The Process of using resources in higher education is influenced by the up and down of the number students. The purpose of this study is to predict the number of students who study in the department of informatics engineering UPN Veteran Yogyakarta for the next periods. This research, data is taken from forlap dikti for Informatics Engineering fom 2009 until 2016 at UPN Veteran Yogyakarta. The method that used to forecast the number of students is a Moving Average method consisting of: Single Moving Average (SMA), Weighted Moving Average (WMA) and Exponential Moving Average (EMA). This study will use the forecasting accuracy namely Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) to select the best model to be used for forecasting. The best model that used for forecasting is Weighted Moving Average (WMA) with weighted 1/3 and average length (n) used for 2. The smallest value for MSE of 5807.96; the smallest MAE value of 55.89 and the smallest value for MAPE of 5.24%. Forecasting of the number of students for four semesters in the future after the even semester of 2016 are respectively: 902; 901,33; 901,56 and 901,48. Keywords : Forecasting, UPN Veteran Yogyakarta, Single moving average(SMA) AbstrakProses penggunaan sumber daya perguruan tinggi setiap tahun dipengaruhi oleh naik turunnya jumlah mahasiswa. Tujuan dari penelitian ini adalah untuk memprediksi jumlah mahasiswa yang kuliah di jurusan teknik informatika UPN Veteran Yogyakarta untuk periode yang akan datang. Data penelitian ini diambil dari forlap dikti untuk Teknik Informatika dari tahun 2009 sampai 2016 UPN Veteran Yogyakarta. Metode yang digunakan untuk melakukan peramalan jumlah mahasiswa adalah metode Moving Average yang tediri dari : Single Moving Average (SMA), Weighted Moving Average (WMA) dan Exponential Moving Average (EMA). Penelitian ini akan menggunkan akurasi peramalan Mean Square Error (MSE), Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE) untuk memilih model terbaik yang akan digunakan untuk peramalan. Model terbaik yang digunakan untuk peramalan yaitu Weighted Moving Average (WMA) dengan pembobot 1/3 dan panjang rata-rata (n) yang dipakai sebesar 2. Nilai terkecil untuk MSE sebesar 5807,96; nilai terkecil MAE sebesar 55,89 dan nilai terkecil untuk MAPE sebesar 5,24 %. Peramalan untuk jumlah mahasiswa empat semester kedepan setelah semester genap 2016 masing-masing adalah : 902; 901,33; 901,56 dan 901,48. Kata Kunci : Peramalan, UPN Veteran Yogyakarta, Single Moving Average(SMA).

Lovebird (Agapornis) is a type of bird that has become the belle of new pet birds lately. The interest of the hobbyist in this one song is because Lovebird has a unique chirp. For beginner lovebird fans, the lack of knowledge and experience about lovebird birds results in various cases of fraud in choosing a quality lovebird. They were disappointed expensive lovebirds that had been purchased but did not match what was expected. Lovebird chirping voice recognition can be learned and recognized through the learning process of speaker recognition, which is part of voice recognition. Speaker recognition captures the frequency of the lovebird's voice, then compares it with the sound frequency of the existing training data. The sound frequency and the long duration of chirping of lovebird birds will be extracted through the Mel-Frequency Cepstral Coefficient (MFCC) method. Information in the form of Mel Frequency Cepstrum Coefficients from input data and training data is then compared to the Dynamic Time Warping method. The methodology used in this study uses the grapple method. The results of this study were obtained an accuracy value of sound validation by 80%. It is hoped that with the capabilities of this system, it can help bird chirping lovers know the sound quality of lovebird birds that are good, moderate, and less. Also, it can help the jury of birds chirping, so that it can be used as an accurate standard in classifying lovebird sounds.

This article discusses the importance of traditional market branding management for maintaining sustainability, with a focus on Niten Market as a successful example. The goal of traditional market branding management is to increase awareness, expand buyer loyalty, attract new buyers, and enhance competitiveness. The article suggests that one way to achieve this is through digital branding, which utilizes information technology to provide a unique and engaging experience for visitors. The article highlights that Niten Market has effectively implemented branding management but needs improvement in the digital branding aspect. The utilization of digital technology has significant potential for supporting future marketing communication activities and enhancing the overall customer experience. The implication of this article is that traditional markets need to consider digital branding as a means to attract and retain customers and remain competitive in the modern marketplace.

The purpose of this research is to predict monthly inflation in the city of Yogyakarta with a simple and easy forecasting model that has high accuracy. The model used is the exponential smoothing-state space or known as the error, trend, and seasonal (ETS) model. This model does not have statistical assumptions, it is easy to analyze using R-package statistics which is an opensource program. This ETS model is built with a combination of trend and nontrend, seasonal and non-seasonal models as well as an additive or multiplicative errors. The monthly inflation data used in this research is secondary data obtained from the Central Bureau of Statistics (BPS) for the city of Yogyakarta from January 2015 to December 2021 with a total of 84 data. The results of this research obtained that the most suitable ETS model for predicting monthly inflation in the city of Yogyakarta is the ETS model (A, N, A). The ETS model (A, N, A) means that the error is additive (A), does not contain a trend (N) and seasonality is additive, so it is written as ETS (A, N, A). The ETS model (A, N, A) obtained in this research has an Akaike information criteria (AIC) value of 145.1996 with an RMSE forecasting accuracy value of 0.2166014 and a MAPE of 127.1662. The results of the forecasting for the next three periods show that the monthly inflation value of Yogyakarta is quite stable, there is an increase and decrease or it fluctuates slightly and is still below 10%.

Difference any exchanging of several kinds information such visual, printed or written form between impairments vision or blind with normal will cause problem especially in written form. For instance in the assistance of a blind child by family with normal vision. One of family role is education to help their children to study and understand about their learning development from home, particularly for blind children. In this study, braille letters can be identified through images obtained using a scanner to help parents and families of a blind child in learning assistance by implementing the Gabor Wavelet feature extraction method. The features used are standard deviation, mean, variance, and median with theta angles of 00,300,450,600,1200,1350,1800 and wavelengths 3,6,13,28, and 58. These features will be combined and used as input as test data and training data. at the Support Vector Machine (SVM) classification stage and generates words in the alphabet. The braille letters detected in this study were small braille letters, capital braille letters, punctuation marks, and numbers. The test is carried out using a multi-class confusion matrix scenario to determine the level of accuracy, precision, and recall. Based on the results of tests carried out using 758 braille data, the accuracy value is 98.15%, the precision value is 97.66% and the recall value is 98.28%. From these results it can be concluded that the Gabor Wavelet feature extraction method and the Support Vector Machine (SVM) can be used to identify braille letters.