Articles

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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
TINJAUAN EKOLOGI DAN ETNOBOTANI GANDARIA (Bouea macrophylla Griffith) Harsono, Tri
JURNAL BIOSAINS Vol 3, No 2 (2017): Jurnal Biosains
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia dikenal memiliki tingkat keragaman hayati yang tinggi. Keragaman hayati tanaman di Indonesia merupakan potensi yang dapat dikembangkan dan bernilai tinggi. Salah satu tanaman yang berpotensi untuk dikembangkan adalah gandaria. Gandaria merupakan tanaman yang dapat tumbuh tinggi. Sebagai salah satu tanaman asli Indonesia, gandaria dimanfaatkan mulai dari buah, daun, hingga batangnya. Pemanfaatan yang optimal dari tanaman gandaria dapat meningkatkan nilai ekonomis. Secara ekologi, gandaria menyebar mulai dari kawasan pantai hingga dataran tinggi. Gandaria merupakan tumbuhan tropik basah dan dapat tumbuh pada tanah yang ringan dan subur. Gandaria mudah beradaptasi pada lingkungan budidayanya dan merupakan salah satu komoditas buah-buahan tropis yang berpotensi baik. Kata kunci : ekologi, gandaria, tanaman
PENGENALAN GAMBAR RAMBU-RAMBU LALU-LINTAS DENGAN METODE KUANTISASI RERATA Harsono, Tri; Basuki, Achmad; Ramadijanti, Nana
Berkala Ilmiah MIPA Vol 16, No 3 (2006)
Publisher : FMIPA UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Salah satu ciri yang bisa dikembangkan dalam pengenalan gambar rambu-rambu lalu-lintas adalah ciri yang melibatkan content pada gambar, yang meliputi ciri dasar gambar seperti warna, bentuk dan tekstur. Pada gambar rambu-rambu lalu lintas, ciri bentuk merupakan ciri dominan yang mengisi content gambar. Untuk mendapatkan ciri bentuk ini ada berbagai macam metode yang banyak digunakan antara lain deteksi tepi, transformasi fourier, integral proyeksi dan kuantisasi. Dalam penelitian ini memanfaatkan ciri bentuk dengan menggunakan teknik kuantisasi untuk menyajikan ciri dari gambar rambu-rambu lalu lintas, yaitu kuantisasi rata-rata. Proses pengenalan dilakukan dengan menggunakan template matching antara vektor gambar rambu-rambu yang dimasukan (vektor query) dengan semua tanda rambu-rambu yang ada dalam database (vektor template). Dengan ukuran vektor yang kecil diharapkan proses template matching ini dapat dilakukan dengan cepat. Dalam hal ini yang dicari adalah nilai selisih terkecil dari vektor query dan vektor template yang ada dalam database. Berdasarkan hasil percobaan yang telah dilakukan, bertambahnya ukuran sampling menghasilkan performansi yang semakin kecil atau prosentase error yang semakin besar, kondisi ini dikarenakan banyak ciri bentuk pada gambar yang hilang, sehingga pengenalan terhadap gambar menimbulkan error yang besar. Hasil percobaan yang cukup baik pada saat ukuran sampling (segmen) 4x4, didapatkan prosentase error rata-rata sebesar 9.19% (jumlah gambar salah sekitar 3 gambar) dengan performansi sebesar 90.81. Kata Kunci: Rambu-rambu lalu-lintas, kuantisasi rata-rata, template matching
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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (10388.993 KB)

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)

Show Abstract | Download Original | Original Source | Check in Google Scholar

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)

Show Abstract | Download Original | 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.
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

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
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.