Khairuramdhani

About me · Tentang Saya

Turning ideas into software that works.

I'm a Computer Engineering student at Universitas Brawijaya who enjoys building software and seeing it through to something people can actually use. Most of my projects run the full path — from preparing the data and training a model to getting it onto a real phone or device. I work mainly in Python and C++, build mobile apps with Flutter, and my main interest is Computer Vision, though I'm just as comfortable around machine learning, data, and IoT. I like picking up new tools and figuring things out as I go.

Saya mahasiswa Teknik Komputer Universitas Brawijaya yang senang membangun software sampai benar-benar bisa dipakai. Sebagian besar proyek saya kerjakan dari awal sampai akhir — menyiapkan data, melatih model, lalu menjalankannya di perangkat nyata. Saya terbiasa dengan Python dan C++, membuat aplikasi mobile dengan Flutter, dan paling tertarik pada Computer Vision, meski nyaman juga di machine learning, data, maupun IoT.

0 End-to-end projects Proyek end-to-end

0 Core stacks · AI · C++ · Flutter Bidang utama

0 Teaching-assistant roles Asisten praktikum

0 Certifications Sertifikasi

Technical skills · Keahlian Teknis

What I work with.

Programming Languages Bahasa Pemrograman

Python
C++
Dart
JavaScript

AI / Machine Learning Kecerdasan Buatan

PyTorch
TensorFlow
scikit-learn
Ultralytics
YOLO
CNN
SVM

Computer Vision Visi Komputer

OpenCV
Real-time detection
Image segmentation
On-device (TF Lite)

Mobile Development Pengembangan Mobile

Flutter
Firebase
Supabase
UI/UX

Embedded / IoT Sistem Tertanam

C++
Arduino
ESP32
FreeRTOS
Sensors
RTC

Data & Analysis Data & Analisis

pandas
NumPy
matplotlib
seaborn
EDA

Tools & Workflow Alat & Workflow

Git
Linux
Anaconda
Weights & Biases
GitHub Actions

Languages Bahasa

Indonesian (native)
English (still improving)

Experience · Pengalaman

Where I've been building & teaching.

Feb 2026 – Present
AI Engineer Intern AITF

Ministry of Communications & Digital Affairs (Komdigi) × Universitas Brawijaya

A selective AI engineering internship under a national program — a cross-institution collaboration between Indonesia's Ministry of Communications and Digital Affairs (Komdigi) and Universitas Brawijaya.
- Built a semi-automatic data-annotation pipeline using Vision-Language Models, cutting manual labelling effort
- Built end-to-end Deep Learning pipelines (data → training → evaluation) for Computer Vision tasks
- Fine-tuned & optimised Large Language Models via parameter tuning and prompt engineering
- Collaborated in a cross-institution national AI research team
- Vision-Language Models
- LLM fine-tuning
- Deep Learning
- Computer Vision
- Python
- PyTorch
- Prompt Engineering
Even Sem. 2024/2025
Teaching Assistant — Data Structures & Algorithms

Faculty of Computer Science, Universitas Brawijaya

Walked students through data structures and algorithms in the lab (in C++), and reviewed their practical assignments.
Odd Sem. 2024/2025
Teaching Assistant — Basic Programming

Faculty of Computer Science, Universitas Brawijaya

Helped first-year students get comfortable with the basics of programming, running lab sessions and giving feedback on their work.

Teaching assistant · practicum session

Education Pendidikan

Computer Engineering (S1) · Teknik Komputer Universitas Brawijaya, Malang · Aug 2023 – Present

SMA Negeri 1 Sindang, Indramayu Natural Sciences (IPA)

Organizations Organisasi

Robotiik — PIC Mechanical & Computer Vision Mar 2024 – Present · Robot design in Fusion 360 & 3D printing; Deep Learning perception with ROS 2

Poros — Web Front-End Mar 2024 – Dec 2025 · HTML, CSS, React; UI design with Figma

Selected projects

Projects

Proyek pilihan · AI, Computer Vision, Embedded & Mobile

Project 01

CERDAS

Cheating Examination Recognition & Detection — YOLO-based

CERDAS spots exam cheating in real time by reading where someone's head and eyes are pointing. I built it from start to finish — collecting and labelling the data, training a YOLO model, then getting it to run smoothly on an Android phone with TensorFlow Lite and Flutter. It keeps up at around 9 FPS and shows a confidence score for each prediction.

CERDAS mendeteksi perilaku mencontek secara real-time dengan membaca arah kepala dan pandangan mata. Saya kerjakan dari awal sampai akhir — data, model YOLO, lalu deploy ke HP Android lewat TensorFlow Lite & Flutter, di kisaran 9 FPS.

Real-time, on-device inference (~9 FPS)
6-class head & gaze orientation detection
Full pipeline: data → training → Android deployment

Python
Ultralytics
YOLO
Anaconda
Flutter
Dart
TensorFlow Lite
Android

github.com/khaichi11/Mobile-Cheating-Detection-YOLO

Documentation · Dokumentasi

CERDAS detecting head down — flagged as violation — Down · violation

CERDAS detecting front-facing — honest — Front · honest

CERDAS detecting head turned left — flagged as violation — Left · violation

CERDAS detecting head turned right — flagged as violation — Right · violation

Confusion matrix for the 6-class CERDAS model — Confusion matrix · 6 classes

Project 02

FishFeed

Automatic Fish-Feeding System · Embedded & IoT

FishFeed is an automatic fish feeder I built around an ESP32. The firmware is written in C++ on the Arduino framework and uses FreeRTOS to juggle tasks, with ultrasonic and turbidity sensors plus an RTC for timing. A Flutter app talks to it through Firebase, so you can check the water, the feed level, and set feeding schedules right from your phone.

FishFeed adalah pemberi pakan ikan otomatis di sekitar ESP32. Firmware C++ (Arduino) memakai FreeRTOS, sensor ultrasonik & turbidity, serta RTC; aplikasi Flutter terhubung lewat Firebase untuk pantau & jadwal dari HP.

C++ firmware on ESP32 with FreeRTOS multitasking
Ultrasonic & turbidity sensing with RTC scheduling
Real-time monitoring via Flutter + Firebase RTDB

C++
Arduino
ESP32
FreeRTOS
Sensors
RTC
Flutter
Firebase RTDB

github.com/khaichi11/Aplikasi-Sistem-Tertanam-FishFeed

Documentation · Dokumentasi

FishFeed circuit prototyping on a breadboard — Circuit prototyping

FishFeed 3D mechanical design in Fusion 360 — 3D mechanical design

Assembled FishFeed device — Assembled device

FishFeed companion app and monitoring dashboard — App & monitoring dashboard

Project 03

Arrhythmia Classification

R-R Interval & QRS Duration using SVM

Here I trained a Support Vector Machine to tell normal heartbeats apart from arrhythmia, using the R-R interval and QRS duration from ECG data. I went through the whole process — exploring the data, normalising the features, building the pipeline, and checking results with a confusion matrix and classification report. It landed at 96% accuracy.

Saya melatih SVM untuk membedakan detak jantung normal dan aritmia dari R-R interval & durasi QRS pada data ECG. Prosesnya lengkap — EDA, normalisasi fitur, pipeline, hingga evaluasi. Hasilnya 96% akurasi.

96% accuracy on the held-out test set
End-to-end ML pipeline with feature normalisation
EDA, confusion matrix & classification report

Python
scikit-learn
pandas
NumPy
matplotlib
Anaconda
SVM

Code-on-College · SVM.ipynb

Documentation · Dokumentasi

Class distribution chart from exploratory data analysis — Class distribution (EDA)

Feature scatter plot of R-R interval versus QRS duration — Feature scatter · RR vs QRS

Classification report showing 96% accuracy — Classification report · 96%

Confusion matrix for the SVM model — Confusion matrix · SVM

Brand & identity

Project 04

Buah-Seru

Fruit-Recognition Educational App for Children

Buah-Seru is a playful app that helps kids learn about fruit: snap a photo, and it tells you what fruit it is along with a few fun facts. Behind the scenes it uses OpenCV (GrabCut) to clean up the image, a CNN trained in TensorFlow to classify it, and TensorFlow Lite + Flutter to run it on a phone.

Buah-Seru aplikasi seru untuk mengenalkan buah ke anak-anak: foto buahnya, lalu muncul namanya plus info singkat. Ada OpenCV (GrabCut), CNN (TensorFlow), dan TensorFlow Lite + Flutter supaya jalan di HP.

CNN classifier deployed on-device (TF Lite)
OpenCV GrabCut object segmentation
Interactive, gamified learning experience

Python
OpenCV
TensorFlow
TF Lite
CNN
GrabCut
Flutter
Dart

github.com/khaichi11/Aplikasi-Deteksi-Buah-CNN

Documentation · Dokumentasi

Buah-Seru welcome screen with a fruit quiz — Welcome screen

Buah-Seru quiz — identify the fruit — Quiz · identify the fruit

Buah-Seru result screen with nutrition facts — Result & nutrition facts

Project 05

K-Means from Scratch

Clustering for Waste Sorting · Implemented Manually

For this one I wrote K-Means clustering by hand in Python, no scikit-learn, just to really understand how it works underneath. I used pandas and NumPy for the data and the distance maths, and matplotlib to visualise the clusters and make sense of the waste-sorting patterns.

Yang ini saya tulis sendiri algoritma K-Means di Python, tanpa scikit-learn, supaya benar-benar paham cara kerjanya. pandas & NumPy untuk data dan jarak, matplotlib untuk visualisasi cluster.

K-Means implemented from scratch (no scikit-learn)
Vectorised distance computation with NumPy
Cluster visualisation & data-pattern analysis

Python
pandas
NumPy
matplotlib
Anaconda
From scratch

Code-on-College · K_Means_Pemilah_Sampah.ipynb

Documentation · Dokumentasi

Dataset construction code for the K-Means project — Dataset construction

Manual K-Means algorithm implementation — Manual K-Means algorithm

Clustering result for January — Clustering result · January

Clustering result for February — Clustering result · February

Project 06 MVP · Working Prototype

PANDAI

Plant Identification with Artificial Intelligence

PANDAI is an Android app that helps primary-school kids get past 'plant blindness' by letting them identify plants in real time, with a bit of gamified learning to keep it fun. It's built with Flutter and Firebase, with a MobileNet model running through TensorFlow Lite, and it ties into SDG 4 and SDG 15. The core flow already works end to end — an MVP rather than a final release.

PANDAI aplikasi Android untuk membantu anak SD mengenal tanaman lewat identifikasi real-time, dengan sentuhan gamifikasi. Dibangun dengan Flutter, Firebase, dan MobileNet lewat TensorFlow Lite, selaras dengan SDG 4 & SDG 15. Alur utamanya sudah berjalan, jadi masih MVP.

MobileNet image classifier via TensorFlow Lite
Flutter app with Firebase & Supabase backend
Aligned with SDG 4 & SDG 15

Flutter
TensorFlow
Python
Firebase
Supabase
MobileNet
TF Lite

github.com/khaichi11/Mobile-APP-Plant-Detection-Mobilenet

Documentation · Dokumentasi

Home & navigation

Scanning a real plant with the PANDAI camera — Scanning a plant

PANDAI real-time plant identification result — Identification result

PANDAI saved plant collection and plant detail — Collection & detail

Certifications & Training · Sertifikasi & Pelatihan

Always picking up something new.

Udemy certificate — Complete A.I. & Machine Learning, Data Science Bootcamp — **Complete A.I. & Machine Learning, Data Science Bootcamp**Udemy

Dicoding certificate — Belajar Dasar AI — **Belajar Dasar AI**Dicoding Indonesia

Dicoding certificate — Belajar Dasar Visualisasi Data — **Belajar Dasar Visualisasi Data**Dicoding Indonesia

Teaching Assistant certificate — Basic Programming, FILKOM UB — **Teaching Assistant · Basic Programming**FILKOM, Universitas Brawijaya

Teaching Assistant certificate — Data Structures & Algorithms, FILKOM UB — **Teaching Assistant · Data Structures & Algorithms**FILKOM, Universitas Brawijaya

Let's connect · Mari Terhubung

Let's build something
together.

Email khairuramdhani@student.ub.ac.id LinkedIn linkedin.com/in/khairuramdhani GitHub github.com/khaichi11

Turning ideas into software that works.

What I work with.

Programming Languages Bahasa Pemrograman

AI / Machine Learning Kecerdasan Buatan

Computer Vision Visi Komputer

Mobile Development Pengembangan Mobile

Embedded / IoT Sistem Tertanam

Data & Analysis Data & Analisis

Tools & Workflow Alat & Workflow

Languages Bahasa

Where I've been building & teaching.

AI Engineer Intern AITF

Teaching Assistant — Data Structures & Algorithms

Teaching Assistant — Basic Programming

Projects

CERDAS

FishFeed

Arrhythmia Classification

Buah-Seru

K-Means from Scratch

PANDAI

Always picking up something new.

Let's build somethingtogether.

Let's build something
together.