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Fitri Insani
Department of Informatics Engineering, UIN Sultan Syarif Kasim Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Toddler nutritional status identification: Support vector machine (SVM) algorithm adoption Affan Asyraffi; Okfalisa Okfalisa; Fitri Insani; Surya Agustian; Riski Mai Candra
Science, Technology, and Communication Journal Vol. 5 No. 2 (2025): SINTECHCOM Journal (February 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v5i2.282

Abstract

Inadequate nutrition in toddlers can lead to health issues and adversely affect their growth, development, and cognitive capabilities. Consequently, it is essential to assess the nutritional status of toddlers to ascertain their health level. This study seeks to ascertain the nutritional health of toddlers utilizing the support vector machine (SVM) methodology, taking into account body weight (BB), height (TB), age, BB/TB ratio, Z-scores for BB/U, Z-scores for TB/U, and Z-scores for BB/TB. The data of 1458 toddlers were evaluated using the knowledge data discovery methodology. This study effectively categorized toddler nutrition into six classifications including malnutrition, undernutrition, adequate nutrition, overnutrition, risk of overnutrition, and obesity. Utilizing the confusion matrix methodology with an 80% training data to 20% test data ratio yields an accuracy of 89.04%. The SVM method is effectively utilized to ascertain the nutritional condition of toddlers, hence enhancing their growth and development.
An IndoBERT-based framework for emotion classification in Indonesian song lyrics Agustar Alfonso; Fitri Insani; Okfalisa Okfalisa; Muhammad Fikry; Fitra Kurnia; Sri Wahyuni
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.372

Abstract

Emotion classification in song lyrics represented a significant research area within natural language processing, yet studies targeting Indonesian-language lyrics remained scarce due to the limited availability of labeled datasets and the absence of domain-specific models. This study developed and evaluated an emotion classification model for Indonesian song lyrics using fine-tuned IndoBERT-base-p2, a transformer-based language model pre-trained on a large Indonesian corpus. A dataset of 1,025 labeled lyric entries was compiled from Kaggle, Genius, and KapanLagi, covering four emotion categories: joy, sadness, fear, and anger. Preprocessing encompassed duplicate removal, case folding, structural marker removal, and non-alphabetic character cleaning. Nine fine-tuning experiments were conducted by systematically varying learning rate and dropout rate, with early stopping applied based on validation loss. The optimal configuration employed a learning rate of 3 × 10-5 and a dropout rate of 0.1, achieving 75.73% accuracy and 75.85% macro-averaged F1-score on the held-out test set. Joy and anger were classified most reliably, attaining F1-scores of 82.76% and 76.47% respectively, while sadness presented the greatest challenge, exhibiting the lowest precision of 64.10% alongside a recall of 80.65%, indicating a systematic tendency of the model to over-predict this class. These findings demonstrated that IndoBERT-base-p2, when fine-tuned with appropriate hyperparameter configuration, served as an effective approach for domain-specific emotion classification in Indonesian song lyrics.
Robust cryptocurrency price forecasting using a Bayesian-optimized CNN–LSTM hybrid model Abdul Aziz Sulton; Fitri Insani; Okfalisa Okfalisa; Lestari Handayani; Nazruddin Safaat Harahap
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.377

Abstract

The rapid growth of cryptocurrency has caused the price movements of digital assets such as Bitcoin (BTC) and Ethereum (ETH) to become highly volatile and difficult to predict. This study aims to develop a cryptocurrency price prediction model using a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture optimized with Bayesian Optimization. The data used in this study consisted of daily historical data for Bitcoin and Ethereum from January 1, 2018, to December 31, 2025, obtained from Yahoo Finance. The research stages included data preprocessing, normalization using Min-Max Scaling, sequence generation using the sliding window method (window sizes of 30, 60, and 90), CNN-LSTM model development, hyperparameter optimization using Bayesian Optimization (30, 50, and 100 trials), and evaluation using regression metrics including MSE, RMSE, MAE, MAPE, and R2. The results showed that the hybrid CNN-LSTM model outperformed the standalone CNN and LSTM models, with RMSE reductions of 27% – 59% for BTC and 18% – 19% for ETH. For Bitcoin data, the best model was obtained using 30 trials with a window size of 30, achieving an RMSE of $2,588.33, MAE of $2,004.23, MAPE of 1.99%, and R2 of 0.9468. Meanwhile, for Ethereum data, the best model was obtained using 50 trials with a window size of 60, achieving an RMSE of $138.56, MAE of $99.75, MAPE of 3.27%, and R2 of 0.9737. These results indicate that the combination of CNN-LSTM and Bayesian Optimization is effective for predicting cryptocurrency prices with non-linear and volatile characteristics.
Application of the smote and backpropagation neural network (BPNN) techniques in the classification of non-alcoholic fatty liver disease (NAFLD) Arif Utama Rambe; Reski Mai Candra; Fitri Insani; Rahmad Abdillah; Siska Kurnia Gusti
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.399

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a liver disorder with a high global prevalence and a significant mortality risk. However, clinical NAFLD datasets often exhibit severe class imbalance, causing machine learning models to become biased toward the majority class. This study aims to classify the mortality risk of NAFLD patients using the backpropagation neural network (BPNN) algorithm combined with the synthetic minority over-sampling technique (SMOTE). To ensure model validity, the follow-up time variable (futime) was excluded to prevent data leakage. The experiments were conducted by comparing different data split ratios (70:30, 80:20, and 90:10) as well as various hidden layer configurations and learning rates. The experimental results indicate that, without SMOTE, the model was trapped in the illusion of high accuracy (92%) while failing to detect mortality cases effectively (recall < 15%). In contrast, the application of SMOTE significantly improved the recall value, reaching 79.85% under the 80:20 data split scenario. These findings demonstrate that the integration of SMOTE and BPNN is highly effective in minimizing missed diagnoses (false negatives) in imbalanced medical datasets.
Co-Authors Abdul Aziz Sulton Affan Asyraffi Agustar Alfonso Arif Utama Rambe Fitra Kurnia Lestari Handayani Muhammad Fikry Nazruddin Safaat Harahap Okfalisa Okfalisa Rahmad Abdillah Reski Mai Candra Riski Mai Candra Siska Kurnia Gusti Sri Wahyuni Surya Agustian