Parijoto fruit is a typical fruit that grows around Mount Muria, Kudus Regency and Mount Merapi in Yogyakarta, Indonesia. This fruit has many health benefits, especially for pregnant women. This fruit production is not much because of its limited growth around Mount Muria. So parijoto fruit is made into powder drink products and syrup, so that it can be consumed in a longer period of time and not only during the harvest. To get a good processed product requires good quality ingredients. Parijoto fruit needs to be sorted and classified. Current technology allows classification to be done by digital image processing. The Gray Level Cooccurrence Matrix (GLCM) method is proposed to extract the texture features from the parijoto fruit and then classify them using the K-Nearest Neighbor (KNN) method. GLCM can describe a spatial linear relationship of the frequency at which gray values are determined by other gray values in one area of investigation. It can simply use the statistical approach of appearance or histogram of the image matrix. In this way, information will be easily relative position of neighboring pixels that are suitable for the classification process using KNN. KKN was chosen because this method was proven to be used for relatively few datasets, but a normalization process was needed to increase accuracy. Based on the results of the implementation of the GLCM and KNN methods for parijoto fruit classification the classification accuracy was 80%.

The ultrasonic range finder sensors is a general-purpose sensor to measure the distance contactless. This sensor categorized as low-cost sensor that widely used in various application. This sensor has a significant deviation that lead to significant error in the measurement result. The error that produced by this sensor tends to increase proportionally to the measured distance. The implementation of the particular algorithm is required to reduce the error value. The model-based calibration is a solution to increase the accuracy. The model-based solutions are no longer feasible if the states of the model have changed. The longer of the usage of the sensor lead to sensor fatigue. Sensor fatigue is one of the causes of model state changes. As long as the drift still within the tolerance limit, the performance of the sensor still can be restored by using calibration method. The model-based calibration calibrates the sensor by using the model. The update of the model must be made whenever the changing of the model state occurred. Since the manual model making process is not an easy task, time and cost required, then the Newton polynomial-based AMG (Automatic Model Generation) have been implemented to this research. The AMG algorithm generates the new sensor model automatically based on the most updated states. This automatic model generation is implemented in the calibration process of the ultrasonic sensor. The implementation of polynomial-based AMG algorithm for sensor calibration have been succeeded to improve the accuracy of the calibrated sensor by 96.4% and reduce the MSE level from 25.6 to 0.914.

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Carbon monoxide is a type of pollutant that is harmful to human health and the environment. On the other hand, carbon monoxide also has benefits for industrial matter. Since the benefits and disadvantages of carbon monoxide, the measurement of carbon monoxide concentration is required. The measurement of carbon monoxide level is not easy moreover with low-cost sensors. The usage of 4 sensors namely TGS2611, TGS2612, TGS2610 and TGS2602 has been used along with feature extractor. The polynomial classifier is required to interpret the feature vector into the amount of substance concentration. The common classifier methods suffer fatal limitations. The polynomial classifiers method offers lower complexity in solution and lower computational effort. Since the involvement of a huge number of data points in the modelling process leads to high degree in the polynomial model. The occurrence of Runge's phenomenon is highly possible in this condition. This phenomenon affects the accuracy level of the generated model. The degree reduction algorithm is required to prevent the occurrence of Runge’s phenomenon. The combination of MAF (Mean Average Filter) and derivative approach as degree reductor algorithm has succeeded in reducing the polynomial model degree. The greater the number degree in the model means the greater the computational load. The model degree reductor algorithm has been succeeded to reduce computational load by 96.6%.