Expounding implementation details of a paper and provide a full computational analysis
This is a problem with a lot of research these days: reproducibility. From my experience publishing an implementation approach to a paper, when no approach was given is a valid academic contribution, as there could be various approaches each with its own advantages and disadvantages. If you can expand your work to include multiple reasonable potential approaches and their outcomes, as well as potential pitfalls that could arise (you probably already went through both of these with your work), it would be a significant worthwhile contribution. 

The next question is whether the journal you submit to sees it this way, and that can be a hit or miss. The only way to know is to try. Try to frame your work as "Approaches to X (where X is the sub-problem you tackled), instead of linking it too heavily with the original work (where X is e.g. the name of the original paper). If everything else fails, you can always try a conference or open publication although the "social credit" for these will be much less.

I also support the idea of publishing your code on github. The more we share the better off we all will be. This open sharing approach has aided in the significant machine learning publishing boom, and fast pace of improvements we are seeing.