Research Articles

The togunggak is a traditional musical instrument made of bamboo. This work observed the unique sound characteristics to define the notes using Fast Fourier Transform (FFT) via a picoscope. The sound characteristics are represented by the dominant frequency with the corresponding intensity. The note of the biggest (tog. 6) to the smallest (tog. 1) bamboo tube is recorded as from G3 to G4. This work reveals that tog. 2 to tog. 5 for togunggak A produce the notes E4, D4, B3, and A3, which is not similar to togunggak B, i.e., E4, D4, C4, Bb3. All bamboo tubes produced fundamental frequency with the presence of two lower partials at 100 Hz and 200 Hz and weaker overtones (except tog. 6)  in their frequency spectrum. Using symbol S for semitone dan T for tone (i.e. 2 semitone), the note interval of the tog. 6 to tog. 1 can be presented as TT2TT2T i.e., the G3, A3, B3, D4, E4, G4 note interval are presented by G3-A3 as T, A3-B3 as T, B3-D4 as 2T, D4-E4 as T and E4-G4 as 2T. The time frequency analysis (TFA) displays all the spectrograms with distinct prominent fundamental frequency peak.

The rapid growth of e-commerce platforms presents unique opportunities to analyze consumer behavior and predict product preferences in the furniture industry. This study explores the use of machine learning techniques to predict consumer choices for furniture products based on favorite listing data from e-commerce platforms. A dataset of 239 furniture products was collected, categorized into three groups: most preferred, moderately preferred, and least preferred. Key attributes, including furniture type, dimensions (width, depth, height), color, material, and price, were analyzed. Machine learning models, specifically Decision Trees and Random Forests, were applied to develop prediction models for these categories. The models were assessed using metrics such as accuracy, precision, sensitivity, and F1-score. Results indicated that the Random Forest model outperformed the Decision Tree, achieving 83% accuracy in predicting preference categories. Feature importance analysis highlighted that price and physical dimensions were the most significant factors influencing consumer preferences. These findings suggest that practical and economic aspects are prioritized over aesthetic features when choosing furniture. The study demonstrates the potential of machine learning in predicting consumer behavior, offering valuable insights for manufacturers and retailers in optimizing product development, inventory management, and marketing strategies.