Voice assistant technology is growing rapidly and its use has begun to spread to daily use. However, voice assistant usages are still limited to standard conversation languages. Meanwhile, Indonesian people are accustomed to informal language in daily conversation. This research gives solution to overcome the problem of voice assistants with informal words or words that will not be found in formal word dictionary. We propose text normalization using Levenshtein distance. Test result shows that normalization using Levenshtein distance outperform the normalization using Longest Common Subsequence (LCS) distance with accuracy difference of 8.34%. Intisari-Teknologi voice assistant (asisten suara) mulai berkembang pesat saat ini. Penggunaannya sudah mulai merambah kepada penggunaan sehari-hari. Namun, voice assistant masih terbatas pada penggunaan bahasa percakapan yang baku. Sementara itu, masyarakat Indonesia terbiasa mengucapkan bahasa tidak baku dalam percakapan sehari-hari. Makalah ini mencakup solusi untuk mengatasi permasalahan voice assistant dengan kata yang tidak baku atau tidak termasuk dalam kamus kata baku. Pendekatan yang digunakan sebagai solusi adalah melakukan normalisasi teks menggunakan jarak Levenshtein. Hasil pengujian menunjukkan bahwa normalisasi dengan jarak Levenshtein mengungguli normalisasi dengan jarak Longest Common Subsequence (LCS) dengan selisih akurasi sebesar 8,34%.
In disease risk spatial analysis, many researchers especially in Indonesia are still modelling population density as the ratio of total population to administrative area extent. This model oversimplifies the problem, because it covers large uninhabited areas, while the model should focus on inhabited areas. This study uses settlement mapping against satellite imagery to focus the model and calculate settlement area extent. As far as our search goes, we did not find any specific studies comparing the use of settlement mapping with administrative area to model population density in computing its correlation to a disease case rate. This study investigates the comparison of both models using data on Tuberculosis (TB) case rate in Central and East Java Indonesia. Our study shows that using administrative area density the Spearman's ρ was considered as "Fair" (0.566, p < 0.01) and using settlement density was "Moderately Strong" (0.673, p < 0.01). The difference is significant according to Hotelling's t test. By this result we are encouraging researchers to use settlement mapping to improve population density modelling in disease risk spatial analysis. Resources used by and resulting from this work are publicly available at https://github.com/mirzaalimm/PopulationDensityVsDisease.
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