First, reread Chapter 2 of the Forecasting: Principles and Practice book that you referenced, specifically section 2.3. It should clarify the difference between cycles and seasonality.
Second, you are struggling to find a way to use the wrong tool. Classical decomposition was designed to decompose a time series into trend/cycle, seasonal and random components, and it is very useful for that specific task. Your data is not seasonal so this model is not appropriate. Very simple.
Third, for ideas on methods that smooth out the random effects and leave the trend/cycle, see Chapter 28 of Irizarry's Introduction to Data Science at https://rafalab.github.io/dsbook/smoothing.html.
Good luck!