nurcahyaari

Belajar-Cplusplus

isi repository ini adalah beberapa hasil belajar dan algoritma di bahasa C++

c-in-nodejs

coma

Configuration manager on the air

Content-Based-Filtering

Content based Filtering is a libary for calculate recommendation using content based filtering method. build on top of nodejs and typescript. using Naturaljs for solve the NLP Task

golang-starter

Golang code boilerplate inspired by clean architecture

jwt-token-manager

Node Library using typescript

kite

Kite is Golang Project structure's code generation

sqlabst

Simple sql abstraction to join tx and db in the same interface

Vector-Space-Model-using-Cosine-Similarity

Vector space model using cosine similarity

nurcahyaari/Light-Armour

version 0.3

nurcahyaari/My-CPP-Library

nurcahyaari/PhotoSwipe-Gunungapipurba

nurcahyaari/PostestWebAPI

this is api for mobile programming lecture

nurcahyaari/Server-Alpro

Repositori ini berisikan backend untuk web pemilihan slot alpro

nurcahyaari/Simple-Todo-With-React-express

nurcahyaari/Collaborative-Filtering

Implemented Item, User and Hybrid based Collaborative Filtering

nurcahyaari/collaborative_filtering

collaborative filtering module, with distances, pearson, tanimoto , user / item based usage including reading via streams for recommendations algorithms

nurcahyaari/ctr

Collaborative modeling for recommendation. Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings.

nurcahyaari/python-Levenshtein

The Levenshtein Python C extension module contains functions for fast computation of Levenshtein distance and string similarity

nurcahyaari/recommendationRaccoon

A collaborative filtering based recommendation engine and NPM module built on top of Node.js and Redis. The engine uses the Jaccard coefficient to determine the similarity between users and k-nearest-neighbors to create recommendations. This module is useful for anyone with a database of users, a database of products/movies/items and the desire to give their users the ability to like/dislike and receive recommendations.