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All Journal DIGINTEL-AI : DIGital INnovation and inTELligence – AI
Agus Budiyantara
Teknik Informatika, Institut Sosial dan Teknologi (ISTEK) Widuri, Jakarta

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Analisis Pola Pembelian Konsumen Di Rumah Makan Tepi Laut Baubau Menggunakan Algoritma Apriori Fadil Firmansyah; Irwansyah; Agus Budiyantara
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 1 (2025): October
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i1.3

Abstract

Baubau seaside restaurants have not utilized transaction data welleven though transaction data can be found behavior or consumer purchasing patterns. This study aims to analyze consumer purchasing patterns based on transaction data to identify consumer purchasing behavior, find items that are often purchased simultaneously in one transaction, and find out the value of support, confidence, and lift ratio of each association rule generated through the analysis process. Of the 701 records contained in the transaction data, the apriori algorithm method is used in analyzing consumer purchasing patterns with support parameters, association rules with confidence parameters and measuring association rules with lift ratio. Based on the results of apriori analysis, three association rules are obtained, namely: purchase of sunu ori tends to followed by mineral water (support 0.102857; confidence 0.808989; lift 2.6339), purchase of iced tea and crispy squid followed by bobara hm (support 0.087143; confidence 0.910448; lift 3.1550), and purchase of crispy squid and bobara hm followed by iced tea (support 0.087143; confidence 0.835616; lift 5.222603).
Klasifikasi Metode Naïve Bayes pada Ulasan Pengguna Aplikasi Dazzcam untuk Pengeditan Foto Vintage di App Store Salsa Dwi Agistina; Irwansyah; Agus Budiyantara; Faldy Irwiensyah
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 2 (2026): April
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i2.9

Abstract

The rapid growth of mobile applications has increased the importance of user-generated reviews as a source of information for evaluating application quality and user satisfaction. Dazzcam, a photo editing application known for its vintage-style filters, has gained significant popularity among iOS users. This study aims to classify user reviews from the App Store into positive and negative sentiment categories using the Naïve Bayes algorithm and to evaluate the performance of the model. A total of 911 reviews were collected and divided into training and testing datasets with a ratio of 80:20. The research methodology includes data preprocessing, feature extraction using TF-IDF, and classification using Naïve Bayes, followed by evaluation with a confusion matrix. The results show that 712 reviews were classified as positive and 199 as negative, with an accuracy of 79.78%, precision of 79.89%, recall of 79.78%, and F1-score of 79.53%. These findings indicate that the Naïve Bayes algorithm demonstrates good performance and can be effectively utilized for sentiment analysis of application reviews.
Co-Authors Fadil Firmansyah Faldy Irwiensyah Irwansyah Irwansyah Salsa Dwi Agistina