DevByte Lab

Build infrastructure& systems

I am Fahri Febriandika Pamungkas, a developer who turns rough requirements into practical software products, dashboards, and automation-friendly systems.

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/ IT Support

/ Full-stack web

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IT Infrastructure

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Real product work, documented through shipped screens

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ReactTailwind CSSNode.jsHapi.js

RecMovies

The Movie Recommendation System project aims to help users find movies that match their preferences and moods through a relevant and personalized recommendation system. By analyzing user responses, interaction data, and movie metadata, the system is designed to reduce search time and improve the viewing experience. While there are risks such as data quality and privacy issues, the project is carried out with careful data management to ensure accuracy and safety.

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Interfaces, dashboards, mobile flows, and experiments

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JavaScriptPHPHTML5+3

Project Bansos

Decision Support System for Providing Social Assistance to Underprivileged Communities Using the Simple Additive Weighting (SAW) Method.

DartFlutterMySQL

WawBeans

Online coffee ordering application that makes it easy for you to order your favorite coffee quikly and conveniently, directly from your smartphone.

HTML5CSS3JavaScript+2

Pahawang-Adventures

Project to manage tourism on Pahawang Island, grouping packages provided by the expedition party.

PythonTensorflowNumPy

Virtual Voice Assistence

Artificial Intelligence Final Project Assignment This program uses Python with the keywords 'search', 'open', 'close', 'recommendation', 'message', 'location', 'promo'. This program also uses the RNN (Recurrent Neural Network) algorithm.

PythonJupyter NotebookPandas+2

Bike Sharing Dashboard Analysis

This project was created to complete the "Getting Started Programming with Python" course on the Dicoding platform. The dataset used was obtained from the following link: https://www.kaggle.com/datasets/lakshmi25npathi/bike-sharing-dataset/discussion?sort=undefined

PythonJupyter NotebookPandas+3

Classification of Heart and Kidney Diseases using Machine Learning Algorithms

This application implements heart and kidney disease classification using several popular machine learning algorithms. Users can select a dataset (heart or kidney disease), choose a classification algorithm (K-Nearest Neighbors, Support Vector Machine, or Random Forest), and view accuracy results and classification reports to compare the performance of these algorithms.

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