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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Universitas Pamulang
Premananda, I Gusti Agung
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Computer Science & IT Engineering

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Anonimisasi Data Penjualan Pakaian di Toko Online Menggunakan Metode K-Anonymity, L-Diversity, dan T-Closeness Hidayat, Rahmat; Premananda, I Gusti Agung; Rakhmawati, Nur Aini
Jurnal Informatika Universitas Pamulang Vol 6, No 2 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i2.9161

Abstract

The development of information technology will have an impact on the development of data. The existence of data might contain sensitive elements that are not intended to become public consumers. Anonymization is a technique that can be applied in publishing data with a different identity or anonymously. K-anonymity is an approach that can anonymization data. Besides that, the l-diversity and t-closeness approaches are also one of the advanced alternatives in data anonymization. The Mondrian algorithm can be implemented in k-anonymity. Therefore, we use the Mondrian algorithm for anonymizing clothing online transaction. The application of these methods can overcome the problem of data privacy contained in the dataset. The results obtained are that the application of the Mondrian algorithm in k-anonymity, l-diversity, and t-closeness has successfully performed data anonymization so that data cannot be consumed freely by other users.
A comparison of meta-heuristic and hyper-heuristic algorithms in solving an urban transit routing problems Muklason, Ahmad; Ahlan Robbani, Shof Rijal; Riksakomara, Edwin; Premananda, I Gusti Agung
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2923-2933

Abstract

Public transport is a serious problem that is difficult to solve in many countries. Public transport routing optimization problem also known as urban transit routing problem (UTRP) is time-consuming process, therefore effective approches are urgently needed. UTRP aims to minimize cost passenger and operator from a combination of route set. UTRP can be optimize with heuristics, meta-heuristics, and hyper-heuristics methods. In several previous studies, UTRP can be optimized with any meta-heuristics and hyper-heuristics methods. In this study we compare the performance of meta-heuristic methods, i.e. ill-climbing, simulated annealing, and hyper-heuristics method based on modified particle swarm optimization algorithm. The experimental results showed that the proposed methods could solve UTRP effectively. Regarding their performance, the results show that despite the generality of hyper-heuristics, their performance are competitive. More specifically, hyper-heuristics method is the best method compared to the other two methods in each dataset. In addition, compared to prior studies results, he proposed hyper-heuristics could outperform them in term of cost passenger of small dataset Mandl. The main contribution of this paper is that to best of our knowledge, it is the first study comparing the performance of meta-heuristics and hyper-heuristics approaches over UTRP.
Co-Authors Ahlan Robbani, Shof Rijal Ahmad Muklason Nur Aini Rakhmawati Rahmat Hidayat Riksakomara, Edwin