Analisis Perbedaan Data Aktual dan Data Visual Cold Storage pada Implementasi SCADA WinCC PLC Siemens

Abstract

Cold Storage di PT XYZ menyimpan bahan esens dan rasa yang sensitif terhadap suhu, sehingga dilengkapi sistem monitoring berbasis PLC Siemens S7-1500 dan SCADA WinCC Unified, namun potensi perbedaan antara suhu aktual dan suhu visual belum dievaluasi. Penelitian ini bertujuan merencanakan, mengimplementasikan, dan menguji sistem monitoring tersebut pada tiga unit Cold Storage (15.1, 16.1, 17.1) menggunakan pendekatan System Development Life Cycle (SDLC) dan metode deskriptif-kuantitatif, dengan analisis Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan persentase error rata-rata. Hasil pengujian menunjukkan selisih waktu konsisten sebesar 4 detik pada seluruh data, akibat akumulasi refresh rate lima lapisan komunikasi sistem. Cold Storage 16.1 memiliki kinerja terbaik (MAE 0,0202°C, RMSE 0,0657°C, error 1,5697%) berkat suhunya yang stabil, sedangkan Cold Storage 15.1 memiliki MAE dan RMSE tertinggi (0,0763°C, 0,1209°C) akibat suhu yang dinamis, dan Cold Storage 17.1 memiliki persentase error tertinggi (4,9588%) karena suhu mendekati 0°C. Sistem ini secara relatif akurat, dengan Cold Storage 16.1 dapat dijadikan acuan performa dan optimasi refresh rate direkomendasikan untuk unit bersuhu dinamis.

The cold storage facility at PT XYZ stores temperature-sensitive ingredients and flavorings; as such, it is equipped with a monitoring system based on a Siemens S7-1500 PLC and WinCC Unified SCADA software. However, the potential discrepancy between the actual temperature and the visual temperature has not yet been evaluated. This study aims to design, implement, and test this monitoring system in three cold storage units (15.1, 16.1, 17.1) using the System Development Life Cycle (SDLC) approach and descriptive-quantitative methods, with analysis of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and average error percentage. Test results showed a consistent time difference of 4 seconds across all data, due to the cumulative effect of the refresh rates across the system’s five communication layers. Cold Storage 16.1 exhibited the best performance (MAE 0.0202°C, RMSE 0.0657°C; error 1.5697%) thanks to its stable temperature, while Cold Storage 15.1 had the highest MAE and RMSE (0.0763°C, 0.1209°C) due to its dynamic temperature, and Cold Storage 17.1 had the highest error percentage (4.9588%) because its temperature was close to 0°C. This system is relatively accurate, with Cold Storage 16.1 serving as a benchmark for performance, optimizing the refresh rate is recommended for units with dynamic temperatures.