Data Science | Figarri Keisha

🎓 The GovTech Edu Data Science Team’s Role in Transforming Education in Indonesia

Thu, 18 Apr 2024 00:00:00 +0000

Overview

This story, specially crafted by Andika Rachman Hakim, Senior Data Scientist and my direct team lead, offers a behind-the-scenes look at how our compact Data Science team at GovTech Edu addressed some of Indonesia’s most pressing educational challenges through AI and data innovation. From automating the validation of 500k+ teacher submissions per month, developing recommendation systems, to launching a GenAI-powered metadata tool. The article highlights our journey building impactful, scalable public-sector solutions through tight collaboration and principled product thinking.

👉 Read the full story on Medium

🌾 Empowering Local Farmers with Data - KaPaN

Mon, 06 Nov 2023 00:00:00 +0000

Extreme weather is inevitable, but crop failure doesn’t have to be.

🏆 Regional Recognition at the United Nations Datathon 2023

As Team 4SKA1, we were honored to be selected as The Best Team in the Asian Region at the United Nations Datathon 2023. This global competition challenged participants to develop innovative data-driven applications combining geospatial and statistical data to advance the Sustainable Development Goals (SDGs).

🚜 The Problem: Climate Uncertainty Meets Agriculture

Farmers in West Java, one of Indonesia’s major rice-producing regions, are increasingly vulnerable to extreme weather events, such as droughts, floods, and irregular rainfall. These threats can severely disrupt food supply and jeopardize farmers’ livelihoods.

Despite the urgency, many farmers lack access to early and localized information to prepare for such risks.

💡 The Solution: KaPaN

We proposed KaPaN (Kalender Padi Nusantara - Nusantara Rice Calendar), a prototype website that serves as an early warning system for extreme weather, tailored specifically for local farmers.

KaPaN empowers users to:

Track when and how long extreme weather is forecasted in their area
Visualize the spatial extent of weather anomalies
Report and share real-time ground-level information with fellow farmers

The name “KaPaN” (Indonesian for “When?”) reflects our goal to answer farmers’ biggest question when facing uncertainty.

🔬 The Method: Extreme Value Analysis

We used Extreme Value Analysis (EVA) (specifically the Block Maxima method) to analyze rare and high-impact events such as intense rainfall and drought.

With the help of Python and the pyextremes library, we simulated thresholds for extreme conditions and provided early warnings that farmers can act on.

🌐 Accessible, Actionable, and Community-Driven

The KaPaN platform is designed to be:

User-friendly: intuitive dashboard tailored for farmers
Insightful: visualizations help interpret trends over time and across regions
Collaborative: enables farmers to contribute ground-level observations

📂 Learn More

🤝 Meet the Team – 4SKA1

We are a passionate group of data practitioners from Indonesia who believe in the power of data for local impact:

Bagoes Rahmat Widiarso
Bhaskoro Muthohar
Figarri Keisha
Muhammad Nassirudin

🍚 Food Insecurity Early Warning System

Fri, 11 Nov 2022 00:00:00 +0000

Overview

At the 2022 United Nations Big Data Hackathon, Team 4SKA1, formed by young Indonesian data scientists including myself, took on the global challenge of Food Insecurity. Our solution was a scalable Early Warning System prototype built using open data and visualized via Google Data Studio. The project won Best Visualization Award in the Youth Track for its clarity, insight, and practical utility.

The Challenge

Climate change and socio-economic instability are increasingly affecting global food security. Yet, early warning systems are often absent, especially in vulnerable regions. Our task was to answer:

Can we monitor and anticipate food insecurity trends globally?
How does a country’s food insecurity affect others through trade flows?

Our Approach

We created a predictive model using data from:

The UN Hackathon 2022 datasets
World Bank, AtlasBig, and OurWorldInData

We engineered three key scores for each country:

Socio-Economic Score (SES): clustering countries based on unemployment, undernourishment, mortality, etc.
Climate Score: using PCA to combine metrics like CO₂ emissions, energy use, forest area.
Food Score: integrating food production, consumption, waste, and trade patterns.

These were combined to train a linear regression model to estimate national food insecurity scores with ~10% error.

The Dashboard Prototype

We built an interactive Google Data Studio dashboard to visualize:

Global food insecurity status by country
SES, Climate, and Food score comparisons
Country-level food trade relationships
Exploratory visualizations to empower policy and humanitarian decisions

👉 View the Dashboard

📄 Download Full Project PDF

📈 Causal Inference - Can We Trust What Happens After?

Fri, 25 Mar 2022 00:00:00 +0000

Overview

How do you know if your new business strategy actually worked or if the trend was going to happen anyway? In this article, I introduce the Difference-in-Differences (DiD) method, a powerful statistical technique for estimating causal impact from observational data, using a real case study from my work at Bukalapak.

We walk through the full pipeline: from identifying the problem with naive before-after comparisons, validating assumptions like parallel trends, to implementing DiD with Python regression models and even conducting a placebo test. I also showcase a custom DiD Python library that makes the process seamless and reproducible.

Read the full article on Medium and discover how we turn business questions into causal answers.

🛍️ Predicting Users’ Intention When Searching on Online Marketplace Platforms

Tue, 25 Feb 2020 00:00:00 +0000

Overview

Ever wondered why some people search with laser focus, while others just explore? In this post, I walk through how we tackled this challenge at Bukalapak by modeling users’ search intentions, specifically whether they are exploring or locating. Using historical behavioral data, we built an unsupervised learning model to predict these intent types based on queries, enabling personalized search strategies and a better user experience. Curious how a simple keyword like “iPhone 5” can lead to very different behaviors? Dive into the full story to find out.

👉 Read more on Medium

Data Science | Figarri Keisha

🎓 The GovTech Edu Data Science Team’s Role in Transforming Education in Indonesia

Overview

Did you find this page helpful? Consider sharing it 🙌

🌾 Empowering Local Farmers with Data - KaPaN

🏆 Regional Recognition at the United Nations Datathon 2023

🚜 The Problem: Climate Uncertainty Meets Agriculture

💡 The Solution: KaPaN

🔬 The Method: Extreme Value Analysis

🌐 Accessible, Actionable, and Community-Driven

📂 Learn More

🤝 Meet the Team – 4SKA1

Did you find this page helpful? Consider sharing it 🙌

🍚 Food Insecurity Early Warning System

Overview

The Challenge

Our Approach

The Dashboard Prototype

Did you find this page helpful? Consider sharing it 🙌

📈 Causal Inference - Can We Trust What Happens After?

Overview

Did you find this page helpful? Consider sharing it 🙌

🛍️ Predicting Users’ Intention When Searching on Online Marketplace Platforms

Overview

Did you find this page helpful? Consider sharing it 🙌