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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika Journal Collabits
Siti Maesaroh
Universitas Mercu Buana
Computer Science & IT Engineering Other

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Web-based Log Analysis System for Website Attack Detection using Boyer-Moore Algorithm and Regular Expression Technique Izzeldin Addarda; Siti Maesaroh
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 2 (2023): July 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i2.48342

Abstract

Di era digitalisasi saat ini, ancaman serangan siber terus meningkat secara global. serangan siber merujuk pada tindakan kejahatan yang dapat menyebabkan gangguan, pemalsuan dan pencurian informasi berharga dari aplikasi atau situs web. Bersumber pada halaman suara.com terdapat lebih dari 700 juta serangan siber yang berlangsung di Indonesia pada tahun 2022 yang diikuti dengan kebocoran informasi kesehatan e-HAC. Hal tersebut berkaitan dengan peranan dari web server guna melayani permintaan HTTP (Hypertext Transfer Protocol). Selain itu, web server bertugas untuk menerjemahkan kode-kode dinamis menjadi kode-kode statis dalam suatu laman website. Berdasarkan hal tersebut dibuatlah sistem log analysis menggunakan algoritma boyer moore dan teknik regular expressions sebagai metode pencarian adanya indikasi serangan website dengan hasil bahwa algoritma boyer moore dan teknik regular expressions dapat menemukan indikasi jenis serangan terhadap website dengan baik dan relatif cepat karena hanya membutuhkan waktu 32.9 detik untuk menganalisis 1553 baris data yang berasal dari log file. Dengan demikian administrator web server dapat dengan mudah mencari atau melihat jenis upaya serangan website terhadap web server oleh pelaku kejahatan siber.  In the current era of digitalization, the threat of cyber attacks continues to increase globally. cyberattacks refer to criminal acts that can lead to tampering, falsification, and theft of valuable information from applications or websites. Sourced on the Suara.com page, there were more than 700 million cyber attacks that took place in Indonesia in 2022, followed by the leak of e-HAC health information. This relates to the role of the web server to serve HTTP (Hypertext Transfer Protocol) requests. In addition, the web server is tasked with translating dynamic codes into static codes on a website page. Based on this, a log analysis system was created using the boyer moore algorithm and regular expressions techniques as a search method for indications of website attacks with the result that the boyer moore algorithm and regular expressions techniques can find indications of types of attacks on websites properly and relatively quickly because it only takes 32.9 seconds to analyze 1553 rows of data coming from the log file. Thus, web server administrators can easily search for or view types of website attack attempts against web servers by cybercriminals.  
Comparison of Long Short Term Memory (LSTM) and LightGBM Algorithms to Improve Inventory Stock Efficiency through Forecasting George Rivaldo; Siti Maesaroh
Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika Vol. 7 No. 2 (2024)
Publisher : Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to address the growing challenges faced by e-commerce businesses in inventory management, particularly the need for accurate forecasting of outgoing goods. The study focuses on comparing the performance of two machine learning algorithms: Long Short Term Memory (LSTM) and LightGBM. Accurate forecasting is crucial to minimize issues such as overstock and stockouts, which can adversely affect profitability. Using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), this study analyzes historical sales data to evaluate the predictive accuracy of both models. The results indicate that the LSTM model provides more accurate predictions compared to LightGBM, demonstrating its effectiveness as a forecasting tool in inventory stock management. These findings highlight the importance of employing advanced machine learning techniques to enhance inventory efficiency, and ultimately, to aid businesses in better decision-making and improved profitability.
Improving E-commerce Platforms with Collaborative Filtering algorithms for Product Recommendations Siti Maesaroh; Putri Nabila; Faiz Muhammad Ramadhan
Journal Collabits Vol. 2 No. 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.27299

Abstract

Online product reviews play a major role in the success or failure of an e-commerce business. In a transaction, buyers will usually find out information on the use of the product or service from online reviews posted by previous customers to get detailed product recommendations and make purchase decisions. Many reviews are created by users who often include strong sentimental opinions. This review of data is very promising and can be used by both customers and the Company. Customers can read reviews to know more about the quality of a product. However, due to the large number of reviews, it is difficult to see and read all consumer evaluations personally to get useful information. One effective approach in providing such recommendations is using Collaborative Filtering (CF) algorithms. This research aims to improve e-commerce platforms by applying Collaborative Filtering algorithms to provide more accurate and relevant product recommendations to users.
Comparative Analysis of Public Sentiment Towards Sri Mulyani and Purbaya as Finance Ministers on the X Platform Using the Indobertweet Model Muhammad Aryaka Zamzami; Siti Maesaroh; Dendy Jonas Managas
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37962

Abstract

The development of social media has positioned platform X (Twitter) as a primary source for expressing public opinion toward government figures and policies. This study aims to analyze public sentiment toward two Indonesian public figures, Sri Mulyani Indrawati and Purbaya Yudhi Sadewa, by utilizing the transformer-based IndoBERTweet model. The data were collected from January 1, 2025, to November 1, 2025. A total of 11,000 tweets related to Sri Mulyani were collected; however, only 2,500 tweets were used for data processing and model training, with a maximum limit of 1,000 tweets per month. Meanwhile, 650 tweets were obtained for Purbaya Yudhi Sadewa. This research employs a supervised learning approach with labeled data consisting of positive, negative, and neutral sentiment classes. Minimal preprocessing was applied, considering that IndoBERTweet is specifically designed to handle the characteristics of social media text. The model was trained for five epochs and evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the IndoBERTweet model can classify sentiment effectively, particularly on the Sri Mulyani dataset, which contains a larger volume of data and achieves an accuracy of over 82%. In contrast, the model’s performance on the Purbaya Yudhi Sadewa dataset shows a lower accuracy of 71%, influenced by the limited amount of data. This study confirms that the quantity and distribution of data significantly affect the performance of transformer-based sentiment analysis models. Based on the sentiment classification results, public sentiment toward Sri Mulyani Indrawati tends to be dominated by negative and neutral sentiments, while sentiment toward Purbaya Yudhi Sadewa shows a distribution dominated by neutral and positive sentiments.
Co-Authors Dendy Jonas Managas Faiz Muhammad Ramadhan George Rivaldo Izzeldin Addarda Muhammad Aryaka Zamzami Putri Nabila