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All Journal Indonesian Journal of Computing and Modeling Eduvest - Journal of Universal Studies
Ravensca Matatula
Program Studi Magister Sistem Informasi Universitas Kristen Satya Wacana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Education Health Professions Mathematics Neuroscience Social Sciences

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Analisis Perbandingan Area Kebakaran Padang Rumput Berbasis Citra Landsat-8 Pada Sumba Tengah Menggunakan Metode Unsupervised Classification Antar Maramba Jawa; Ravensca Matatula; Stanny Dewanty Rehatta; Sri Yulianto Joko Prasetyo; Charitas Fibriani
Indonesian Journal of Computing and Modeling Vol 3 No 1 (2020)
Publisher : Pusat Studi Sistem Informasi dan Pemodelan Mitigasi Tropika

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Abstract

Data statistik tahun 2019 menunjukan hingga saat ini luas padang rumput di kawasan Sumba Tengah cenderung mengalami penurunan dikarenakan seringnya terjadi kebakaran pada lokasi tersebut. Data ini menjadi pendorong akan kebutuhan informasi yang tepat dan cepat terkait tata letak dan perubahan tata letak padang rumput untuk tetap menjaga ketahanan lingkungan sekitar. Teknologi penginderaan jarak jauh telah menjadi tool yang baik dalam pemecahan masalah ini. Melalui klasifikasi citra satelit penginderaan jarak jauh, perubahan lokasi kebakaran dapat dianalisis dengan baik. Berdasarkan hal tersebut penelitian ini akan menganalisis perubahan padang rumput pasca kebakaran pada daerah Sumba Tengah khususnya sebagai wilayah penelitian pada index vegetasi citra satelit lansat 8 dengan memakai Unsupervised Classification. Tujuan dari penelitian ini adalah melakukan klasifikasi untuk memperkirakan luas dan sebaran perubahan padang rumput di Sumba Tengah.Hasil penelitian Kabupaten Sumba Tengah November 2019 memiliki lebih banyak lahan hijau (vegetasi tinggi) dibandingkan dengan nilai NDVI pada citra landsat Kabupaten Sumba Tengah Maret 2020. Sedangkan presentase NDVI November 2019 dan Maret 2020 penurunan yang cukup besar terjadi pada lahan hijau atau padang rumput dengan nilai vegetasi 0.492 ke 0.415 Analisis menunjukkan adanya perbedaan nilai vegetasi pada klasifikasi lahan hijau atau padang rumput menunjukkan terjadinya kebakaran, sehingga naiknya nilai vegetasi klasifikasi lahan kering.
Covid-19 Sentiment Analysis Using Convolutional Neural Network / Reccurent Neural Network Method Ravensca Matatula; Danny Manongga; Hendry Hendry
Eduvest - Journal of Universal Studies Vol. 2 No. 8 (2022): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1819.418 KB) | DOI: 10.59188/eduvest.v2i8.514

Abstract

Social media is a very important tool in this modern era , one of which is namely twitter. Twitter allows user for give opinion / opinion to various issues and topics hot / viral trending . Trending on twitter is so so fast in the process of spreading so that Twitter becomes a medium of information that often become a media issue conspiracy . Covid-19 is a moderate epidemic / disease _ experienced the whole world when this . Issues circulating in the population , they believe that Covid-19 is a real pandemic and a conspiracy , issue this make population confused differentiate Among second issue that . Because of that required a fast and accurate analysis _ for produce valid results , that Covid-19 a real thing _ or conspiracy seen from opinion population and corner views written on Twitter. CNN/RNN or combined from RNN(LSTM) and CNN methods are method used _ for classify opinion population about Covid-19 issues . Study this also done with compare is correct RNN/CNN accuracy same like deep RNN even more fast for in the process . Research results state that accuracy from combined RNN/CNN no different remote , even RNN/CNN in the process more fast than deep RNNs. Research results about opinion / opinion residents on twitter who believe about Covid-19 is conspiracy more low than residents who have confidence about Covid-19 is something the real thing . Percentage classification opinion / opinion from sentiment positive by 63.15% and opinion / opinion sentiment negative by 28.60%, this is results calculation use RNN/CNN method , with accuracy reached 58%. Accuracy from method used _ make Covid-19 issues that exist in the population no Becomes hoax news so population more alert against the ongoing Covid-19 pandemic happen.
Co-Authors Antar Maramba Jawa Charitas Fibriani Danny Manongga Hendry Sri Yulianto Joko Prasetyo Stanny Dewanty Rehatta