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All Journal Prosiding National Conference for Community Service Project Jurnal Pendidikan Vokasi Raflesia Journal Of Artificial Intelligence And Software Engineering Jurnal Pengabdian kepada Masyarakat : Kreasi Mahasiswa Manajemen
Jessika, Jessika
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Journal : Journal Of Artificial Intelligence And Software Engineering

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Sentiment Analysis Of Instagram Comments On The BPS Province X Account Using The Naive Bayes Algorithm Based On Machine Learning Jessika, Jessika; Khaidar, Al; Nurdin, Nurdin; Muliana, Syarifah
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7815

Abstract

Sentiment analysis is an approach in natural language processing that aims to identify and categorize user opinions or attitudes towards an entity based on text data. The data used consists of the last 500 uploaded captions obtained through the Phantombuster tool. The analysis stages include data crawling, preprocessing (removal of duplicate and empty data, tokenization, stopword removal, and case folding), printing using the Naïve Bayes algorithm, and visualization of the classification results. Based on the processing results, it was found that the majority of the data was classified as neutral (97.65%), while the rest was divided into positive (1.57%) and negative (0.78%) categories, with a model accuracy of 94%. Although the model accuracy is relatively high, the dominance of the neutral class indicates an imbalance in data distribution (imbalanced data) which can affect the quality of the generalization model.
Analysis Of Customer Understanding Level Of The E-Policy System In The Sinar Mas Online Insurance Application In The Lhokseumawe Branch Work Area Muliana, Syarifah; Nurdin, Nurdin; Alqhifari, Azka; Khaidar, Al; Jessika, Jessika
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7824

Abstract

Digital transformation in the insurance industry is driving companies to adopt electronic systems, including the implementation of e-policies as a replacement for physical policy documents. This study aims to analyze the level of customer understanding of the e-policy system on the Sinar Mas Online Insurance application in Lhokseumawe branch. The research method used is a quantitative approach with data collection techniques through distributing questionnaires to 100 active customers. The results show that most customers are aware of the existence of e-policies, but still face obstacles in understanding their functions, legality, and how to access documents through the Sinar Mas Online application. Factors such as age, education level, and experience using digital services have been shown to influence the level of customer understanding. This study recommends the need for continuous education and the development of a more intuitive application interface to improve digital literacy and user convenience in accessing e-policies. These findings are expected to provide evaluation material for companies in improving their information systems and digital communication strategies for customers.
Co-Authors Alqhifari, Azka Andirantony, Andirantony Baehaqi Carlie, Jhon Debora, Vira Dewi, Astrid Mustika Fatmawati, Fatmawati Feehily, Nick Hassa, Simah Ardillah Jalil, Arifuddin Julinda, Julinda Khaidar, Al Kusnadi, Ryo Muliana, Syarifah Nasution, M. Wahyu Nova Susanti Nurdin Nurdin Rosyaadah, Haniifah Salsabila, Lutfia Nada Saputra, M. Bayu Syukur, Hamdani Tengku, Tengku Theodora, Elny Widyanto, Dea Pradita Yulianti, Kartikaning Diyah Zahra, Faustina Zahra, Nurazlin Saydina