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All Journal eProceedings of Engineering Advance Sustainable Science, Engineering and Technology (ASSET)
Yanti Rusmawati
Telkom University
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Model Formal Dan Verifikasi Sistem Layanan Presensi Rfid Dengan Logika Temporal: Studi Kasus Di Universitas Telkom, Indonesia Damar Huda; Yanti Rusmawati; Muhammad Arzaki
eProceedings of Engineering Vol 4, No 3 (2017): Desember, 2017
Publisher : eProceedings of Engineering

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Abstract

Sistem presensi RFID (Radio Frequency Identification) di Universitas Telkom adalah suatu sistem layanan yang bertujuan untuk meningkatkan efisiensi dalam proses pencatatan kehadiran mahasiswa. Saat ini masalah masih banyak ditemukan sehingga keandalan sistem layanan presensi masih belum optimal. Salah satu masalahnya adalah presensi mahasiswa terkadang tidak terekam pada sistem tersebut. Dalam tugas akhir ini, penulis terlebih dulu membuat dokumen tertulis (diagram aktivitas) dan spesifikasi sistem secara rinci dan jelas berdasarkan hasil observasi di lapangan. Hasil tersebut akan diformalisasikan ke dalam model formal dengan logika temporal tertentu. Selanjutnya penulis menggunakan metode formal untuk melakukan verifikasi spesifikasi keamanan (safety) dan keterjangkauan (liveness) berdasarkan model yang dibentuk. Tugas akhir ini memberikan suatu contoh translasi dari diagram aktivitas ke dalam model formal untuk sistem layanan presensi RFID. Lebih jauh, penulis menunjukkan bahwa metode formal dapat mendukung verifikasi sistem layanan presensi RFID di Universitas Telkom. Kata Kunci: metode formal, RFID, diagram aktivitas, model formal
Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction Henri Tantyoko; Dade Nurjanah; Yanti Rusmawati
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.1003

Abstract

Earthquakes are natural events caused by the movement of the earth's plates, often triggered by the energy release from hot liquid magma. Predicting earthquakes is crucial for raising public awareness and preparedness in seismically active areas. This study aims to predict earthquake activity by identifying patterns in seismic events using Sequential Pattern Mining (SPM). To enhance the prediction accuracy, Sequential Rule Mining (SRM) is applied to derive rules with confidence values from these patterns. The results show that using betweenness centrality as a weight increases the prediction accuracy to 83.940%, compared to 78.625% without weights. Using eigenvector centrality as a weight yields an accuracy of 83.605%. These findings highlight the potential of using centrality measures to improve earthquake prediction systems, offering valuable insights for disaster preparedness and risk mitigation.
Co-Authors Dade Nurjanah Damar Huda Henri Tantyoko Muhammad Arzaki