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Here, we describe our work in developing Indonesian Mind Map Generator that employs several Indonesian natural language understanding tools as its main engine. The Indonesian Mind Map Generator 1 aims to help the user in easily making a Mind Map object. The system consists of several Indonesian natural language understanding tools such as Indonesian POS Tagger, Indonesian Syntactic Parser, and Indonesian Semantic Analyzer. The methods used for developing each of Indonesian natural language understanding tools are devised to such an extend that they are enable to alleviate the low availability of Indonesian language resources. For Indonesian POS Tagger, we employed HMM and subsequently enhanced the result by using affix tree. As for the Indonesian Syntactic Parser, we compared the performance of CYK and Earley parser, which are known as common dynamic algorithms in PCFG. The Indonesian Semantic Analyzer consists of several components such as lexical semantic attachment, reference resolution, and Semantic Analyzer itself that transforms the parse tree result into first order logic representation. In our work, instead of using a rich resource on semantic information for each vocabulary, we defined several rules for the lexical semantic attachment based on POS Tags and certain words. Finally, to develop the Mind Map generator, we used the radial drawing method to visualize the first order logic representation and we also built a Mind Map editor to allow a user in modifying the Mind Map result. To evaluate the result, we conducted the experiments for each component mentioned previously. The POS Tagger accuracy achieved 96.5%, the Syntactic Parser achieved accuracy of 47.22%, and the Semantic Analyzer achieved accuracy of 62.5%. The final result of Mind Map object was evaluated by 5 respondents. The results of evaluationshowed that, for the simple sentence, the Mind Map object can be easily understood.

Padatnya kawasan perkotaan akibat pertumbuhan penduduk menyebabkan timbulnya permukiman kumuh. Kondisi ini sering dijumpai pada permukiman yang berada di sempadan sungai Provinsi DKI Jakarta, khususnya Kampung Manggarai. Keberadaan lahan permukiman ini berdampak pada perubahan proporsi tutupan lahan, sehingga mengurangi area resapan air. Studi ini mengaplikasikan konsep sponge city yang telah disesuaikan dengan kebutuhan wilayah studi. Metode pengumpulan data dilakukan dengan wawancara, observasi, dan perolehan data sekunder. Metode analisis dilakukan dengan membandingkan kriteria ketiga elemen rencana, yaitu (a) Green Neighborhood (b) Permeable Road (c) Utility terhadap kondisi eksisting menggunakan metode gap analysis untuk menghasilkan rekomendasi. Studi ini dilakukan dengan merancang sebuah kawasan kumuh untuk menyelesaikan persoalan banjir. Beberapa rekomendasi yang dapat dilakukan diantaranya: (1) Penambahan area resapan air; (2) Penataan daerah terbangun dengan penyediaan bangunan hunian vertikal; (3) Pengaplikasian konstruksi jalan dengan material ramah lingkungan dan memiliki daya resap air yang tinggi seperti permeable paving block; (4) Penyediaan fasilitas manajemen limpasan air hujan seperti sumur resapan, bioswales, dan kolam retensi.

We proposed the usage of dependency tree information to increase the accuracy of Indonesian factoid question answering. We employed MSTParser and Universal Dependency corpus to build the Indonesian dependency parser. The dependency tree information as the result of the Indonesian dependency parse is used in the answer finder component of Indonesian factoid question answering system. Here, we used dependency tree information in two ways: 1) as one of the features in machine learning based answer finder (classifying each term in the retrieved passage as part of a correct answer or not); 2) as an additional heuristic rule after conducting the machine learning technique. For the machine learning technique, we combined word based calculation, phrase based calculation and similarity dependency relation based calculation as the complete features. Using 203 data, we were able to enhance the accuracy for the Indonesian factoid QA system compared to related work by only using the phrase information. The best accuracy was 84.34% for the correct answer classification and the best MRR was 0.954.