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All Journal Jurnal Teknik ITS Techno.Com: Jurnal Teknologi Informasi Register: Jurnal Ilmiah Teknologi Sistem Informasi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika Systemic: Information System and Informatics Journal Jurnal Mantik Jurnal Teknologi Informasi dan Terapan (J-TIT) Journal of Applied Engineering and Technological Science (JAETS) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Indonesian Journal of Electrical Engineering and Computer Science GUYUB: Journal of Community Engagement Journal of Applied Data Sciences El-Mujtama: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science

Ali Ridho Barakbah

Politeknik Elektronika Negeri Surabaya

Author-ID : 240788

Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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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)

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, automaticÂ clustering, K-means.

Javanese Character Feature Extraction Based on Shape Energy Wibowo, Galih Hendra; Sigit, Riyanto; Barakbah, Aliridho
EMITTER International Journal of Engineering Technology Vol 5, No 1 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Javanese character is one of Indonesias noble culture, especially in Java. However, the number of Javanese people who are able to read the letter has decreased so that there need to be conservation efforts in the form of a system that is able to recognize the characters. One solution to these problem lies in Optical Character Recognition (OCR) studies, where one of its heaviest points lies in feature extraction which is to distinguish each character. Shape Energy is one of feature extraction method with the basic idea of how the character can be distinguished simply through its skeleton. Based on the basic idea, then the development of feature extraction is done based on its components to produce an angular histogram with various variations of multiples angle. Furthermore, the performance test of this method and its basic method is performed in Javanese character dataset, which has been obtained from various images, is 240 data with 19 labels by using K-Nearest Neighbors as its classification method. Performance values were obtained based on the accuracy which is generated through the Cross-Validation process of 80.83% in the angular histogram with an angle of 20 degrees, 23% better than Shape Energy. In addition, other test results show that this method is able to recognize rotated character with the lowest performance value of 86% at 180-degree rotation and the highest performance value of 96.97% at 90-degree rotation. It can be concluded that this method is able to improve the performance of Shape Energy in the form of recognition of Javanese characters as well as robust to the rotation.

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 | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v2i1.17

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.

Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm Subhan, Muh; Sudarsono, Amang; Barakbah, Ali Ridho
EMITTER International Journal of Engineering Technology Vol 5, No 2 (2017)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v5i2.214

Radical content in procedural meaning is content which have provoke the violence, spread the hatred and anti nationalism. Radical definition for each country is different, especially in Indonesia. Radical content is more identical with provocation issue, ethnic and religious hatred that is called SARA in Indonesian languange. SARA content is very difficult to detect due to the large number, unstructure system and many noise can be caused multiple interpretations. This problem can threat the unity and harmony of the religion. According to this condition, it is required a system that can distinguish the radical content or not. In this system, we propose text mining approach using DF threshold and Human Brain as the feature extraction. The system is divided into several steps, those are collecting data which is including at preprocessing part, text mining, selection features, classification for grouping the data with class label, simillarity calculation of data training, and visualization to the radical content or non radical content. The experimental result show that using combination from 10-cross validation and k-Nearest Neighbor (kNN) as the classification methods achieve 66.37% accuracy performance with 7 k value of kNN method[1].

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v3i1.34

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, seismicÂ parameters.

Botnet Detection Using On-line Clustering with Pursuit Reinforcement Competitive Learning (PRCL) Mahardhika, Yesta Medya; Sudarsono, Amang; Barakbah, Ali Ridho
EMITTER International Journal of Engineering Technology Vol 6, No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Botnet is a malicious software that often occurs at this time, and can perform malicious activities, such as DDoS, spamming, phishing, keylogging, clickfraud, steal personal information and important data. Botnets can replicate themselves without user consent. Several systems of botnet detection has been done by using classification methods. Classification methods have high precision, but it needs more effort to determine appropiate classification model. In this paper, we propose reinforcedÂ approach to detect botnet with On-line Clustering using Reinforcement Learning. Reinforcement Learning involving interaction with the environment and became new paradigm in machine learning. The reinforcement learning will be implemented with some rule detection, because botnet ISCX dataset is categorized as unbalanced dataset which have high range of each number of class. Therefore we implemented Reinforcement Learning to Detect Botnet using Pursuit Reinforcement Competitive Learning (PRCL) with additional rule detection which has reward and punisment rules to achieve the solution. Based on the experimental result, PRCL can detect botnet in real time with highÂ accuracy (100% for Neris, 99.9% for Rbot, 78% for SMTP_Spam, 80.9% for Nsis, 80.7% for Virut, and 96.0% for Zeus) and fast processing time up to 176 ms. Meanwhile the step of CPU and memory usage which are 78 % and 4.3 GBÂ for pre-processing, 34% and 3.18 GB for online clustering with PRCL, and Â 23% and 3.11 GB evaluation. The proposed method is one solution for network administrators to detect botnet which has unpredictable behavior in network traffic.

Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya Putra, Berlian Juliartha Martin; Helen, Afrida; Barakbah, Ali Ridho
EMITTER International Journal of Engineering Technology Vol 6, No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Diskominfo Surabaya, as a government agency, received much community participatory for improvement of governmental services, with increasing number of 698, 2717, 4176 and 4298 participatory data respectively in 2011, 2012, 2013 and 2014. It is challenging for Diskominfo Surabaya to set a target by giving the response back within 24 hours. Due to task complexity to address the degree of participatory and to categorize the group of participatory, they faced difficulty to fulfill the target. In this research, we present a new system for measuring the sentiment degree of community participatory. We provide 5 functions in our system, which are: (1) Data Collection, (2) Data Preprocessing, (3) Text Mining, (4) Sentiment Analysis and (5) Validation. We propose our rule-based technique for the sentiment analysis of opinion mining with detection of 8 important parts, which are (1) Verb, (2) Adjective, (3) Preposition, (4) Noun, (5) Adverb, (6) Symbol, (7) Phrase, and (8) Complimentary. For applicability of our proposed system, we made a series of experiment with 410 data of community participatory in Twitter for Diskominfo Surabaya and compared with other sentiment classification algorithms which are SVM and Naive Bayes Classifier. Our system performed 77.32% rate of accuracy and outperformed to other comparing algorithms.

Spatio Temporal with Scalable Automatic Bisecting-Kmeans for Network Security Analysis in Matagaruda Project Hisyam, Masfu; Barakbah, Ali Ridho; Syarif, Iwan; S, Ferry Astika
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Internet attacks are a frequent occurrence and the incidence is always increasing every year, therefore Matagaruda project is built to monitor and analyze internet attacks using IDS (Intrusion Detection System). Unfortunately, the Matagaruda project has lacked in the absence of trend analysis and spatiotemporal analysis. It causes difficulties to get information about the usual seasonal attacks, then which sector is the most attacked and also the country or territory where the internet attack originated. Due to the number of unknown clusters, this paper proposes a new method of automatic bisecting K-means with the average of SSE is 93 percents better than K-means and bisecting K-means. The usage of big spark data is highly scalable for processing massive data attack.

Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features Kurniasari, Arvita Agus; Barakbah, Ali Ridho; Basuki, Achmad
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

The existing image search system often faces difficulty to find a appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by that the users’ intention for searching image is different with dominant information of the image collected from feature extraction. In this paper we present a new approach for content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something - supposed as an object. We applies segmentation of image as content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract color feature of image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)’s approach with 15 kernels. For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.

Indonesian Online News Extraction and Clustering Using Evolving Clustering Muhammad Alfian; Ali Ridho Barakbah; Idris Winarno
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.537

43,000 online media outlets in Indonesia publish at least one to two stories every hour. The amount of information exceeds human processing capacity, resulting in several impacts for humans, such as confusion and psychological pressure. This study proposes the Evolving Clustering method that continually adapts existing model knowledge in the real, ever-evolving environment without re-clustering the data. This study also proposes feature extraction with vector space-based stemming features to improve Indonesian language stemming. The application of the system consists of seven stages, (1) Data Acquisition, (2) Data Pipeline, (3) Keyword Feature Extraction, (4) Data Aggregation, (5) Predefined Cluster using Automatic Clustering algorithm, (6) Evolving Clustering, and (7) News Clustering Result. The experimental results show that Automatic Clustering generated 388 clusters as predefined clusters from 3.000 news. One of them is the unknown cluster. Evolving clustering runs for two days to cluster the news by streaming, resulting in a total of 611 clusters. Evolving clustering goes well, both updating models and adding models. The performance of the Evolving Clustering algorithm is quite good, as evidenced by the cluster accuracy value of 88%. However, some clusters are not right. It should be re-evaluated in the keyword feature extraction process to extract the appropriate features for grouping. In the future, this method can be developed further by adding other functions, updating and adding to the model, and evaluating.

Co-Authors A.A. Ketut Agung Cahyawan W Abd. Rasyid Syamsuri Achmad Basuki Achmad Basuki Achmad Basuki Achmad Basuki Adnan Rachmat Anom Besari Afifah, Izza Nur Afrida Helen Afrida Helen, Afrida Agata, Dias Agus Kurniasari, Arvita Ahsan, Ahmad Syauqi Al Islami, M Tafaquh Fiddin Alde, Muhammad Riski Alfi Fadliana Amali, Darari Nur Amalia Wirdatul Hidayah Amalo, Elizabeth Anggraeni Amang Sudarsono, Amang Andhik Ampuh Yunanto Andy Yuniawan ANITA DAMAYANTI Anom Besari, Adnan Rachmat Arna Fariza Arvita Agus Kurniasari Aziz, Adam Shidqul Bayu Dwiyan Satria Bima Sena Bayu Dewantara Budi Santosa Dadet Pramadihanto Dadet Pramadihanto Darari Nur Amali Desi Amirullah, Desi Desy Intan Permatasari, Desy Intan Devira Nanda Kuswhara, Devira Nanda Dewanto, Raden Sanggar Dias Agata Edelani, Renovita Edi Satriyanto Entin Martiana Kusumaningtyas Fahrudin, Tresna Maulana Fahrudin, Tresna Maulana Fauzi Nafi'Ubadah, Kriza Febrianto, Ardiansyah Indra Ferry Astika Saputra Haikal Yuniarta Krisgianto, Ricko Hamida, Silfiana Nur Hermawan, Aditya Afgan Hermawan, Aditya Afgan Hidayah, Amalia Wirdatul Hidayah, Nadila Wirdatul Hisyam, Masfu Hisyam, Masfu Huda, Achmad Thorikul I Made Akira Ivandio Agusta Idris Winarno Idris Winarno Ilham Iskandariansyah Indah Yulia Prafitaning Tiyas, Indah Yulia Prafitaning Indra Adji Sulistijono Insani, Fawzan Irene Erlyn Wina Rachmawan, Irene Erlyn Wina Isbat Uzzin Nadhori, Isbat Uzzin iwan Syarif Iwan Syarif Khotibul Umam Kindarya, Fabyan Kohei Arai Kohei Arai Kurniasari, Arvita Agus Kurniasari, Arvita Agus Kusuma, Dedy Hidayat Kusuma, Selvia Ferdiana Louis Nashih Uluwan Arif M Udin Harun Al Rasyid, M Udin Harun Mahardhika, Yesta Medya Marlisa Sigita, Marlisa Maulana, Wahyu Ikbal Mayangsari, Mustika Kurnia Miko, Taufan Radias Mirza Ghulam Rifqi Mirza Ghulam Rifqi Mohammad Nur Shodiq Mohammad Nur Shodiq Mohammad Nur Shodiq, Mohammad Nur Mu'arifin, Mu'arifin Muarifin ., Muarifin Muarifin Muarifin Muhammad Alfian Muhammad Rois Muhammad Wahyu Nugroho Sakti Nadila Wirdatul Hidayah Nana Ramadijanti, Nana Ni'Ma, Najma Akmalina Nur Rosyid Mubatada'i Nur Rosyid Mubtadai, Nur Rosyid Oktavia Citra Resmi Rachmawati Piko Permata Ilham Prasetyo Primajaya, Grezio Arifiyan Puspasari Susanti Putra, Berlian Juliartha Martin Rachmawati, Oktavia Citra Resmi Rasyada, Ihda Ratri Cahyaning Winedhar Renovita Edelani Ridho, Bistiana Syafina Riyanto Sigit Riyanto Sigit, Riyanto Rizka Rahayu Sasmita Rudi Kurniawan S, Ferry Astika S, Ferry Astika Sa'adah, Umi Saputra, Muhammad Krisnanda Vilovan Sesulihatien, Wahjoe Tjatur Setiawardhana Setiawardhana Setiawardhana, Setiawardhana Shabirin, Irsal Subhan, Muh Sumarsono, Irwan Suryani, Indah Yudi Susanti, Puspasari Susetyoko, Ronny Syd. Ali Zein Farmadi, Syd. Ali Zein Tahta Alfina Tessy Badriyah Tessy Badriyah, Tessy Tita Karlita Tita Karlita Tresna Maulana Fahrudin Tri Hadiah Muliawati, Tri Hadiah Tri Harsono Tri Harsono ULURRASYADI, FAIZ Wahjoe Tjatur Sesulihatien Wahjoe Tjatur Sesulihatien Wahyu Widodo Wibowo, Galih Hendra Wibowo, Galih Hendra Widodo, Edi Wahyu Wina Rachmawan, Irene Erlyn Wina Rachmawan, Irene Erlyn Yuliana Setiowati, Yuliana Zainal Arief

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