Teknologi Fusi Multi Sensor Menggunakan Kalman Filter Pada Sistem Combine Harvester Untuk Pengukuran Luas Lahan Yang Telah Di Panen

Sawungsari, Gabriel (2026) Teknologi Fusi Multi Sensor Menggunakan Kalman Filter Pada Sistem Combine Harvester Untuk Pengukuran Luas Lahan Yang Telah Di Panen. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perusahaan jasa sewa mesin combine harvester "Tani Berhasil" menghadapi kerugian finansial akibat pengukuran luas lahan panen secara manual yang rentan terhadap manipulasi. Untuk mengatasi masalah ini, penelitian ini mengembangkan sebuah sistem pengukuran berbasis Internet of Things (IoT) yang terintegrasi pada mesin combine harvester untuk menyediakan data yang objektif dan waktu-nyata. Sistem ini memanfaatkan pendekatan fusi multi-sensor dengan mengintegrasikan data dari modul GPS presisi tinggi (u-blox ZED-F9P) dan Inertial Measurement Unit (IMU MPU6050). Metodologi inti dari penelitian ini adalah implementasi algoritma fusi hibrida: Kalman Filter digunakan untuk menghasilkan estimasi sudut roll dan pitch yang stabil dengan meredam derau getaran mesin, sementara Complementary Filter digunakan untuk estimasi sudut yaw yang akurat dengan menggabungkan data giroskop yang responsif dengan data arah absolut (Course Over Ground) dari GPS untuk mengoreksi pergeseran (drift). Seluruh proses akuisisi dan pengolahan data dikendalikan oleh mikrokontroler ESP32-S3. Data hasil fusi kemudian ditransmisikan melalui modul seluler SIM800L ke basis data cloud dan divisualisasikan pada dashboard web untuk pemantauan jarak jauh. Hasil pengujian menunjukkan bahwa algoritma fusi berhasil menghasilkan data orientasi yang stabil dalam kondisi statis maupun dinamis. Pada pengujian lapangan, sistem mampu merekam jejak pergerakan mesin secara andal dan menunjukkan konsistensi tinggi dengan selisih relatif rata-rata hanya 0,79% bila dibandingkan dengan metode estimasi manual. Sistem ini terbukti menjadi solusi yang efektif untuk menyediakan data pengukuran yang transparan dan dapat diaudit, sehingga dapat meminimalkan kerugian dan meningkatkan efisiensi manajemen operasional.
Kata kunci: Fusi Sensor, Kalman Filter, Complementary Filter, GPS ZED-F9P, IMU MPU6050, Combine Harvester, IoT, Pengukuran Luas Lahan.
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The combine harvester rental service company, "Tani Berhasil", faces financial losses due to manual land area measurements that are prone to manipulation. To address this issue, this research develops an Internet of Things (IoT)-based measurement system integrated into a combine harvester to provide objective, real-time data. The system utilizes a multi-sensor fusion approach by integrating data from a high-precision GPS module (u-blox ZED-F9P) and an Inertial Measurement Unit (IMU MPU6050). The core methodology of this research is the implementation of a hybrid fusion algorithm: a Kalman Filter is used to generate stable roll and pitch angle estimations by suppressing machine vibration noise, while a Complementary Filter is used for accurate yaw estimation by combining responsive gyroscope data with the absolute direction from the GPS's Course Over Ground (COG) to correct for drift. The entire data acquisition and processing pipeline is controlled by an ESP32-S3 microcontroller. The fused data is then transmitted via a SIM800L cellular module to a cloud database and visualized on a web dashboard for remote monitoring. Test results demonstrate that the fusion algorithm successfully produces stable orientation data under both static and dynamic conditions. In field tests, the system reliably recorded the machine's trajectory and showed high consistency, with an average relative difference of only 0.79% when compared to manual estimation methods. This system proves to be an effective solution for providing transparent and auditable measurement data, thereby minimizing losses and enhancing operational management efficiency.
Keywords: Sensor Fusion, Kalman Filter, Complementary Filter, GPS ZED-F9P, IMU MPU6050, Combine Harvester, IoT, Land Area Measurement.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Fusi Sensor, Kalman Filter, Complementary Filter, GPS ZED-F9P, IMU MPU6050, Combine Harvester, IoT, Pengukuran Luas Lahan.Sensor Fusion, Kalman Filter, Complementary Filter, GPS ZED-F9P, IMU MPU6050, Combine Harvester, IoT, Land Area Measurement.
Subjects:	T Technology > T Technology (General) > T57.5 Data Processing T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5102.9 Signal processing. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7867.5 Noise T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7888.3 Digital computers T Technology > TS Manufactures > TS176 Manufacturing engineering. Process engineering (Including manufacturing planning, production planning)
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	Gabriel Sawungsari
Date Deposited:	11 Aug 2026 03:04
Last Modified:	11 Aug 2026 03:04
URI:	http://repository.its.ac.id/id/eprint/144269

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