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All Journal ILKOM Jurnal Ilmiah Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Isra, Ali
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Apriori Algorithm Application for Consumer Purchase Patterns Analysis Situmorang, Boldson Herdianto; Isra, Ali; Paragya, Dhatu; Akbar Adhieputra, David Aulia
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9260

Abstract

The Apriori algorithm is a data mining association rule algorithm for finding relationship patterns between one or more items in a dataset. Apriori algorithm is often used in transaction data analysis or market basket analysis. Apriori algorithm is used to find out consumer purchase patterns in e-commerce systems and provide product recommendations to consumer by extaracting associations or events from transactional data. This study is purposed for deeply analyze the steps, performance of Apriori algorithm, and give relevant an example of case study to better explain the steps of Apriori algorithm application, as well as the results achieved. 
A Deep Learning Approach for Tourism Destination Recommendation Using IndoBERT and TF-IDF Silfianti, Widya; Syah, Rama Dian; Suhendra, Adang; Isra, Ali; Darmayantie, Astie; Ohorella, Noviawan Rasyid
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3069.241-251

Abstract

The rapid development of information technology has transformed various sectors, including tourism, where recommendation systems play a vital role in providing personalized services. Tourists are often faced with a wide range of destination choices, making decision-making increasingly complex. To address this, Artificial Intelligence (AI) and Natural Language Processing (NLP) can be leveraged to enhance recommendation accuracy through deeper analysis of destination descriptions. This study proposes a tourism destination recommendation system combining IndoBERT, SimCSE, and TF-IDF methods. IndoBERT was applied to capture semantic and contextual meaning in the Indonesian language, SimCSE improved sentence-level embeddings, and TF-IDF extracted essential keywords from descriptions. The system was implemented on a website to generate personalized recommendations based on user input. Evaluation results demonstrated that the composition of IndoBERT and TF-IDF achieved strong performance, with precision, recall, and F1-score values of 1.0 at a similarity threshold of 0.20. However, higher thresholds reduced recall and F1-score, indicating that a lower threshold provided a better balance between accuracy and coverage. The recommendation outputs matched user preferences, and functional testing showed that all website features performed successfully. These findings highlight the effectiveness of combining semantic and keyword-based methods for tourism recommendation. Future work could expand the dataset, integrate user feedback, and benchmark against other state-of-the-art models to further enhance system performance.
Co-Authors Adang Suhendra Akbar Adhieputra, David Aulia Darmayantie, Astie Ohorella, Noviawan Rasyid PARAGYA, DHATU Rama Dian Syah Silfianti, Widya Situmorang, Boldson Herdianto