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All Journal TEKNIK INFORMATIKA International Journal of Applied Mathematics and Computing.
Erlita Sulistiati
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Computer Science & IT Mathematics

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EVALUASI EFEKTIVITAS MENU BNI SMS BANKING PADA SISTEM OPERASI ANDROID MENGGUNAKAN FRAMEWORK DECIDE Erlita Sulistiati; Nur Amalia; Fenty Eka
JURNAL TEKNIK INFORMATIKA Vol 9, No 2 (2016): Jurnal Teknik Informatika
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (721.748 KB) | DOI: 10.15408/jti.v9i2.5603

Abstract

ABSTRAK Desain sebuah antarmuka pengguna dapat dievaluasi menggunakan sebuah framework yaitu DECIDE. Tujuan dilakukan evaluasi adalah mengukur seberapa jauh tingkat usability dari sebuah antarmuka pengguna. Sistem operasi android yang banyak digunakan oleh smartphone memiliki berbagai macam kemudahan bagi penggunanya. Salah satunya bisa melakukan transaksi perbankan melalui aplikasi berbasis mobile. BNI SMS Banking adalah salah satu antarmuka yang disediakan untuk memudahkan pengguna melakukan transaksi keuangan tanpa harus menghapalkan sintaks SMS. Berdasarkan hasil evaluasi, skor usability yang dimiliki oleh BNI SMS Banking adalah 90%. Dengan kata lain pengguna merasa puas dengan adanya menu SMS Banking pada sistem operasi android.  Kata kunci : usability evaluation, DECIDE Framework, User Interaction Design
Algorithmic Simulation for Optimization in Combinatorial Mathematics Using Heuristic Techniques Ahmad Budi Trisnawan; Syed Asif Ali; Erlita Sulistiati
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.274

Abstract

This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like Branch-and-Bound and Dynamic Programming computationally expensive and inefficient for larger problem sizes. The primary objective of this research is to evaluate the performance of heuristic methods, including Simulated Annealing (SA), Genetic Algorithms (GA), and Iterative Computation techniques, such as Tabu Search (TS) and Particle Swarm Optimization (PSO). These methods are tested for their ability to provide approximate solutions efficiently. The findings reveal that while ACO provided the best solution quality, it had the longest runtime. TS was the fastest, though with slightly lower solution quality. SA and GA demonstrated a balance between solution quality and computational efficiency, but their performance heavily depended on parameter tuning. The hybridization of SA and GA showed potential for improving solution quality but introduced additional complexity. The research concludes that heuristic methods, especially when combined, offer viable solutions for large-scale combinatorial optimization problems, though the trade-off between solution quality and computational time must be considered when selecting an algorithm.
Co-Authors Ahmad Budi Trisnawan Fenty Eka Nur Amalia Syed Asif Ali