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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics PROSIDING CSGTEIS 2013 JURNAL PENELITIAN SAINTIKA EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA Martabe : Jurnal Pengabdian Kepada Masyarakat INTEK: Jurnal Penelitian Jurnal Mantik Jurnal Informatika dan Rekayasa Elektronik Jurnal Pembelajaran dan Biologi Nukleus Best Journal (Biology Education, Sains and Technology) JURNAL PIONIR Indonesian Journal of Electrical Engineering and Computer Science GUYUB: Journal of Community Engagement

Tri Harsono

Politeknik Elektronika Negri Surabaya

Author-ID : 276645

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health 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.

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.

KAJIAN KERAGAMAN JENIS DAN LAJU PERTUMBUHAN KAPANG DALAM ACAR LIMAU KASTURI (CITROFORTUNELLA MICROCARPA) MARANAN MASY ARAKAT MELAYU nasution, Muhammad Yusuf; Hasairirr, Ashar; Harsono, Tri
JURNAL PENELITIAN SAINTIKA Vol 11, No 2 (2011): SEPTEMBER 2011
Publisher : JURNAL PENELITIAN SAINTIKA

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

This research aims to study the diversity of species, growth rate and colony number of mold growth inMusk Lime Pickle in various storage time. The research method is Non Factorial Experiment,complete random sampling (RAL) .. The results indicated that in Musk Lime Pickle, there are 6 moldssuch as: Aspergillus fumigatus, Aspergillus niger, Aspergillus tamari, Fusarium solani, Mucor mucedo,and Penicillium digitatum. The duration of storage decreased the growth rate of mold, and the diversityof mold is not increase. The mold that growth more is Aspergillus fumigatus and Penicillium digitatum, while the least is Mucor mucedo and Fusarium solani. The species of mold found in morenumber in the storage of one week and the few number without treatment.

Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm Sa'ada, Nailus; Harsono, Tri; Basuki, Ahmad
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Images contain a lot of information that can be used in a variety of areas. One of the images that have much information inside is satellite image. In order to extract the information properly, the image processing step should be performed properly. The segmentation process plays an important role in image processing, especially for feature extraction. Many ways were developed to perform the segmentation image. In this study, we apply DBSCAN clustering to segment images on whirlwind cloud feature extraction problems. DBSCAN is a density-based classifier method which means it is suitable to group a density-based data. While the image used in the segmentation process is the Himawari 8 satellite image which also contains density-based data. It contains various information about clouds condition like cloud type, cloud temperature, cloud humidity, rainfall potential based on cloud temperature, etc. This study uses Himawari 8 satellite images as input where the images taken are images several hours before a wirlwind event in an area, while the cluster method used is the DBSCAN algorithm. Clustering is done to get the extraction features of a wirlwind in the form of centroid points that characterize the movement of a cloud. Segmentation performance was observed based on the number of centroid points as a result of clustering several types of clouds in an area before a wirlwind occurred. Based on segmentation testing using the DBSCAN algorithm for cloud data in an area for several hours before a wirlwind, better segmentation performance was obtained compared to the segmentation results of the Meng hee heng k-means algorithm for the same test data specifications. DBSCAN separates a type of cloud in more detail that makes it easier to record each centroid of each cluster around the scene. It is even able to cluster small groups of clouds independently so that these small groups of clouds can also be detected as features.

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia Mohammad Nur Shodiq; Dedy Hidayat Kusuma; Mirza Ghulam Rifqi; Ali Ridho Barakbah; Tri Harsono
JOIV : International Journal on Informatics Visualization Vol 2, No 1 (2018)
Publisher : Society of Visual Informatics

A model of artiï¬cial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Surveyâ€™s (USGS). Six clusters was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richterâ€™s scale magnitude.

Adaptive Neural Fuzzy Inference System and Automatic Clustering for Earthquake Prediction in Indonesia Mohammad Nur Shodiq; Dedy Hidayat Kusuma; Mirza Ghulam Rifqi; Ali Ridho Barakbah; Tri Harsono
JOIV : International Journal on Informatics Visualization Vol 3, No 1 (2019)
Publisher : Politeknik Negeri Padang

Earthquake is a type of natural disaster. The Indonesian archipelago located in the world's three mega plates; they are Australian plate, Eurasian plate, and Pacific plate. Therefore, it is possible for applied of earthquake risk of mitigation. One of them is to provide information about earthquake occurrences. This information used for spatiotemporal analysis of earthquakes. This paper presented Spatial Analysis of Magnitude Distribution for Earthquake Prediction using adaptive neural fuzzy inference system (ANFIS) based on automatic clustering in Indonesia. This system has three main sections: (1) Data preprocessing, (2) Automatic Clustering, (3) Adaptive Neural Fuzzy Inference System. For experimental study, earthquake data obtained Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) and the United States Geological Survey’s (USGS), the year 2010-2017 in the location of Indonesia. Automatic clustering process produces The optimal number of cluster, that is 7 clusters. Each cluster will be analyzed based on earthquake distribution. Its calculate the b value of earthquake to get the seven seismicity indicators. Then, implementation for ANFIS uses 100 training epochs, Number of membership function (MFs) is 2, MFs type input is gaussian membership function (gaussmf). The ANFIS result showed that the system can predict the non-occurrence of aftershocks with the average performance of 70%.

Social Media Engineering for Issues Feature Extraction using Categorization Knowledge Modelling and Rule-based Sentiment Analysis M Tafaquh Fiddin Al Islami; Ali Ridho Barakbah; Tri Harsono
JOIV : International Journal on Informatics Visualization Vol 5, No 1 (2021)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.1.397

A company maintains and improves its quality services by paying attention to reviews and complaints from users. The complaints from users are commonly written using human natural language expression so that their messages are computationally difficult to extract and proceed. To overcome this difficulty, in this study, we presented a new system for issues feature extraction from users’ reviews and complaints from social media data. This system consists of four main functions: (1) Data Crawling and Preprocessing, (2) Categorization Knowledge Modelling, (3) Rule-based Sentiment Analysis, and (4) Application Environment. Data Crawling and Preprocessing provides data acquisition from users’ tweets on social media, crawls the data and applies the data preprocessing. Categorization Knowledge Modelling provides text mining of textual data, vector space transformation to create knowledge metadata, context recognition of keyword queries to the knowledge metadata, and similarity measurement for categorization. In the Rule-based Sentiment Analysis, we developed our own rules of computatioal linguistics to measure polarity of sentiment. Application Environment consists of 3 layers: database management, back-end services and front-end services. For applicability of our proposed system, we conducted two kinds of experimental study: (1) categorization performance, and (2) sentiment analysis performance. For categorization performance, we used 8743 tweet data and performed 82% of accuracy. For categorization performance, we made experiments on 217 tweet data and performed 92% of accuracy.

Incremental Associative Mining based Risk-Mapping System for Earthquake Analysis in Indonesia Renovita Edelani; Ali Ridho Barakbah; Tri Harsono; Louis Nashih Uluwan Arif
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Indonesia is one of the largest archipelagic countries in the world that has the highest risk of an earthquake. The major causes of earthquakes in this country are plate movements and volcanic activity. Earthquakes in Indonesia has a cause and effect relationship between each province. This disaster caused severe damage including a lot of people to get killed, injured and lose their money and property. We must minimize the impact of the earthquake by forming earthquake risk mapping. The risk of seismicity in Indonesia can vary each year, so it needs to be analyzed how the changes in risk are each addition of earthquake data. This paper proposes an earthquake risk mapping system with Associative Mining based on incremental earthquake data that have the highest values of confidence rates from the seismic association between provinces in Indonesia. The system uses the Incremental Association rule method to see the trend in the value of changes in confidence for each addition of earthquake data every 5 years. This system proposes 3 main features, which are (1) Data Retrieval and Preprocessing, (2) Association Rule Mining, (3) Incremental Associative Mining based risk mapping. For the experimental study, the system used data from 1963-2018. The results show that the provinces of Maluku, North Maluku, Nusa Tenggara Timur, North Sulawesi, and Papua have an incremental association risk of an earthquake.

Improved echocardiography segmentation using active shape model and optical flow Riyanto Sigit; Calvin Alfa Roji; Tri Harsono; Son Kuswadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.11821

Heart disease is one of the most dangerous diseases that threaten human life. The doctor uses echocardiography to analyze heart disease. The result of echocardiography test is a video that shows the movement of the heart rate. The result of echocardiography test indicates whether the patient’s heart is normal or not by identifying a heart cavity area. Commonly it is determined by a doctor based on his own accuracy and experience. Therefore, many methods to do heart segmentation is appearing. But, the methods are a bit slow and less precise. Thus, a system that can help the doctor to analyze it better is needed. This research will develop a system that can analyze the heart rate-motion and automatically measure heart cavity area better than the existing method. This paper proposes an improved system for cardiac segmentation using median high boost filter to increase image quality, followed by the use of an active shape model and optical flow. The segmentation of the heart rate-motion and auto measurement of the heart cavity area is expected to help the doctor to analyze the condition of the patient with better accuracy. Experimental result validated our approach.

Mobile sensing in Aedes aegypti larva detection with biological feature extraction Dia Bitari Mei Yuana; Wahjoe Tjatur Sesulihatien; Achmad Basuki; Tri Harsono; Akhmad Alimudin; Etik Ainun Rohmah
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

According to WHO, Dengue fever is the most critical and most rapidly mosquito-borne disease in the world over 50 years. Currently, the presence and detection of Aedes aegypti larvae (dengue-mosquitoes vector’s) are only quantified by human perception. In large-scale data, we need to automate the process of larvae detection and classification as much as possible. This paper introduces the new method to automate Aedes larvae. We use Culex larva for comparison. This method consists of data acquisition of recorded motion video, spatial movement patterns, and image statistical classification. The results show a significant difference between the biological movements of Aedes aegypti and Culex under the same environmental conditions. In 50 videos consisting of 25 Aedes larvae videos and 25 Culex larvae videos, the accuracy was 84%.

Co-Authors Achmad Basuki Achmad Basuki Achmad Basuki Adha Putra, Chairunas Afifah, Izza Nur Ahmad Basuki Ahmad Basuki Ali Ridho Barakbah Alimudin, Akhmad Amang Sudarsono, Amang Arna Fariza Arwita, Widya Bima Sena Bayu Dewantara Calvin Alfa Roji Dadet Pramadihanto David Fahmi Abdillah Dia Bitari Mei Yuana Edi Wahyu Widodo Farah Devi Isnanda Hamida, Silfiana Nur Hasairirr, Ashar Huda, Achmad Thorikul Idris Winarno Indah Yulia Prafitaning Tiyas Indah Yulia Prafitaning Tiyas, Indah Yulia Prafitaning Iqbal Sabilirrasyad Ira Prasetyaningrum Irene Erlyn Wina Rachmawan Irene Erlyn Wina Rachmawan Irene Erlyn Wina Rachmawan, Irene Erlyn Wina Irwansyah Irwansyah iwan Syarif Jamilatul Badriyah Kharismadhany, Ekky Kusuma, Dedy Hidayat Louis Nashih Uluwan Arif M Tafaquh Fiddin Al Islami Maretha Ruswiansari, Maretha Maysarah, Maysarah Mirza Ghulam Rifqi Mirza Ghulam Rifqi Moch. Rochmad Mochammad Choirur Roziqin Mohammad Nur Shodiq Mohammad Nur Shodiq Mohammad Nur Shodiq Mohammad Nur Shodiq, Mohammad Nur Mu'arifin, Mu'arifin Muarifin . Muarifin ., Muarifin Muarifin Muarifin Nailus Sa'ada nasution, Muhammad Yusuf Ningtiyas, Sri Kandi Atma Rachmawati, Oktavia Citra Resmi Renovita Edelani Renovita Edelani Ritonga, Yusran Efendi Riyanto Sigit Rizal Mukra Rohmah, Etik Ainun Roziqin, Mochammad Choirur Rudi Kurniawan Samsul Huda Samsul Huda Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Shafwan S. Pulungan, Ahmad Shiori Sasaki Son Kuswadi Suci Rahmawati, Suci Susanti, Puspasari Taufan Radias Miko Tessy Badriyah, Tessy Wahjoe Tjatur S. Wahjoe Tjatur Sesulihatien Widodo, Edi Wahyu Wina Rachmawan, Irene Erlyn Wiratmoko Yuwono Yasushi Kiyoki

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