No abstract

The aim of this research is to determine the performance of PV panels, wind turbines, battery storage and power imported from the grid to the system which will ensure a reliable energy supply, as well as the technical feasibility of a smart microgrid system. Indonesia’s renewable energy potential for electricity reaches 443 GW, where solar energy is the largest potential, namely 4.8 KWh m–2 or equivalent to 112 000 GWp, but only 10 MWp has been utilized. The most basic problem in this system, namely, the uncertainty of wind energy and solar energy, one of the most vital factors in the optimal size of a renewable energy-based smart microgrid system is the reliability of the system being built. The method used in this research is to collect data on the availability of wind energy and solar energy as well as load analysis on the smart microgrid system. As a result, the resulting power output was 6.2 MWh during the experiment. The highest average Performance Ratio (PR) of the solar energy power generation system, namely 77 % in February 2020. Optimized with Battery Life (OBL) model produces a power output of 102.4 kWh and has an overall system efficiency of 81.92 %.

The need for beef in Indonesia continues to increase. In 2020, it will require imports of 300 thousand t of meat or the equivalent of 1.7 × 106 cattle a year. To overcome the problem, the biogas-energized livestock feed making machine (copper) was designed. The advantage of this copper is the process of making the livestock feed can efficient and effective. Results of the calculations, known that with increasing miller rotation output, the resulting capacity shows an increase. Also, it can use drive motors that are available on the market with a power capacity of 50 W. Hence; this machine is very efficient in the use of electricity, high economic value, convenient and easy move to other places. Electricity for the engine is designed with renewable energy, namely biogas from co-digestion substrates namely animal feed waste, kitchen waste, cow dung and excreta disposal from septic tanks. This co-generation is expected to improve the life of the breeders

Abstract. Plagiarism occurs when the students have tasks and pursued by the deadline. Plagiarism is considered as the fastest way to accomplish the tasks. This reason makes the author tried to build a plagiarism detection system with Winnowing algorithm as document similarity search algorithm. The documents that being tested are Indonesian journals with extension .doc, .docx, and/or .txt. Similarity calculation process through two stages, the first is the process of making a document fingerprint using Winnowing algorithm and the second is using Jaccard coefficient similarity. In order to develop this system, the author used iterative waterfall model approach. The main objective of this project is to determine the level of plagiarism. It is expected to prevent plagiarism either intentionally or unintentionally before our journal published by displaying the percentage of similarity in the journals that we make.

Program Studi Sistem Informasi adalah salah satu program studi di Universitas Jember yang berdiri sejak tahun 2009. Sampai saat ini sudah cukup banyak mahasiswa yang telah menyandang gelar sarjana, khususnya angkatan 2009-2013 , namun tidak banyak yang berhasil menyelesaikan studinya tepat waktu sehingga berdampak pada penilaian akreditasi dari program studi tersebut. Mahasiswa memiliki beban pembelajaran sekurang-kurangnya 144 SKS dengan masa studi selama 4- 5 tahun untuk memperoleh gelar sarjana. Berdasarkan permasalahan tersebut, terdapat berbagai cara untuk mengklasifikasi ketepatan waktu kelulusan mahasiswa, salah satunya dengan metode jaringan syaraf tiruan Backpropagation. Data yang digunakan yaitu data lulusan mahasiswa Program Studi Sistem Informasi Universitas Jember angkatan tahun 2011-2013. Atribut yang digunakan untuk klasifikasi berjumlah 9 atribut, yaitu nilai Indeks Prestasi (IP) semester 1 sampai 6, jumlah SKS yang ditempuh, semester saat terakhir kali memprogram matakuliah Kuliah Kerja Nyata (KKN) dan Praktik Kerja Lapang (PKL). Kelas yang digunakan untuk klasifikasi yaitu ketepatan waktu lulus mahasiswa tersebut. Penentuan ketepatan waktunya yaitu jika masa studi kurang dari sama dengan 60 bulan, maka mahasiswa tersebut lulus tepat waktu dan jika lebih dari 60 bulan maka tidak tepat waktu. Penerapan metode klasifikasi ini dilakukan dengan menggunakan learning rate 0.1, 0.3, 0.5, 0.7, dan 0.9 dengan batas iterasi masing-masing 1.000, 2.000, dan 3.000 iterasi. Nilai akurasi tertinggi yaitu sebesar 98,82% pada iterasi ke-2000 dan 3000, masing-masing dengan learning rate = 0,7 dan 0,9 untuk iterasi ke-2000 dan learning rate = 0,5, 0,7 dan 0,9 untuk iterasi ke-3000. Hasil tersebut didapat dari jumlah data benar sebanyak 167 data dari 169 data secara keseluruhan. Kata Kunci: Data Mining, Klasifikasi, Jaringan Syaraf Tiruan, Metode Backpropagation Neural Network.