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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Jason Permana
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Computer Science & IT Mathematics Other

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Simulasi Jaringan untuk Sistem Terdistribusi E-Commerce Joomla dengan GNS3 Kevin Jonathan JM; Marchella Angelina; Arya Wira Kristanto; Nicholas Martin; Jason Permana
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 12 No. 2 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v12i2.31571

Abstract

Di era ini, E-Commerce bukanlah hal yang asing lagi karena hadir dalam berbagai bentuk, seperti website, dan untuk mengakomodasi layanan tersebut, dibutuhkan server database dan server web untuk menjaga semua data yang diperlukan. Ketika berbicara tentang E-Commerce, hosting server mungkin akan dilakukan oleh pihak lain, karena koneksi internet dibutuhkan antara klien E-Commerce (klien web) dan server hosting. Melalui penggunaan GNS3, dimungkinkan untuk mensimulasikan koneksi-koneksi yang disebutkan di atas secara virtual. Dalam artikel jurnal ini, koneksi antara web client sebagai outside zone, web server, Joomla server, dan client server sebagai demilitarized zone (DMZ), dan host client sebagai inside zone akan dibangun melalui Cisco ASA untuk mensimulasikan akses yang aman ke website yang dihosting oleh web server dan Joomla server melalui web client. Hasil simulasi menunjukkan keberhasilan untuk mengakses Web Server dan Joomla Server menggunakan web browser melalui client pada outside zone sementara client pada inside zone berhasil mengakses Web Server dan Joomla Server baik melalui web browser maupun ping di terminal.
CLASSIFICATION OF NATURAL DISASTERS IN WEST SEMARANG BASED ON WEATHER DATA USING DEEP LEARNING Nicholas Martin; Jason Permana; Tony Tony
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6147

Abstract

Natural disasters like floods, landslides, and fires pose serious threats to both life and mental well-being, especially in vulnerable areas like West Semarang, which frequently experiences extreme weather. To mitigate these risks, an accurate classification system is essential for timely prevention and response. This study compares the performance of three neural network models—Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in classifying natural disasters using weather data. LSTM and GRU are particularly effective for handling long-term dependencies and addressing vanishing gradient problems common in time series data. Data for the study comes from the Semarang City Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG), spanning 2019 to 2022. The models achieved a high accuracy of 95.8%, but this is due to an imbalanced dataset—70 records of natural disasters versus 1377 without—resulting in classification favoring "no disaster." Among the models, LSTM performed the best, reaching optimal accuracy in just 20.0671 seconds per epoch. This suggests LSTM is the most effective model for this classification task.
Co-Authors Arya Wira Kristanto Kevin Jonathan JM Marchella Angelina Nicholas Martin Nicholas Martin Tony Tony