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All Journal Sistemasi: Jurnal Sistem Informasi Syntax Literate: Jurnal Ilmiah Indonesia
Darmawan Setiawan, Darmawan
Universitas Bakrie
Humanities Computer Science & IT Education Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Feature Extraction Optimization to Improve Naïve Bayes Accuracy in Sentiment Analysis of Bulukumba Tourism Objects Setiawan, Darmawan; Umar, Najirah; Nur, M. Adnan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4580

Abstract

This research employs social media (Twitter) to apply sentiment analysis ascertain the degree of public satisfaction with the Bulukumba tourist attraction. Unstructured text data is a major challenge in sentiment analysis. For this reason, implementing the Naïve Bayes algorithm is an effective approach for conquering this challenge because of its ability to handle text data well. This study aims to evaluate the performance of multinomial Naïve Bayes by testing a combination of minimum document frequency (min-df) and maximum document frequency (max-df) parameter values in determining the level of accuracy. This analysis stage includes collecting data from Twitter related to the Bulukumba tourist attraction. Preprocessing carried out includes data cleaning, casefolding, text normalization, tokenization, stopword removal, and stemming. Feature extraction using Count Vectorizer and TF-IDF weighting. The process ends with 10-Fold Cross-Validation by separating the data into training data and test data for sentiment analysis classification, as well as evaluation using the Confusion Matrix. In this research, there are 10 test scenarios with various combinations of min-df and max-df. The values of employed min-df consists of 0.001, 0.002, 0.005, 0.01, 0.02 and max-df consists of 0.5 and 0.8. The results of implementing Multinomial Naïve Bayes in this test show that classification accuracy increases with effective min-df and max-df parameter settings. The greatest accuracy was 0.7910 in testing a combination of min-df parameter values of 0.001 and max-df 0.8. Meanwhile, the average accuracy for each test was obtained the highest value of 0.7272 with min-df of 0.002 and max-df of 0.5 and 0.8 respectively.
Pengaruh Kualitas Layanan Terhadap Kepuasan Nasabah pada Bank Muamalat Jubaidi, Jubaidi; Setiawan, Darmawan; Firdaus, Bintang; Fitri, Beinda Desita Zalasia; Aziizah, Azzah Nur
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i11.17078

Abstract

Bagi perusahaan, mencapai mutu yang prima dan unggul merupakan hal yang krusial guna menjamin kepuasan pelanggan dan konsumen. Sedangkan bagi nasabah pelayanan yang berkualitas suatu hal yang sangat penting untuk membangun kepercayaan. Terbentuknya kepuasan nasabah menawarkan beberapa keuntungan yang akan memberikan kontribusi terhadap peningkatan pendapatan. Oleh sebab itu penelitian ini bertujuan untuk menilai pengaruh tangible, realibity, responssiveness, assurance, dan emphaty sebagai dimensi kualitas layanan terhadap kepuasan nasabah pada bank Muamalat Cabang Samarinda. Data yang dianalisis berasal dari kuesioner yang disebarkan kepada nasabah sebanyak 100 orang. Analisis datamenggunakan analisis regresi linier berganda melalui program SPSS 22. Hasil penelitian menunjukkan bahwa secara simultan semua variabel tangible, realibity, responssiveness, assurance, dan emphaty berpengaruh secara signifikan terhadap kepuasan nasabah Bank Muamalat Cabang Samarinda.
Co-Authors Aziizah, Azzah Nur Firdaus, Bintang Fitri, Beinda Desita Zalasia Jubaidi Najirah Umar Nur, M. Adnan