Population growths are increasing the demand for water. This can cause an imbalance of water availability to fulfill domestic water demand. Therefore, it is necessary to analyze the water availability and water demand so that water resources are maintained and can fulfill the community needs in the present and future. This research aims to analyze the water availability and domestic water demand in Semarang Regency. The methods consist of calculating monthly rainfall, calculating water balance using F. J Mock model, calculating the water demand regarding to the water availability, and calculating how much water can be obtained from rain harvesting. The results show the highest annual water availability in Semarang Regency is Pringapus district 48,559,764.55 m3/year, while the the lowest is Kaliwungu district 17,352,024.13 m3/year. The highest domestic water demand are in West Ungaran district, East Ungaran district, and Bergas district, while the lowest are in Bancak and Kaliwungu district. The total water demand is 6% of the total water availability, means there is no water deficit in Semarang Regency. The total volume of rainwater harvested is 120,163,412 m3/year which means the volume of rainwater harvested in one year can fulfill the domestic water demand in Semarang Regency.
Automatic short answer scoring methods have been developed with various algorithms over the decades. In the Indonesian language, the string-based similarity is more commonly used. This method is difficult to accurately measure the similarity of two sentences with significantly different word lengths. This problem has been handled by the Geometric Average Normalized-Longest Common Subsequence (GAN-LCS) method by eliminating non-contributive words utilizing the Longest Common Subsequence method. However, students’ answers may vary not only in character length but also in the words they choose. For instance, some students tend only to write the abbreviations or acronyms of the phrase instead of writing meaningful words. As a result, it will reduce the intersection character between the reference answer and the student answer. Moreover, it can change the sentence structure even though it has the same meaning by definition. Therefore, this study aims to improve GAN-LCS method performance by incorporating the abbreviation checker to handle the abbreviations or acronyms found in the reference answer or student answer. The dataset used in this study consisted of 10 questions with 1 reference answer for each question and 585 student answers. The experimental results show an improvement in GAN-LCS performance that could run 34.43% faster. Meanwhile, the Root Mean Square Error (RSME) value became lower by 7.65% and the correlation value was increased by 8%. Looking forward, future studies may continue to investigate a method for automatically generate the abbreviations dictionary.
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