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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal Of Artificial Intelligence And Software Engineering Jurnal Teknologi Rekayasa Informasi dan Komputer
Muliana, Syarifah
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Computer Science & IT

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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.
Monitoring Jadwal Belajar Mengajar Prodi Teknik Multimedia Dan Jaringan Dengan Raspberry Pi Muliana, Syarifah; Nasir, Muhammad; Mursyidah, Mursyidah
Jurnal Teknologi Rekayasa Informasi dan Komputer Vol 2, No 1 (2019): JURNAL TRIK - POLITEKNIK NEGERI LHOKSEUMAWE
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jtrik.v2i1.2575

Abstract

Abstrak— Politeknik Negeri Lhokseumawe menyediakan Web server untuk mengakses berbagai macam informasi sekitaran jurusan dan program studi khususnya Prodi Teknik Multimedia dan Jaringan. Web bukan hanya untuk menyampaikan sebatas informasi pada prodi, tetapi juga dapat memonitoring jadwal belajar mengajar meggunakan Raspberry Pi sebagai server mini, pengganti komputer yang biasanya digunakan untuk server. Selama ini monitoring jadwal belajar mengajar dilakukan secara manual, sistem ini dibuat untuk memonitoring secara otomatis. Perancangan sistem berbasis Web dengan pemprograman HTML, PHP, MYSQL dan SMS gateway dengan gammu sebagai servicenya. Operasi yang berjalan dalam SMS gateway ini yaitu, pesan broadcast yang dapat mengirimkan pesan kebanyak tujuan sesuai dengan jadwal dan auto respon atau sistem dapat menerima pesan dan mengirim kembali ke nomor tujuan jika ada secara otomatis. Parameter nilai yang diiambil berdasarkan data waktu yang diatur dalam sistem. Dengan adanya sistem ini dapat mempermudah prodi Teknik Multimedia dan Jaringan dalam memonitoring jadwal belajar mengajar.Kata Kunci : Web, SMS Gateway, Gammu, Raspberry Pi
Analysis of Customer Churn Classification for Sinarmas Syariah Lhokseumawe Insurance Services Using Deep Learning Tabnet and Explainable AI Muliana, Syarifah; Taufiq , Taufiq; Ula, Munirul
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12864

Abstract

Customer churn is a major challenge in the insurance industry because it directly affects customer retention, business sustainability, and company profitability. Early identification of customers at risk of churn is therefore essential for developing effective retention strategies. This study proposes an interpretable customer churn prediction framework for Sinarmas Syariah Lhokseumawe Insurance Services by integrating TabNet deep learning with Shapley Additive Explanations (SHAP). The dataset consists of 2,000 customer records containing demographic information, insurance transactions, premium payments, claims history, and customer interaction data. Due to the imbalanced class distribution, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training dataset to improve model learning while preventing data leakage. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The experimental results demonstrate that the proposed approach achieved an accuracy of 98.77%, precision of 86.67%, recall of 92.86%, F1-score of 89.66%, and ROC-AUC of 0.995, indicating excellent classification performance. Furthermore, SHAP analysis revealed that communication, premi_2025, and reason_to_purchase were the most influential features affecting churn predictions. These findings highlight the importance of customer engagement, premium management, and purchasing motivations in customer retention. The proposed TabNet-SHAP framework provides both high predictive performance and model interpretability, making it a valuable decision-support tool for customer retention strategies in the insurance sector.
Co-Authors Alqhifari, Azka Jessika, Jessika Khaidar, Al Muhammad Nasir Munirul Ula Mursyidah Mursyidah Nurdin Nurdin Taufiq , Taufiq