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All Journal Jurnal Krisnadana
I Putu Agus Eka Darma Udayana
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.
Inventory Forecasting System Using LSTM for Vape Store I Putu Agus Eka Darma Udayana; Ni Putu Suci Meinarni; I Gusti Ayu Agung Randhika Kerlania; I Putu Utama Arta; I Made Adi Sutrisna
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.v5i1.981

Abstract

Inventory management remains a critical challenge for small and medium-sized retail businesses, including Gonvapestore, a vape retailer in Bali, where stock decisions are often made intuitively. This study aims to design and implement a stock forecasting system using the Long Short-Term Memory (LSTM) algorithm to enhance the accuracy of monthly inventory predictions. The research follows the Knowledge Discovery in Database (KDD) process, encompassing data selection, preprocessing, and time series transformation through a sliding window approach. The LSTM model was developed using TensorFlow and Keras, and its forecasting accuracy was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics. Experimental results show that the LSTM model achieved superior performance compared to ARIMA, with RMSE and MAE values of 3.14 and 2.71, respectively, versus 6.05 and 5.21 for ARIMA. Product-level evaluation using MAPE indicates that Icy Lychee achieved a relatively low error rate of 37%, while Icy Mango (50%) and Icy Watermelon (52%) exhibited higher error rates, suggesting model performance may vary across product categories. These results demonstrate the LSTM model’s superior ability to capture nonlinear sales patterns compared to traditional statistical approaches. The model was integrated into a Django-based web system with a real-time visualization dashboard and sales logging features. The proposed system offers a practical and intelligent decision-support tool for retail inventory management, reducing stockout and overstock risks through data-driven forecasting.
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 Gusti Ayu Agung Randhika Kerlania I Made Adi Sutrisna I Putu Utama Arta Ni Made Jeni Aprilia Dewi Ni Putu Suci Meinarni Putu Yoka Angga Prawira