Sistem Keamanan Sepeda Motor Dengan Mengganti Saklar Starter Menggunakan Fingerprint

Rizkyana, Raju; Surya, Awang

doi:10.37373/jttm.v2i1.90

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Backpropagation merupakan algoritma pembelajaran pada ANN yang terawasi (supervised learning) diperkenalkan oleh David Rumelhart dan James McClelland pada tahun 1986 [12]. Proses optimasi dilakukan dengan membuat sebuah data training sehingga menghasilkan sebuah data output sesuai yang diharapkan [13]. Proses optimasi data sangat dipengaruhi oleh intensitas data training, semakin banyak pola yang dimasukkan ke dalam parameter training maka sistem ANN akan memberikan keputusan atau hasil optimasi dengan lebih akurat, begitu pula terhadap efektivitas dan kecepatan mencapai konvergensinya akan semakin cepat tercapai [14].…”

Section: Pendahuluanunclassified

Optimasi konsumsi bahan bakar pada mesin bensin menggunakan artificial neural network

Nugroho

Nurhasan

Wijanarko

et al. 2023

jttm

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The engine control unit (ECU) is the primary component of the current injection technique used in spark ignition (SI) engines. The combustion system, which includes injection timing and ignition timing, is controlled by the engine map recorded in the ECU. To maximize an engine's performance, ECU remapping can be used. Remapping is currently not efficient because the data is typically employed in a trial-and-error fashion. To solve these issues, artificial neural network (ANN) techniques are being developed. In this study, engine remapping for fuel economy optimization is predicted using an artificial neural network (ANN). The purpose of this study is to use ANN to compare motorbike performance between before and after engine remapping (Artificial Neural Network). A set of dynamometers is used in this study approach to assess the performance of the 4 stroke, 160 CC spark plug ignition engine in terms of fuel consumption, power, and torque. A current (standard) engine map that contains a number of data from the electronic control unit (ECU) is used to start the optimization process. Pertalite and Pertamax, which are purchased from the closest gas station, are the fuels used in this study. In addition, optimization utilizing ANN is carried out with the goal of 10% increases in torque and power while maintaining a consistent fuel consumption. Following re-implementation into the ECU, the outcomes of the ANN training will be tested again on the dynamometer. The study's finding is that by using the training function, it is possible to improve machine performance using the regression value of R = 0.98993 and the ANN prediction outcomes. Comparing the optimized engine performance to the factory default engine performance, torque increased 10.9% and power rose 9.5%.

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Section: Pendahuluanunclassified

Optimasi konsumsi bahan bakar pada mesin bensin menggunakan artificial neural network

Nugroho

Nurhasan

Wijanarko

et al. 2023

jttm

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“…Selain itu, pemanfaatan Radio Frequency Identification (RFID) untuk menghidupkan sepeda motor sehingga hanya pemilik yang mempunyai tag reader yang bisa menggunakannya [4]. Ada juga fingerprint digunakan sebagai saklar untuk menghidupkan motor sehingga hanya sidik jari yang terdaftar yang dapat menghidupkan motor [5].…”

Section: Pendahuluanunclassified

Sistem Internet of Things dan Transmisi Data Menggunakan Aplikasi Telegram pada Sistem Keamanan Sepeda Motor

Kholik¹,

Ibrahim

Rahmadewi³

2022

STRING

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Sepeda motor adalah alat transportasi kendaran beroda dua yang digerakan oleh sebuah mesin. Dengan adanya sepeda motor kita akan lebih cepat dan mudah mencapai lokasi tujuan kita. Hal itu membuat pengguna sepeda motor dijalan raya terlihat lebih banyak dari pada kendaraan lainnya. Akibat banyaknya kendaraan sepeda motor maka bertambahnya tindak kriminalitas pencurian sepeda motor. Faktor utama yang mendasari pencurian sepeda motor karena faktor ekonomi. Oleh karna itu, dibuat sistem keamanan sepeda motor berbasis IoT dengan aplikasi Telegram. Dimana sistem keamananya menggunakan GPS Quectel L80, fingerprint dan bisa juga mengontrol motor dengan aplikasi telegram. Hasil pengujian perintah pada sistem keamanan sepeda motor menggunakan aplikasi telegram didapat hasil waktu delay yang stabil pada setiap percobaan. Dimana nilai rata-rata delay pada pengujian perintah Engine ON dan Engine OFF sebesar 5,01. Sedangkan nilai rata-rata delay pada pengujian ADD sidik jari sebesar 4,4 detik dan nilai rata-rata delay pada pengujian Delete sidik jari sebesar 2,3 detik. Pada penilaian Quality of Service dari hasil pengujian menggunakan wireshark dapat dilihat hasil rata-rata pada jaringan baik, karena memenuhi syarat standarisasi dengan nilai indeks 3.3. Dimana nilai rata-rata delay didapatkan sebesar 170,168865 ms, nilai rata-rata throughput didapatkan sebesar 17573,68780 kilo bytes/17k, dan nilai packet loss sebesar 0%.

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“…Several technologies are used to create motorcycle security systems, including the use of Radio Frequency Identification (RFID) [6], [7]; fingerprint sensor [8], [9], [10]; using password [11]; and Global Positioning System (GPS) module [12], [13].…”

Section: Introductionmentioning

confidence: 99%

“…Unregistered fingerprints will not start the motorcycle engine and will activate the motorcycle alarm. To begin, the registered fingerprint motorbike engine must be clean and dry (not wet) [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Battery Electric Power Consumption in a Fingerprint System or a Motor Manual Lock System

Amalia

Andari

Arif

2023

protk

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Motorcycle security systems using fingerprint sensors are needed to avoid theft because of their high level of security compared to manual systems. This study underscores the importance of using this fingerprint sensor for motorcycle security. The formulation of the problem in this study is how much electricity is required in the motorcycle battery for this fingerprint security system. This system uses various components, such as the R503 fingerprint sensor, Arduino Uno, buzzer, 4-channel relay, ignition key, and motorcycle starter. Testing of this system is performed by inserting a finger in a wet, oily, dusty, or normal condition, and the result is that this sensor is capable of detecting the entire condition of the finger. Attempts to start a motorcycle with all ten fingers showed that the left index finger started faster under normal conditions, in 1.55 seconds compared to the manual method, which took 2.05 seconds longer. It also measures power consumption and calculates how long the battery can power this device. The result is that the battery can power this device for 180 hours without interruption

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Sistem Keamanan Sepeda Motor Dengan Mengganti Saklar Starter Menggunakan Fingerprint

Cited by 4 publications

References 5 publications

Optimasi konsumsi bahan bakar pada mesin bensin menggunakan artificial neural network

Optimasi konsumsi bahan bakar pada mesin bensin menggunakan artificial neural network

Sistem Internet of Things dan Transmisi Data Menggunakan Aplikasi Telegram pada Sistem Keamanan Sepeda Motor

Comparison of Battery Electric Power Consumption in a Fingerprint System or a Motor Manual Lock System

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