Abstrak

Implementation of automation technology for grading tobacco leaf was very promising. In Indonesia, grading tobacco leaf was done manually and relied on the skill and experience of tobacco leaf graders. Large tobacco plantation needed many graders, and the workers needed to be trained, to become a skilled grader. It would take a long time and substantial cost to prepare sufficient graders. Even if the plantation had enough graders, monotonous and long duration of work would raise the human error. Therefore, we proposed a method for grading tobacco leaf based on color and quality using image processing techniques. This work covered quality inspection of tobacco leaf, namely leaf defect detection and classification of tobacco leaf based on color. Image processing techniques such as image thresholding, morphological operation, blob detection, and color analysis of tobacco leaf were employed to determine the grade of tobacco leaf. From the experiment, the proposed method was able to detect a leaf defect and able to classify tobacco leaf with 91.667% accuracy.

Abstrak

The need for fuel oil has increased along with the increase of population, the number of vehicles and industries. An increase in demand for fuel oil is used by some people to make a profit by selling mixed fuel oil at the same price as the price set by the government. The purpose of this study is to create a prototype device that can characterize the type of fuel oil and create a classification system to determine the level of fuel purity with 40 kHz ultrasonic waves based on the parameters of wave velocity using the K-Nearest Neighbor (KNN) algorithm.This device works by using a 40 kHz ultrasonic wave that is connected to an ultrasonic transmitter. The propagated wave will be received by the ultrasonic receiver. The wave received by the receiver will be amplified and connected to the comparator circuit so that it can be processed by a microcontroller. Data obtained using this tool are wave travel time, wave velocity, density, and attenuation. The data used for classification systems using the KNN algorithm is wave velocity.Classification using the KNN algorithm can identify the level of fuel purity based on the parameters of the wave velocity obtained from ultrasonic wave gauges with an accuracy of 72.50%. Wave velocity which is measured using ultrasonic waves is directly proportional to the actual speed with the largest percentage of deviations that is 0.34%.

In a wireless communication, a signal power amplifier is needed to carry signals over a long distance. Many signal amplifiers are made of tube as the amplifying component and as such they suffer low efficiency. This study presents results of the research on designing and implementing solid state 144MHz signal amplifier employing MOSFET BLF278 as the active component of the amplifier. The amplifier has an automatic activation switch, an input terminal, a 50 ohm impedance output terminal and a bypass system for two way communication. This amplifier has been tested on frequencies between 144.00 MHz to 146.90 MHz in 100 KHz steps without adjusting the tuning. It is found that the signal amplifier delivers RF powers up to 500 watt at the working voltage of 48 volt with the efficiency of 77%. The amplifier has a Standing Wave Ratio (SWR) of 1:1 when amplifying and 1.1:1.0 when in bypass mode. The signal amplifier has been tested successfully for radio communication between Sukoharjo in Central Jawa and Surabaya in East Jawa