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All Journal Jurnal Krisnadana
Putu Yoka Angga Prawira
Informatics, Institut Bisnis Dan Teknologi Indonesia,Denpasar, Bali, Indonesia
Computer Science & IT Control & Systems Engineering

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Comparison of Deep Learning Methods for Product Sales Forecasting at The Catur Sasih Cooperative Four Seasons Hotel I Putu Agus Eka Darma Udayana; Putu Yoka Angga Prawira; Ni Made Jeni Aprilia Dewi
Jurnal Krisnadana Vol 5 No 3 (2026): Jurnal Krisnadana May - July 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/avs92s73

Abstract

This study aims to assist Koperasi Catur Sasih Hotel Four Seasons in determining the most appropriate sales forecasting method based on the characteristics of the cooperative’s sales data, as fluctuating demand creates challenges in determining optimal inventory levels. The methods compared in this study are Long Short-Term Memory (LSTM) and Autoformer, using monthly sales data from January 2020 to December 2024 for three products, namely Yakult, Bavarois Roti Pizza/Sisir, and Marlboro Lights 20. The results indicate that the Autoformer method provides more accurate sales predictions than the LSTM method in forecasting product sales at Koperasi Catur Sasih, as evidenced by the lowest error values according to the RMSE, MAE, and MAPE metrics. For the Yakult product, the Autoformer method achieved an RMSE of 82.4660, an MAE of 72.2140, and a MAPE of 9.65%. For the Bavarois Roti Pizza/Sisir product, the Autoformer method produced an RMSE of 18.2666, an MAE of 15.4194, and a MAPE of 11.37%. Furthermore, for the Marlboro Lights 20 product, the Autoformer method resulted in an RMSE of 44.3490, an MAE of 37.0785, and a MAPE of 12.36%. In addition, noise reduction testing by removing the first three months of 2020 as an anomalous data period resulted in a decrease in error values for both methods. Nevertheless, Autoformer consistently outperformed LSTM in sales forecasting, both before and after the noise removal process.
Sentiment Analysis on E-commerce Reviews Using GRU and Naive Bayes I Putu Agus Eka Darma Udayana; Putu Yoka Angga Prawira; I Gede Bagus Arya Merta Tika; I Gede Iwan Sudipa
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.982

Abstract

The rapid growth of e-commerce platforms in Indonesia has heightened the need to understand customer sentiment for improving service quality and user experience. This study conducts sentiment analysis on customer reviews from two e-commerce platforms under the Baliyoni Group: Tokodaring.balimall.id and Balimall.id. A total of 45,257 reviews were collected through database dumps, consisting of 41,519 reviews from Tokodaring and 3,738 from Balimall. The raw textual data underwent a comprehensive preprocessing pipeline involving duplicate removal, tokenization, normalization, stopword elimination, stemming, and sentiment labeling. Two classification models were implemented and compared: Gated Recurrent Unit (GRU), a deep learning model that captures sequential dependencies, and Naïve Bayes, a probabilistic classifier utilizing TF-IDF features. Evaluation results indicate that the GRU model achieved higher precision and F1-score, whereas Naïve Bayes obtained perfect recall but lower precision. Specifically, for Balimall reviews, Naïve Bayes achieved an accuracy of 90.06%, precision of 84.03%, recall of 100%, and an F1-score of 91.32%. These results mirror the Tokodaring dataset performance, confirming the model’s ability to effectively identify positive sentiment but also its tendency to overestimate it, leading to false positives on negative reviews. Overall, the GRU model demonstrated a more balanced performance, making it more suitable for analyzing informal and diverse user-generated text on Indonesian e-commerce platforms. This research provides insights into developing robust sentiment analysis systems to support data-driven decision-making in digital commerce environments.
Co-Authors I Gede Bagus Arya Merta Tika I Gede Iwan Sudipa I Putu Agus Eka Darma Udayana Ni Made Jeni Aprilia Dewi