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Isomorphic Solutions of the N-queens Problem uddin, Sawal; Sitompul, Opim Salim; Nababan, Erna Budhiarti
SNATIKA Vol 1, No 1 (2011): SNATIKA 2011
Publisher : APTIKOM

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Abstract

AbstractThe N-queens problem is a classic example in mathematics as well as computer science that receives many attentions from researchers for nearly two centuries. Despite its usefulness in teaching computational intelligence algorithms, another interesting features of the N-queens problem is the notion of isomorphism and transformation groups. Once a solution of the N-queens problem is found, the isomorphic solutions can be transformed easily by performing two kinds of permutations, i.e. rotation and reflection. In this paper, we proposed a two-stage approach to find the isomorphic solutions to the N-queens problem. In the first stage, a genetic algorithm is used to find a solution of the N-queens problem. In the second stage, the solution is transformed into seven isomorphic solutions. Using this approach, a complete solution for the N-queens problem can be obtained. The results obtained show how the complete isomorphic solutions of 5-, 6-, 7-, and 8-queens can be generated in a very short execution time.
Klasifikasi Konten Berita Dengan Metode Text Mining Kurniawan, Bambang; Effendi, Syahril; Sitompul, Opim Salim
Dunia Teknologi Informasi - Jurnal Online Vol 1, No 1 (2012): Jurnal Dunia Teknologi Informasi
Publisher : Universitas Sumatera Utara

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Abstract

Banyak instansi yang bergerak dalam  penyaluran informasi atau berita sudah mulai menggunakan sisitem berbasis web untuk menyampaikan berita secara up to date. Pada umumnya berita yang disampaikan dalam portal tersebut terdiri dari beberapa kategori seperti berita politik, olahraga, ekonomi dan lain sebagainya. Namun, dalam membagi berita  ke dalam kategori-kategori tersebut untuk saat ini masih dilakukan secara manual. Hal ini sangat merepotkan apabila berita yang ingin diunggah berjumlah banyak. Oleh karena itu perlu adanya sistem yang bisa mengklasifikasikan berita secara otomatis. Text mining merupakan metode klasifikasi yang merupakan variasi dari data mining berusaha menemukan pola yang menarik dari sekumpulan data tekstual yang berjumlah besar. Sedangkan algoritma naïve bayes classifier merupakan lagoritmape ndukung untuk melakukan klasifikasi. Dalam penelitian ini data yang digunakan berupa berita yang berasal dari beberapa media online. Berita terdiri dari 4 kategori yaitu politik, ekonomi, olahraga, entertainment. Setiap kategori tediri dari 100 berita; 90 berita digunakan untuk proses training dan 10 berita digunakan untuk proses testing. Hasil dari penelitian ini menghasilkan sistem klasifikasi berita berbasis web dengan menggunakan bahasa pemrograman PHP dan database MySQL menunjukkan bahwa berita testing bisa terklasifikasi secara otomatis seluruhnya.
Rancangan Permainan Othello Berbasis Android Menggunakan Algoritma Depth-First Search Handayani, Mauza Saputri; Arisandi, Dedy; Sitompul, Opim Salim
Dunia Teknologi Informasi - Jurnal Online Vol 1, No 1 (2012): Jurnal Dunia Teknologi Informasi
Publisher : Universitas Sumatera Utara

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Abstract

Aplikasi game merupakan aplikasi yang banyak diminati oleh pengguna mobile phone untuk mendapatkan hiburan dan edukasi. Perkembangan game didukung oleh semakin canggihnya teknologi mobile phone yang ada baik secara model maupun operating system. Android adalah salah satu jenis sistem operasi mobile phone yang sedang berkembang saat ini. Salah satu jenis game bersistem operasi android pada mobile phone yang beredar luas antara lain game kecerdasan buatan, misalnya Othello. Othello merupakan salah satu permainan papan berbasis strategi yang dimainkan oleh dua pemain pada papan yang berukuran 8 baris dan 8 kolom. Setiap pemain memiliki bidak berbeda warna yaitu hitam dan putih. Penerapan kecerdasan buatan menggunakan algoritma DFS (Depth First Search) dengan algoritma Negamax yang dioptimasi dengan Alpha Beta Pruning ini dapat mengurangi ruang pencarian sehingga proses penelusuran dan evaluasi dapat dilakukan lebih cepat. Aplikasi ini dikembangkan dengan menggunakan bahasa pemrograman Java berbasis Eclipse IDE. Hasil yang diperoleh adalah sebuah aplikasi permainan Othello yang dapat diterapkan pada mobile phone berbasis Android.
Perancangan Permainan Flood Filling pada Platform Android Fikri, Hasnul Arief; Rahmat, Romi Fadillah; Sitompul, Opim Salim
Dunia Teknologi Informasi - Jurnal Online Vol 1, No 1 (2012): Jurnal Dunia Teknologi Informasi
Publisher : Universitas Sumatera Utara

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Abstract

Flood filling adalah game kombinasi warna yang dibuat menggunakan algoritma flood fill. Algoritma flood fill adalah algoritma yang menentukan area yang terhubung terhadap node pada multidimensional array. Perancangan permainan mobile flood filling pada platform Android ini adalah sebuah analisis, desain, dan implementasi algoritma flood filling dalam pembuatan mobile game. Pembuatan aplikasi ini bertujuan untuk  menjelaskan cara kerja aplikasi dan penerapannya di lingkungan Android. Dua buah mode permainan baru juga dikembangkan agar permainan ini  lebih menarik. Aplikasi ini dikembangkan dengan metode perancangan UML dan bahasa pemrograman Java. Hasil yang diperoleh adalah aplikasi permainan flood filling yang kompatibel untuk perangkat Android dan lulus user requirement review dalam perancangan mobile game.
STUDI APLIKASI MARKETING BERBASIS WEBSITE DAN PENGARUHNYA TERHADAP PENINGKATAN JUMLAH PELANGGAN PADA USAHA HOTEL CHERRY MEDAN Lubis, Muhammad Alfin Soekri; Rismayani, Rismayani; Sitompul, Opim Salim
Jurnal Keuangan & Bisnis Program Studi Magister Manajemen Sekolah Tinggi Ilmu Ekonomi Harapan Vol 5, No 3 (2013): November
Publisher : Yayasan Pendidikan Harapan

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Abstract

The number of website visitors www.cherryhotelsmedan.com and since 2010 to 2012 has decreased. The average length of access that also tends to decrease. The purpose of this study is to determine how much the factors to build brand awareness and loyalty, direct response promotion, market education, as well as public relations services and support for the decision to use the website influence this. Also to find out the most dominant factor affecting consumers visiting the website www.cherryhotelsmedan.com.The population is Cherry visitors accessing the website www.cherryhotelsmedan.com ever. By using multiple regression analysis, it is concluded that 78.5% visited the website www.cherryhotelsmedan.com decision influenced by brand awareness and loyalty, direct response promotion, market education, as well as public relations services and support, while 21.5% influenced by other factors not examined in this study. Factors direct response promotion gives the most dominant influence on the decision to visit the website www.cherryhotelsmedan.com. Factor market education and public relations does not have a significant influence on the decision to visit the website www.cherryhotelsmedan.com, this suggests that the rise or fall of website visitors www.cherryhotelsmedan.com not influenced both factors.
Biased support vector machine and weighted-smote in handling class imbalance problem Hartono, Hartono; Sitompul, Opim Salim; Tulus, Tulus; Nababan, Erna Budhiarti
International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1285.257 KB) | DOI: 10.26555/ijain.v4i1.146

Abstract

Class imbalance occurs when instances in a class are much higher than in other classes. This machine learning major problem can affect the predicted accuracy. Support Vector Machine (SVM) is robust and precise method in handling class imbalance problem but weak in the bias data distribution, Biased Support Vector Machine (BSVM) became popular choice to solve the problem. BSVM provide better control sensitivity yet lack accuracy compared to general SVM. This study proposes the integration of BSVM and SMOTEBoost to handle class imbalance problem. Non Support Vector (NSV) sets from negative samples and Support Vector (SV) sets from positive samples will undergo a Weighted-SMOTE process. The results indicate that implementation of Biased Support Vector Machine and Weighted-SMOTE achieve better accuracy and sensitivity.
Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q Sibero, Alexander F.K.; Sitompul, Opim Salim; Nasution, Mahyuddin K.M.
Data Science: Journal of Computing and Applied Informatics Vol 2 No 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v2.i2-324

Abstract

Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. Even though this algorithm is known to be an appealing clustering method,many efforts to improve its performance are still pursued in various research works. In order to gain faster computation time, for instance, running SOM in parallel had been focused in many previous research works. Utilization of the Graphics Processing Unit (GPU) as a parallel calculation engine is also continuously improved. However, total computation time in parallel SOM is still not optimal on processing large dataset. In this research, we propose a combination of Dynamic Parallel and Hyper-Q to further improve the performance of parallel SOM in terms of faster computing time. Dynamic Parallel and Hyper-Q are utilized on the process of calculating distance and searching best-matching unit (BMU), while updating weight and its neighbors are performed using Hyper-Q only. Result of this study indicates an increase in SOM parallel performance up to two times faster compared to those without using Dynamic Parallel and Hyper-Q.
Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem Nababan, Erna Budhiarti; Sitompul, Opim Salim; Cancer, Yuni
Data Science: Journal of Computing and Applied Informatics Vol 2 No 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v2.i2-326

Abstract

Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA.
Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network Rahmat, Romi Fadillah; Dennis, Dennis; Sitompul, Opim Salim; Purnamawati, Sarah; Budiarto, Rahmat
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1056.819 KB) | DOI: 10.12928/telkomnika.v17i5.11276

Abstract

In this paper, we propose an approach to detect and geotag advertisement billboard in real-time condition. Our approach is using AlexNet’s Deep Convolutional Neural Network (DCNN) as a pre-trained neural network with 1000 categories for image classification. To improve the performance of the pre-trained neural network, we retrain the network by adding more advertisement billboard images using inductive transfer learning approach. Then, we fine-tuned the output layer into advertisement billboard related categories. Furthermore, the detected advertisement billboard images will be geotagged by inserting Exif metadata into the image file. Experimental results show that the approach achieves 92.7% training accuracy for advertisement billboard detection, while for overall testing results it will give 71,86% testing accuracy.
PEMODELAN PERENCANAAN TERINTEGRASI UNTUK RANTAI SUPLAI DAN STOK PENGAMAN MULTI ESELON Hasibuan, Irwitadia; Sitompul, Opim Salim; Lidya, Maya Silvi
JISTech (Journal of Islamic Science and Technology) Vol 4, No 1 (2019)
Publisher : UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jistech.v4i1.5344

Abstract

Business environment has strong competition from year to year. This is because various changes and uncertainties fill the competition. Most of the changes or uncertainties in the business world are caused by the increasing of consumer bargaining power in business practices. Consumers have high power in determining their requests that must be fulfilled by business people. Changes or uncertainties are most of the main factors that cannot be anticipated when the business world has strong and uncertain competition. These uncertainties require business people to design an appropriate plan in order to minimize costs, especially inventory costs with consumer demand are still fullfilled. In that design plan, business people must be able to optimize the supply chain. In industrial systems, supply chain optimization and its response are strongly influenced by inventories. Inventories and its numbers are important issues in the supply chain that must be integrated with the optimization of the supply chain to manage demand uncertainty and to maintain customer service levels. This study designs an integrated planning model for supply chain and multi-echelon inventory in determining the location and the numbers of inventory in a supply chain with a general configuration with considering the uncertainty of consumer demand or  all things coming from production time