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All Journal Jurnal Nasional Teknologi Informasi dan Aplikasinya
Putu Praba Santika
Universitas Udayana
Computer Science & IT Engineering

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Perancangan UI/UX Aplikasi PLATES untuk Perencanaan Gaya Hidup Sehat I Gede Adrian Satria Pratama S.; I Gusti Agung Gede Arya Kadyanan; Putu Praba Santika
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p13

Abstract

Public awareness of healthy living in Indonesia continues to grow, yet many still struggle to maintain healthy habits consistently. According to UNICEF (2022), Indonesia's adult obesity rate went from 14.8% in 2013 to 21.8% in 2018. caused by poor diet, physical inactivity, and lack of accessible, personalized nutritional information. To address this, PLATES (Personal Lifestyle Assistant for Tracking Eating and Sustainability) was developed as a mobile application that supports users in maintaining a healthy lifestyle by applying the Design Thinking methodology. Features include a calorie calculator, personalized meal plans, screen time reminders, health challenges, and NutriCamera for scanning food intake. Usability testing with 25 respondents aged 18 to over 51 years yielded an average SUS score of 78.3, indicating good usability across diverse user groups. The findings demonstrate that PLATES can support behavior change and health monitoring effectively. Aiming to guarantee healthy lives and advance well-being for everyone at all ages, the Sustainable Development Goal (SDG) number 3 is in line with this answer.
Klasifikasi Genre Buku Berdasarkan Sinopsis Menggunakan Naïve Bayes dan Logistic Regression Anak Agung Anom Witaradiani; I Gede Arta Wibawa; Putu Praba Santika
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p13

Abstract

Genre is an important element in book categorization based on specific content characteristics or themes. However, manual classification processes are no longer efficient due to the increasing volume of literature. This study aims to compare the performance of Naïve Bayes and Logistic Regression algorithms in book genre classification based on synopses. The dataset used is secondary data obtained from Kaggle. The dataset consists of 4,535 samples after the preprocessing stage, with feature representation using the TF-IDF method. To address class distribution imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The experimental results show that Logistic Regression achieved the best performance with 75.19% accuracy and 75.16% F1-score, while Naïve Bayes achieved 72.22% accuracy and 72.11% F1-score. Based on this evaluation, Logistic Regression is considered more effective in classifying book genres from synopsis text.
Co-Authors Anak Agung Anom Witaradiani I Gede Adrian Satria Pratama S. I Gede Arta Wibawa I Gusti Agung Gede Arya Kadyanan