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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Universitas Pamulang Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Premananda, I Gusti Agung
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Computer Science & IT Control & Systems Engineering 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.
Optimasi Rute Rencana Perjalanan Pesawat Menggunakan Algoritma Late Acceptance Hill Climbing (Studi Kasus : Travelling Salesman Challenge 2.0) Muklason, Ahmad; Premananda, I Gusti Agung
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106842

Abstract

Permasalahan Traveling Salesman Problem (TSP) merupakan permasalahan klasik yang popular diteliti dalam bidang optimasi kombinatorika. Permasalahan ini bertujuan menentukan rute perjalanan terpendek untuk  mengunjungi setiap lokasi tepat satu kali dan diakhir perjalanan harus kembali ke lokasi awal perjalanan dimulai. Permasalahan ini telah digolongkan sebagai permasalahan NP-Hard, sehingga membutuhkan algoritma non-deterministic untuk dapat menyelesaikan permasalahan ini. Dalam permasalahan nyata, salah satu penerapan TSP ada pada permasalahan untuk menentukan rute perjalanan termurah untuk mengunjungi beberapa kota di beberapa negara. Kompetisi Travelling Salesman Challenge 2.0 (TSC 2.0) mengangkat permasalahan ini dalam sebuah kompetisi pada tahun 2018. Untuk menyelesaikan studi kasus tersebut, penelitian ini menyembangkan algoritma Late Acceptance Hill Climbing (LAHC) menggunakan metode hiper-heuristik. Algoritma LAHC merupakan algoritma yang sederhana namun telah terbukti mampu mengoptimasi dengan baik pada beberapa permasalahan TSP. Algoritma LAHC diuji coba pada 14 dataset dari TSC 2.0. Hasil penelitian menunjukan algoritma LAHC menghasilkan solusi yang kompetitif dengan mampu menurunkan biaya perjalanan dengan rata-rata 58% dan menghasilkan hasil yang lebih baik dengan rata-rata 9% dari algoritma Threshold Acceptance (TA) yang digunakan sebagai algoritma pembanding. AbstractThe Traveling Salesman Problem (TSP) is a classic problem that is popularly researched in the field of combinatorics optimization. This problem aims to determine the shortest travel route to visit each location exactly once and, at the end of the trip, must return to where the trip started. This problem has been classified as an NP-Hard problem. Therefore it requires a non-deterministic algorithm to solve it. In the real world, one of the applications of TSP is the problem of determining the cheapest travel routes to visit several cities in several countries. The Traveling Salesman Challenge 2.0 (TSC 2.0) competition raised this issue in a competition in 2018. This study developed the Late Acceptance Hill Climbing (LAHC) algorithm using the hyper-heuristic method to complete the case study from TSC 2.0. The LAHC algorithm is simple but has been proven to optimize well for several TSP problems. The LAHC algorithm was tested on 14 datasets from TSC 2.0. The results show that the LAHC algorithm produces competitive solutions by reducing travel costs by an average of 58% and making better results by an average of 9% than the Threshold Acceptance (TA) algorithm used as a comparison algorithm.
Automated Course Timetabling Optimization Using Tabu-Simulated Annealing Hyper-Heuristics Algorithm Muklason, Ahmad; Marom, Ahsanul; Premananda, I Gusti Agung
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.4835

Abstract

The topic of solving Timetabling Problems is an interesting area of study. These problems are commonly encountered in many institutions, particularly in the educational sector, including universities. One of the challenges faced by universities is the Course Timetabling Problem, which needs to be addressed regularly in every semester, taking into consideration the available resources. Solving this problem requires a significant amount of time and resources to create the optimal schedule that adheres to the predefined constraints, including both hard and soft constraints. As a problem of computational complexity, University Course Timetabling is NP-hard, meaning that there are no exact conventional algorithms that can solve it in polynomial time. Several methods and algorithms have been proposed to optimize course timetabling in order to achieve the optimal results. In this study, a new hybrid algorithm based on Hyper-Heuristics is developed to solve the course timetabling problem using the Socha Dataset. This algorithm combines the strengths of Simulated Annealing and Tabu Search to balance the exploitation and exploration phases and streamline the search process. The results show that the developed algorithm is competitive, ranking second out of ten previous algorithms, and finding the best solution in six datasets.
Co-Authors Ahlan Robbani, Shof Rijal Ahmad Muklason Marom, Ahsanul Nur Aini Rakhmawati Rahmat Hidayat Riksakomara, Edwin