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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
NURYASIN, MUHAMMAD FAUZI
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Electrical & Electronics Engineering Engineering

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WIRELESS SENSOR NETWORK (WSN) UNTUK PREDICTIVE MAINTENANCE LAMPU SINYAL KERETA API NURYASIN, MUHAMMAD FAUZI; WIBAWA, BAMBANG MUKTI; TAUFIK, MOHAMMAD
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 2 (2020): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektro
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i2.418

Abstract

ABSTRAKLampu sinyal memiliki peranan yang penting dalam persinyalan kereta api. Pemeliharaan lampu sinyal perlu diperhatikan agar kondisi lampu sinyal tetap sesuai standar. Penelitian ini merancang Wireless Sensor Network (WSN) untuk mengakuisisi parameter lampu sinyal agar dapat dipantau, dianalisis, dan disimpan. Parameter yang diakuisisi berdasarkan dari standar peraturan Menteri Perhubungan Indonesia mengenai lampu sinyal kereta api antara lain intensitas cahaya, tegangan, suhu, dan kelembapan lingkungan lampu sinyal. Sistem tersusun dari tiga subsistem, yaitu sensor node, gateway, dan server. Sistem dapat melakukan pemantauan intensitas lampu sinyal, suhu dan kelembapan lingkungan lampu sinyal, serta tegangan lampu sinyal yang dikirim secara nirkabel yang dapat mencapai jarak hingga 200 m memanfaatkan spektrum radio frekuensi 433 Mhz. Pada saat ada gangguan terhadap lampu sinyal, sistem telah mampu melakukan predictive maintenance berdasarkan standarisasi lampu sinyal.Kata kunci: lampu sinyal, sensor node, gateway, server, wireless sensor network ABSTRACTSignal lights has an important role in railway signaling. Proper maintenance must be taken to maintain signal lights so that signal lights condition remain in accordance with the standard. This study designed the Wireless Sensor Network (WSN) to acquire signal lights parameter information so that it can be monitored, analyzed and stored. The parameters acquired are based on the Indonesian Minister of Transportation's regulatory standards regarding railway signal lights such as light intensity, voltage, temperature, and humidity of signal lights. System consists of three subsystems, namely sensor node, gateway, and server. System can monitor signal lights intensity, ambient temperature, ambient humidity, and voltage of signal lights then send those parameters which can reach distances of up to 200 m utilizing the radio frequency spectrum 433 Mhz. When there are interferences with signal lights, system has been able to perform predictive maintenance based on the signal lights standardization.Keywords: signal lights, sensor node, gateway, server, wireless sensor network
Wireless Sensor Network (WSN) untuk Predictive Maintenance Lampu Sinyal Kereta Api NURYASIN, MUHAMMAD FAUZI; WIBAWA, BAMBANG MUKTI; TAUFIK, MOHAMMAD
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 2: Published May 2020
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i2.418

Abstract

ABSTRAKLampu sinyal memiliki peranan yang penting dalam persinyalan kereta api. Pemeliharaan lampu sinyal perlu diperhatikan agar kondisi lampu sinyal tetap sesuai standar. Penelitian ini merancang Wireless Sensor Network (WSN) untuk mengakuisisi parameter lampu sinyal agar dapat dipantau, dianalisis, dan disimpan. Parameter yang diakuisisi berdasarkan dari standar peraturan Menteri Perhubungan Indonesia mengenai lampu sinyal kereta api antara lain intensitas cahaya, tegangan, suhu, dan kelembapan lingkungan lampu sinyal. Sistem tersusun dari tiga subsistem, yaitu sensor node, gateway, dan server. Sistem dapat melakukan pemantauan intensitas lampu sinyal, suhu dan kelembapan lingkungan lampu sinyal, serta tegangan lampu sinyal yang dikirim secara nirkabel yang dapat mencapai jarak hingga 200 m memanfaatkan spektrum radio frekuensi 433 Mhz. Pada saat ada gangguan terhadap lampu sinyal, sistem telah mampu melakukan predictive maintenance berdasarkan standarisasi lampu sinyal.Kata kunci: lampu sinyal, sensor node, gateway, server, wireless sensor network ABSTRACTSignal lights has an important role in railway signaling. Proper maintenance must be taken to maintain signal lights so that signal lights condition remain in accordance with the standard. This study designed the Wireless Sensor Network (WSN) to acquire signal lights parameter information so that it can be monitored, analyzed and stored. The parameters acquired are based on the Indonesian Minister of Transportation's regulatory standards regarding railway signal lights such as light intensity, voltage, temperature, and humidity of signal lights. System consists of three subsystems, namely sensor node, gateway, and server. System can monitor signal lights intensity, ambient temperature, ambient humidity, and voltage of signal lights then send those parameters which can reach distances of up to 200 m utilizing the radio frequency spectrum 433 Mhz. When there are interferences with signal lights, system has been able to perform predictive maintenance based on the signal lights standardization.Keywords: signal lights, sensor node, gateway, server, wireless sensor network
Kombinasi Deteksi Objek, Pengenalan Wajah dan Perilaku Anomali menggunakan State Machine untuk Kamera Pengawas NURYASIN, MUHAMMAD FAUZI; MACHBUB, CARMADI; YULIANTI, LENNI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 1: Published January 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i1.86

Abstract

ABSTRAKSaat ini sistem kamera pengawas mengandalkan manusia dalam melakukan penerjemahan pada rekaman gambar yang terjadi. Perkembangan computer vision, machine learning, dan pengolahan citra dapat dimanfaatkan untuk membantu peran manusia dalam melakukan pengawasan. Penelitian ini merancang sistem kerja kamera yang terdiri dari tiga modul yaitu deteksi objek, pengenalan wajah, dan perilaku anomali. Deteksi objek memakai HOG-SVM, pengenalan wajah menggunakan CNN dengan arsitektur VGG-16 memanfaatkan transfer learning, dan perilaku anomali memakai spatiotemporal autoencoder berdasarkan threshold. Ketiga modul tersebut diuji menggunakan metrik akurasi, presisi, recall, dan f1-score. Ketiga modul diintegrasikan dengan state machine menjadi satu kesatuan sistem. Kinerja modul memiliki akurasi 88% untuk deteksi objek, 98% untuk pengenalan wajah, dan 78% untuk perilaku anomali. Hasil tampilan riil dapat diakses secara sederhana dan nirkabel melalui web.Kata kunci: HOG-SVM, CNN, VGG-16, spatiotemporal autoencoder, state machineABSTRACTNowadays, the surveillance camera system relies on human to interpret the recorded images. Computer vision, machine learning, and image processing can be utilized to assist the human role in supervising. This study designed a camera work system consisting of three main modules, namely object detection, face recognition, and anomaly behavior. Object detection used the HOG-SVM combination. Facial recognition used CNN with the VGG-16 architecture that utilized transfer learning. Anomalous behavior used spatiotemporal autoencoder based on threshold. Modules are tested using the metrics of accuracy, precision, recall, and f1-score. The three modules are integrated using a state machine into one system. The performance of the module had 88% accuracy for object detection, 98% for facial recognition, and 78% for anomalous behavior. Real time video recording can be accessed wireless via web-based.Keywords: HOG-SVM, CNN, VGG-16, spatiotemporal autoencoder, state machine
Co-Authors Carmadi Machbub Mohammad Taufik Wibawa, Bambang Mukti YULIANTI, LENNI