Building intelligence system that create impact.

Statistics and Data Science student at Universitas Negeri Semarang with hands-on experience building real-world AI applications. I specialize in LLM-based systems, AI agents, and end-to-end machine learning solutions, with experience developing and deploying machine learning and deep learning models for practical use cases. TensorFlow Developer Certified through DeepLearning.AI, with a strong foundation in deep learning and practical model development. I have contributed to SINTA- and Scopus-indexed research publications and have been recognized through multiple national AI and data science competition awards. Passionate about building scalable, production-ready AI systems that deliver measurable real-world impact.

7+ Research Papers
4 National Awards
2+ Years of Experience

Open to collaboration

Galih Kusuma Wijaya
GK

AI Engineer Intern

Flyrank AI

FlyRank is an AI-powered SEO platform built to automate and scale content intelligence. I worked as an AI Engineer intern building production backend systems across the full stack — containerized REST APIs (FastAPI, Node.js) with PostgreSQL, Supabase, JWT auth, and background job queues on Inngest — alongside LLM pipelines with guardrails and cost observability. Built and deployed a Telegram-based agentic workflow via n8n and MCP, orchestrating LLMs with Notion, email, bookkeeping, and web search into one pipeline, achieving an 83% task success rate processing ~300k tokens/day at ~$0.0013/call. Reduced token consumption 31% on a RAG chatbot via a custom-built prompt optimizer. As for the Capstone project I built Unspun. Unspun is a bias-aware product search engine with a parallel retrieval pipeline (SerpAPI), deterministic affiliate bias audit layer, and a single Cerebras gpt-oss-120b inference call for full synthesis, deployed on Vercel with Next.js and FastAPI serverless backend.

Leader

Data Champion Society UNNES

When I took over, I rewrote the vision and mission from scratch, restructured all divisions, and replaced the existing program with a 9-month project-based capstone where members produce publishable outputs every month, supported by 3 sessions per month with practitioners, lecturers, and partner communities. I also designed and led the internal Satria Data selection pipeline to surface the campus's top data talent.

Research Assistant

Universitas Negeri Semarang

Embedded in an active publication pipeline, I co-authored a Scopus-indexed paper on optimized LSTM and BiLSTM for electricity load forecasting, developed the full NLP and K-Means methodology for a SINTA journal on food program prioritization, and contributed the modeling and research implementation for a Scopus-indexed study applying Graph Attention Networks (GAT) to identify optimal gene combinations in Spirulina platensis for maximum lipid yield.

001 Client Project

ARA: RAG Chatbot for Abrasea

Built end-to-end RAG pipeline for Abrasea.com, a government-funded mangrove conservation platform. Designed a dual-source knowledge base combining platform-specific documentation and peer-reviewed scientific journals on mangrove ecology and blue carbon, embedded with BGE-M3 (1024-dim) and stored in Pinecone. Orchestrated the full agentic workflow on n8n with Nvidia Nemotron Ultra 3 as the inference model. Evaluated across 100 domain-specific queries using a custom RAGAS framework, validated by two frontier judges (GPT-5.5 and Claude Sonnet 5), achieving Faithfulness 0.99, Answer Relevancy 0.99, and Context Utilization 0.91 at $0.003/query.

RAG n8n Vector Database LLM-as-a-Judge Agentic AI
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002

Conjify: Anemia Screening via Conjunctival Imaging

Built the full AI pipeline for Conjify, a mobile-first web app for non-invasive anemia self-screening via smartphone camera. I designed and trained a dual-head EfficientNet-B0 that simultaneously classifies anemia presence and estimates hemoglobin levels quantitatively, trained on CP-AnemiC dataset (710 images) using multi-task learning with progressive unfreezing. Integrated Grad-CAM to extract spatial activation statistics for structured Llama 3.3 70B clinical interpretation in plain Indonesian. Model achieved 84% accuracy, AUC-ROC 0.902, and Hb regression MAE 1.515 g/dL. Deployed on Hugging Face Spaces via Gradio API.

Computer VisionEfficientNet-B0Multi-task LearningGrad-CAMLLMGradio
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003 Internship Capstone

Unspun: Bias-Aware Product Search Engine

Capstone project built during my internship at Flyrank AI, also submitted to the DevNetwork [API + Cloud + AI] Hackathon 2026 under SerpApi "Best AI Use Case" track. Designed a parallel retrieval pipeline that concurrently fires organic search, Reddit-scoped sentiment, and Google Shopping pricing via SerpAPI, feeding a deterministic bias audit layer that quarantines affiliate-heavy and listicle domains before any LLM sees the results. A single Cerebras gpt-oss-120b inference call then handles the full synthesis: sentiment ranking, astroturf flagging, savings delta computation, and quarantine reason tightening. Post-render, a separate Google Trends endpoint resolves interest curves per ranked product. Engineered with hard latency budgets (6.5s parallel cap, 9s synthesis ceiling) and graceful degradation so Reddit and Shopping failures degrade to empty rather than blocking the organic pipeline. Deployed on Vercel with Next.js App Router + TypeScript frontend and FastAPI Python serverless backend, zero database.

TypeScript FastAPI Cerebras SerpAPI
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004

NutriVision

Built as the AI component of a cross-path capstone at Coding Camp DBS x Dicoding. I implemented EfficientNetV2 as the backbone with BiFPN for multi-scale feature fusion and FCOS as the detection head, a fully anchor-free approach using stride-based convolution instead of predefined anchor boxes. The app identifies fast food items from major brands and estimates their exact nutritional content.

Computer VisionEfficientNetV2FCOSBiFPNAnchor-Free Detection
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005

NALAR: Media Literacy Simulation

Built an agentic AI simulation platform where specialized LLM workflows autonomously generate scenario briefings, audience personas, editorial assets, network propagation, and post-incident analysis. Designed a multi-stage AI orchestration pipeline with structured JSON outputs, prompt specialization, and multi-model routing across Gemini 2.5 Flash and Groq, monitored through Langfuse observability. Engineered a real-time AI-driven propagation engine that recursively simulates branching social interactions on a force-directed graph, alongside token-budget guardrails, hybrid rule-based + LLM evaluation, and secure server-side orchestration using Next.js App Router and Supabase.

Next.jsTypeScriptAIGroqGeminiSupabasereact-force-graph-2d
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006

Personal AI Agent (WhatsApp)

Built and deployed a personal AI agent directly into my own WhatsApp, handling automated responses, task assistance, and context-aware interactions through a bot pipeline running on personal infrastructure.

AI AgentWhatsAppAutomationPython
007

PyGrind

A browser-only, 100% AI-driven coding practice platform for AI/ML and backend engineers. Every task, curriculum path, and code review is generated dynamically by an LLM (Groq or Gemini) using the user's own API key, with no backend and no database. Built an adaptive tier system where problem difficulty scales per topic, a Monaco-based practice loop with real-time AI code review gating progression, and an owner-maintained HTML handbook synced as reference material for every problem.

ReactTypeScriptMonaco EditorLLM IntegrationZustandTailwind CSS
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008 2nd Place · DIMAS-TI Data Mining

LexiLSTM: Campus Complaint Classifier

The real challenge was not the model, it was the absence of labeled data. I used weak supervision with a domain lexicon and fuzzy string matching to auto-generate pseudo-labels across six complaint categories from informal Indonesian social media text. I compared standard BiLSTM, BiLSTM + self-attention, and BiLSTM + multi-head attention. The key finding: added complexity does not consistently win when training labels are noisy. The hybrid approach still delivered +18% accuracy over baseline, with attention weights providing interpretable token-level insight.

NLPBiLSTMSelf-AttentionWeak SupervisionPython
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009

Hydros: AI-Powered Urban Water Investigation Platform

Built for the IEEE OneAquaHealth Global Hackathon 2026, an evidence-first investigation tool that turns a photo and location of an urban waterway into a structured risk assessment grounded in the One Health model. Enforced a strict observation → evidence → inference separation in the type system itself, so an AI-flagged observation can never silently become a contamination claim — only a dedicated risk-assessment layer may conclude, and “insufficient data” is a first-class outcome. Designed a multi-stage pipeline: NVIDIA NIM for descriptive visual analysis, human-in-the-loop confirmation of every observation, Cerebras gpt-oss-120b for research planning and web-evidence synthesis plus One Health exposure-pathway mapping (each pathway cited and conditional), and Groq for the final reasoning step. Added deterministic degradation alerts, geohash-based site clustering with risk trend tracking, and FHIR R4 / JSON-LD (FAIR-compliant) data exports. Deployed with Next.js, TypeScript, Supabase, and MapLibre GL.

Next.jsTypeScriptCerebrasNVIDIA NIMGroqSupabaseMapLibre GL
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2026 Scopus

Comparative Study of LSTM-Based Models with Hyperparameter Optimization for Short-Term Electricity Load Forecasting

Iqbal Kharisudin, Insyiraah Oxaichiko Arissinta, Sabrina Aziz Aulia, Muhamad Abdul Qodir Dani, Galih Kusuma Wijaya

BAREKENG: Jurnal Ilmu Matematika dan Terapan

A comparative study of LSTM-based architectures with systematic hyperparameter optimization for short-term electricity load forecasting, evaluating which configuration best captures temporal dependencies in consumption data across varying load patterns.

DOI →
2026 Proceedings

Pemodelan Navigasi Adaptif Tunadaksa dengan Integrasi Indeks Aksesibilitas dan Algoritma A*: Studi Kasus di Kampus FMIPA UNNES

Adaptive Navigation Modeling for the Physically Disabled with Accessibility Index Integration and A* Algorithm: A Case Study at FMIPA UNNES Campus

Galih Kusuma Wijaya, Muharima Sahara, Tsalisa Chulaili Syahri Nova, Muhammad Abdul Qodir Dani, Ratna Nur Mustika Sanusi, Iqbal Kharisudin

PRISMA: Prosiding Seminar Nasional Matematika

An algorithmic model integrating a Physical Disability Accessibility Index (IAT) using fuzzy logic with the A* search algorithm to identify optimal and inclusive navigation routes for individuals with physical disabilities.

DOI →
2026 Proceedings

Prediksi IHSG Berdasarkan Sentimen Publik terhadap Kebijakan Ekonomi Nasional Menggunakan Model IndoBERT–LSTM

IHSG Prediction Based on Public Sentiment towards National Economic Policies Using the IndoBERT–LSTM Model

Galih Kusuma Wijaya, Maulana Anandyta Narayana, Muhammad Akbar Anugrah Syafa, Muhammad Alifian Yusuf, Iqbal Kharisudin, Virgania Sari

PRISMA: Prosiding Seminar Nasional Matematika

A study developing a predictive model for the Indonesia Composite Index (IHSG) by integrating public sentiment from social media using IndoBERT alongside a Long Short-Term Memory (LSTM) time-series model to improve forecasting accuracy.

DOI →
2025 SINTA

Regional Prioritization for Free Nutritious Food Programs through Social Data Integration and Public Sentiment Analysis Using K-Means and NLP

Ratna Nur Mustika Sanusi, Galih Kusuma Wijaya, Nur Achmey Selgi Harwanti

UJM: UNNES Journal of Mathematics

An integrated approach combining K-Means clustering on socioeconomic indicators with NLP-based sentiment analysis from social media to build a context-aware regional prioritization model for Indonesia's Free Nutritious Meal program.

DOI →
2025 Proceedings

Comparative Study of Autoencoder and LSTM-AE for Extreme Temperature Anomaly Detection in Semarang

Galih Kusuma Wijaya, Aliyya Anggraeni, Tsalisa Chulaili Sahri Nova, Muhammad Alifian Yusuf, Iqbal Kharisudin

ICDSOS - POLSTAT STIS International Conference

A comparison of standard Autoencoder and LSTM-Autoencoder for detecting extreme temperature anomalies in Semarang's historical climate data, evaluating which architecture better captures temporal dependencies in anomaly patterns.

DOI →
2025 Proceedings

Dinamika Sentimen Publik dalam Suksesi Pemerintahan Indonesia berdasarkan Analisis Data Media Sosial

Public Sentiment Dynamics in Indonesian Government Succession Based on Social Media Data Analysis

Galih Kusuma Wijaya, Adelia Venie Diniar, Shata Alwan Jalaluddin, Iqbal Kharisudin

PRISMA: Prosiding Seminar Nasional Matematika

A sentiment analysis of Indonesian social media across the presidential succession from Jokowi to Prabowo, mapping how public sentiment shifted across key political milestones and what it reveals about digital discourse and public trust.

DOI →
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1st Place

GRAVITASI Essay Competition

Universitas Sumatera Utara · Oct 2025

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2nd Place

DIMAS-TI Data Mining Competition

AMLI (Asosiasi MIPA LPTK Indonesia) · Nov 2025

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1st Place

Lomba Esai Nasional Rumah Disabilitas

Rumah Disabilitas Indonesia · Dec 2025

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Special Honor: Distinction

FPCI Global Impact Day 2025 Essay Competition

President University · Jul 2025

Hard Skills

Python SQL RAG LangChain MCP LLM Deployment LangGraph n8n Agentic AI Vector Databases TensorFlow

Soft Skills

Leadership Teamwork Critical Thinking Research Writing Data Storytelling

Certifications

Let's build something.

Open to research, collaboration, and full-time or hybrid roles.