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All Journal Buletin Poltanesa ITEJ (Information Technology Engineering Journals)
Sujacka Retno
Malikussaleh University
Agriculture, Biological Sciences & Forestry Computer Science & IT Education

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Detecting Bot Comments on a Product in Shopee Using the Gradient Boosting Method Manda Sari; Rizal Rizal; Sujacka Retno
Poltanesa Vol 26 No 1 (2025): June 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v26i1.3344

Abstract

Shopee is one of the largest e-commerce platforms in Southeast Asia, providing a product comment feature that serves as a primary reference for prospective buyers to assess product quality and seller reputation. Unfortunately, the prevalence of fake comments generated by bots—characterized by rigid language, repetitive patterns, and excessive praise—raises concerns about the authenticity of available reviews. This issue can negatively influence consumers’ purchasing decisions. This study aims to develop an automated system capable of detecting bot comments using the Gradient Boosting algorithm. A total of 3,000 comments were manually collected from various product categories and labeled directly by the researchers. The comment data were then processed through several stages, including text cleaning, tokenization, and lemmatization, to prepare for model analysis. The trained model demonstrated excellent performance, achieving an accuracy of 94.09%, precision of 95.99%, recall of 83.23%, and an F1-score of 89.13%. Based on these results, it can be concluded that the Gradient Boosting algorithm is highly effective in classifying bot comments and can help improve consumer trust and security in online shopping.
Forecasting of Palm Oil CPO Production Results at PTPN III Batang Toru Plantation Using The Autoregressive Integrated Moving Average Method Sylva Putri Utari; Asrianda Asrianda; Sujacka Retno
ITEJ (Information Technology Engineering Journals) Vol. 10 No. 2 (2025): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i2.254

Abstract

The increasing demand for palm oil as a raw material for food and energy industries has driven the need for accurate forecasting methods to optimize palm oil production management. This study aims to forecast Crude Palm Oil (CPO) production at PTPN III Batang Toru Plantation using the Autoregressive Integrated Moving Average (ARIMA) method. Monthly time series data from January 2020 to January 2024, including Fresh Fruit Bunches (FFB), loose fruit, and CPO yields, were analyzed to build the forecasting model. The Augmented Dickey-Fuller (ADF) test confirmed that the data is stationary without differencing. Based on the ACF, PACF, and white noise tests, the ARIMA(1,0,1) model was identified as the best fit. The forecasting results indicated a potential increase in CPO production from January 2025 to December 2026. However, alternative models like CPOF showed poor accuracy, with a high MAPE of 442.12%, suggesting the need for further model refinement. Despite limitations, the ARIMA method remains effective for short-term forecasting and supports data-driven decision-making in the plantation sector.
Co-Authors Asrianda Asrianda Manda Sari Rizal Rizal Sylva Putri Utari