Habibi, Mohammad Sufa Ammar and Navsih, Muhammad Ghinan (2025) PREDIKSI KEBUTUHAN PEGAWAI HARIAN PT ANTAR SURYA JAYA MENGGUNAKAN LONG-SHORT TERM MEMORY (LSTM). Project Report (Praktek Kerja Lapang dan Magang). Universitas Pembangunan Nasional "Veteran" Jawa Timur.
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Abstract
PT Antar Surya Jaya faces challenges in the production process due to uncertainty in the fulfillment of daily labor. The need for daily workers is often sudden and difficult to predict, so the availability of labor is not always optimal. This causes a slowdown in production and a decrease in the amount of output because available workers have to bear additional workloads. To overcome this problem, this research proposes a prediction model for daily labor requirements using Long-Short Term Memory (LSTM). LSTM is used to capture historical patterns of production demand and labor availability data to improve the accuracy of labor demand prediction. The results show that the proposed model is able to provide more accurate predictions than conventional methods, so it can help companies in planning labor requirements more effectively. With the application of this model, the process of calling workers can be done in a more structured and proactive manner, reducing uncertainty and increasing production efficiency.
| Item Type: | Monograph (Project Report (Praktek Kerja Lapang dan Magang)) | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76.6 Computer Programming | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Data Science | ||||||||||||
| Depositing User: | Mohammad Sufa Ammar Habibi Habibi | ||||||||||||
| Date Deposited: | 07 Jul 2026 07:13 | ||||||||||||
| Last Modified: | 07 Jul 2026 07:13 | ||||||||||||
| URI: | https://repository.upnjatim.ac.id/id/eprint/54766 |
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