Projects
A detailed examination of key projects, outlining the challenges, processes, and outcomes.
Case Study 1: "Bersama Bill ID" AI Biller Identification System
Context
Artajasa, a leading payment infrastructure firm, required high-impact, innovative technology demonstrations for its annual "Members Meeting." This event is attended by key clients and stakeholders, including many executives from banks across Indonesia.
Challenge
With the event just about 2 months away at the time of my internship, a new series of compelling products was needed to serve for the flagship tech demo. The challenge was ours, the developers to continue developing and deploy the ideas from the remaining time left. One of the ideas lying around is "Bersama Bill ID", the idea is to pay bills using our face as the authenticator. This was assigned to me as the solo-developer for the optional purpose of POC for its annual Members Meeting should the task be done before the event.
Process
The project was executed in a series of intensive, week-long sprints, encompassing the full development lifecycle.
Foundational Learning & Architecture
The initial phase involved the rapid acquisition of unfamiliar technologies, including Go for backend microservices and Flutter for cross-platform mobile development. Simultaneously, the end-to-end system architecture was designed.
Core ML & Backend Development
The core machine learning model was developed with TensorFlow to intelligently identify customer faces as the authentication wall for billing payments. Concurrently, the supporting backend microservices and APIs were built using Go.
Mobile Frontend & Integration
The user-facing mobile application was developed using Flutter. This phase focused on creating an intuitive interface and ensuring seamless integration with the backend APIs.
Testing, Deployment & Polish
The final week was dedicated to system hardening. The application was containerized using Docker for stable deployment, followed by rigorous testing to ensure a faultless live demonstration.
Results
The final deliverable was "Bersama Bill ID," an AI-powered mobile application fully functional for the scopes of POC. The application was successfully demonstrated live on stage at the Members Meeting to hundreds of industry executives without technical failure.
I have successfully delivered a complete, full-stack, AI-driven POC product from concept to deployment in 1.5 months, working as the sole developer.
Case Study 2: IS 2024 Full-Stack Event Platform
Context
As the Head of Web Engineering for the IS 2024 university event, I was responsible for the digital infrastructure. The project was initiated during an accelerated summer semester, which created a high-pressure environment with a condensed timeline.
Challenge
The event required a centralized, custom-built web platform to manage all participant activities, from registration to scheduling. At the time I had no experience with fullstack programming, and no existing infrastructure was in place. The entire application had to be built from scratch against an extremely compressed deadline, with failure impacting all event participants.
Process
The process blended structured planning with intensive execution to meet the stringent deadline.
Architecture & Planning
The project began with system architecture design, including database schema, API endpoint definitions, and core user flow mapping. This initial planning was critical to prevent costly rework.
Intensive Solo Development
A two-week intensive development phase followed, where I single-handedly coded the majority of the backend logic and frontend interface to meet tightly timed initial critical milestones. This was done to prevent collaboration losses, as we all had no prior full-stack experience to know if we were heading into the right direction. During that time I rapidly reiterate experimenting back and forth to point no.1 and getting my hands dirty to get every basics right before further core feature integrations.
Integration & Testing
Core features were progressively integrated into a cohesive platform by our team. Continuous testing was also conducted to identify and resolve bugs, ensuring application stability.
Deployment & Handover
The platform was deployed to a live server. Essential documentation was prepared, and a handover session was conducted with event staff to empower them to manage the platform's operations.
Solution
The result was a robust, responsive, full-stack web application that served as the digital backbone for the IS 2024 event. The platform provided seamless participant's task submission and scoring by the committee, sophisticated attendance system, live information portals, and a full administrative control panel.
Results
The platform was launched successfully on schedule, with all core features fully functional.
The application reliably served hundreds of student participants throughout the event with no critical downtime and throttling.
This project solidified my full-stack capabilities and demonstrated the ability to deliver mission-critical software under extreme pressure.
Case Study 3: Applied ML in Geoscience: Low Resistivity Reservoirs
Context
A research initiative at the Faculty of Industrial Technology (FTTM) was investigating physics-based models for hydrocarbon reservoirs in low-permeability rocks. The project's analytical capabilities were constrained by the limitations of its existing data we had relevant to the topic.
Challenge
The research required a specialist to bridge the gap between theoretical physics and computational science. The core challenge was to translate complex physical equations into efficient Python code, develop improved data visualizations, and integrate predictive machine learning models to derive new insights from well logs regarding the probability of hydrocarbon reservoirs.
Process
Analysis & Enhancement
I began by conducting a thorough analysis of the existing python-based basic well log interpretation visualization models to identify areas for improvement.
Model Development
I applied various machine learning techniques to the reservoir data to build and test predictive models, aiming to enhance the model's forecasting capabilities.
Implementation
A key part of the process involved working directly with the professor to translate complex geophysical domain knowledge into functional, data-driven software solutions.
Results
The final deliverable included enhanced visualization tools and several predictive machine learning models applied to the reservoir data. The new models and visualizations provided the research team with enhanced tools for data analysis and prediction.