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This Author published in this journals
All Journal Indonesian Journal of Data and Science TIERS Information Technology Journal Jurnal Ilmiah Telsinas Elektro, Sipil dan Teknik Informasi
Ni Wayan Jeri Kusuma Dewi
Institut Bisnis dan Teknologi Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : indonesian journal of data and science

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Sales Forecasting Analysis Using Fuzzy Time Series and Simple Linear Regression Methods at Toko Ari Ni Luh Sri April Yanti; Ni Wayan Jeri Kusuma Dewi; I Gede Made Yudi Antara; Desak Made Dwi Utami Putra; Putu Wirayudi Aditama
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.368

Abstract

Introduction: Forecasting, often referred to as prediction, can actually help assess conditions or predict future sales. In the business world forecasting is crucial because it can help companies plan their future operations especially when faced with sudden increases and decreases in sales and stockpiles. Especially in retail forecasting is extremely helpful in purchasing merchandise, managing inventory in the warehouse, and reducing losses due to changing customer preferences. Ari's shop, located on Jalan Raya Samu, Singapadu Kaler, Gianyar, Bali, also experiences increases and decreases in monthly sales. Therefore, it is hoped that this sales forecasting can help maintain more stable and smooth operations. Methods: This study used two methods to forecast sales: Fuzzy Time Series (FTS) and Simple Linear Regression (SLR), to predict figures from Ari's shop's monthly sales data. Both methods use the same dataset, which is Ari's Store sales data for 13 months, from January 2024 to January 2025. The forecast results are then compared using the Mean Absolute Percentage Error (MAPE), which measures the model's accuracy in predicting results. Results: Based on the sales forecasts performed, both models produced fairly accurate predictions due to their low MAPE values, below 10%. Of the two methods, Simple Linear Regression provided more accurate results with a MAPE of 3.57%. Meanwhile, the Fuzzy Time Series method produced a MAPE of 5.53%. This difference in values indicates that the linear regression model is more appropriate for Ari's Store sales data, especially since the data pattern tends to follow a linear trend.
Comparison of Naïve Bayes and SVM in Sentiment Analysis of ChatGPT for Learning on X and YouTube Ni Putu Eka Swari; Ni Wayan Jeri Kusuma Dewi; Ni Ketut Utami Nilawati; Aniek Suryanti Kusuma; Ni Luh Wiwik Sri Rahayu Ginantra
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.382

Abstract

The rapid development of artificial intelligence technology has encouraged users to actively express opinions on social media platforms such as X and YouTube, including discussions on the use of ChatGPT as a learning support tool. This study aims to analyze public sentiment toward the use of ChatGPT in learning contexts by comparing the performance of the Naïve Bayes and Support Vector Machine (SVM) classification methods. A total of 5,500 comments from platform X and 5,543 comments from YouTube were collected through a crawling process using relevant keywords during the period from January 2023 to December 2025. The data were preprocessed and labeled into three sentiment classes (positive, negative, and neutral) using a lexicon-based approach with the INSET Lexicon. Feature extraction was conducted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that the SVM classifier consistently outperformed the Naïve Bayes method on both platforms. On platform X, SVM achieved an accuracy of 76.67%, while Naïve Bayes obtained 74.60%. On YouTube, SVM achieved an accuracy of 73.10%, significantly higher than Naïve Bayes at 62.04%. These findings indicate that SVM is more effective for sentiment analysis of social media data related to the use of ChatGPT in learning environments
Co-Authors Aniek Suryanti Kusuma, Aniek Suryanti Defri Sucahyono Desak Made Dwi Utami Putra I Gede Made Yudi Antara Ni Ketut Utami Nilawati Ni Luh Sri April Yanti Ni Luh Wiwik Sri Rahayu Ginantra Ni Putu Eka Swari Putu Wirayudi Aditama