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All Journal Knowledge Engineering and Data Science Transaction on Informatics and Data Science Prosiding SeNTIK STI&K
Akhriza, Tubagus M.
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media

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Recurrent Session Approach to Generative Association Rule based Recommendation Armanda, Tubagus Arief; Wardhani, Ire Puspa; Akhriza, Tubagus M.; Admira, Tubagus M. Adrie
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p199-214

Abstract

This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations.  The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot.  In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability.
TOPIK KONTRAS BERBASIS LIFT POSITIF-NEGATIF: STRATEGI BARU REKOMENDASI KONTEN Akhriza, Tubagus M.; Setyowibowo, Sigit; Armanda, Tubagus Arief; Admira, Tubagus M Adrie
Prosiding Seminar SeNTIK Vol. 8 No. 1 (2024): Prosiding SeNTIK 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Pemetaan topik tradisional seperti Latent Dirichlet Allocation (LDA) sering kali berfokus pada distribusi global kata tanpa memperhatikan hubungan spesifik antar topik. Penelitian ini memperkenalkan strategi baru dalam rekomendasi konten berbasis Association Rule Learning (AR) dengan Lift positif dan negatif. Metode ini tidak hanya mampu menemukan asosiasi yang sering muncul bersama (Lift positif), tetapi juga mengungkap kontras antara topik-topik yang jarang muncul bersama (Lift negatif), memberikan wawasan lebih dalam yang tidak dapat diidentifikasi oleh LDA. Pendekatan ini diterapkan pada dataset berita politik Indonesia untuk membangun sistem rekomendasi konten yang lebih cerdas dan dinamis, yang mampu menawarkan topik-topik kontras yang menarik minat pengguna serta memperkaya pengalaman mereka. Hasil eksperimen menunjukkan bahwa metode ini lebih fleksibel dalam mengidentifikasi keterkaitan dan anomali antar topik, sehingga memberikan rekomendasi yang lebih relevan dan terarah..
Comparison of the Accuracy Between Naive Bayes Classifier and Support Vector Machine Algorithms for Sentiment Analysis in Mobile JKN Application Reviews Septiani, Erni; Akhriza, Tubagus M.; Husni, Mochamad
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12232

Abstract

The Mobile JKN (National Health Insurance) application is a form of BPJS Health's commitment to implementing health insurance programs since 2014. The large number of reviews of the Mobile JKN application on the Google Play Store requires sentiment analysis with an algorithm that produces the best accuracy. This research compares the accuracy obtained from the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. This algorithm is implemented directly in sentiment analysis and combined with the Synthetic Minority Over-Sampling Technique (SMOTE) technique to overcome data imbalance. The data in this research was obtained from reviews of the Mobile JKN application on the Google Play Store using the data scraping method. We use data scraping and labeling processes before performing sentiment analysis. The sentiment analysis process includes text preprocessing and processing (modeling) by dividing the data into 30%, 40%, and 50% test data, with the rest becoming training data. The results of this research showed that the algorithm with the best accuracy was the NBC algorithm using the SMOTE technique with 50% test data and the SVM algorithm without the SMOTE technique with 50% test data. Both give the same accurate results, namely 0.90 or 90%. Experiments show that the amount of test data and the application of SMOTE affect the accuracy of the two compared algorithms.
Co-Authors Admira, Tubagus M Adrie Admira, Tubagus M. Adrie Armanda, Tubagus Arief Ire Puspa Wardhani Mochamad Husni Septiani, Erni Sigit Setyowibowo