Analisis Jumlah Pengguna pada Traffic IP-based dengan Multi Criteria Decision Making - MARISA PREMITASARI (Institut Teknologi Nasional, Bandung, 2020)
AbstrakTrafik telekomunikasi sudah bermigrasi ke IP-based Traffic. Salah satunya adalah Laboratorium TIK (Teknologi Informasi dan Komunikasi) ITENAS yang meng-generate invarian trafik. Pada penelitian ini, penulis melakukan monitoring pasif dan aktif untuk mendapatkan berbagai invarian trafik. Monitoring pasif didapatkan dari software ISP Moratel dan SOPHOS Firewall. Monitoring aktif dilakukan dengan capture data secara live pada waktu jam sibuk. Invarian trafik yang berhasil di-captured adalah incoming traffic, outgoing traffic, speed, volume, date dan downtime. Jam sibuk diambil berdasarkan dugaan sementara mulai pukul 10.00-16.00. Invarian ini menjadi input dari sistem untuk dijadikan kriteria dan jam sibuk dijadikan atribut. Kriteria dan atribut diolah dengan metoda Multi Criteria Decision Making yaitu SAW (Simple Additive Weighting) dan AHP(Analytical Hierarchy Process). Output dari sistem adalah prediksi jumlah pengguna di jam sibuk dengan skala fuzzy rules. Hasil penelitian menyimpulkan pukul 11.00 AM-12.00 PM adalah jam tersibuk dengan jumlah user terbanyak.Kata kunci: monitoring aktif, monitoring pasif, kriteria, atribut,bobotAbstractTelecommunication traffic has migrated to IP-based traffic .One of the industry is Laboratorium TIK ITENAS (Teknologi Informasi dan Komunikasi) which generates traffic invariant. In this study, the authors conducted passive and active monitoring to obtain various traffic invariance. Passive monitoring were obtained from ISP Moratel software and SOPHOS Firewall. Active monitoring were done by capturing live data during peak hours. Traffic invariance that have been captured consist incoming traffic, outgoing traffic, speed, volume, date and downtime. Busy hours were taken based on personal estimation start from 10.00-16.00. This invariance became the system’s input which has been used as criteria and peak hours are used as attributes. Criteria and attributes were processed using the Multi Criteria Decision Making method, namely SAW (Simple Additive Weighting and AHP (Analytical Hierarchy Process). The output of the system is user’s number prediction with fuzzy scale. The result concluded that 11.00 AM-12.00 PM is the busiest hours with the most number of usersKeywords: active monitoring, passive monitoring, criterion, attributes, weight