Tri Harsono
Teknik Elektronika, Politeknik Elektronika Negeri Surabaya (PENS) – ITS

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Desain Antar Muka Platform Reselient Untuk Manajemen Bencana Winarno, Idris; Yuwono, Wiratmoko; Harsono, Tri
PROSIDING CSGTEIS 2013 CSGTEIS 2013
Publisher : PROSIDING CSGTEIS 2013

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Abstract

Abstrak - Salah satu sistem informasi kebencanaanyang ada saat ini adalah Sahana.Sahana memilikiketerbatasan dalam integrasi dengan aplikasi pendukungkebencanaan yang dikembangkan olehpihaklain. Hal initerjadi karena tidak adanya platform standar yangmemiliki protokol yang terbuka untuk dapatdimanfaatkan oleh pengembang aplikasi atau sistem.Olehkarena itu dibutuhkan sebuah sistem informasikebencanaan yang bersifat universal dimana memilikiprotokol yang dapat dimanfaatkan oleh pengembang agaraplikasi yang dibuat dapat diintegrasikan secara langsungterhadap sistem informasi kebencanaan dengan diawalidengan pembuatan desain antar muka dari sisteminformasi tersebut.Penelitian ini membuat desain antarmuka yang bersifat universal dimana fitur-fiturnya lebihlengkap dari Sahana. Secara garis besar desain antarmuka antara Sahana dan Sistem informasi kebencanaanhampir sama tetapi pada sistem informasi kebencanaanterdapat tambahan beberapa fitur yaitu ManejemenTrackingdengan penggunaan UI Boostrap didalampembangunannya.Kata Kunci : Sistem Informasi, Bencana Alam, platform,antar muka
Demam Berdarah dalam Perspektif Urban : Analisa Statistik untuk Awareness Strategy Tjatur S., Wahjoe; Prasetyaningrum, Ira; Harsono, Tri; Sasaki, Shiori; Kiyoki, Yasushi
PROSIDING CSGTEIS 2013 CSGTEIS 2013
Publisher : PROSIDING CSGTEIS 2013

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Abstrak—Demam berdarah saat ini menjadi penyakit yangbanyak mengancam banyak kota besar didunia saat ini.Mengingat kompleksitas pada penyebaran penyakit ini, perluadanya strategi yang komprehensip dan bersifat preventif.Untuk mendapatkan strategi yang tepat perlu adanya analisastatistic komprehensip antara semua factor yang mempengaruhidemam berdarah yaitu perubahan iklim, peningkatan humanmovement dan kultur budaya dalam kebersihan. Penelitian iniberfokus pada general case analisa statistik yang diharapkandapat menjadi dasar bagi strategi awareness demam berdarah.Kata kunci: demam berdarah, analisa statistic, strategiawareness
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.
Evacuation System in a Building Using Cellular Automata for Pedestrian Dynamics ., Muarifin; Harsono, Tri; Barakbah, Aliridho
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

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Abstract

The sense of safety in public facilities for pedestrians can be shown by the availability of good infrastructure, particularly the building. One of the aspects that can make pedestrians feel comfortable and safe is the availability of evacuation facilities in emergency situation. When a disaster strikes, people would start to panic and this will cause problems, especially during an evacuation.During panic in an evacuation process, pedestrians tend to act blindly and walk randomly and mindlessly. They might follow one another when they get panic. This is called as herding behavior. Regarding the evacuation systems, cellular automata is the basic method used to represent human motion. The movement of pedestrian is an important aspect during an evacuation process and this can be analyzed and implemented by using Cellular Automata. It is a simple method yet it can solve complex problems.Total evacuation time becomes the indicators in measuring the efficiency of this system. The result of comparison method shows that the proposed method could work better in certain conditions. In addition, the results of the experiments during panic and normal situation show similar characteristics especially regarding density aspect, yet evacuation time during panic situation takes longer time. The experiment’s results by using the actual data also has similar tendency with the evacuation time.Keywords: evacuation time, cellular automata, panic behavior, pedestrian
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.
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.
PENGEMBANGAN MODEL PEMBELAJARAN FISIKA UMUM BERBASIS PENDIDIKAN KARAKTER DI PROGRAM STUDI PENDIDIKAN FISIKA FMIPA UNIMED ,, Derlina; Harsono, Tri; ., Sabani
Jurnal Pendidikan Fisika Vol 3, No 1 (2014)
Publisher : Universitas Negeri Medan

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Abstract

Tujuan utama penelitian ini adalah untuk meningkatkan karakter dan hasil belajar mahasiswa yang meliputi: pembelajaran berdasarkan masalah, pembelajaran kooperatif dengan berbagai tipe, dan inquiry training, melalui penyusunan perangkat pembelajaran untuk beberapa komptensi dasar mata kuliah Fisika Umum dengan model-model pembelajaran berbasis karakter. Jenis penelitian adalah penelitian pengembangan. Perangkat pembelajaran yang disusun meliputi (1) silabus, (2) rencana pelaksanaan pembelajaran (RPP), (3) bahan ajar, (4) lembar kerja mahasiswa (LKM), dan (5) pedoman/alat evaluasi. Target khusus yang ingin dicapai adalah (1) peningkatan hasil belajar mahasiswa, dan (2) mengembangkan karakter mahasiswa antara lain sikap jujur, tanggung jawab, disiplin, berlaku hormat, kerjasama, kemampuan berkomunikasi dan kreativitas.
SPATIO-TEMPORAL ASSOCIATIVE MINING FOR EARTHQUAKE DATA DISTRIBUTION IN INDONESIA Edelani, Renovita; Barakbah, Aliridho; Harsono, Tri
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.428

Abstract

Indonesia is a country that has the highest seismically activity in the world. This country has really high earthquake frequency because of it traversed by three plate meeting plate and located in Ring of Fire area. The shaking events from an earthquake are very strong and propagate in all directions, capable of destroying even the strongest civilian buildings, so there is no doubt that there are many victims of human lives. The other facts, earthquake in Indonesia have seismic relation between the provinces. In this paper, we present a new earthquake Spatio-temporal mapping system based on the association confidence value from the result of associative mining process on earthquake data distribution in Indonesia. The system proposed three main functions which are (1) Data Acquisition which taken from four data provider, then preprocess and combine it become one, (2) Associative Mining process to get the rule of association earthquake between provinces in Indonesia, and (3) Earthquake Association Spatio-Temporal Model from the highest confidence value and Visualization. We use data from several earthquake data providers from 1900 until 2018.  To perform our proposed Spatio-temporal earthquake association mapping system, we divided the data to become a 5-year discrete partition. After that, we mining the rule and get the highest confidence value from each period. This confidence value is used for modeling and visualization of our Spatio-temporal mapping system. As a result of this study, we manage to generate earthquake association risk mapping from 13 provinces that had earthquake connectivity between each other. The provinces are Aceh, Sumatera Utara, Bengkulu, East Java, Bali, NTB, NTT, Maluku, North Maluku, Gorontalo, North Sulawesi, Papua dan West Papua.
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 | Download Original | Original Source | Check in Google Scholar | Full PDF (946.111 KB) | DOI: 10.24003/emitter.v3i1.38

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.
PENGEMBANGAN MODEL PEMBELAJARAN FISIKA UMUM BERBASIS PENDIDIKAN KARAKTER DI PROGRAM STUDI PENDIDIKAN FISIKA FMIPA UNIMED ,, Derlina; Harsono, Tri; ., Sabani
Jurnal Pendidikan Fisika Vol 3, No 1 (2014)
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22611/jpf.v3i1.3271

Abstract

Tujuan utama penelitian ini adalah untuk meningkatkan karakter dan hasil belajar mahasiswa yang meliputi: pembelajaran berdasarkan masalah, pembelajaran kooperatif dengan berbagai tipe, dan inquiry training, melalui penyusunan perangkat pembelajaran untuk beberapa komptensi dasar mata kuliah Fisika Umum dengan model-model pembelajaran berbasis karakter. Jenis penelitian adalah penelitian pengembangan. Perangkat pembelajaran yang disusun meliputi (1) silabus, (2) rencana pelaksanaan pembelajaran (RPP), (3) bahan ajar, (4) lembar kerja mahasiswa (LKM), dan (5) pedoman/alat evaluasi. Target khusus yang ingin dicapai adalah (1) peningkatan hasil belajar mahasiswa, dan (2) mengembangkan karakter mahasiswa antara lain sikap jujur, tanggung jawab, disiplin, berlaku hormat, kerjasama, kemampuan berkomunikasi dan kreativitas.