<b>ANALISIS HUBUNGAN CURAH HUJAN DENGAN KEJADIAN BANJIR DAN KEKERINGAN PADA WILAYAH DENGAN SISTIM USAHATANI BERBASIS PADI DI PROPINSI JAWA BARAT</b><br> (ANALYSIS OF RELATIONSHIP BETWEEN RAINFALL AND FLOOD AS WELL AS DROUGHT EVENTS ON AREA WITH RICE ...

There are significantly decreasing of rainfall in wet season and dry season, and changed in onset of early season, that all of them can make crouded in plan of planting date, field actifity especially for food crops africulture. In the other side, climate is one of condition that has been ready and can not change, where probability of climate change will be reality that should be happened every time. Increasing frequency of climate extrem will give high impact in agriculture, especialy in rice-based farming system. This paper describes the climate risk based on statistical approaches. The climate risk is focused on flood and drought event. The analysis used was a chance occurrence based on time series data of rainfall and flood/droughts (affected and puso) based on median value from time series data. The goal of this research are : (1) to know rainfall critical value that can be influence flood and drought event in some of central food crops i West Java, (2) to know probability of flood and drought event in some of central food crops in West Java. The result of this research show that critical value of the rainfall that can be influence flood and drought event is very variety. Average of for flood event for paddy field near coastal based on median approach is 140 mm/month with probability 0,6. For another location, 166 mm/month with probability 0,68. Average of critical value of the rainfall for drought event is 64 mm/month for paddy field near coastal with probability 0,73. For another location, critical rainfall value is 119 mm/month with probability 0,76. For spesific research or detail scale (district or sub distric) we can use rainfall critical value and probablity based on data in that specific location because the data is more representative local riil condition.

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Mathematical modelling of ammonia volatilization from slurry stores and its effect on Cryptosporidium oocyst viability

The Journal of Agricultural Science ◽

10.1017/s0021859600071240 ◽

1995 ◽

Vol 124 (1) ◽

pp. 55-60 ◽

Cited By ~ 6

Author(s):

G. D. Ruxton

Keyword(s):

Mathematical Model ◽

Time Series ◽

Mathematical Modelling ◽

Ammonia Volatilization ◽

Time Series Data ◽

Cryptosporidium Oocyst ◽

Critical Value ◽

Series Data ◽

Simple Mathematical Model ◽

Ammonia Loss

SUMMARYA simple mathematical model is shown to provide reasonably good predictions of time series data on ammonia loss from three experiments on slurry stores. The model produces predictions of changes in the distribution of ammonia with depth. Experimental evidence suggests that ammonia concentrations above a critical value appear to destroy the viability of Cryptosporidium oocysts. Using this criterion, the model predicts that, even under the most favourable circumstances, oocysts are unlikely to remain viable in slurry stores except in the top few centimetres. The effect of stirring the slurry is considered.

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Kluster Tanaman Pangan Unggulan di Provinsi Jawa Tengah

Jurnal Kajian Ilmiah ◽

10.31599/jki.v21i1.303 ◽

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

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METODE SSA PADA DATA PRODUKSI PERIKANAN TANGKAP DI PROVINSI JAWA BARAT

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2017.9.2.2870 ◽

2017 ◽

Vol 9 (2) ◽

pp. 95

Author(s):

Fadhilah Fitri ◽

Nurul Fiskia Gamayanti ◽

Gumgum Gunawan

Keyword(s):

Time Series ◽

Fish Consumption ◽

Time Series Data ◽

Singular Spectrum Analysis ◽

Model Specification ◽

Series Data ◽

West Java ◽

Forecasting Accuracy ◽

Nonparametric Approach ◽

Fish Catch

Indonesia is an archipelagic country where 2/3 of its territory is ocean. The vastness of Indonesia's oceans is expected to produce abundant sea products that can meet the needs of Indonesian consumers, especially fish. Adequacy of the amount of fish consumption can be assessed through the number of fish catch. Based on data at the Ministry of Marine Affairs and Fisheries in 2015, West Java has a low growth of fish consumption, 6.05% in 2010-2014. Therefore, it is necessary to forecast the results of fish catch for several years ahead so it can be known whether the provision of fish consumption will be fulfilled or not. One method that can be used is Singular Spectrum Analysis (SSA). The SSA method is a flexible method because it uses a nonparametric approach. That is, in its application, this method does not require the model specification of time series data, as well as parametric assumptions. Forecasting accuracy of a method is said to be good if it has a MAPE value less than 20%. MAPE of SSA method forecast is 6.19% so that SSA method is suitable for forecasting of capture fishery production in West Java Province. The forecast for fishery production in West Java Province in 2015 for the first, second, third, and fourth quarter were 53,978.49 Ton, 54,406.91 Ton, 50,889.11 Ton, and 56,896.96 Ton, respectively.

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Identifying climate risk causing maize (Zea mays L.) yield fluctuation by time-series data

Natural Hazards ◽

10.1007/s11069-019-03605-4 ◽

2019 ◽

Vol 96 (3) ◽

pp. 1213-1222

Author(s):

Yuhe Ji ◽

Guangsheng Zhou ◽

Lixia Wang ◽

Shudong Wang ◽

Zongshan Li

Keyword(s):

Time Series ◽

Zea Mays ◽

Zea Mays L ◽

Time Series Data ◽

Climate Risk ◽

Series Data

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INTEGRASI PASAR AYAM BROILER DI SENTRA PRODUKSI DAN PASAR INDONESIA

JURNAL PANGAN ◽

10.33964/jp.v30i1.526 ◽

2021 ◽

Vol 30 (1) ◽

pp. 31-44

Author(s):

Jojo Jojo ◽

Harianto Harianto ◽

Rita Nurmalina ◽

Dedi Budiman Hakim

Keyword(s):

Time Series ◽

Broiler Chickens ◽

Broiler Chicken ◽

Time Series Data ◽

Vector Autoregression ◽

Market Competition ◽

Series Data ◽

The West ◽

West Java ◽

Vector Autoregression Model

Ayam broiler merupakan salah satu komoditas strategis yang tumbuh pesat, dihasilkan subsektor peternakan. Permasalahan umum pemasaran ayam broiler antara lain fluktuasi harga dan kompetisi pasar terhadap harga ayam antar pedagang besar antar kota menyebabkan terjadinya keterkaitan harga ayam satu kota dengan kota lainnya, antar peternak dan pedagang eceran. Tujuan penelitian ini untuk mengkaji integrasi pasar ayam broiler Jawa Barat dan pasar Indonesia. Data yang digunakan merupakan data sekunder time series bulanan, yaitu 72 bulan, periode Januari 2014 – Desember 2019. Data dianalisis menggunakan pendekatan model VAR (Vector Autoregression). Hasil analisis menunjukkan bahwa pasar ayam broiler Jawa Barat tidak memiliki integrasi dengan pasar Indonesia baik dalam jangka panjang ataupun jangka pendek. Broiler chickens are a strategic commodity that is growing rapidly, produced by the livestock sub-sector. General problems of broiler chicken marketing include price fluctuations and market competition on chicken prices among wholesalers between cities, causing a linkage between the price of chickens from one city to another, between breeders and retail traders. The purpose of this study was to examine the integration of the West Java broiler market and the Indonesian market. The data used are secondary monthly time series data, namely 72 months, for the period January 2014 - December 2019. The data were analyzed using the VAR (Vector Autoregression) model approach. The results of the analysis show that the West Java broiler chicken market does not have integration with the Indonesian market either in the long or short term.

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IDENTIFIKASI PERUBAHAN POLA CURAH HUJAN MELALUI PERIODOGRAM STANDAR

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2017.9.1.2859 ◽

2017 ◽

Vol 9 (1) ◽

pp. 103

Author(s):

Gumgum Darmawan ◽

Budhi Handoko ◽

Zulhanif Zulhanif

Keyword(s):

Time Series ◽

Open Source ◽

Open Source Software ◽

Time Series Data ◽

Seasonal Pattern ◽

Series Data ◽

West Java ◽

Periodogram Analysis

Rainfall is time series data that has seasonal pattern, usually period 12. The pattern of rainfall seasonal itself often changes. In this paper, the pattern of rainfall will identify by Periodogram Analysis. We use Time series data from one of city in west Java province. By this analysis, it is proved that the pattern of rainfall has been change. Computation itself, we use macro of Open Source Software R (OSSR).

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Mapping Paddy Field Extent and Temporal Pattern Variation in a Complex Terrain Area using Sentinel 1-Time Series Data: Case Study of Magelang District, Indonesia

International Journal of Geoinformatics ◽

10.52939/ijg.v17i2.1763 ◽

2021 ◽

pp. 79-88

Author(s):

S Arjasakusuma ◽

S.S. Kusuma ◽

W.K. Mahendra ◽

N. Astriviany

Keyword(s):

Time Series ◽

Paddy Field ◽

Complex Terrain ◽

Temporal Pattern ◽

Time Series Data ◽

Unsupervised Classification ◽

Series Data ◽

High Temporal Resolution ◽

Field Mapping ◽

Sar Data

The availability of free Synthetic Aperture Radar (SAR) data of Sentinel 1A/B, with the high temporal resolution, has provoked the usage of time-series backscatter values from the SAR data for mapping paddy field extent and crop phenology. However, paddy field extent mapping over complex terrain areas is rarely conducted, and the effect of terrain shadows on the accuracy of paddy field classification has not been addressed yet. This study attempted to identify the effect of terrain shadows on the paddy field mapping accuracy using monthly median composites Sentinel S1A data in 2018 and to perform the effort to minimize the misclassification by incorporating dem-derived terrain ruggedness index (TRI) in the random forest classifier. Lastly, this research also aimed to identify the general variation of the time-series backscatter of VV-and-VH polarization in the paddy field by employing unsupervised K-means classification. Our study showed that terrain shadow contributed to the misclassification of the identified paddy field due to the similar low backscatter values to the paddy field. Incorporating TRI can eliminate the misclassified paddy field, which subsequently increased the accuracy of paddy field mapping by 8 – 9% (user accuracy) and 4 – 5% (producer accuracy). In addition, means of clustered classes from unsupervised classification over paddy field areas generated temporal patterns related to the cropping frequencies and agreement with the weather pattern of the study area. This indicated that the cropping phase of paddy could be identified using the temporal pattern from 12-month median composites of the radar backscatter.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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