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All Journal International Journal of Electrical and Computer Engineering HAYATI Journal of Biosciences ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Prosiding SNATIKA Vol 01 (2011) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics ILKOM Jurnal Ilmiah Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Aina Musdholifah
Department Of Computer Science And Electronics, Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Combination of Ant Colony Optimization with Local Triangular Kernel Clustering for Vehicle Routing Problem with Time Windows Aina Musdholifah; Rahman Indra Kesuma
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp355-362

Abstract

VRP is a common problem that occurred in logistics, including determination a route of products from the source to the destination. VRPTW is variation of VRP that use routing concepts in the serving process at the certain time interval. Recently, many methods are used to solve this optimization problem, for example ACO. LTKC-ACO was developed to improve the ACO solutions that apply LTKC to obtain a number of classes that are considered as the candidate list in ACO. Local Search is also used to avoid ACO getting stuck in the local optimum. In this study, two types of LTKC-ACO are developed that’s related to time windows parameter usage in clustering. The experimental result of 56 Solomon’s datasets showed that LTKC-ACO can improve the ACO solutions on 73,21% of datasets and can out performed then the other methods, especially on the datasets that have longer scheduling of service time.
Two Level Clustering for Quality Improvement using Fuzzy Subtractive Clustering and Self-Organizing Map Erick Alfons Lisangan; Aina Musdholifah; Sri Hartati
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp373-380

Abstract

Recently, clustering algorithms combined conventional methods and artificial intelligence. FSC-SOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster centers are processed twice i.e. searching for data with high density at FSC then updating the cluster centers at SOM. FSC-SOM was tested using 10 datasets that is measured with F-Measure, entropy, Silhouette Index, and Dunn Index. The result showed that FSC-SOM can improve the cluster center of FSC with SOM in order to obtain the better quality of clustering results. The clustering result of FSC-SOM is better than or equal to the clustering result of FSC that proven by the value of external and internal validity measurement.
Digital Image Based Identification of Rice Variety Using Image Processing and Neural Network Lilik Sumaryanti; Aina Musdholifah; Sri Hartati
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp182-190

Abstract

The increased of consumer concern on the originality of rice  variety and the quality of rice leads to originality certification of rice by existing institutions. Technology helps human to perform evaluations of food grains using images of objects. This study developed a system used as a tool to identify rice varieties. Identification process was performed by analyzing rice images using image processing. The analyzed features for identification consisted of six color features, four morphological features, and two texture features. Classifier used LVQ neural network algorithm. Identification results using a combination of all features gave average accuracy of 70,3% with the highest classification accuracy level of 96,6% for Mentik Wangi and the lowest classification accuracy of 30%  for Cilosari.
A new hybrid parallel genetic algorithm for multi-destination path planning problem Luthfiansyah Ilhamnanda Yusuf; Aina Musdholifah
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp584-591

Abstract

This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles.
Co-Authors Abednego Dwi Septiadi Aditya Permana Anastasya Latubessy Anjani, Sarah Anny Kartika Sari Arda Yunianta Br Bangun, Elsi Titasari Ekki Rinaldi Erick Alfons Lisangan Faisal Ramadhan Hamda, Hizbullah Hananto, Andhika Rafi Hani Febri Mustika Hani Febri Mustika Haviluddin Haviluddin Herdiesel Santoso Herlina Jayadiyanti Jayadiyanti, Herlina Lilik Sumaryanti Luthfiansyah Ilhamnanda Yusuf Mirwan Ushada Mohd Shahizan Othman Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Rahman Indra Kesuma Rahmat Budiarsa Retantyo Wardoyo Rinaldi, Ekki Rosihan Rosihan, Rosihan Sapuletea, Chrissandy Sri Hartati Sri Kusrohmaniah, Sri Sri Mulyana Tempola, Firman Timothy K. Shih Tsuyoshi Okayama Wahyu Srimulyani Yudha Islami Sulistya Yunianta, Arda Yusof, Omar Obarukab Norazah