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All Journal JURNAL PENDIDIKAN TAMBUSAI VISA: Journal of Vision and Ideas
Ilyas, Maulana
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Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences

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Idealfit: Aplikasi Penentuan Berat Badan Ideal dan Rekomendasi Kesehatan Berbasis Chatbot AI Juanuari, Juanuari; Widodo, Rahmat Tri; Ilyas, Maulana; Manzis, Ilham; Prasetya, Sabdha; Pratama, Reza; Nainggolan, Esron Rikardo
Jurnal Pendidikan Tambusai Vol. 10 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v10i1.36490

Abstract

Kelebihan berat badan dan obesitas masih menjadi permasalahan kesehatan yang banyak terjadi di Indonesia akibat pola hidup modern yang kurang seimbang. Untuk membantu masyarakat memahami kondisi tubuh secara mandiri, penelitian ini mengembangkan aplikasi IdealFit sebagai media edukasi kesehatan digital. Aplikasi dibangun menggunakan Flutter dan Dart dengan metode Rule-Based Learning untuk menghasilkan kategori tubuh, diagnosis awal, dan saran pencegahan berdasarkan data pengguna. IdealFit juga dilengkapi chatbot interaktif yang terintegrasi dengan API Gemini untuk memberikan informasi kesehatan umum. Perancangan sistem dilakukan menggunakan diagram UML, sedangkan pengujian menggunakan metode Blackbox Testing. Hasil implementasi menunjukkan bahwa seluruh fitur berfungsi dengan baik dan memberikan informasi yang relevan. IdealFit dapat dimanfaatkan sebagai media edukasi kesehatan digital yang mudah digunakan dan mendukung peningkatan kesadaran masyarakat terhadap pentingnya menjaga berat badan ideal dan pola hidup sehat.
Perbandingan Algoritma K-Nearest Neighbor (K-NN) dan Naive Bayes dalam Klasifikasi Tingkat Kemiskinan di Indonesia Juanuari, Juanuari; Ilyas, Maulana; Widodo, Rahmat Tri; Manzis, Ilham; Budiarti, Yusnia; Napiah, Musriatun
VISA: Journal of Vision and Ideas Vol. 6 No. 1 (2026): Journal of Vision and Ideas (VISA)
Publisher : IAI Nasional Laa Roiba Bogor

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Abstract

Poverty is a major issue in sustainable development in Indonesia that requires a data-driven analysis approach to produce more accurate identification. This study aims to compare the performance of the K-Nearest Neighbor (K-NN) and Naive Bayes algorithms in classifying poverty levels in Indonesia based on social and economic data. The dataset was obtained from the Kaggle platform with the title "Classification of Poverty Levels in Indonesia", which contains 514 district/city data with various poverty indicators. The data was divided with a ratio of 80% for training and 20% for testing, then classification was carried out using the K-NN algorithm with a value of K = 5 and Naive Bayes. Evaluation was carried out using a confusion matrix with metrics of accuracy, precision, recall, and F1-score. The results showed that K-NN provided the best results with an accuracy of 97.09%, precision of 100%, recall of 75.00%, and F1-score of 85.71%, while Naive Bayes achieved an accuracy of 95.15%, precision of 73.33%, recall of 91.67%, and F1-score of 81.48%. This study resulted in better performance of this model compared to the results of previous studies. Therefore, the K-NN algorithm with the right parameters can be used as an effective method to support the data-based poverty level classification process and assist the government in poverty alleviation management and planning policies.
Co-Authors Juanuari, Juanuari Manzis, Ilham Musriatun Napiah Nainggolan, Esron Rikardo Prasetya, Sabdha Pratama, Reza Rahmat Tri widodo Yusnia Budiarti