The purpose of this study is to conduct a quality control analysis of carded and combed yarn produced using the six sigma method. The approach used in working with the Six Sigma method is DMAIC (define, measure, analyze, improve and control). The results is obtained from the carded and combed yarn products are there are 4 types of defects, such as the strength of the thread does not match the standard, contami, bad rolls and tangled thread. The average DPMO value is both threads of 7,786 with an average sigma level of 4. The root causes of product defects are operators’ fatigue, age of machines, no machine checks, cotton fibers from the eastern countries and lack of ventilation. The conclusion obtained by using the DMAIC (define, measure, analyze, improve and control) to analyze 4 types of strength defects is that the strength defects do not match the standard is the most dominant type of defect. In addition, the results of the DPMO value and sigma level indicate that the product quality is good, exceeding the average sigma level of the company in Indonesia.

Internet of Things or commonly referred to as IoT is a concept that has the aim to expand the utilization of the internet. The application of IoT can be done in various fields, both in the fields of science, industry, health, and geographical. One example of the application of IoT in this study is for net AI classification, according to Government Regulation No. 82 of 2001 the classification of water quality is divided into 4 classes, namely classes one, two, three and four. By utilizing IoT and implementing the Naïve Bayes algorithm as a basis for classification, it can facilitate monitoring of water itself whether it is feasible or not for use in everyday life. In addition to applying the Naïve Bayes algorithm as an algorithm for classification, it also designs and builds tools for collecting water data, using temperature, pH, and turbidity sensors. The results obtained are the accuracy of 99.16 % for the temperature sensor, 96.89% for the pH sensor and 100% for the water turbidity sensor.