Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer
Hendra Kurniawan
Jurusan Teknik Informatika, Universitas Maritim Raja Ali Haji
Computer Science & IT Control & Systems Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimasi Jaringan Syaraf Tiruan Backpropagation Dengan Particle Swarm Optimization Untuk Prediksi Pasang Surut Air Laut Nerfita Nikentari; Hendra Kurniawan; Nola Ritha; Denny Kurniawan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 5: Oktober 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1089.662 KB) | DOI: 10.25126/jtiik.2018551055

Abstract

Indonesia merupakan negara maritim yang lebih dari 70 % wilayahnya adalah lautan. Lautan memiliki banyak fenomena alam yang mempengaruhi kehidupan sehari-hari masyarakat bahari atau masyarakat yang hidup tergantung pada laut. Salah satu fenomena alam dari laut adalah pasang surut. Pasang surut air laut dalam hal ini tinggi memegang peranan penting pada masyarakat diberbagai aspek seperti transportasi, pariwisata dan ekonomi. Prediksi tinggi pasang surut air dapat bermanfaat untuk memudahkan segala aktifitas masyarakat bahari. Penelitian ini menggunakan metote Particle Swarm Optimization (PSO) dan Jaringan Syaraf Tiruan (JST) untuk prediksi tinggi pasang surut air laut. Metode Particle Swarm Optimization dan Neural Network memiliki beberapa parameter inputan seperti, jumlah neuron input, learning rate, swarm, c1,c2 inertia min, inertia max. Data yang digunakan sebanyak 1000 yang terbagi menjadi  700 data training dan 300 data testing. Hasil pengujian menunjukkan akurasi prediksi adalah 91.56 %  dengan menggunakan 90 swarm, learning rate 0,9 dan iterasi sebanyak 20 kali. AbstractIndonesia is a maritime country where 70% of its territory is  the ocean. Oceans have many natural phenomena that affect the daily lives of maritime communities or people who live dependent on the sea. One of the natural phenomena of the sea is tide level. Tide level plays an important role in the community both directly and indirectly such as transportation, tourism and the economy. Predictions of tide level can be useful to facilitate all marine activities. This study uses Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANN) to forecast tide level. PSO is used to optimize the minimum error value on the network in order to get the ideal ANN network. The Particle Swarm Optimization and Neural Network methods have several input parameters such as number of input neurons, learning rate, swarm, c1, c2 inertia min, inertia max. The number of data being used in this reseach is 1000 which divided into 700 training data and 300 testing data. The test results shows the prediction accuracy level is 0. 078373 using 90 swarms, learning rate is 0.9 and iteration is 20 times. 
Hospital Nurse Scheduling Optimization Using Simulated Annealing and Probabilistic Cooling Scheme Ferdi Chahyadi; Azhari SN; Hendra Kurniawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 1 (2018): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.23056

Abstract

Nurse’s scheduling in hospitals becomes a complex problem, and it takes time in its making process. There are a lot of limitation and rules that have to be considered in the making process of nurse’s schedule making, so it can fulfill the need of nurse’s preference that can increase the quality of the service. The existence variety of different factors that are causing the nurse scheduling problem is so vast and different in every case. The study is aimed to develop a system used as an equipment to arrange nurse’s schedule. The working schedule obtained will be checked based on the constraints that have been required. Value check of the constraint falsification used Simulated Annealing (SA) combined with cooling method of Probabilistic Cooling Scheme (PCS). Transitional rules used cost matrix that is employed to produce a new and more efficient state. The obtained  results showed that PCS cooling methods combined with the transition rules of the cost matrix generating objective function value of  new solutions better and faster in processing time than the cooling method exponential and logarithmic. Work schedule generated by the application also has a better quality than the schedules created manually by the head of the room.
Co-Authors Azhari SN Chahyadi, Ferdi Denny Kurniawan Nikentari, Nerfita Nola Ritha