Non-Cash Food Aid is a monthly social aid program from government for beneficiary families by electronic account mechanism. This electronic account later has function for this family to buy food materials in grocery stores or e-stores which cooperate with bank. The village heads make proposal about the poor family in their area as beneficiary family for this Non-Cash Food Aid after having observation. However, there is problem for deciding and filtering the data of citizens whether they deserve or not to accept this social aid. Somehow, few of middle class people are included in the data of the poor as beneficiary family. In this case, there is no method to decide the beneficiary of Non-Cash Food Aid. Therefore, the aim of this research is the methods application of Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Based on the research result, there is decision support system for Non-Cash Food Aid data by using AHP and SAW method. Based on test result of old and new ranking system with 10 data sampling, there are 9 different data in this ranking system. It happened because the old system calculation only used 1 criterion, monthly income. Meanwhile, new ranking system used all criterions having value and quality

The level of rice productivity is influenced by several inhibiting factors, for example disease attack in rice plants. The slow and inappropriate treatment of rice plant can make the crop failure so that rice production and farmers' income decrease. The symptoms of rice disease are difficult to distinguish, especially in severe symptoms. Collaboration with other fields, especially computer science, is needed to classify diseases automatically so that the farmers can take action for plant treatment and the spread of disease can be controlled quickly. The classification of diseases based on images requires the best features/characteristics so that the disease can be classified. In this research, Deep Learning method, especially Convolutional Neural Network with EfficientNet B3 architecture, can extract features very well. In this research, the classification of brown spot and bacterial leaf disease by applying EfficientNet B3 with transfer learning reached 79.53% accuracy and 0.012 loss/error.

Telepon pintar sudah menjadi gaya hidup masyarakat saat ini. Selain untuk komunikasi, telepon pintar juga digunakan sebagai alat pencari informasi yang cepat serta bebas, tidak bergantung tempat dan waktu. Pencarian informasi berdasarkan posisi pengguna. Pencarian informasi tempat yang berdasarkan posisi dan waktu dapat dilakukan dengan menggunakan location based service. Permasalahan yang timbul ialah akurasi pembacaan lokasi dari alat yang ada dalam telepon seluler tersebut, yaitu global positioning system (GPS). Pada saat pembacaan, pergeseran titik posisi pengguna dapat terjadi sehingga posisi yang diperoleh tidak berada tepat pada posisi aktual di peta digital. Penelitian ini mengukur akurasi pembacaan dari GPS yang berada dalam perangkat telepon pintar Android. Data diambil berdasarkan studi kasus pada posisi lokasi sekolah SMA Negeri yang ada di Palembang. Pengambilan data dengan membuat aplikasi yang dipasang dalam perangkat telepon pintar yang secara langsung dapat mengambil, mengolah, sampai menampilkan data posisi pada peta digital di telepon pintar pengguna. Setelah data didapat, pengolahan data dengan rumus Haversine dilakukan untuk mengetahui seberapa besar pergeseran lokasi sebagai nilai akurasi alat. Pergeseran lokasi dengan membandingkan hasil pembacaan alat GPS yang berada dalam perangkat Android terhadap GPS komersial (GPS Garmin). Hasil akurasi pergeseran pembacaan yang diperoleh adalah sebesar 10.9489 meter.<br /><br />Kata Kunci: akurasi, Android, global positioning system, location based service

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Thesis report entitled “Application of AHP and TOPSIS Method as Decision Support System in Determining Position Promotion for Teachers of Vocational High School in Surakarta” is based on research by the author carried out located in Jl. Dr. Wahidin 33 Surakarta on 28 September 2018 – 6 Agustus 2019. The purpose of this paper is to make Application of AHP and TOPSIS Method as Decision Support System in Determining Position Promotion for Teachers of Vocational High School in Surakarta to integrate the selection of recipients in order to avoid manipulation of the data in the provision of accurate decisions. Data collection was conducted by the author uses descriptive method that includes field studies and literature. Field studies were conducted are interviews with the Vocational High School related problems. While the literature study is useful to get a theoretical basis in the form of expert opinion on matters which is the object of research. It is also used to assist writers in the execution of a research report by the author. The result of the manufacturing Application of AHP and TOPSIS Method as Decision Support System in Determining Position Promotion for Teachers of Vocational High School Surakarta consists of input data including teachers data, criteria data, the data classifications, and data input analysis. The results of position promotions for teachers on Vocational High School Surakarta was given to best alternative with score of 88.12, while on using with AHP and TOPSIS method was given to best alternative with score of 0.7238.