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All Journal Jurnal Bumigora Information Technology (BITe) Reputasi: Jurnal Rekayasa Perangkat Lunak
Tanwir, Tanwir
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Implementasi Konsultasi Stunting Balita Menggunakan Large Language Models (LLMs) Tanwir, Tanwir; Hidjah, Khasnur; Susilowati, Dyah
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Mei 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v6i1.8961

Abstract

Stunting pada balita merupakan masalah kesehatan kritis di Indonesia yang memerlukan intervensi berbasis teknologi untuk meningkatkan akses informasi nutrisi. Penelitian ini bertujuan mengembangkan chatbot konsultasi stunting berbasis Large Language Models (LLMs) guna menyediakan rekomendasi kesehatan yang akurat dan mudah diakses. Metode yang digunakan berupa Model LLaMA 3 di-fine-tuning menggunakan dataset Q&A spesifik stunting berisi 7.642 entri, kemudian dievaluasi dengan matrik ROUGE untuk mengukur kesesuaian semantik respons. Hasil menunjukkan model Stunting mencapai skor ROUGE-1 (72,24%), ROUGE-2 (64,54%), ROUGE-L (70,42%), dan ROUGE-Lsum (70,96%), secara signifikan melampaui model baseline seperti LLaMA3, Deepseek-R1, dan Mistral. Chatbot diimplementasikan dalam aplikasi web berbasis cloud dengan arsitektur terdistribusi, dilengkapi enkripsi SSL dan HTTPS untuk menjamin keamanan data. Sistem ini memungkinkan interaksi real-time antara pengguna dan model LLMs melalui antarmuka berbasis Gradio. Temuan penelitian mengonfirmasi potensi LLMs dalam menyederhanakan layanan kesehatan preventif, khususnya di daerah dengan sumber daya terbatas
Peningkatan Kinerja Klasifikasi Scabies Sapi MenggunakanEdited Nearest Neighbours (ENN) pada Model Random Forestdan XGBoost Ihsan, M. Khaerul; Maulana, Muhammad; Tanwir, Tanwir; Mas’ud, Abi; Hanif, Naufal; Resmiranta, Dading Oktaviadi
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.6055

Abstract

Background: Scabies disease in cattle causes significant economic losses for farmers due to declines in the animals’physical condition and productivity.Objective: This study aims to evaluate the effectiveness of the Edited Nearest Neighbours (ENN) method in improvingclassification performance for scabies in cattle.Methods: This research employs machine learning methods, including Random Forest and XGBoost. A dataset of 600clinical symptom samples was converted to numerical data and cleaned of noise using the ENN technique.Result: Applying ENN significantly improved the accuracy of both the Random Forest and XGBoost models, increasing itfrom around 0.60 to 0.91. In addition, both models achieved a perfect recall of 1.00, indicating maximum capability todetect positive cases.Conclusion: This study concludes that noise reduction using ENN can produce a more accurate and reliable diagnosticsystem. This method is highly recommended to optimize the performance of classification algorithms on animal clinicaldata with high levels of inconsistency. 
Co-Authors Dyah Susilowati Khasnur Hidjah Mas’ud, Abi Muhammad Maulana Naufal Hanif Resmiranta, Dading Oktaviadi Tanwir Tanwir