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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Sistem Informasi dan Informatika (SIMIKA) Journal Sensi: Strategic of Education in Information System Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Jurnal Inovatif : Inovasi Teknologi Informasi dan Informatika Journal Collabits
Siti Maesaroh
Universitas Mercu Buana
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : journal collabits

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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.
Analysis of Best-Selling Product Sales at Hatfina Hijab Using Association Rule Mining for Business Intelligence Khairunnisa Ramadhan; Siti Maesaroh; Nadia Kayla Dhinita
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.27287

Abstract

This study analyzes customer purchasing patterns at Hatfina Hijab and translates the resulting association rules into actionable Business Intelligence (BI) recommendations. The dataset consists of 174 sales transactions recorded from February to April 2024. After data cleaning, product items were transformed into a binary transaction matrix and processed using association rule mining in RapidMiner. The baseline rule set was obtained using a minimum support of 0.30 and a minimum confidence of 0.50. The revised analysis complements the original confidence values with support and lift to distinguish rules that are frequent from those that represent genuinely positive product associations. Nineteen baseline rules were reported, of which sixteen have lift values greater than 1.00 and therefore indicate positive associations. The strongest confidence was found in the rule Hampers Paket Hemat 2 Hijab and Hampers Paket Hemat 1 Hijab -> Souvenir Sajadah (confidence = 1.000; lift = 1.475). Parameter sensitivity analysis shows that stricter support-confidence thresholds reduce the retained rule set from 19 rules at 0.30/0.50 to 14 rules at 0.35/0.60, 13 rules at 0.40/0.70, 3 rules at 0.45/0.80, and 1 rule at 0.50/0.90. The findings are interpreted into recommendations for product bundling, cross-selling, promotional design, and coordinated inventory planning. The study demonstrates how association rule mining can function as an analytical component of BI and Decision Support Systems (DSS), while emphasizing that association patterns indicate co-occurrence rather than causality.
Co-Authors Dendy Jonas Managas Erliyani, Ita Faiz Muhammad Ramadhan George Rivaldo Izzeldin Addarda Khairunnisa Ramadhan Mohamad Yusuf Muhammad Aryaka Zamzami Muhammad Zakki Mardhi Nadia Kayla Dhinita Nungky Awang Candra Nungky Chandra Putri Nabila Roy Mubarak Rushendra, Rushendra