Designing a Safety Condition Monitoring System for Body Harness Installation Using the Decision Tree Method
DOI:
https://doi.org/10.33795/jartel.v16i1.9557Keywords:
Body Harness, Microcontroller, Decision Tree, GSM, Proximity, Mpx 5010Abstract
Work safety in high-altitude activities remains a major issue, particularly due to body harness installation errors such as hooks not being attached to anchor points or excessive tension on safety ropes. This study aims to develop a sensor-based body harness safety system and Decision Tree algorithm for real-time automatic monitoring. The system uses inductive proximity sensors to detect hook attachment and MPX5010 pressure sensors to measure excessive pressure on the harness rope. Sensor data is processed by a Decision Tree algorithm embedded in an Arduino Uno microcontroller to classify safe and unsafe conditions. In unsafe conditions, the system sends an emergency notification in the form of a misscall via the SIM800L GSM module. Sensor testing results show a 100% success rate for proximity and pressure sensors. Overall system testing using a confusion matrix results in 88% classification accuracy without false negatives. The proximity sensor functions as hook attachment verification, while the pressure sensor is the main parameter with a threshold of 3.425 kPa. The results of the study show that the system is capable of improving work safety at heights through effective early warning.
References
M. Mushidah, M. F. Aliansyah, and A. Maghfiroh, “Hubungan Penggunaan Alat Pelindung Diri Body Harness terhadap Kejadian Kecelakaan Kerja Jatuh dari Ketinggian pada Teknisi Pemasangan Jaringan di PT Telkom Akses Kendal,” SAINTEKES: Jurnal Sains, Teknologi dan Kesehatan, vol. 2, no. 4, pp. 586–592, 2023, doi: 10.55681/saintekes.v2i4.216.
W. P. Ningrum, I. Siboro, L. M. Zainul, and D. Saputra, “Penggunaan Full Body Harness pada Pekerja Perancah di PT Graha Mandala Sakti Balikpapan,” Identifikasi, vol. 9, no. 2, pp. 858–863, 2023, doi: 10.36277/identifikasi.v9i2.283.
M. E. Santika, M. Prasetyo, D. Nugroho, and B. Murtianta, “Evaluasi Kinerja Sensor Proximity Induktif sebagai Alternatif Pengganti RFID pada Prototipe Rail Guided Vehicle Berbasis Arduino,” vol. 19, no. x, pp. 611–622, 2025.
A. I. Shaleh, A. U. Bani, and B. G. Sudarsono, “Design and Manufacture of Air Pressure Measuring Instruments with Arduino Microcontroller-Based Pressure Water Sensors,” Journal of Mathematics and Technology, vol. 1, no. 1, pp. 17–28, 2022. [Online]. Available: http://journal.binainternusa.org/index.php/matech/article/view/22/15
Widharma, “Sensor Tekanan pada Alat Kesehatan,” 2020.
Suyanto, Machine Learning Tingkat Dasar dan Lanjut Edisi 2. INFORMATIKA, 2018.
D. Septhya et al., “Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 1, pp. 15–19, 2023, doi: 10.57152/malcom.v3i1.591.
G. Tendra, “Sistem Penyiraman Pestisida Otomatis Menggunakan Arduino Uno dan GSM Shield SIM800L,” vol. 12, no. 2, pp. 13–19, 2020.
D. T. Pandiangan, “Perancangan Sistem Alat Kontrol Lampu Menggunakan Perintah SMS dengan Modul GSM SIM800L Berbasis Metode Arduino,” JUKI: Jurnal Komputer dan Informatika, vol. 3, no. 2, pp. 52–58, 2021, doi: 10.53842/juki.v3i2.61.
A. Daniati and W. W. Fadilla, “Analisis Kepatuhan Penggunaan Alat Pelindung Diri (APD) Full Body Harness pada Pekerja PLN ULP Amuntai Tahun 2020,” Jurnal Lentera Kesehatan Masyarakat, vol. 1, no. 2, pp. 50–57, 2022.
M. A. Hasanah, S. Soim, and A. S. Handayani, “Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir,” Journal of Applied Informatics and Computing, vol. 5, no. 2, pp. 103–108, 2021, doi: 10.30871/jaic.v5i2.3200.
F. Fassa, A. F. Setiawan, and N. Agnidjunaedi, “Analisis Kesadaran Pekerja terhadap Penggunaan Alat Pelindung Diri (APD) pada Pekerjaan di Ketinggian dalam Proyek Konstruksi,” vol. 10, no. 2, pp. 45–54, 2024.
J. M. A. F. Dina Rachmawaty, “Penerapan Metode Klasifikasi Decision Tree untuk Memprediksi Kelulusan Tepat Waktu,” Journal of Industrial Engineering Technology, vol. 2, no. 1, pp. 61–74, 2022, doi: 10.24176/jointtech.v2i1.7432.
P. B. N. Setio, D. R. S. Saputro, and Bowo Winarno, “Klasifikasi dengan Pohon Keputusan Berbasis Algoritme C4.5,” PRISMA: Prosiding Seminar Nasional Matematika, vol. 3, pp. 64–71, 2020.
Anggara Trisna Nugraha, Edy Prasetyo Hidayat, Purwidi Asri, Briyen Rangga Prayoga W, and Diego Ilham Yoga Agna, “Prototipe Sistem Pengendalian dan Pemantauan Cargo Hold Bilge Kapal dengan Metode Decision Tree Berbasis Mikrokontroler,” Jurnal 7 Samudra, vol. 8, no. 2, pp. 93–108, 2023, doi: 10.54992/7samudra.v8i2.130.
