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All Journal Jurnal Transformatika Mobile and Forensics
Pramudhita, Agung
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Neuroscience

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IDENTIFIKASI PENYAKIT JANTUNG MENGGUNAKAN MACHINE LEARNING: STUDI KOMPARATIF Sintiya, Endah Septa; Rizdania, Rizdania; Afrah, Ashri Shabrina; Pramudhita, Agung
Jurnal Transformatika Vol 21, No 1 (2023): July 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.6274

Abstract

Heart disease is the number one cause of death globally. This condition is followed by an unhealthy lifestyle. Heart disease prediction needs to be done considering the importance of health. The presence of machine learning has made it easier for humans to make early detection of patterns that are close to heart disease. Prediction of heart disease is important given the behavior of people who are still prone to risk factors. Conditions where predictions using machine learning for heart disease have not been compared with many using machine learning methods. Predictions of heart disease are needed along with the interrelationships of the variables. This research compares 6 machine learning methods for disease classification with KNN, Naïve Bayes, Decision tree, Random forest, logistic regression, and SVM. The final classification obtained ranking accuracy with the highest value of 82% in the KNN method with the confusion matrix test, precision, accuracy, re-call, and fi-score. These results can be applied to real case studies of heart disease
Forensic Digital Analysis of Telegram Applications Using the National Institute Of Justice and Naïve Bayes Methods Apriyani, Meyti Eka; Maskuri, Rahmad Alfian; Ratsanjani, M.Hasyim; Pramudhita, Agung; Rawansyah, Rawansyah
Mobile and Forensics Vol. 5 No. 2 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v5i2.7893

Abstract

Currently, Telegram is an instant messaging application that is often used by the Indonesian people as a means of long-distance communication with other users. Telegram also has good security features to protect all data from its users. However, Telegram has a positive impact on its users. This security feature can be used by several people to protect against digital crimes, especially cases of sexual harassment. To overcome the existing crimes, analysis, and forensic methods are needed to help solve crimes. This research is guided by the investigation process using the National Institute Of Justice (NIJ) method and the Naïve Bayes method to classify the conversations found. It can be concluded that MOBILedit Forensic Express has a poor performance in finding digital evidence in the Telegram application and FTK Imager is very good at finding digital evidence in the Telegram application. In this research, the classification process using the Naïve Bayes method has been able to classify conversations that contain sexual harassment or not. Evaluation of the classification method uses a confusion matrix to determine the best classification model.
Co-Authors Ashri Shabrina Afrah Deddy Kusbianto Eka Apriani, Meyti Endah Septa Sintiya Maskuri, Rahmad Alfian Nurhalimah Ratsanjani, M.Hasyim Rawansyah, Rawansyah Rizdania, Rizdania