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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Ayu Ratna Juwita
Universitas Buana Perjuangan Karawang, Karawang
Computer Science & IT Decision Sciences, Operations Research & Management

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Penerapan Metode Naive Bayes Dalam Klasifikasi Spam SMS Menggunakan Fitur Teks Untuk Mengatasi Ancaman Pada Pengguna Fathimah Noer Azzahra; Tatang Rohana; Rahmat Rahmat; Ayu Ratna Juwita
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5070

Abstract

One of the negative impacts of current digital advances is the increasing number of SMS spam. Spam SMS poses a security risk to users because they can contain malicious links or requests for personal information that are used for malware, smishing, or fraud attacks. However, with the various protection measures available, not all spam SMS can be classified and prevented effectively. However, this problem can be minimized by creating an anti-spam SMS model which aims to classify SMS types. So this research aims to classify types of SMS that contain spam and spam by applying the Naïve Bayes algorithm. In this study, the dataset consisted of 5572 records consisting of 2 categories, namely spam and ham. This algorithm is able to show satisfactory performance in differentiating spam and spam messages because, according to the diversity of literature, the Naïve Bayes algorithm is suitable for use in English language datasets. The evaluation model displays good results with accuracy reaching 93.2%, precision 93.7%, recall 93.2%, and F1-score 91.6%. In addition, analysis in the research using the Receiver Operating Characteristic (ROC) curve shows an accuracy rate of 97.3%, indicating that the model has very good performance in classifying spam in SMS messages. However, there is still room for improvement through the use of new methods and larger and more diverse data sets. This research has an important involvement in working on communication security and user experience in using short message services.
Analisis Sentimen Pemboikotan Produk dengan Pendekatan Algoritma Naïve Bayes Media Sosial X Rizky Rifaldi; Jamaludin Indra; Adi Rizky Pratama; Ayu Ratna Juwita
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5420

Abstract

This research aims to analyze sentiment regarding the problem of product boycotting by the public using the Naive Bayes algorithm. 1426 data were collected from social media x to study consumer behavior towards certain products. Through the application of the Naive Bayes algorithm, sentiment analysis was carried out to identify patterns in consumer opinions regarding boycotting the products studied. Experimental results show that the Naive Bayes algorithm succeeded in achieving 81% accuracy in classifying sentiment towards products. This shows the algorithm's ability to analyze consumer sentiment effectively, which can provide valuable insights for companies in understanding public perception and managing the reputation of their products. The practical implication of this research is the importance of utilizing sentiment analysis techniques in marketing strategy and brand management to increase product competitiveness in a competitive market.
Penerapan Metode Regresi Logistik Untuk Memprediksi Peristiwa Biner Pasien Pasca Operasi Kanker Payudara Sylvia Sujana; Ayu Ratna Juwita; Rahmat Rahmat; Sutan Faisal
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5521

Abstract

Breast cancer is the second leading cause of death in women worldwide. To overcome this growing problem, this study designed a model that can predict breast cancer by utilizing datasets and then processed using the Logistic Regression Prediction method. This method is appropriate for predicting the data used because of its ability to handle dependent variables that are categorical and provide outups in the form of probabilities. This study uses a dataset of 306 samples with 4 attributes. Data used Research steps include data collection, preprocessing, modeling with logistic regression and evaluating results using matrices such as confusion matrix, MAE, MSE, and R-Square. The results showed a prediction accuracy of 86%, with an MSE value of 0.137 and R-Square of 0.309. This study shows the effectiveness of logistic regression in predicting the survival of patients after breast cancer surgery. However, by applying different algorithms, this study can select the best set of significant attributes to increase the prediction accuracy value in postoperative breast cancer patients.
Co-Authors Adi Rizky Pratama Fathimah Noer Azzahra Indra, Jamaludin Rahmat Rahmat Rizky Rifaldi Sutan Faisal Sylvia Sujana Tatang Rohana