Profile Highlights

Skill & Achievement

Stacks I use, milestones I reached, and publications I’ve shared.

Programming Languages

PHP
Python
Golang
C++
Java
Java Script

Backend Framework

Laravel
Django
Flask
FastAPI

DevOps

GitHub Actions
Jenkins
Docker
Kubernetes
Docker Compose

Cloud

Google Cloud Project
AWS
Azure
Runpod (AI Training Cloud)

Monitoring

Prometheus
Grafana

AI Development

TensorFlow
PyTorch
YOLO
Roboflow
MLflow
Dagshub
TFRecord/IDX

Other Tools

Power BI
Git
Figma
Postman

Achievements

Distinction Graduate Bangkit Academy Machine Learning Path Batch 1 2024

Distinction Graduate Bangkit Academy Machine Learning Path Batch 1 2024

Participated in the Bangkit Academy 2024, a prestigious independent study program organized by Google, GoTo, and Traveloka. The program offers comprehensive training in Machine Lea...

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Distinction Graduate Bangkit Academy Machine Learning Path Batch 1 2024

Distinction Graduate Bangkit Academy Machine Learning Path Batch 1 2024

10 Jul 2024

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Participated in the Bangkit Academy 2024, a prestigious independent study program organized by Google, GoTo, and Traveloka. The program offers comprehensive training in Machine Learning, with mentorship from industry experts and instructors, focusing on both technical skills and soft skills development.

2nd Place Winner in Hackathon Competition by KMTETI, UGM Faculty of Engineering

2nd Place Winner in Hackathon Competition by KMTETI, UGM Faculty of Engineering

Secured 2nd place in a hackathon competition organized by KMTETI at the Faculty of Engineering, Universitas Gadjah Mada (UGM). Our team developed a mobile application called Planti...

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2nd Place Winner in Hackathon Competition by KMTETI, UGM Faculty of Engineering

2nd Place Winner in Hackathon Competition by KMTETI, UGM Faculty of Engineering

02 Jun 2023

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Secured 2nd place in a hackathon competition organized by KMTETI at the Faculty of Engineering, Universitas Gadjah Mada (UGM). Our team developed a mobile application called Planties, designed to address environmental challenges by simplifying planting activities, thereby promoting sustainable living and environmental stewardship.

Ranked 4th in the Hackathon competition held by Ciputra University Surabaya.
14 May 2023

Ranked 4th in the Hackathon competition held by Ciputra University Surabaya.

4th place winner in a 24-hour hackathon held onsite at Ciputra University Surabaya. Created an application called KAKATUA that functions to find out about domestic tourism with a c...

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Ranked 4th in the Hackathon competition held by Ciputra University Surabaya.

Ranked 4th in the Hackathon competition held by Ciputra University Surabaya.

14 May 2023

4th place winner in a 24-hour hackathon held onsite at Ciputra University Surabaya. Created an application called KAKATUA that functions to find out about domestic tourism with a camera feature called AI Landmark Detection.

Publications

Classification of Bean Leaf Lesions Using Modified EfficientNetV2 for Implementation in TensorFlow Lite
15 Sep 2025 View Paper →

Classification of Bean Leaf Lesions Using Modified EfficientNetV2 for Implementation in TensorFlow Lite

Developed a lightweight and accurate model for classifying bean leaf diseases (healthy, Angular Leaf Spot, Bean Rust) using a modified EfficientNetV2B0 architecture. Achieved 97.76...

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Classification of Bean Leaf Lesions Using Modified EfficientNetV2 for Implementation in TensorFlow Lite

Classification of Bean Leaf Lesions Using Modified EfficientNetV2 for Implementation in TensorFlow Lite

15 Sep 2025

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Developed a lightweight and accurate model for classifying bean leaf diseases (healthy, Angular Leaf Spot, Bean Rust) using a modified EfficientNetV2B0 architecture. Achieved 97.76% test accuracy, 6.18 MB TFLite model size, and 0.0594 s inference time. Grad-CAM was used for model interpretability. Optimized for real-time, on-device deployment in agriculture, particularly in resource-limited settings. Highlights potential for scalable, efficient plant disease detection solutions.

Classification of Apple Leaf Diseases Using a Modified EfficientNet Model
11 Mar 2025 View Paper →

Classification of Apple Leaf Diseases Using a Modified EfficientNet Model

Developed and evaluated a modified EfficientNet architecture for classifying apple leaf diseases from images, achieving 99.1% training, 97.4% validation, and 84.5% test accuracy wi...

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Classification of Apple Leaf Diseases Using a Modified EfficientNet Model

Classification of Apple Leaf Diseases Using a Modified EfficientNet Model

11 Mar 2025

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Developed and evaluated a modified EfficientNet architecture for classifying apple leaf diseases from images, achieving 99.1% training, 97.4% validation, and 84.5% test accuracy with up to 50% fewer parameters. The model maintained high accuracy in complex backgrounds and is well-suited for real-world agricultural use, especially in low-resource settings.