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All Journal Jurnal Bangkit Indonesia MDP Student Conference
Stefanni, Stefanni
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering

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Pengaruh Bauran Pemasaran Terhadap Keputusan Pembelian Pada Madu Kembang Joyo Sriwijaya Di Palembang Stefanni, Stefanni; Meirisa, Faradila
MDP Student Conference Vol 3 No 2 (2024): The 3rd MDP Student Conference 2024
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v3i2.7427

Abstract

The purpose of this study is to examine partially and simultaneously how the influence of marketing mix on purchasing decisions at Madu Kembang Joyo Sriwijaya in Palembang. The sample used amounted to 250 respondents. Data collection method with questionnaire and using Likert measurement scale. This research approach uses associative research. The technique for sampling uses non-probability sampling with the purposive sampling method. The results of this study show that the independent variable has a partial effect on the dependent variable. Simultaneously, the independent variable affects the dependent variable with a Sig < 0.05.
Analisis Sentimen Pengguna X Terhadap Kebocoran Data Pribadi Menggunakan Algoritma Naïve Bayes Classifier Stefanni, Stefanni; Zulfachmi, Zulfachmi; Zulkipli, Zulkipli; Saputra, Aggry
Jurnal Bangkit Indonesia Vol 14 No 1 (2025): Bulan Maret 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i1.434

Abstract

Personal data breaches in Indonesia have become increasingly prevalent, posing significant risks to both individuals and businesses. With the rapid advancement of information technology, this issue has sparked intense discussions on social media, particularly on the X (Twitter) platform. This study aims to analyze user sentiment on X regarding personal data breaches by classifying opinions into positive, negative, and neutral sentiment categories. Additionally, this research evaluates the performance of the classification model using a confusion matrix to measure accuracy, precision, recall, and f1-score. The method used in this study is the Naïve Bayes Classifier, with 70% training data (433 data points) and 30% testing data (186 data points). The data was obtained through web crawling and preprocessed before performing sentiment classification. The results indicate that negative sentiment dominates with 43.54%, followed by positive sentiment at 28.50%, and neutral sentiment at 27.96%. Model evaluation achieved an accuracy of 98.92%, with negative precision 100%, neutral precision 100%, and positive precision 96.30%. Meanwhile, recall for both positive and negative sentiment reached 100%, while recall for neutral sentiment was 96.23%. The f1-score for negative, neutral, and positive sentiment was 1.0, 0.988, and 0.981, respectively. These findings demonstrate that the Naïve Bayes Classifier performs exceptionally well in classifying sentiment related to personal data breaches. The dominance of negative sentiment in the classification results reflects high public concern over this issue, highlighting the urgent need for enhanced data security measures and privacy protection in Indonesia.
Co-Authors faradila meirisa Saputra, Aggry Zulfachmi, Zulfachmi Zulkipli Zulkipli