An extended ID3 decision tree algorithm for spatial data

Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polygon features. The result is a spatial decision tree with 138 leaves and the accuracy is 74.72%

An extended ID3 decision tree algorithm for spatial data

Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polygon features. The result is a spatial decision tree with 138 leaves and the accuracy is 74.72%

A Survey on Improved Hybrid Classification Methods in Data Mining

Data mining is powerful concept with great potential to predict future trends and behaviour. It refers to the extraction of hidden knowledge from large data set using various techniques. But as the amount of data generated is increasing exponentially, harnessing such voluminous data has become a major challenge. To address this problem there is proposed various improved classification methods into Data mining. All this methods use hybrid algorithm to improve classification in data mining. Here hybrid algorithm is nothing but logical combination of multiple pre-existing techniques to enhance performance and provide better results

Burn Area Processing to Generate False Alarm Data for Hotspot Prediction Models

Developing hotspot prediction models using decision tree algorithms require target classes to which objects in a dataset are classified.  In modeling hotspots occurrence, target classes are the true class representing hotspots occurrence and the false class indicating non hotspots occurrence.  This paper presents the results of satellite image processing in order to determine the radius of a hotspot such that random points are generated outside a hotspot buffer as false alarm data.  Clustering and majority filtering were performed on the Landsat TM image to extract burn scars in the study area i.e. Rokan Hilir, Riau Province Indonesia.  Calculation on burn areas and FIRMS MODIS fire/hotspots in 2006 results the radius of a hotspot 0.90737 km.  Therefore, non-hotspots were randomly generated in areas that are located 0.90737 km away from a hotspot. Three decision tree algorithms i.e. ID3, C4.5 and extended spatial ID3 have been applied on a dataset containing 235 objects that have the true class and 326 objects that have the false class. The results are decision trees for modeling hotspots occurrence which have the accuracy of 49.02% for the ID3 decision tree, 65.24% for the C4.5 decision tree, and 71.66% for the extended spatial ID3 decision tree

Preparing for Future Forest Fires: Emerging Technologies and Innovations

Forest fires are part of the global ecosystems occurring for a long time in earth history.  These forest fires are part of the processes which establish the ecosystems and directly influence plant species composition within the ecosystems. However, the anthropogenic effect has changed this relationship causing an increasing number of forest fires Human activities have also changed world climate and future climate is expected to increase in temperature with dire consequences on the earth environment. These changes will profoundly impact on the earth’s socio-economic and human well-being. One of the effects of higher global temperature is increasing forest fires occurrences with stronger intensities.  There is a need to develop innovation and new technologies to manage these future fires. This paper aims to review various innovations and new technologies that can be used for the whole spectrum of forest fire management, from forest fire prediction to forest restoration of burnt areas. Emerging technologies such as geospatial technologies, the Internet of Things (IoT), Artificial Intelligence, 5G & enhanced connectivity, the Internet of Behaviors (IoB), virtual and augmented reality, and robotics are discussed and potential applications to forest fire management are discussed. Adaptation of these technologies is vital in the effective management of future forest fires. Key words: Climate Change, Future Fires, InnovationsKebakaran hutan merupakan bagian dari ekosistem global yang terjadi sejak lama dalam sejarah bumi. Kebakaran hutan ini merupakan bagian dari proses yang membentuk ekosistem dan secara langsung mempengaruhi komposisi spesies tumbuhan di dalam ekosistem. Namun, efek antropogenik telah mengubah hubungan ini yang menyebabkan peningkatan jumlah kebakaran hutan Aktivitas manusia juga telah mengubah iklim dunia dan iklim di masa depan diperkirakan akan meningkatkan suhu dengan konsekuensi yang mengerikan pada lingkungan bumi. Perubahan ini akan sangat berdampak pada sosial ekonomi bumi dan kesejahteraan manusia. Salah satu dampak dari peningkatan suhu global adalah meningkatnya kejadian kebakaran hutan dengan intensitas yang lebih kuat. Ada kebutuhan untuk mengembangkan inovasi dan teknologi baru untuk mengelola kebakaran di masa depan ini. Tulisan ini bertujuan untuk mengkaji berbagai inovasi dan teknologi baru yang dapat digunakan untuk seluruh spektrum penanggulangan kebakaran hutan, mulai dari prediksi kebakaran hutan hingga restorasi hutan pada kawasan yang terbakar. Teknologi yang muncul seperti teknologi geospasial, Internet of Things (IoT), Artificial Intelligence, 5G & konektivitas yang ditingkatkan, Internet of Behaviors (IoB), virtual dan augmented reality, dan robotika dibahas dan aplikasi potensial untuk manajemen kebakaran hutan dibahas. Adaptasi teknologi ini sangat penting dalam pengelolaan kebakaran hutan yang efektif di masa depan. Kata kunci: Perubahan Iklim, Kebakaran di Masa Depan, Inovas

Analisis Kesesuaian Lahan Padi Gogo Berbasis Sifat Tanah dan Cuaca Menggunakan ID3 Spasial(Land Suitability Analysis for Upland Rice based on Soil and Weather Characteristics using Spatial ID3)

Penurunan minat generasi muda terhadap sektor pertanian menjadi permasalahan sekaligus tantangan bagi Indonesia dalam hal kedaulatan pangan, dimana kebutuhan suplai pangan justru akan terus meningkat setiap tahunnya. Pemerintah Indonesia telah menetapkan rencana strategis berupa pengembangan sembilan komoditas utama,  salah  satunya adalah  padi yang merupakan bahan pangan utama masyarakat Indonesia. Pengembangan dapat dilakukan dengan menerapkan kemajuan teknologi untuk keefektifan produksi pangan, dengan tujuan utama adalah ekstensifikasi lahan pertanian. Arahan kesesuaian lahan berupa karakteristik lahan dan cuaca yang sesuai sangat penting dalam menunjang hal tersebut, yang dapat diperoleh melalui evaluasi kesesuaian lahan. Penelitian ini melakukan kajian analisis berupa evaluasi kesesuaian lahan padi gogo menggunakan algoritme ID3 spasial berdasarkan sifat tanah dan cuaca. Algoritme ID3 spasial merupakan pengembangan dari algoritme ID3 konvensional untuk menangani klasifikasi data yang melibatkan faktor spasial. Dataset terbagi menjadi dua kategori, yakni layer penjelas merepresentasikan delapan sifat tanah (elevasi, drainase, relief, kejenuhan basa, kapasitas tukar kation, tekstur tanah, kemasaman tanah, dan kedalaman mineral tanah) dan dua data cuaca (curah hujan dan temperatur), serta layer target merepresentasikan kesesuaian lahan padi gogo pada area studi, Kabupaten Bogor, Provinsi Jawa Barat, Indonesia. Analisis kesesuaian lahan menghasilkan dua model yang memperoleh simpul akar (relief) dan akurasi yang sama (87.28%), dengan jumlah aturan yang berbeda, yakni 144 oleh model A dan 69 oleh model B. Penambahan faktor cuaca merupakan hal tepat yang dibuktikan oleh keterlibatannya pada dua model keputusan spasial, sehingga dapat memberikan informasi curah hujan dan temperatur yang dibutuhkan dalam pengoptimalan pertanian padi gogo