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Muhammad Iqbal Habibie
Agency for the Assessment and Application of Technology (BPPT)
Computer Science & IT

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WEB SCRAPING OF DISEASE INFORMATION FROM SOCIAL MEDIA TWITTER Muhammad Iqbal Habibie; Taufiq Widiaputra; Yulianingsani Yulianingsani
Jurnal Teknoinfo Vol 16, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i2.1871

Abstract

Environmental degradation caused by land conversion, trash (both domestic and industrial), and natural catastrophes is all variables that contribute to the establishment of disease susceptibility. Experts throughout the world suggest “ONE HEALTH” as a strategy for dealing with the threat of zoonoses. The One Health concept is a worldwide strategy to expand interdisciplinary collaboration and communication in all aspects of health care for humans, animals, and the environment. To overcome this disease of zoonoses, we developed a system of information zoonoses and Emerging Infectious Disease (SIZE). In this system of SIZE, we gather the disease information from social media. The disease information was collected from Twitter are Demam Berdarah Dengue (DBD), malaria disease, Antraks Disease, Canine Madness (Anjing Gila), Bird Flu (flu burung), and Ebola Disease. Twitter is a social media platform that has become a constant resource developing for data collectors. To perform this task to get the data of disease information, related tweets and Twitter user details the data collection using web scraping. Data Collection from Twitter was carried out by applying web scraping technology using python language. The scraping experiment from twitter in this study has succeeded in retrieving disease information from 2015-2020 using an advanced tool for Twitter scrapping called Twint using the python script. As the results lately have been increased number of tweets of diseases from canine madness (anjing gila) 34477, followed by Malaria Disease (28046) and Demam Berdarah Dengue (DBD) 11950 in 2020.
A MULTICRITERIA INDEX USING NEURAL NETWORK TO EVALUATE THE POTENTIAL LANDS OF MAIZE Muhammad Iqbal Habibie; Nety Nurda
Jurnal Teknoinfo Vol 16, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i2.1873

Abstract

The criteria for planting maize should be consistent with sensible and ecological criteria to determine the potential lands. However, there is still a lack of proven methodology for this evaluation. The purpose of this analysis was to determine the parameters that affect the multi-criteria decision of maize, with the aim of a new method on the land suitability analysis. The land suitability analysis proposed was based on GIS-analysis and management parameters such as distance from roads, rivers, slope, LULC, elevation, soil type, NDVI, SAVI, rainfall, and temperature. We have found a sample of 4590 maize in Tuban, East Java, Indonesia. Based on the above criteria, maize has been classified into four groups according to FAO. Moreover, we analyzed was done using Neural Network. Results showed that the integrated AHP with Neural Network to evaluate the lands inferred that 66.7 percent of the study area was classified as highly suitable, 30.2 percent were moderately suitable, and 3 percent were marginally suitable for Maize Cultivation in Tuban Regency. The approach presented in this analysis can be extended in this analysis can be extended to other maize areas also other crops as a decision-making system.
THE APPLICATION OF MACHINE LEARNING USING GOOGLE EARTH ENGINE FOR REMOTE SENSING ANALYSIS Muhammad Iqbal Habibie
Jurnal Teknoinfo Vol 16, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v16i2.1872

Abstract

The spatial dimensions and temporal resolutions of the change detection analyses have been limited by traditional methodologies (i.e., desktop computing, open source). For decades, Remote Sensing (RS) have been collected large amounts of data, which are difficult to manage and analyzed using standard software packages and desktop computing resources. For this, Google developed the Google Earth Engine (GEE) cloud computing to successfully meet the issues of large data analysis. GEE is a cloud-based computing as a planetary-scale geospatial platform for Earth science data and analysis, allows these spatiotemporal constraints to be lifted and handle massive amounts of geodata over wide areas and to monitor the environment over long periods of time. We summarize the GEE data catalog’s big geospatial data such as Climate and weather for surface temperature, climate, atmospheric and weather. It also contains Imagery like Landsat, Sentinel, MODIS and High-resolution Imagery and Geophysical information contains of terrain, land cover, cropland, and other geophysical data. Furthermore, supervised machine and unsupervised machine algorithms   were used for several applications for Land Use Land Cover (LULC), hydrology, urban planning, natural disasters, and climate assessments. The research describes the utilization to resolve the big data using machine learning algorithm.
Co-Authors Nety Nurda Taufiq Widiaputra Yulianingsani Yulianingsani