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All Journal Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat Journal of Information Technology and Informatics Engineering
Muhamad Firly
Universitas Pamulang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Other

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Peningkatan Cyber Security dan Penggunaan Sosial Media dalam Teknologi Informasi di Era Digital di SMK Media Informatika Muhamad Firly; Muhamad Ridwan Nurrulloh; Maulana Farras Fathurrahman; Abdul habib Hasibuan
Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat Vol 1 No 1 (2025): Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat
Publisher : PT Jurnal Cendekia Indonesia

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This community service program aims to improve the knowledge and skills of students at SMK Media Informatika in cybersecurity and responsible use of social media in the digital age. The training consisted of interactive sessions covering fundamental information security concepts, awareness of cyber threats, and ethical social media practices. Evaluation results indicated significant improvements in students’ understanding, threat recognition skills, and responsible social media conduct. Furthermore, the training effectively raised awareness of digital security within the school and positively influenced students’ daily behavior. It isrecommended to integrate cybersecurity and social media literacy topics into the curriculum and to conduct ongoing training programs to continuously enhance student competencies.
Prediksi Tingkat Kepuasan Pasien Fisioterapi Menggunakan Algoritma Naive Bayes Kaila Nazuwa; Indra Bagoes Mu’afa; Muhamad Firly; Ahmad Taher; Refo Altalario Bintang Anugrah; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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This study aims to predict patient satisfaction levels in physiotherapy services using the Naive Bayes algorithm. Patient satisfaction is a key indicator of healthcare service quality, and this prediction is based on attributes such as age, gender, session duration, and therapist expertise. The dataset, consisting of 31 entries, was analyzed using RapidMiner software. The classification process applied the Naive Bayes model, known for its simplicity, computational efficiency, and strong performance even with limited data. Evaluation results showed an accuracy rate of 90%, with balanced precision and recall between the "satisfied" and "dissatisfied" categories. These find-ings demonstrate that data mining techniques can serve as valuable tools to support continuous improvement in physiotherapy service quality.
Co-Authors Abdul habib Hasibuan Ahmad Taher Indra Bagoes Mu’afa Kaila Nazuwa Maulana Fansyuri Maulana Farras Fathurrahman Muhamad Ridwan Nurrulloh Refo Altalario Bintang Anugrah