The purpose of this study is to compare the results of malaria risk zone mapping using scoring method and fuzzy method against the actual data (API 2014). Variables used in determining malaria risk area are temperature, densiy of vegetation and land cover. Satellite image manipulation is performed using remote sensing technology. As a result, the average of every pixel in the district area for temperature, density of vegetation, and land cover is obtained. The result is then processed using the scoring model and fuzzy model. Accuracy tests have been conducted for 91 districts where the fuzzy model has an accuracy rate of 61.54% while the scoring model produce an accuracy of 18.68%. The test result shows that the fuzzy model tends to produce a higher grade than the actual grade. Fuzzy model produces class "High" but actually "Low" (17.85%), fuzzy model produces class "Medium" but actually "Low" (2.20%), or fuzzy models produces class "High" but actually "Medium" (14.29%). This could be caused by a form of intervention for mosquito nest eradication conducted in some districts, that is quite effective. Besides, some districts are not participating actively in detecting the number of malaria cases, hence, the actual data provided by Puskesmas tends to be lower. Intisari-Makalah ini membandingkan hasil pemetaan daerah resiko malaria oleh metode skoring dan metode fuzzy terhadap data aktual (API 2014). Variabel yang digunakan dalam menentukan daerah resiko malaria adalah suhu, kerapatan vegetasi, dan penutup lahan. Dari hasil pengolahan citra satelit dengan teknologi penginderaan jauh diperoleh ratarata setiap piksel dalam satu area kecamatan untuk suhu, kerapatan vegetasi, dan penutup lahan yang kemudian diolah dengan model skoring dan model fuzzy. Telah dilakukan uji akurasi untuk 100 kecamatan. Hasilnya menunjukkan bahwa model fuzzy memiliki tingkat akurasi 61,54%, sedangkan model skoring menghasilkan akurasi sebesar 18,68%. Dari hasil pengujian terlihat bahwa model fuzzy cenderung menghasilkan kelas yang lebih tinggi dibandingkan kelas pada data aktualnya, misalnya model fuzzy menghasilkan kelas "Tinggi" namun aktualnya "Rendah" (17,58%), model fuzzy menghasilkan kelas "Sedang" tapi aktualnya "Rendah" (2,20%), ataupun model fuzzy menghasilkan kelas "Tinggi" tapi aktualnya "Sedang" (14,29%). Hal ini bisa disebabkan oleh bentuk intervensi untuk pemberantasan sarang nyamuk (PSN) yang dilakukan di beberapa kabupaten sudah cukup efektif seperti kelambunisasi dan penyemprotan. Selain itu untuk beberapa kabupaten juga masih pasif dalam mendeteksi jumlah kasus malaria sehingga data aktual yang disediakan puskesmas cenderung rendah.

Perencanaan pengadaan obat yang baik pada puskesmas diperlukan guna mendukung pelayanan kesehatan yang diberikan oleh puskesmas, dalam mengatasi masalah perencanaan permintaan obat agar sesuai dengan kebutuhan yang ada, penulis menggunakan metode fuzzy mamdani dalam optimasi permintaan obat, ada beberapa tahapan dalam fuzzy yaitu pembentukan himpunan fuzzy, aplikasi fungsi implikasi (aturan), komposisi aturan, penegasan (defuzzy) menggunakan metode MOM (Mean of Maksimum). Parameter yang digunakan ialah stok awal, penerimaan, persediaan, pemakaian, stok akhir dan permintaan. Perhitungan sistem dilakukan dengan menggunakan data obat selama 2 tahun, pengujian dilakukan sebanyak 1 tahun untuk membandingkan hasil permintaan puskesmas dan permintaan sistem. Dari hasil pengujian total permintaan sistem lebih kecil dibandingkan total permintaan Puskesmas, maka didapat optimasi sistem sebesar 7,623% untuk 3 data obat sehingga dapat meningkatkan efisiensi dana anggaran sebesar Rp. 3.168.223, sehingga dapat disimpulkan bahwa  metode Fuzzy Mamdani merupakan metode yang memberikan solusi yang optimal.

The purpose of this paper is to implement Fuzzy Simple Additive Weighting (F-SAW) method in determination of water quality for the development of Sangkuriang catfish, using six criteria, i.e. temperature, pH, DO, brightness, plankton, and odor, using 15 alternatives (pool). There are two test scenarios that have been done in this research to obtain system accuracy level, which are: 1) result of system compared with assessment from an expert; and 2) the results of the system compared with assumption of the study that the alternative with the best data should be ranked first and the alternative with the worst data should be rated last. The results obtained from the first scenario shows the level of accuracy is 87%, while the results obtained from the second scenario shows 100% accuracy rate. Intisari-Tujuan dari makalah ini adalah untuk mengimplementasikan metode Fuzzy Simple Additive Weighting (F-SAW) dalam penentuan kualitas air untuk perkembangan ikan lele Sangkuriang, dengan menggunakan enam kriteria, yaitu suhu, pH, DO, kecerahan, kadar plankton, dan bau, dengan menggunakan 15 alternatif (kolam). Terdapat dua skenario pengujian yang telah dilakukan dalam makalah ini untuk memperoleh tingkat akurasi sistem, yaitu: 1) hasil sistem dibandingkan dengan penilaian dari seorang pakar; dan 2) hasil sistem dibandingkan dengan asumsi bahwa alternatif dengan data terbaik harus mendapat peringkat pertama dan alternatif dengan data terburuk harus mendapat peringkat terakhir. Hasil yang diperoleh dari skenario pertama menunjukkan tingkat akurasi mencapai 87%, sedangkan hasil yang diperoleh dari skenario kedua menunjukan tingkat akurasi mencapai 100%.

The problem of waste has not been handled well, especially in cities, including the city of Kupang. Placing the right location of the trash can be one of solutions to the waste problem. The purpose of this study is to combine decision support systems and geographic information systems to determine the location of TPS locations. There are two stages of analysis, the Brown Gibson method to determine which alternative is best for construction temporary landfill and the second analysis using the GIS approach to determine suitable point. The alternative is Neigborhoods (RT) in the Nefonaek, Kupang. The results showed that in the RT22, RT17, and RT18 which is outside the buffer area were selected as the best candidates for the new location of TPS. The system is tested in two ways, testing the blackbox using questionnaire on two respondents, and the accuracy that compares the results of the system and the results of expert. From the results of the blackbox testing, the percentage values for each GUI, Function, and information obtained were 94%, 92.5%, and 97.5%. And from Accuracy testing, obtained the value of accuracy on the first staff is 86.67% and for the second staff the accuracy value is 80%. From the two staffs obtained an average accuracy of 83.34%.

Smart tourism is one component of smart village or smart city that aims to improve service quality of tourism. Smart tourism is expected to provide a better tourist experience for tourists by utilizing information technology. The purpose of this study is to develop for a culinary recommendation application to support smart tourism. Simple Multi-Attribute Rating Technique (SMART) was employed to develop the application which is based on geographic information systems. The application gives culinary places’ recommendation by considering several attributes, such as facilities, prices, menu variations, and distances (most recommender systems of culinary places do not use distance as the main attribute). In addition, this recommendation system is also one of the solutions in implementing green computing, namely “telecommuting” by reducing transportation emissions. Two test scenarios, namely user acceptance, and accuracy testing have been carried out. The user acceptance testing yielded 75.2% which indicated that the application was good, while the result of the accuracy testing was 83.33% which was considered high.