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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JAKPP : Jurnal Analisis Kebijakan & Pelayanan Publik Jurnal Informatika: Jurnal Pengembangan IT
Nurharjadmo, Wahyu
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Computer Science & IT Engineering Environmental Science

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PDF-Document Chatbot Responses using Large Language Models to Enable Smart City Engagement Khadija, Mutiara Auliya; Nurharjadmo, Wahyu; Aziz, Abdul; Primasari, Ina
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8262

Abstract

Traditional documents, including Rencana Pembangunan Jangka Menengah Daerah (RPJMD), Strategic Plans (Renstra), and e-masterplans, have undergone a remarkable transformation, evolving from their conventional printed formats to the dynamic realm of electronic versions. While this shift holds the promise of enhanced accessibility and convenience for the public, the full potential of these resources remains unrealized due to inherent challenges. On the other hand, a Generative AI approach is employed for the creation of an intelligent chatbot. Our primary contribution lies in the PDF-Document Chatbot Response utilizing Large Language Models (LLMs) GPT 3.5 Turbo from OpenAI, aimed at fostering engagement within Smart City. The dataset consists of Masterplan documents for Smart City development in Yogyakarta City, presented in PDF format and employing the Indonesian language. This research leverages the Large Language Models (LLMs) GPT-3.5 Turbo from OpenAI, in conjunction with user input and prompts. The development process for crafting this chatbot utilizes the LangChain Framework and Pinecone for storing vector embeddings. The results underscore the chatbot's capability to generate coherent responses closely aligned with the context found within the PDF document.
Analysis of Formulation and Implementation Preparation: Waste to Energy Plant Development Policy in Surakarta City Ayuningtyas, Tiara Kusuma; Nurharjadmo, Wahyu
JAKPP (Jurnal Analisis Kebijakan & Pelayanan Publik) Volume 7 No. 1, Juni 2021
Publisher : Departemen Ilmu Administrasi FISIP UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31947/jakpp.v7i1.10668

Abstract

The problem of waste as an urgent problem in Surakarta became the beginning of the idea the Waste to Energy of PLTSa Development in Putri Cempo Landfill. This study aims to describe the process of policy formulation and analyze the implementation preparation of PLTSa development policy in Surakarta. This descriptive qualitative study was obtained from interviews with 8 informants and analyze data from results of interviews, documentation, and observations according aspects of policy formulation process and policy implementation preparation. The findings show that the policy formulation process has through several formulation steps of the Draft Regional Regulation Formulation arranged by the executive and still in the draft has planned to be approved in 2020. Changes of central regulation on tipping fee and concerns about potential state losses had hampered the draft. Eventually, the draft has planned to be finished in 2020 and become regional regulation in 2021 but could not be continued because of the budget was refocusing for Covid-19 handling. Nevertheless, the political will of Surakarta about this matter is very serious. This is realized by taking preparatory steps to implement the policy in parallel with the policy formulation process. In the process of preparing for implementation, the Surakarta City Government has made preparations on organizational activities and interpretations. In organizational activities, designation of the implementor has been carried out institutionally and personally, establishment of implementation management with leading sector and collegial pattern, and the scheduling which is now in the construction phase. While the organizational activity being prepared is making SOP, this is somewhat hampered due to limited resources. In the interpretation activity, Surakarta City Government has not yet fully made official communication and socialization preparations to the residents considering that the implementation is unclear, so that it is still passive to residents around the PLTSa construction.  
Generative Indonesian chatbot for university major selection using transformers embedding Khadija, Mutiara Auliya; Harjito, Bambang; Saberi, Morteza; Paradhita, Astrid Noviana; Nurharjadmo, Wahyu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3474-3482

Abstract

Selecting a university major is a crucial decision that impacts students' future career paths and personal fulfillment. Traditional guidance methods often lack the personalization and timeliness needed to support students effectively. This study explores the use of Indonesian generative artificial intelligence (AI) chatbots and transformer embeddings to enhance decision-making for university major selection. By leveraging advanced AI techniques, such as bidirectional encoder representations from transformers (BERT) and Gemini embeddings, the research aims to provide personalized, interactive, and contextually relevant guidance. Experiments showed that BERT embeddings achieved the highest accuracy, with recurrent neural network (RNN) and long short-term memory (LSTM) models also performing well but facing issues with overfitting. Gemini embeddings provided strong performance but slightly less effective than BERT. The findings suggest that BERT-based models with RNN are superior for developing decision-support systems in 92% accuracy. Future work should focus on further optimization and integration of user feedback to ensure the relevance and effectiveness of these AI tools in educational settings.
Co-Authors Abdul Aziz Ayuningtyas, Tiara Kusuma Bambang Harjito, Bambang Khadija, Mutiara Auliya Paradhita, Astrid Noviana Primasari, Ina Saberi, Morteza