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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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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.

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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