SISTEM REKOMENDASI AKTIVITAS PADA APLIKASI MOOD TRACKER BERBASIS ANDROID MENGGUNAKAN TWO-TOWER NEURAL NETWORK

JAILANI, AHMAD and Yusliani, Novi and Darmawahyuni, Annisa (2023) SISTEM REKOMENDASI AKTIVITAS PADA APLIKASI MOOD TRACKER BERBASIS ANDROID MENGGUNAKAN TWO-TOWER NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

The person's mental health is influenced by many factors, one of which is mood. Mood is important to manage, because mood can affect daily productivity. This research aims to create an Android-based mood tracker software that can be used to monitor mood. This software has a recommendation system that can recommend activities to do so that the mood gets better. The recommendation system uses a Two-tower Neural Network. The results of testing the activity recommendation model using the Top-K Accuracy method show that the number of activities that suitable to be recommended is as many as 10 activities and the model can recommend activities that are different from the previous activity recommendations with the new dataset, even though the model is still classified as overfitting. The results of testing Android applications using unit tests and UAT (User Acceptance Test) show that the application meets user needs and runs well.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sistem Rekomendasi, Mood (Suasana Hati), Android, Two-tower Neural Network, Collaborative Filtering, Top-K Classification Accuracy
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Ahmad Jailani
Date Deposited:	10 Apr 2023 02:36
Last Modified:	10 Apr 2023 02:36
URI:	http://repository.unsri.ac.id/id/eprint/94042

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