRNN method was chosen for its ability to extract spatial and temporal features from music audio signals. The data used in this study includes a dataset of calming songs and the mother's emotional profile. The system works by analyzing the audio characteristics of music and matching them with the mother's preferences and psychological conditions. Test results indicate that the CRNN model is capable of classifying and recommending music with good accuracy. The developed recommendation system is expected to be a tool for mothers with postpartum depression to manage their mental health independently and effectively.

Item Type:	Thesis (Other)
Additional Information:	RSSI 618.76 Nab s-1 2022
Uncontrolled Keywords:	Postpartum Depression. Sistem Rekomendasi Musik. Convolutional Recurrent Neural Network.
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD30.213 Management information systems. Dashboards. Enterprise resource planning.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	03 Jun 2026 08:25
Last Modified:	03 Jun 2026 08:25
URI:	http://repository.its.ac.id/id/eprint/133533

Actions (login required)

View Item