Adias Pradana, Marchel and Baihaqi Arrisalah, Muhammad (2025) PENGEMBANGANDANIMPLEMENTASISISTEM APLIKASI MOBILEFINDTOFINEDIYAYASANDICODING INDONESIA. Project Report (Praktek Kerja Lapang). Fakultas Ilmu Komputer, Surabaya. (Unpublished)
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Abstract
Many travelers often face challenges in managing their belongings during trip planning and execution. The difficulty in ensuring that all necessary items are packed often results in stress and inconvenience. Addressing this issue, the FindtoFine application was developed to simplify the packing process, reduce the risk of lost items, and enhance the overall travel experience. In this application, users can leverage machine learning technology to scan their belongings by simply taking photos. This technology enables the application to visually recognize and categorize items, helping users quickly and accurately create an inventory, ensuring no important items are forgotten during the trip. FindtoFine is the result of a project conducted during the Practical Work (PKL) program at Bangkit Academy, an initiative by Google, Gojek, Tokopedia, and Traveloka aimed at developing the technological skills of Indonesian students. Bangkit Academy also offers intensive training in Machine Learning, Mobile Development, and Cloud Computing for 5 months. The program is organized by the Dicoding Indonesia Foundation, located at Dicoding Space, Jalan Batik Kumeli No 50, Cibeunying Kaler District, Sukaluyu Village, RT 10, RW 07, Bandung City, West Java, 40123. During the Practical Work, students apply the knowledge gained through real projects such as the development of the FindtoFine application. This project provides practical experience and deeper understanding of technology development in the industry. Through this PKL, students can also build professional networks and receive direct mentorship from experts in the technology field. The development method of the FindtoFine application involves several key stages. First, for the Machine Learning aspect, the team collected and balanced the dataset using oversampling, then split the data into training and testing sets. Images were scaled and classified using the ResNet50 architecture for transfer learning and trained using TensorFlow, achieving an 85% accuracy on the test data. The final model was saved in H5 format and integrated with FastAPI for further development. Second, for Mobile Development, TensorFlow Lite was applied to create both user and admin applications with real-time connections using Firebase. Finally, for Cloud Computing, the nfrastructure was set up on Google Cloud Platform, including Cloud Run, Firebase, and Cloud Storage. The final results of this program demonstrate the successful integration of machine learning, cloud computing, and mobile development in creating an innovative and useful solution. This application simplifies the user experience with photo-based inventory features, post-trip item verification, and a user-friendly interface. For future implementation, it is recommended that the integration of these three learning tracks continues to be developed to create more advanced applications and services. The synergistic approach between machine learning for data analysis, cloud computing for efficient data processing and storage, and mobile evelopment for creating user-friendly applications will result in applications that are more responsive, secure, and scalable.
| Item Type: | Monograph (Project Report (Praktek Kerja Lapang)) | ||||||||
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| Subjects: | T Technology > T Technology (General) | ||||||||
| Depositing User: | Marchel Adias Pradana | ||||||||
| Date Deposited: | 27 May 2025 06:58 | ||||||||
| Last Modified: | 27 May 2025 06:58 | ||||||||
| URI: | https://repository.upnjatim.ac.id/id/eprint/36766 |
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