Fitriana, Lailatul Annisa and Ramadhani, Rafaela Shyra Ashma' (2026) Perancangan dan Implementasi Sistem Kalkulasi Kapasitas Warehouse (CAPAS – Warehouse Capacity Prediction Software) pada PT Cipta Krida Bahari Logistic. Project Report. [s.n.], [s.l.]. (Unpublished)
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Abstract
PT Cipta Krida Bahari (CKB) Logistic merupakan perusahaan logistik yang bergerak di bidang pengelolaan warehouse dan distribusi barang di Indonesia. Dalam menjalankan operasional warehouse, perusahaan membutuhkan alat bantu yang dapat menghitung kebutuhan kapasitas penyimpanan, tenaga kerja (manpower), dan peralatan material handling (MHE) secara akurat dan efisien. Proses perencanaan yang selama ini dilakukan secara manual dinilai membutuhkan waktu yang lama dan berpotensi menghasilkan estimasi yang kurang tepat. Dalam rangka menyelesaikan permasalahan tersebut, dikembangkan sebuah sistem berbasis web bernama CAPAS (Warehouse Capacity Prediction Software). Sistem ini dibangun menggunakan arsitektur full-stack dengan frontend berbasis Vue 3 + Vite dan backend berbasis FastAPI (Python). CAPAS menyediakan tiga modul utama, yaitu kalkulasi kebutuhan storage, kalkulasi kebutuhan manpower, dan kalkulasi kebutuhan Material Handling Equipment (MHE). Modul storage menggunakan pendekatan rule-based untuk perhitungan bulking serta model machine learning Random Forest untuk prediksi lokasi penyimpanan berdasarkan dataset. Modul manpower menggunakan metode Linear Regression untuk estimasi kebutuhan tenaga kerja berbasis data historis. Modul MHE menggunakan sistem rule-based berdasarkan unit of measure (UOM) untuk merekomendasikan jenis dan jumlah alat. Sistem CAPAS telah berhasil diimplementasikan dan dapat diakses melalui https://capas.ckblogistics.id/. Hasil User Acceptance Test (UAT) menunjukkan bahwa seluruh fitur sistem berjalan dengan baik dan memenuhi kebutuhan operasional warehouse PT CKB Logistic.
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PT Cipta Krida Bahari (CKB) Logistics is a logistics company specializing in warehouse management and goods distribution across Indonesia. To support warehouse operations, the company requires a tool capable of accurately and efficiently calculating storage capacity requirements, workforce (manpower) needs, and Material Handling Equipment (MHE) requirements. Previously, these planning processes were performed manually, which was time-consuming and prone to estimation inaccuracies. To address these challenges, a web-based system called CAPAS (Warehouse Capacity Prediction Software) was developed. The system was built using a full-stack architecture, with Vue 3 + Vite as the frontend framework and FastAPI (Python) as the backend framework. CAPAS provides three main modules: storage requirement calculation, manpower requirement calculation, and Material Handling Equipment (MHE) requirement calculation. The storage module employs a rule-based approach for bulking calculations and a Random Forest machine learning model to predict storage locations based on historical datasets. The manpower module utilizes Linear Regression to estimate workforce requirements using historical operational data. Meanwhile, the MHE module applies a rule-based system based on Unit of Measure (UOM) classifications to recommend the appropriate types and quantities of equipment. The CAPAS system has been successfully implemented and is accessible at https://capas.ckblogistics.id/. User Acceptance Testing (UAT) results indicate that all system features function properly and effectively meet the operational requirements of PT CKB Logistics' warehouse management processes.
| Item Type: | Monograph (Project Report) |
|---|---|
| Uncontrolled Keywords: | Warehouse, Capacity Planning, Machine Learning, Vue 3, FastAPI, Random Forest, Linear Regression, Pergudangan, Perencanaan Kapasitas, Pembelajaran Mesin, Random Forest, Regresi Linear, Vue 3, FastAPI |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.888 Web sites--Design. Web site development. |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Lailatul Annisa Fitriaana |
| Date Deposited: | 11 Jun 2026 01:07 |
| Last Modified: | 11 Jun 2026 01:07 |
| URI: | http://repository.its.ac.id/id/eprint/133707 |
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