Fazlurrahman, Jalu Iman (2026) Rancang Bangun Sistem Integrasi Data Sensor Untuk Monitoring Real-Time Pada Sistem Pengolahan Sampah. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Permasalahan pengelolaan sampah di lingkungan perguruan tinggi menjadi isu yang perlu mendapat perhatian serius. Berdasarkan hasil kuesioner yang dilakukan dengan 80 orang di Departemen Teknik Elektro Otomasi (DTEO) Institut Teknologi Sepuluh Nopember Surabaya, sebanyak 57,6% mahasiswa menyatakan fasilitas tempat sampah belum memadai, 51,1% merasa sering terganggu oleh sampah yang berserakan, dan 53,2% mengaku tidak melakukan pemilahan sampah. Kondisi tersebut diperparah dengan tidak adanya sistem pemantauan kapasitas tempat sampah secara real-time, sehingga pengangkutan masih dilakukan berdasarkan jadwal tetap (fixed schedule) tanpa mempertimbangkan kondisi aktual di lapangan, yang mengakibatkan overflow sampah kerap terjadi dan mengganggu kenyamanan lingkungan DTEO ITS. Untuk menjawab permasalahan tersebut, penelitian ini merancang dan membangun sistem integrasi data sensor untuk monitoring real-time pada sistem pengelolaan sampah di lingkungan DTEO ITS. Sistem yang dikembangkan mencakup alur integrasi data dari ESP32 yang mengirimkan data berat (load cell) dan volume (ultrasonik), serta dari Raspberry Pi yang mengirimkan hasil deteksi berupa confidence klasifikasi, latensi pengiriman data, dan informasi lokasi dari pendeteksian objek sampah, seluruhnya dikirimkan ke server berbasis Laravel melalui protokol HTTP berbasis RESTful API. Data yang diterima server kemudian disimpan sebagai data historis dan ditampilkan secara real-time melalui dashboard website, sehingga pengelola dapat memantau kondisi tempat sampah secara aktual dan pengangkutan dapat dilakukan berdasarkan data riil. Hasil pengujian menunjukkan bahwa sistem berhasil mencapai tingkat keberhasilan transmisi data 100% dari total 23.493 pengiriman selama beberapa sesi pengujian selama 13 hari, dengan latensi rata-rata 39,90 ms. Pipeline data berhasil memproses 23.575 event pengujian tanpa kehilangan data, dengan rata-rata latensi pemrosesan server-side sebesar 51,80 ms
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Waste management in higher education institutions has become an issue that requires serious attention. Based on a questionnaire conducted in the Department of Automation Engineering, Sepuluh Nopember Institute of Technology (ITS), Surabaya, 57.6% of students stated that the available waste bin facilities were inadequate, 51.1% reported being frequently disturbed by scattered waste, and 53.2% admitted that they did not practice waste segregation. This condition is aggravated by the absence of a real-time waste bin capacity monitoring system, causing waste collection to be carried out based on a fixed schedule without considering actual field conditions. As a result, waste bin overflows frequently occur and negatively affect the comfort of the DTEO ITS environment. To address these issues, this study designed and developed a sensor data integration system for real-time monitoring of waste management in the DTEO ITS environment. The developed system integrates data from an ESP32 that transmits weight measurements in grams from a load cell sensor and volume measurements in percentages from an ultrasonic sensor, as well as data from a Raspberry Pi that sends detection results including classification confidence, data transmission latency, and location information of detected waste objects. All data are transmitted to a Laravel-based server through the HTTP protocol using a RESTful API architecture. The received data are then stored as historical records and displayed in real time through a web-based dashboard, enabling administrators to monitor waste bin conditions and perform waste collection based on actual data. The testing results showed that the system achieved a 100% data transmission success rate across a total of 23,493 transmissions during multiple testing sessions conducted over 13 days, with an average latency of 39.90 ms. The data pipeline successfully processed 23,575 test events without any data loss, achieving an average server-side processing latency of 51.80 ms
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