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All Journal Informatics and Computer Engineering Journal Internet of Things and Artificial Intelligence Journal Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Siregar, Kalfida Eka Wati
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Pemodelan Aplikasi Pengelolaan Data Wajib Pajak Menggunakan Pendekatan SDLC Siregar, Kalfida Eka Wati; Fadil, Ulfi Muzayyanah; Armansyah, Armansyah
Informatics and Computer Engineering Journal Vol 5 No 1 (2025): Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v5i1.7992

Abstract

Efficient and accurate taxpayer data management is very important in supporting tax administration.However, the data management system used in Tuntungan 1 Village currently still relies on traditional methodsthat are less integrated, thus limiting the efficiency and accuracy in managing taxpayer data. This study aims todesign a taxpayer data management information system model using the Software Development Life Cycle (SDLC)approach and prototype method. The SDLC stages include planning, user needs analysis, and model design withUML diagrams that map the process flow and interactions between system components clearly and systematically.The results of the study are in the form of a comprehensive information system model, which is expected to increaseefficiency and accuracy in managing taxpayer data. Based on the evaluation, this system model shows a feasibilityvalue of 87%, which indicates good potential to be applied in supporting more structured and integrated taxpayerdata management.
Sentiment analysis of Faculty of Science and Technology students' satisfaction with the 2024 graduation using the Naïve Bayes method Siregar, Kalfida Eka Wati; Ramadani, Wily Supi; Sitepu, Anggi Jelita; Fadil, Ulfi Muzayyanah; Furqan, Mhd.
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i2.940

Abstract

Sentiment analysis of UINSU student graduation based on academic data is one of the efforts to understand the factors that affect the success of student studies. This research aims to analyze the sentiment of UINSU student graduation by utilizing academic data such as cumulative grade point average (GPA), number of credits taken, and other relevant attributes, using the Naive Bayes method. Naive Bayes was chosen because of its ability to classify data efficiently and accurately, even though the data used has noise or inconsistency. The research process begins with collecting student data from the university database, and then data cleaning is carried out to ensure the quality of the data used. Next, the data is processed and classified using the Naive Bayes algorithm in Weka software to predict graduation status based on academic parameters. The results show that the Naive Bayes method is able to produce quite high accuracy in predicting student graduation, with accuracy values ranging from 75% to more than 85% depending on parameter selection and data cleaning. GPA is the most influential attribute on the prediction results, while other attributes such as class activity and organizational experience also contribute, although not as much as GPA. These findings provide important insights for the campus in designing more effective academic coaching and planning programs and can be a reference in the development of data mining-based decision support systems to improve the quality of computer science graduates.
Pemodelan Aplikasi Pengelolaan Data Wajib Pajak Menggunakan Pendekatan SDLC Siregar, Kalfida Eka Wati; Fadil, Ulfi Muzayyanah; Armansyah, Armansyah
Informatics and Computer Engineering Journal Vol 5 No 1 (2025): Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v5i1.7992

Abstract

Efficient and accurate taxpayer data management is very important in supporting tax administration.However, the data management system used in Tuntungan 1 Village currently still relies on traditional methodsthat are less integrated, thus limiting the efficiency and accuracy in managing taxpayer data. This study aims todesign a taxpayer data management information system model using the Software Development Life Cycle (SDLC)approach and prototype method. The SDLC stages include planning, user needs analysis, and model design withUML diagrams that map the process flow and interactions between system components clearly and systematically.The results of the study are in the form of a comprehensive information system model, which is expected to increaseefficiency and accuracy in managing taxpayer data. Based on the evaluation, this system model shows a feasibilityvalue of 87%, which indicates good potential to be applied in supporting more structured and integrated taxpayerdata management.
Implementation of the Naive Bayes Algorithm in Spam Detection in SMS Messages Fadil, Ulfi Muzayyanah; Siregar, Kalfida Eka Wati; Ramadani, Wily Supi
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.26

Abstract

This study discusses the application of the Naive Bayes algorithm to detect spam messages in Short Message Service (SMS) services. The background of this study is the increasing spread of spam messages containing advertisements, fraud, and malicious content, which necessitates an automated system to distinguish spam from non-spam. The methods used in this study include collecting labeled SMS data, preprocessing (text cleaning, tokenization, stopword removal, and stemming), and feature extraction using the Term Frequency-Inverse Document Frequency (TF-IDF) technique. The Naive Bayes model was trained on a Kaggle dataset and tested in Google Colab to evaluate classification performance using accuracy, precision, and recall metrics. The results showed that the Multinomial Naive Bayes model achieved an accuracy of 96.86%, with a strong ability to recognize ham (non-spam) messages and exemplary performance in detecting spam messages. These findings demonstrate that the Naive Bayes algorithm is effective and efficient at classifying Indonesian-language text messages, making it a suitable basis for developing a more innovative, faster automatic SMS spam detection system.
Co-Authors Armansyah Armansyah Fadil, Ulfi Muzayyanah Mhd Furqan Ramadani, Wily Supi Sitepu, Anggi Jelita