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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Informatika
Ni Kadek Dwi Rusjayanthi, Ni Kadek
Jurusan Teknologi Informasi, Fakultas Teknik, Universitas Udayana
Computer Science & IT

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AI-Driven Mobile Childrens Book Generator A Case Study of Tema Insani Psychology Consultancy Pasha Catra Parama, Anak Agung Ngurah; Ayu Wirdiani, Ni Kadek; Dwi Rusjayanthi, Ni Kadek
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 13 No 1 (2025): Vol. 13, No. 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2025.v13.i01.p01

Abstract

The advancement of technology drivesving digitalization across various sectors, including psychology and literacy in Indonesia. This motivates children’s psychology consultant Tema Insani to enhance children’s literacy interest by considering psychological conditions, neurodevelopmental disorders, and developmental age, which have so far relied heavily on parents’ role in providing books. The issue presents a solution in the form of a mobile-based application that can generate bedtime stories tailored to children’s age, disorders, and preferences. The app includes a reading tracker to monitor children’s literacy progress based on reading frequency. The development uses the Agile methodology and Dart with Flutter SDK as the primary development language. The black box testing results show that using the app with children at night can increase reading frequency by replacing device usage with bedtime stories. The development of the app is expected to support Tema Insani’s efforts to bridge psychology, technology, and children’s literacy interest in Indonesia.
Performance Evaluation of LSTM and GRU Models for Movie Genre Classification Based on Subtitle Dialogs Using Augmented Data and Cross-Validation Yonita Putri Utami, Ni Luh Putu; Singgih Putri, Desy Purnami; Dwi Rusjayanthi, Ni Kadek
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25897

Abstract

This study aims to evaluate and compare the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in classifying movie genres based on subtitle dialogs. To address data imbalance across genres, data augmentation was applied to create balanced datasets with 500 and 700 samples per genre, in addition to the original dataset. The classification models were built using Word2Vec for word embedding, followed by LSTM and GRU architectures with a single hidden layer and dropout regularization. Model performance was assessed using accuracy and further validated through 5-fold cross-validation. The best test accuracy was achieved with the dataset containing 700 samples per genre, reaching 91% for LSTM and 92% for GRU. Cross-validation showed stable performance with average accuracies of 0.68 for LSTM and 0.67 for GRU. A paired t-test analysis yielded a p-value of 0.341, indicating no statistically significant difference between the two models. These findings suggest that both LSTM and GRU are effective for genre classification based on subtitle dialogs. The use of data augmentation is a key contribution of this study, enabling improved model performance on underrepresented genres. This research supports the development of automated movie recommendation systems that utilize subtitle-based genre prediction.
Co-Authors Desy Purnami Singgih Putri Dwi Putra Githa Frannata Wangsa, Jedy Goestaman I ketut Gede Darma Putra I Nyoman Piarsa Made Henny Aryani, Made Ni Kadek Ayu Wirdiani, Ni Kadek Pasha Catra Parama, Anak Agung Ngurah Sukla Mandika, Ketut Gde Yonita Putri Utami, Ni Luh Putu Yudiadewi, Made Aprisintia