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Open access Jurnal SINTA 2

BATINARA: Hybrid LLM-BERT-ML Chatbot for Safe Mental Health Support

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 20 No. 2 (2026) · pp. 129–140

DOI
10.22146/ijccs.112432
ISSN
2460-7258, 1978-1520
Published
2026-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study addresses the pressing need for safe and personalized digital mental health support by mitigating the inherent risk of Large Language Models (LLMs) generating unsafe or unethical responses during high-risk psychological crises. We developed BATINARA, a chatbot system based on a Neuro-Symbolic Hybrid framework. This architecture integrates a Predictive Module (IndoBERT for crisis detection, Random Forest for multi-label emotion classification) with a Generative LLM Module (OpenAI API). Ethical control is enforced by the Dynamic Context Integration Logic (D-CIL), which utilizes clinical rules to uphold the Principle of Nonmaleficence. Key results demonstrate the system’s ability to: (1) Enforce safety protocols through the automatic override of LLM responses when suicidal ideation is detected (Recall IndoBERT 0.9977). (2) Achieve high contextual accuracy in multi-label emotion detection (F1 = 0.94), which supports dynamic personalization via Dynamic Prompt Modulation based on specific therapeutic styles and user PHQ-9/GAD-7 clinical scores. (3) Enhance interaction transparency through the real-time visualization of detected emotions. This Neuro-Symbolic hybrid approach proves effective in mitigating clinical risks associated with generative AI, resulting in adaptive and ethically sound therapeutic interactions.

Keywords

Collaboration network of the authors

5 authors · 5 co-authorship links

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  • Universitas Teknologi Yogyakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Agus Rakhmadi MidoUniversitas Teknologi Yogyakarta1
Candra Heru SaputraUniversitas Teknologi Yogyakarta1
RiantoAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">BATINARA: Hybrid LLM-BERT-ML Chatbot for Safe Mental Health Support</dc:title>
	<dc:creator>Walangitan, Jeanette</dc:creator>
	<dc:creator>Ujianto, Erik Iman Heri</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science; Artificial Intelligence (AI); Natural Language Processing (NLP); Health Informatics</dc:subject>
	<dc:subject xml:lang="en-US">Neuro-Symbolic AI; Mental Health Chatbot; Dynamic Prompt Modulation; Emotion Detection; Safety Net Module</dc:subject>
	<dc:description xml:lang="en-US">This study addresses the pressing need for safe and personalized digital mental health support by mitigating the inherent risk of Large Language Models (LLMs) generating unsafe or unethical responses during high-risk psychological crises. We developed BATINARA, a chatbot system based on a Neuro-Symbolic Hybrid framework. This architecture integrates a Predictive Module (IndoBERT for crisis detection, Random Forest for multi-label emotion classification) with a Generative LLM Module (OpenAI API). Ethical control is enforced by the Dynamic Context Integration Logic (D-CIL), which utilizes clinical rules to uphold the Principle of Nonmaleficence. Key results demonstrate the system’s ability to: (1) Enforce safety protocols through the automatic override of LLM responses when suicidal ideation is detected (Recall IndoBERT 0.9977). (2) Achieve high contextual accuracy in multi-label emotion detection (F1 = 0.94), which supports dynamic personalization via Dynamic Prompt Modulation based on specific therapeutic styles and user PHQ-9/GAD-7 clinical scores. (3) Enhance interaction transparency through the real-time visualization of detected emotions. This Neuro-Symbolic hybrid approach proves effective in mitigating clinical risks associated with generative AI, resulting in adaptive and ethically sound therapeutic interactions.</dc:description>
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	<dc:date>2026-04-30</dc:date>
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	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 2 (2026): April; 129-140</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
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	<dc:rights xml:lang="en-US">Copyright (c) 2026 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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