I am Divo Dwinov Gyantoro,
Software
Engineer

An intensive machine learning training program focused on developing end-to-end ML solutions using Microsoft Azure, including data preparation, model training, evaluation, and deployment in the cloud environment.

• Learned modern machine learning techniques using Microsoft Azure.
• Applied end-to-end ML pipelines from data processing to model deployment.
• Worked on industry-based case studies and cloud-based projects.

Awardee of Codepolitan Fullstack Developer Scholarship. Actively learningfullstack web development technologies and best practices using Alibaba Cloud services.

• Selected as a recipient of the Alibaba Cloud Scholarship among thousands of applicants.
• Studied fullstack web development using the MEVN Stack (MongoDB, Express.js, Vue.js, Node.js).
• Trained in cloud computing and integration with Alibaba Cloud Services.
• Developed a final project using MEVN Stack with deployment to cloud infrastructure.

Active student member at Universitas Indonesia. Participated in workshops,tech talks, and collaborative projects focused on web development, cloud computing, andmobile apps.

• Completed a comprehensive curriculum in Google Cloud, DevOps, SRE, and API development while strengthening soft skills including critical thinking, adaptability, and professional communication.

• Handled and managed networks to ensure optimal connectivity.
• Responsible for the maintenance and upkeep of hardware.
• Managed the software to ensure it is always up-to-date and functioning properly, including Visual Studio Code, NetBeans, Cisco Packet Tracer, VirtualBox, XAMPP, and others.

Represented SMAN 1 Cikarang Utara as a student delegate in the National Science Olympiad (OSN) for Informatics. Focused on system development and software engineering fundamentals.

• Continuing Education Program–Center for Computing and Information Technology Faculty of Engineering.

• Bachelor of Communication and Information Technology Program at Asia e University.

• Representative of SMAN 1 Cikarang Utara in the 2022 National Science Olympiad(Informatics Category).
• 1st class rank, 4th,5th,6th semester
• Putera Puteri environmental ambassador SMAN 1 Cikarang Utara

This article introduces deep learning as an advanced subset of machine learning that mimics the human brain through artificial neural networks. It explains the fundamental structure of neural networks, the learning process through forward propagation, loss calculation, and backpropagation using gradient descent. Through a penguin classification example, readers learn how feature inputs are processed through layers to output a probability-based prediction. The article provides an accessible yet technical walkthrough of how deep learning models are trained and how they evolve to make accurate decisions in classification tasks.

This article explores love as a quantum phenomenon that transcends classical space and time. Love exists like a particle in superposition—both present and absent—changing only when observed. Through the lens of entanglement, love connects two beings beyond spatial limits. Like quantum tunneling, love can penetrate energy barriers probabilistically. It is also described as the dynamic interplay of matter and antimatter, releasing unique existential energy. Ultimately, love is not just an emotion but a constant uncertainty and the only certainty within an uncertain universe.

This article explains clustering in machine learning, an unsupervised method that groups data based on feature similarity without known labels. K-Means is a common algorithm that assigns points to clusters by iteratively updating cluster centers. Since labels aren’t known, evaluation uses metrics like silhouette score to measure how well clusters are separated.

This article explains multiclass classification in machine learning, where the goal is to predict one label out of multiple possible classes. It uses algorithms like One-vs-Rest and Multinomial (softmax) to estimate class probabilities. Evaluation metrics include accuracy, recall, precision, and F1-score to measure how well the model predicts the correct class.

This article explains binary classification in machine learning, where the goal is to predict one of two possible labels (e.g., true or false). It uses logistic regression and the sigmoid function to estimate the probability of a class. Evaluation metrics include accuracy, recall, precision, F1-score, and AUC-ROC, which help measure the model’s performance in distinguishing between the two classes effectively.

This article explains the concept of regression in supervised machine learning, where models are trained using labeled data to predict numeric outcomes. The training process involves splitting data into training and validation sets, fitting a model with a regression algorithm such as linear regression, and evaluating its accuracy using metrics like MAE, MSE, RMSE, and R².

This article explains the basic types of machine learning: supervised and unsupervised. Supervised learning uses labeled data to train models, including regression for predicting numbers and classification for categorizing data. Classification can be binary (yes/no) or multiclass (multiple categories). In contrast, unsupervised learning, like clustering, finds patterns in data without labels by grouping similar items. Each method is suited for different prediction tasks depending on the data and goals.

This essay draws a metaphor between pigs and humans, highlighting how both are limited by their environments and perspectives. Just as pigs are confined and unable to lift their heads without falling, humans too must sometimes experience failure or hardship to see new possibilities and grow. The piece reflects on resilience, self-improvement, and the transformative power of overcoming challenges.

This article explores how scroll-driven animation can improve UI performance and efficiency compared to traditional JavaScript libraries like AOS or GSAP. By leveraging the Scroll-Driven Animations API and Intersection Observer API, developers can create responsive animations using declarative CSS and JavaScript, significantly reducing code complexity and CPU usage—up to 80% fewer lines and lower CPU load during scroll. The article includes a demo and technical explanations of properties like animation-timeline and animation-range, showing how to create smooth, scroll-based animations natively without relying on heavy external libraries.

This article explores how AJAX revolutionizes modern websites by enabling data exchange with the server without reloading the entire page. It highlights the difference between traditional and AJAX requests, explains how JSON is used for lightweight data transfer, and provides real-life examples like Wikipedia's live search. Ideal for beginner web developers looking to understand AJAX and its role in creating faster, more responsive web applications.

• Java
• JavaScript
• Dart
• Pyhton
• CSS3

• Google Cloud Platform (GCP)
• Alibaba Cloud
• Firebase

• Jenkins
• Docker
• GIT
• GitHub
• GitLab
• Apache
• NGINX
• Apache Tomcat
• Apache MAVEN

• Spring
• Hibernate
• Laravel
• Express.js
• Node.js
• Next.js
• Vue.js
• Bootstrap.js

• MySQL
• MariaDB
• MongoDB
• SQLite
• Firebase Realtime DB

• Flutter(Dart)
• Android(Java)
• Web (HTML5, CSS3, MEVN)

• Figma
• Adobe Photoshop
• Canva
• Maze

• Analyzing situations comprehensively and making objective desicions.
• Effective time management and organization.
• Innovative and creative problem-solving.
• Strong teamwork and independent working skills.
• Disciplined and prompt in completing assigned tasks.

• English
• Indonesia