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All Journal InComTech: Jurnal Telekomunikasi dan Komputer Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Hani Harafani
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Other

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Perbandingan Metode Deep Learning dalam Mengklasifikasi Citra Scan MRI Penyakit Otak Parkinson Waeisul Bismi; Hani Harafani
InComTech : Jurnal Telekomunikasi dan Komputer Vol 12, No 3 (2022)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v12i3.15068

Abstract

Penyakit Parkinson merupakan gangguan neurodegenerative yang bersifat progresif dan relative umum pada system saraf pusat yang menyebabkan kesulitan dalam bergerak. Biasanya penyakit ini sering terjadi pada individu berusia lebih dari 60 tahun dipengaruhi oleh factor genetic dan lingkungan. Deteksi dini pada penyakit Parkinson dapat mencegah gejala hingga usia tertentu sehingga meningkatkan harapan hidup. Dalam penelitian ini bertujuan untuk menggunakan gambar otak dari Magnetic Resonace Imaging (MRI) untuk mengetahui bagaimana penyakit tersebut menyebar, dengan menggunakan metode deep learning menggunakan model atau arsitektur InceptionV3, VGG16, VGG19, NasnetMobile, dan MobileNet dengan melalui proses Input data - augmentasi - preprocessing - Classification (model a b c d ) - result dan pembelajaran mesin pada kumpulan data klinis dan paraklinis untuk mendiagnosis secara akurat meggunakan dataset yang berasal dari Parkinsons Brain MRI sebanyak 2 kelas yaitu kelas normal dan Parkinson. Hasil dari penelitian menggunakan deep learning berdasarkan kelima algoritma yang digunakan tersebut diperoleh nilai akurasi terbaik dari seluruh model arsitektur adalah arsitektur MobileNet sebesar 99,75% dengan kappa score 99,30% dengan total durasi komputasi selama 2 jam satu menit
Analisa Kualitas Aplikasi Absensi Mobile Greatday untuk Mengukur Kepuasan Pengguna Mengunakan Metode Webqual 4.0: Penelitian Imam Budiawan; Fahrizal; Martua HamiSiregar; Mushliha; Hani Harafani; Ade Setiawan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 3 No. 4 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 3 Nomor 4 (April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v3i4.1447

Abstract

The online attendance application has now become one of the systems that is starting to be widely used by companies to support ease of work in carrying out attendance activities. Starting from daily absences, permit applications, leave applications, employee overtime data to attendance recaps which can be accessed easily to present a management report. The problem that occurs is that the information service often has errors, namely the Greatday mobile application still does not receive good service or is still less responsive to users who need good attendance information services. To measure user satisfaction of the Greatday mobile attendance application, accurate and appropriate methods and measuring tools are needed, such as Webqual 4.0. Based on the test results for the coefficient of determination (R Square), the figure 0.666, the coefficient of determination is equal to 66.6%. This figure means that usability (X1), information quality (X2) and interaction quality (X3) influence consumer satisfaction (Y). Meanwhile, the remaining 100% − 66.6% = 33.4% is influenced by other variables outside the variables that were not examined.
IMPLEMENTATION OF THE SEMONT APPLICATION AS A SIGNATURE-BASED INTRUSION DETECTION AND PREVENTION SYSTEM ON THE SMAN 1 RANCAEKEK COMPUTER NETWORK Alwi Al Hadad; Hani Harafani
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 10 No. 4 (2025): November
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

SMAN 1 Rancaekek is highly dependent on web applications that are vulnerable to injection attacks (SQL Injection, XSS, RCE, LFI), web defacement, and brute force, exacerbated by the absence of real-time monitoring, which has led to serious security incidents. This study aims to design and implement the Semont (Sentinel Monitoring) application as a signature-based Intrusion Detection and Prevention System (IDPS) to detect and prevent such cyber attacks, monitor network traffic on Port 80 (HTTP) and Port 443 (HTTPS), and generate comprehensive reports. The research method involved network system analysis and Semont design. The main contribution of this research is the development of a lightweight, efficient, and intuitive IDPS solution capable of protecting sensitive data and website visual integrity with minimal overhead. The analysis results show that Semont successfully detected and blocked 100% of simulated attacks, significantly changing the security posture of the SMAN 1 Rancaekek website from vulnerable to secure, supported by detailed logging and real-time notifications to Telegram. In conclusion, Semont proved to be highly effective in detecting and preventing common cyber attacks, meeting the need for proactive defense in educational environments, although the signature-based method is limited to zero-day attacks, which can be improved through the integration of anomaly detection in the future.
Co-Authors Ade Setiawan FAHRIZAL Hadad, Alwi Al Imam Budiawan Martua HamiSiregar Mushliha Waeisul Bismi