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All Journal Jurnal Infomedia Jurnal E-Komtek
Neng Ayu Herawati
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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Retrieval-Augmented Generation (RAG) Chatbot for Handling Customer Complaints in the Energy Sector Ibnu Prastowo Haryono Putro; Jefry Antoni; Maulana Krisna Adhitya; Neng Ayu Herawati; Ayu Purwarianti; Nugraha Priya Utama
Jurnal Infomedia: Teknik Informatika, Multimedia, dan Jaringan Vol 10, No 2 (2025): Jurnal Infomedia
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jim.v10i2.7169

Abstract

Fast and accurate customer service is critical in the energy sector, especially for large-scale utilities like PLN. This study introduces a novel Retrieval-Augmented Generation (RAG)-based chatbot tailored for PLN’s internal operational context to automate customer complaint resolution in Bahasa Indonesia. In contrast to previous approaches that utilize only fine-tuned LLMs or retrieval-based question answering, our system uniquely integrates internal complaint records stored in internal database with a local Indonesian-optimized LLM through LangChain orchestration. The proposed architecture features temporal and linguistic preprocessing, vector embedding using FAISS, and a dynamic clarification-fallback mechanism, ensuring context-aware and grounded responses. This work contributes a scalable framework for deploying generative AI in high-stakes public utility settings, emphasizing data privacy, language fidelity, and real-time applicability. Evaluation results both simulated and human-reviewed demonstrate the chatbot’s effectiveness, achieving BLEU-4 of 46.5 and ROUGE-L of 0.63, with 92% of answers rated helpful. These findings underscore the model's potential to enhance customer experience and operational efficiency in Indonesia’s energy sector.
A Machine Learning-Based Early Warning System for Electricity Outage Due to Extreme Weather Helmy Satria Martha Putra; Alit Kesatria Mendala; Intan Jelita Saragih; Neng Ayu Herawati; Ayu Purwarianti; Nugraha Priya Utama
Jurnal E-Komtek Vol 9 No 2 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2484

Abstract

Electricity is a critical resource that supports various sectors in Indonesia, especially during extreme weather. Outages have become serious for operational risks during extreme weather. This study proposes a machine learning-based early warning system to predict electricity outages caused by extreme weather. Historical weather and outage data were combined using spatial alignment. Key innovation of this study involved geospatial feature enrichment via HDBSCAN, Yeo-Johnson transformation, robust scaling, and class resampling using SMOTE, ADASYN, and SMOTE-ENN. Four ensemble classification models (Random Forest, XGBoost, AdaBoost, and LightGBM) were evaluated. LightGBM with SMOTE yielded the highest recall (0.99) and the fewest false negatives. These findings suggest a solution for a proactive early warning system risk mitigation in electricity under extreme weather conditions.
Co-Authors Alit Kesatria Mendala Ayu Purwarianti Helmy Satria Martha Putra Ibnu Prastowo Haryono Putro Intan Jelita Saragih Jefry Antoni Maulana Krisna Adhitya Nugraha Priya Utama