This study aims to compare the results of the accuracy and speed of the system in diagnosing skin diseases using the case based reasoning (CBR) method with the indexing method and without using indexing. Self-organizing maps (SOM) are used as an indexing method and the process of finding similarity values uses the nearest neighbor method. Testing is done with two scenarios. The first scenario uses CBR without indexing self-organizing maps, the second scenario uses CBR with indexing self-organizing maps. The accuracy of the diagnosis of skin diseases at a threshold ≥80 for CBR without indexing self-organizing maps is 93.46% with an average retrieve time of 0.469 seconds while CBR testing using SOM indexing is 92.52% with an average retrieve time of 0.155 seconds. The results of comparison of CBR methods without using show higher results than using SOM indexing, but the process of retrieving CBR using SOM is faster than not using indexing
Hotspot is one of indications for forest and land fires. Analysis of hotspot data needs to be done as an early warning activity to prevent the occurrence of forest and land fires. The previous study analyzed hotspot clusters using the incremental spatio-temporal density-based clustering (ST-DBSCAN) algorithm. Hotspots in a cluster are considered as strong indicator for forest and land fires. However, clustering of hotspots is implemented on the command line interface. Through this interface, users are required to execute manually the commands to perform the clustering task on the dataset. The disadvantage of command line interface is that the commands have to be typed precisely and mistypes will cause errors. Therefore, this study aims to build a web-based application for clustering hotspot data using the incremental ST-DBSCAN module. This study uses hotspot dataset in the period 2014 to 2017. The application was developed using R programming language and Shiny framework. The method of Adaptive Software Development (ASD) was adopted in this study. The main functions of the application are incremental clustering on hotspot data and visualization of clustering result. The testing using the black box approach show that all features of the application work properly. Based on the result of usability assessment, the user satisfaction level reach 78.45% meaning that the application is quite easy to be used.
Pepaya merupakan salah satu jenis buah kaya nutrisi yang banyak memberikan manfaat bagi kesehatan. Warna memungkinkan sebuah objek dapat dikenali dan diidentifikasi dengan baik. Sebelumnya telah banyak penelitian yang serupa. Namun dari beberapa penelitian sebelumnya, nilai keakuratan dalam klasifikasinya masih kurang akurat yang dikarenakan menggunakan proses dan metode yang kurang tepat. Sehingga diperlukan sistem pengolahan citra digital menggunakan kecerdasan buatan yang dapat mengklasifikasi tingkat kematangan pada buah papaya dengan menggunakan metode dan proses yang tepat. Pada penelitian ini, kami mengusulkan Klasifikasi Tingkat Kematangan Buah Pepaya Berdasarkan Fitur Warna Menggunakan Jaringan Syaraf Tiruan. Dengan menggunakan 90 dataset citra pepaya RGB. Proses dan metode yang diusulkan yaitu, akuisisi citra, tahap preprocessing, tahap segmentasi dengan metode otsu, operasi morfologi, kemudian tahap klasifikasi dengan jaringan saraf tiruan. Sehingga pada pengujian dan pelatihan berdasarkan klasifikasi menghasilkan nilai akurasi sebesar 100%. Diharapkan sistem ini dapat membantu pekebun dalam mengklasifikasi tingkat kematangan buah pepaya dan terciptanya pengembangan teknologi budidaya dalam peningkatan produktivitas pepaya.
Indonesia is a large maritime country, and most of its territorial waters are larger than its land area. Due to the vastness of the oceans, the large number of large and small islands makes Indonesia a potential area for marine cultivation. In general, the existing data based on the Central Statistics Agency (BPS) of Marine Aquaculture Production for each province in Indonesia only applies to production data which only produces detailed data on total marine aquaculture production in tonnes per year, and takes a long time. To classify very large data, a method is needed that can use the K-Means algorithm to classify the highest, middle, and lowest opportunities in the field of marine aquaculture from 2004 to 2018. The results implemented in python consisted of 26 provinces in klaster 1 (C1), 3 provinces in klaster 2 (C2), and 5 provinces in klaster 3 (C3).
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