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All Journal Jurnal Mantik Interdisciplinary Social Studies
Minggu, Emanuel
Universitas Amikom Yogyakarta
Computer Science & IT Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences

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Utilization of Information Technology for Tourism Development of Lake Kelimutu, Ende Regency, East Nusa Tenggara With a Virtual Tour Based on Mobile Web Emanuel Minggu; Bambang Soedijono; Dhani Ariatmanto
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

The development of technology that is increasingly rapid day by day, gives rise to many new innovations from technology. One of the technological development innovations is virtual tours which are starting to be widely used, for example in some applications to introduce a location. However, the use of virtual tour applications as a medium for promoting tourism in Indonesia is still very small. Virtual tour Kelimutu lake tourism in Ende regency, East Nusa Tenggara, was created to be able to visually display information from the natural attractions of lake kelimutu The development methodology used in this study is the Multimedia Development Life Cycle (MDLC) methodology which is a multimedia software design method that emphasizes the 6 stages of multimedia development with this virtual tour users can see the state of 3600 natural lake attractions kelimutu made with immersive photography techniques. By presenting information in the form of a 3600 panoramic image, it makes it easier for users to visually display information from the tourist attraction
Classification of Hotspots Causing Forest and Land Fires Using the Naive Bayes Algorithm Zainul, Muchamad; Minggu, Emanuel
Interdisciplinary Social Studies Vol. 1 No. 5 (2022): Special Issue
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/iss.v1i5.62

Abstract

Forest and land fires that occurred in Indonesia have caused many losses for the community. Forest fires generally occur in August and September, coinciding with the dry season in most parts of Indonesia. One indicator of the occurrence of forest fires is hotspots. This study uses one of the data mining techniques, namely classifying hotspots in Riau Province. This study used a dataset of forest fires in Pelalawan Regency from 2015 to 2019 using the Naïve Bayes algorithm. The hot spots to be analyzed consist of temperature, humidity, rainfall, wind speed, and class. The highest accuracy of the dataset of forest and land fires in 2019 is 96.95%. The classification method using the Naïve Bayes algorithm can be used to predict the emergence of hotspots in the future so that they can take preventive measures before forest and land fires occur.
Co-Authors Bambang Soedijono Dhani Ariatmanto Zainul, Muchamad