scholarly journals Persepsi Kualitas Mahasiswa Program Studi DIII Akuntansi Polines Selama Praktek Kerja Lapangan (PKL) di Industri

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Moh.Hasanudin Marliyati ◽  
Sri Murtini ◽  
Resi Yudhaningsih ◽  
Retno Retno
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Communication Skills ◽  
Time Series Data ◽  
Soft Skills ◽  
Series Data ◽  
Central Java ◽  
Hard Skills ◽  
Main Components

<p>This research aimed at exploring the quality of accounting diploma <br />students during their internship program in industries. The term of student’s <br />quality described in this research isexplained using 5 main components as follows: (1) communication skills (2) teamwork (3) independence (4) creativity (5) accounting and information technology (IT)-related skills. The research’s sample is industries where students of Diploma in Accounting of State Polytechnic of Semarang (SPS) took their intership and the students themselves whom have completed their internship program for three months in various institutions such as private enterprises, state owned enterprises, local government offices spread out around Central Java. The data on this research is time series data taken from 2015 to 2016 and was collected using questionnaires from the corresponding industries about the students competencies both hard skills and soft skills. <br />Data was scored using Likert scale, ranges from Poor (1) to Excellent (5) and <br />analyzed using statistic descriptive. The result showed that average students’ <br />quality during their internship was good. Among the 5 skills observed, the <br />corresponding industries ranked teamwork skills as the highest, followed by <br />independence, creativity, communication skills and the accounting and IT -related skills. It is expected that the result can be used for future development of Accounting Program Study of SPS.</p>

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

2020 ◽  
Vol 9 (3) ◽  
pp. 306-315
Author(s):  
Febyani Rachim ◽  
Tarno Tarno ◽  
Sugito Sugito
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Series Method ◽  
Fuzzy Variables ◽  
Central Java ◽  
Time Series Method ◽  
Fuzzy Logical

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE

scholarly journals Inferring phage-bacteria infection networks from time series data

2016 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage-bacteria infection network establishes which virus types infects which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold-standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage-bacteria infection networks. This method uses time series data of fluctuating population densities to estimate the complete interaction network without having to test each phage-bacteria pair individually. We use in silico experiments to analyze the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network and mitigate against the possibility of evolutionary changes to infection during the time-course of measurement.

scholarly journals Analisis Potensi Gempa di Selatan Pulau Jawa Berdasarkan Pengamatan GPS

2020 ◽  
Vol 11 (3) ◽  
pp. 151
Author(s):  
Irwan Meilano ◽  
Agidia L. Tiaratama ◽  
Dudy D. Wijaya ◽  
Putra Maulida ◽  
S. Susilo ◽  
...  
Keyword(s):  
Time Series ◽  
Subduction Zones ◽  
Velocity Vector ◽  
Time Series Data ◽  
Principal Component ◽  
Reference Station ◽  
Tectonic Activity ◽  
Series Data ◽  
Local Data ◽  
Central Java

ABSTRAKPulau Jawa merupakan salah satu pulau yang memiliki kepadatan penduduk tinggi dengan aktivitas tektonik yang sangat aktif. Hal ini dikarenakan Pulau Jawa terletak di zona konvergensi Lempeng Indo-Australia dan Lempeng Eurasia. Aktivitas tektonik ini menghasilkan kegempaan di zona subduksi dan sesar di daratan Penelitian ini menganalisis pola vektor kecepatan yang dihasilkan melalui pengolahan data stasiun pengamatan GPS (Global Positioning System) CORS (Continuously Operating Reference Station) BIG (Badan Informasi Geospasial) di wilayah Pulau Jawa bagian selatan. Data koordinat harian dianalisis dengan metode PCA (Principal Component Analysis) untuk memisahkan sinyal tektonik berupa data deret waktu global dan non-tektonik berupa data deret waktu lokal dengan penerapan aturan pemilihan varian dominan nilai eigen dalam pembetukan PC (Principal Component) dan orthogonal vektor eigen sebagai bobot dalam meminimalkan korelasi. Hasil dari data deret waktu global dan lokal digunakan untuk menghitung besar kecepatan pergeseran dari tahun 2011 sampai 2018. Hasil pengolahan menunjukkan besar resultan vektor kecepatan pada data awal berselang 0,06 sampai 10,46 mm/tahun, pada data global antara 0,06 mm/ tahun sampai 10,39 mm/tahun, dan data lokal sebesar 0,0037 sampai 1,99 mm/tahun. Variasi spasial vektor kecepatan pengamatan GPS data domain PCA menunjukkan variasi pergeseran horizontal di wilayah Banten bergerak ke arah timur laut; Jawa Barat, Daerah Istimewa Yogyakarta, dan Jawa Tengah bergerak ke arah tenggara; dan Jawa Timur bergerak ke arah timur laut. Hasil dari inversi data pergeseran terhadap slip pada zona subduksi, menunjukkan terjadinya kekurangan slip atau terjadi coupling pada zona subduksi Jawa bagian timur dan barat, sementara terjadi kelebihan slip pada bagian tengah yang merupakan efek postseismic dari gempa Pangandaran 2006.Kata kunci: GPS, PCA, potensi gempa, vektor kecepatanABSTRACTJava is one of the island that has a high population density with very active tectonic activity. This is because Java Island is located in the convergence zone of the Indo-Australian Plate and the Eurasian Plate. This tectonic activity produces seismicity in subduction zones and inland faults. This study analyzes the velocity vector patterns generated through data processing of the GPS (Global Positioning System) CORS (Continuously Operating Reference Station) BIG (Geospatial Information Agency) observation station in the southern part of Java. Daily coordinate data were analyzed using PCA (Principal Component Analysis) method to separate time series of tectonic signals as global data and non-tectonic time series data as local data by applying the rules for selecting dominant variants of eigen values for PC formation and orthogonal eigen vectors as weights in minimizing correlations. The results from global and local time series data were used to calculate the magnitude of the displacement velocity from 2011 until 2018. The processing results show the resultant velocity vector in the initial data intermittent 0.06 to 10.46 mm/year, global data from 0.06 to 10.39 mm/year, and local data of 0.0037 to 1.99 mm/year. The spatial variation of the velocity vector in PCA domain data shows the horizontal displacement in the Banten region to the northeast; West Java, Yogyakarta Special Region, Central Java to southeast; and East Java moving to northeast. The results of the inversion of the surface displacement to slip data in the subduction zone show that there is a slip deficiency or coupling occurs in the subduction zones of Eastern and Western Java, while there is excess slip in the Central Java which is a post-seismic effect of the 2006 Pangandaran earthquake.Keywords: earthquake potential, GPS, PCA, velocity vector

scholarly journals Kluster Tanaman Pangan Unggulan di Provinsi Jawa Tengah

2021 ◽  
Vol 21 (1) ◽  
pp. 55-68
Author(s):  
Choiroel Woestho ◽  
Milda Handayani ◽  
Adi Wibowo Noor Fikri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Food Plants ◽  
Series Data ◽  
Food Crops ◽  
Food Crop ◽  
Land Area ◽  
Location Quotient ◽  
Central Java ◽  
Central Java Province

The food crop sector has an important role for regions in Indonesia. Food plants can be a determinant for an area in meeting the needs of the people in that area. In addition, the food crop sector, if developed, can become revenue for the region. This study aims to analyze the leading food plants in 35 districts / cities in Central Java Province. By using the location quotient (LQ) method and the Regional Specialization Index. The data used is time series data from 2014 to 2019 in 35 districts / cities in Central Java Province for food crops based on land area and production. The results obtained for the average LQ value of food crops based on land area, there are only 12 districts / cities which are the basis for superior food crops with Wonogiri Regency at the top. Meanwhile, based on the average LQ value based on production, only 11 districts / cities are the basis for superior food crops with Semarang Regency being the top. For the specialization index based on both land area and production, there is no Regency / City that specializes in Central Java Province.   Keywords: Foodcrop Sector, Location Quotient, Specialization Index, Central Java   Abstrak   Sektor tanaman pangan mempunyai peranan penting bagi daerah di Indonesia. Tanaman pangan dapat menjadi penentu bagi suatu daerah dalam memenuhi kebutuhan masyarakat yang ada di daerah tersebut. Selain itu, sektor tanaman pangan jika dikembangkan dapat menjadi pendapatan bagi daerah. Penelitian ini bertujuan untuk menganalisis tanaman pangan unggulan yang ada di 35 Kabupaten/Kota pada Provinsi Jawa Tengah. Dengan menggunakan metode location quotient (LQ) dan Indeks Spesialisasi Regional. Data yang digunakan adalah data time series selama tahun 2014 hingga tahun 2019 pada 35 Kabupaten/Kota di Provinsi Jawa Tengah untuk tanaman pangan berdasarkan luas lahan dan produksi. Hasil yang diperoleh untuk nilai rata – rata LQ tanaman pangan berdasarkan luas lahan, hanya terdapat 12 Kabupaten/Kota yang menjadi basis bagi tanaman pangan unggulan dengan Kabupaten Wonogiri berada di urutan teratas. Sementara berdasarkan nilai rata – rata LQ berdasarkan produksi, hanya 11 Kabupaten/Kota yang menjadi basis tanaman pangan unggulan dengan Kabupaten Semarang menjadi urutan teratas. Untuk indeks spesialisasi baik berdasarkan luas lahan dan produksi, tidak ada Kabupaten/Kota yang mempunyai spesialisasi terhadap Provinsi Jawa Tengah.   Kata kunci: Tanaman Pangan, Indeks Lokalisasi, Indeks Spesialisasi, Jawa Tengah

scholarly journals A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

2020 ◽  
Vol 12 (18) ◽  
pp. 2888 ◽  
Author(s):  
Nishanta Khanal ◽  
Mir Abdul Matin ◽  
Kabir Uddin ◽  
Ate Poortinga ◽  
Farrukh Chishtie ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Input Image ◽  
Series Data ◽  
Data Set ◽  
Temporal Smoothing ◽  
Land Cover Data ◽  
Cover Class

Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.

scholarly journals Inferring phage–bacteria infection networks from time-series data

2016 ◽  
Vol 3 (11) ◽  
pp. 160654 ◽  
Author(s):  
Luis F. Jover ◽  
Justin Romberg ◽  
Joshua S. Weitz
Keyword(s):  
Time Series ◽  
Time Course ◽  
Community Dynamics ◽  
Time Series Data ◽  
Plaque Assay ◽  
Interaction Network ◽  
Series Data ◽  
Factors Affecting ◽  
Evolutionary Changes

In communities with bacterial viruses (phage) and bacteria, the phage–bacteria infection network establishes which virus types infect which host types. The structure of the infection network is a key element in understanding community dynamics. Yet, this infection network is often difficult to ascertain. Introduced over 60 years ago, the plaque assay remains the gold standard for establishing who infects whom in a community. This culture-based approach does not scale to environmental samples with increased levels of phage and bacterial diversity, much of which is currently unculturable. Here, we propose an alternative method of inferring phage–bacteria infection networks. This method uses time-series data of fluctuating population densities to estimate the complete interaction network without having to test each phage–bacteria pair individually. We use in silico experiments to analyse the factors affecting the quality of network reconstruction and find robust regimes where accurate reconstructions are possible. In addition, we present a multi-experiment approach where time series from different experiments are combined to improve estimates of the infection network. This approach also mitigates against the possibility of evolutionary changes to relevant phenotypes during the time course of measurement.

ASSESSMENT OF THE QUALITY OF TIME-SERIES DATA

Epidemiology ◽  
1996 ◽  
Vol 7 (Supplement) ◽  
pp. S69
Author(s):  
S TONG
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data

scholarly journals ANALISIS ARIMA DAN WAVELET UNTUK PERAMALAN HARGA CABAI MERAH BESAR DI JAWA TENGAH

2020 ◽  
Vol 9 (3) ◽  
pp. 247-262
Author(s):  
Chrisentia Widya Ardianti ◽  
Rukun Santoso ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Parametric Method ◽  
Haar Wavelet ◽  
Time Span ◽  
Series Data ◽  
Central Java ◽  
The One ◽  
Better Than ◽  
Analysis Of Time Series

Time series is a type of data collected according to the sequence of times in a certain time span. Time series data can be used as a predictor of future conditions. Analysis of time series data, one of the ARIMA units, is a parametric method that requires an assumption to get valid results. Data stationarity is one of the factors that must be fulfilled. Wavelet is a non-parametric method that is able to represent time and frequency information simultaneously, so that it can analyze non-stationary data. This research presents forecasting the price of red chili in Central Java using ARIMA and wavelet with the approach of the Multiscale Autoregressive (MAR) model. The best model is the one with the smallest MSE value. The results showed that the ARIMA(0,1,1) model was said to be the best model with MSE = 2252142. However, because the assumption of normality is not fulfilled, an alternative process is done with wavelet. Wavelet approach results show that the MAR model Haar filter level (j) = 4 with MSE = 2175906 is better than Daubechies 4 filter 4 level (j) = 1 with MSE = 3999669. Therefore, the Haar wavelet is considered better in the time series analysis. Keyword : ARIMA, wavelet, MAR, forecasting, MSE

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