Ali Ridho Barakbah
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Published : 30 Documents
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Analisa Perbandingan Metode Hierarchical Clustering, K-Means dan Gabungan Keduanya dalam Cluster Data (Studi Kasus: Problem Kerja Praktek Teknik Industri ITS) Alfina, Tahta; Santosa, Budi; Barakbah, Ali Ridho
Jurnal Teknik ITS Vol 1, No 1 (2012): Jurnal Teknik ITS (ISSN 2301-9271)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

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Abstract

Saat ini, konsep data mining semakin dikenal sebagai tools penting dalam manajemen informasi karena jumlah informasi yang semakin besar jumlahnya. Salah satu teknik yang dikenal dalam data mining adalah clustering,  berupa proses pengelompokan sejumlah data atau objek ke dalam cluster (group) sehingga setiap dalam cluster tersebut akan berisi data yang semirip mungkin dan berbeda dengan objek dalam cluster yang lainnya. Clustering memiliki dua metode, yaitu partisi dan hierarki. Dua metode ini memiliki kelebihan dan kekurangan masing-masing, dan dengan menggabungkan keduanya dapat diperoleh hasil cluster yang lebih baik. Dari hasil cluster dengan menggunakan data problem Kerja Praktek Jurusan Teknik Industri ITS, maka diperoleh hasil bahwa gabungan metode Single Linkage Clustering dan K-means memberikan hasil cluster yang lebih baik dengan parameter uji cluster variance dan metode silhouette coefisien.
Centronit: Initial Centroid Designation Algorithm for K-Means Clustering Barakbah, Ali Ridho; Arai, Kohei
EMITTER International Journal of Engineering Technology Vol 2, No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

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Abstract

Clustering performance of the K-means highly depends on the correctness of initial centroids. Usually initial centroids for the K- means clustering are determined randomly so that the determined initial centers may cause to reach the nearest local minima, not the global optimum. In this paper, we propose an algorithm, called as Centronit, for designation of initial centroidoptimization of K-means clustering. The proposed algorithm is based on the calculation of the average distance of the nearest data inside region of the minimum distance. The initial centroids can be designated by the lowest average distance of each data. The minimum distance is set by calculating the average distance between the data. This method is also robust from outliers of data. The experimental results show effectiveness of the proposed method to improve the clustering results with the K-means clustering.Keywords: K-means clustering, initial centroids, Kmeansoptimization.
Differential Spatio-temporal Multiband Satellite Image Clustering using K-means Optimization With Reinforcement Programming Rachmawan, Irene Erlyn Wina; Barakbah, Ali Ridho; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

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Abstract

Deforestration is one of the crucial issues in Indonesia because now Indonesia has worlds highest deforestation rate. In other hand, multispectral image delivers a great source of data for studying spatial and temporal changeability of the environmental such as deforestration area. This research present differential image processing methods for detecting nature change of deforestration. Our differential image processing algorithms extract and indicating area automatically. The feature of our proposed idea produce extracted information from multiband satellite image and calculate the area of deforestration by years with calculating data using temporal dataset. Yet, multiband satellite image consists of big data size that were difficult to be handled for segmentation. Commonly, K- Means clustering is considered to be a powerfull clustering algorithm because of its ability to clustering big data. However K-Means has sensitivity of its first generated centroids, which could lead into a bad performance. In this paper we propose a new approach to optimize K-Means clustering using Reinforcement Programming in order to clustering multispectral image. We build a new mechanism for generating initial centroids by implementing exploration and exploitation knowledge from Reinforcement Programming. This optimization will lead a better result for K-means data cluster. We select multispectral image from Landsat 7 in past ten years in Medawai, Borneo, Indonesia, and apply two segmentation areas consist of deforestration land and forest field. We made series of experiments and compared the experimental results of K-means using Reinforcement Programming as optimizing initiate centroid and normal K-means without optimization process.Keywords: Deforestration, Multispectral images, landsat, automatic clustering, K-means.
Semantic Songket Image Search with Cultural Computing of Symbolic Meaning Extraction and Analytical Aggregation of Color and Shape Features Amirullah, Desi; Barakbah, Ali Ridho; Basuki, Achmad
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

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Abstract

The term "Songket" comes from the Malay word "Sungkit", which means "to hook" or "to gouge". Every motifs names and variations was derived from plants and animals as source of inspiration to create many patterns of songket. Each of songket patterns have a philosophy in form of rhyme that refers to the nature of the sources of songket patterns and that philosophy reflects to the beliefs and values of Malay culture. In this research, we propose a system to facilitate an understanding of songket and the philosophy as a way to conserve Songket culture. We propose a system which is able to collect information in image songket motif variations based on feature extraction methods. On each image songket motif variations, we extracted philosophy of rhyme into impressions, and extracting color features of songket images using a histogram 3D-Color Vector quantization (3D-CVQ), shape feature extraction songket image using HU Moment invariants. Then, we created an image search based on impressions, and impressions search based on image. We use techniques of search based on color, shape and aggregation (combination of colors and shapes). The experiment using impression as query : 1) Result based on color, the average value of true 7.3, total score 41.9, 2) Result based on shape, the average value of true 3, total score 16.4, 3) Result based on aggregation, the average value of true 3, total score 17.4. While based using Image Query : 1) Result based on color, the average precision 95%, 2) Result based on shape, average precision 43.3%, 3) Based aggregation, the average precision 73.3%. From our experiments, it can be concluded that the best search system using query impression and query image is based on the color.Keyword : Image Search, Philosophy, impression, Songket, cultural computing, Feature Extraction, Analytical aggregation.
Automatic Representative News Generation using On-Line Clustering Sigita, Marlisa; Barakbah, Ali Ridho; Kusumaningtyas, Entin Martiana; Winarno, Idris
EMITTER International Journal of Engineering Technology Vol 1, No 1 (2013)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

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Abstract

The increasing number of online news provider has produced large volume of news every day. The large volume can bring drawback in consuming information efficiently because some news contain similar contents but they have different titles that may appear. This paper presents a new system for automatically generating representative news using on-line clustering. The system allows the clustering to be dynamic with the features of centroid update and new cluster creation. Text mining is implemented to extract the news contents. The representative news is obtained from the closest distance to each centroid that calculated using Euclidean distance. For experimental study, we implement our system to 460 news in Bahasa Indonesia. The experiment performed 70.9% of precision ratio. The error is mainly caused by imprecise results from keyword extraction that generates only one or two keywords for an article. The distribution of centroid’s keywords also affects the clustering results.Keywords: News Representation, On-line Clustering, Keyword Aggregation, Text Mining.
Reinforced Intrusion Detection Using Pursuit Reinforcement Competitive Learning Tiyas, Indah Yulia Prafitaning; Barakbah, Ali Ridho; Harsono, Tri; Sudarsono, Amang
EMITTER International Journal of Engineering Technology Vol 2, No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

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Abstract

Today, information technology is growing rapidly,all information can be obtainedmuch easier. It raises some new problems; one of them is unauthorized access to the system. We need a reliable network security system that is resistant to a variety of attacks against the system. Therefore, Intrusion Detection System (IDS) required to overcome the problems of intrusions. Many researches have been done on intrusion detection using classification methods. Classification methodshave high precision, but it takes efforts to determine an appropriate classification model to the classification problem. In this paper, we propose a new reinforced approach to detect intrusion with On-line Clustering using Reinforcement Learning. Reinforcement Learning is a new paradigm in machine learning which involves interaction with the environment.It works with reward and punishment mechanism to achieve solution. We apply the Reinforcement Learning to the intrusion detection problem with considering competitive learning using Pursuit Reinforcement Competitive Learning (PRCL). Based on the experimental result, PRCL can detect intrusions in real time with high accuracy (99.816% for DoS, 95.015% for Probe, 94.731% for R2L and 99.373% for U2R) and high speed (44 ms).The proposed approach can help network administrators to detect intrusion, so the computer network security systembecome reliable.Keywords: Intrusion Detection System, On-Line Clustering, Reinforcement Learning, Unsupervised Learning.
Differential Spatio-temporal Multiband Satellite Image Clustering using K-means Optimization With Reinforcement Programming Rachmawan, Irene Erlyn Wina; Barakbah, Ali Ridho; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Original Source | Check in Google Scholar | Full PDF (946.111 KB)

Abstract

Deforestration is one of the crucial issues in Indonesia because now Indonesia has worlds highest deforestation rate. In other hand, multispectral image delivers a great source of data for studying spatial and temporal changeability of the environmental such as deforestration area. This research present differential image processing methods for detecting nature change of deforestration. Our differential image processing algorithms extract and indicating area automatically. The feature of our proposed idea produce extracted information from multiband satellite image and calculate the area of deforestration by years with calculating data using temporal dataset. Yet, multiband satellite image consists of big data size that were difficult to be handled for segmentation. Commonly, K- Means clustering is considered to be a powerfull clustering algorithm because of its ability to clustering big data. However K-Means has sensitivity of its first generated centroids, which could lead into a bad performance. In this paper we propose a new approach to optimize K-Means clustering using Reinforcement Programming in order to clustering multispectral image. We build a new mechanism for generating initial centroids by implementing exploration and exploitation knowledge from Reinforcement Programming. This optimization will lead a better result for K-means data cluster. We select multispectral image from Landsat 7 in past ten years in Medawai, Borneo, Indonesia, and apply two segmentation areas consist of deforestration land and forest field. We made series of experiments and compared the experimental results of K-means using Reinforcement Programming as optimizing initiate centroid and normal K-means without optimization process.Keywords: Deforestration, Multispectral images, landsat, automatic clustering, K-means.
Centronit: Initial Centroid Designation Algorithm for K-Means Clustering Barakbah, Ali Ridho; Arai, Kohei
EMITTER International Journal of Engineering Technology Vol 2, No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Original Source | Check in Google Scholar

Abstract

Clustering performance of the K-means highly depends on the correctness of initial centroids. Usually initial centroids for the K- means clustering are determined randomly so that the determined initial centers may cause to reach the nearest local minima, not the global optimum. In this paper, we propose an algorithm, called as Centronit, for designation of initial centroidoptimization of K-means clustering. The proposed algorithm is based on the calculation of the average distance of the nearest data inside region of the minimum distance. The initial centroids can be designated by the lowest average distance of each data. The minimum distance is set by calculating the average distance between the data. This method is also robust from outliers of data. The experimental results show effectiveness of the proposed method to improve the clustering results with the K-means clustering.Keywords: K-means clustering, initial centroids, Kmeansoptimization.
Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia Shodiq, Mohammad Nur; Barakbah, Ali Ridho; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

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Abstract

Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System), for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014.Keywords: Clustering, visualization, multidimensional data, seismic parameters.
Smart I’rab: Smart Aplicasion for Arabic Grammar Learning Farmadi, Syd. Ali Zein; Barakbah, Ali Ridho; Kusumaningtyas, Entin Martiana
EMITTER International Journal of Engineering Technology Vol 1, No 1 (2013)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

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Abstract

Arabic grammar, known as nahwu, is necessary to comprehend the Holy Qur’an that is completely written in Arabic. However, many people get trouble to study this skill because there are various kinds of word formation and sentences that may be created from a single verb, noun, adjective, subject, predicate, object, adverb or another formation. This research proposes a new approach to identify the position and word function in Arabic sentence. The approach creates smart process that employs Natural Language Processing (NLP) and expert system with modeling based on knowledge and inference engine in determining the word position. The knowledge base determines the part of speech while the inference engine shows the word function in the sentence. On processing, the system uses 82 templates consisting of 34 verb templates, 34 subject pronouns, 14 pronouns for object or possessive word. All the templates are in the form of char array for harakat (vowel) and letters which become the comparators for determining the part of speech from input word sentence. Output from the system is an i’rab (the explanation of word function in sentence) written in Arabic. The system has been tested for 159 times to examine word and sentence. The examination for word that is done 117 times has not made any error except for the word that is really like another word. While the detection for word function in sentence that is done 42 times experiment, there is no error too. An error happens when the part of speech from the word being examined is not included in the system yet, influencing the following word function detection.Keywords: I’rab, Arabic grammar, NLP, expert system, knowledge base, inference engine