1. Problem Definition
I have already made seven blogs (including this W7 blog) of the total 22 blogs that have to be made. How to know the time I used to set up W22 blog will be predict from now. I will be made the learning curve from the each time writing blog data. The results of the learning curve is expected to note the time of writing the blog in the foreseeable future.
2. Development of Feasible Alternatives
Learning Curve Theory is concerned with the idea that when a new job, process or activity commences for the first time it is likely that the workforce involved will not achieve maximum efficiency immediately. Repetition of the task is likely to make the people more confident and knowledgeable and will eventually result in a more efficient and rapid operation. Eventually the learning process will stop after continually repeating the job. As a consequence the time to complete a task will initially decline and then stabilise once efficient working is achieved. The cumulative average time per unit is assumed to decrease by a constant percentage every time that output doubles. Cumulative average time refers to the average time per unit for all units produced so far, from and including the first one made. 
3. Development Outcomes for Alternative
Will be made two types of learning curve calculation :
– Learning curve from the actual data trendline
– Learning curve from the calculation formula.
4. Selection of Criteria
Learning curve formula is : 
Zu = K (un)
u = the output unit number;
Zu = the number of input resource units needed to produce output unit u;
K = the number of input resource units needed to produce the first output unit;
s = the learning curve slope parameter expressed as a decimal;
n = log s / log 2 = the learning curve exponent.
5. Analysis of the Alternatives
The actual data of time writing each blog as table below.
From actual data above, learning process can be forecast into week 22 by logarithmic trendline as graphic below. In trend line method, logarithmic is selected as best equation of writing duration since it has the highest R‐squared and it shape is make sense.
The table below is representing result of learning curve calculation by formula (s = 90%).
From table above, can be made graphical curve as below.
6. Selection of Preferred Alternative
The result of learning curve, time for W22 blog posting are:
Trendline from actual data : writing time 406.96 minutes ; average time 237.26 minutes
From formula calculation : writing time 37.56 minutes ; average time 43.30 minutes
7. Performance Monitoring & Post Evaluation of Results
There are striking difference between result of both learning curve. Learning curve formula is more precise used for activities that are repetitive and not appropriate to be used for blog posting with different topics and different difficulty levels every week. Also in this case, the actual trendline arguably can not represent learning processes that occur until week 22, because the time data not getting down. So for this case has not occurred a significant learning process that deserves to be made trendline forward.
 Learning Curve Theory. (n.d.). Retrieved from http://www.managementaccountant.in/2007/04/learning-curve-theory.html
 Sullivan, W. G., Wicks, E. M., & Koelling, C. P. (2012). Engineering Economy (15th ed., p. 86). Boston: Prentice Hall.
 MAHAKAM13: W9_TRI_ Blog Posting Learning Curve. (n.d.). Retrieved from http://aacemahakam.blogspot.com/2012/10/w9tri-blog-posting-learning-curve.html