Rigorous, real analysis, proof of De Moivre–Laplace theorem

The undergraduate books in probability theory that I know do not provide a rigorous proof of even a weak version of the central limit theorem. Instead, they rely on Lévy's continuity theorem, whose proof they choose to omit due to it allegedly being too technical. It seems (see comments here) that the proof of the De Moivre–Laplace theorem which is just a special case of the central limit theorem is not as difficult to prove and I've been searching for a sufficiently rigorous proof. However, in order to prove it everyone either refers to the central limit theorem which they as aforementioned do not provide a proof of or they provide some non-rigorous proof.

I've searched on google for quite a while now and I am unable to find a proof that is rigorous and does not refer to the central limit theorem. This makes me think that a proper proof of the De Moivre–Laplace theorem is beyond the scope of undergraduate mathematics students as well.

Is this correct? If not, how does one prove this thing? Preferably I'd like it to be proven with some ordinary real analysis methods if possible. If I am to do it by myself, where do I start? And no, I don't like the wikipedia proof.

Excerpts from Kai Lai Chung's book Elementary probability theory with stochastic processes (Springer, 1974, Undergraduate texts in mathematics):