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<![CDATA[PERAMALAN PRODUKSI TEH HIJAU DENGAN PENDEKATAN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE ]]>
<![CDATA[Wijaksono, Satrio]]>;
<![CDATA[Sulistijanti, Wellie]]>
<![CDATA[ PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Semarang ]]>
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<![CDATA[Tea plantation is one aspect of the lucrative agricultural sector in Indonesia.The world's need for very large plantation commodities, especially tea. theproduction of tea shoots as a raw material greatly determines the continuity oftea production as a whole. Tea production usually fluctuates from time to time.To establish a management view of future tea productivity forecasting, we canuse the Autoregressive Integrated Moving Average (Time Series) AutoregressiveIntegrated Moving Average (ARIMA) analysis. With the MSE value of 0.03668the best ARIMA model obtained is ARIMA (1.0.0), with general forecastingmodel: The data used is the production data of tea sales at PT. Rumpun Sari Medini from 2011-2016. The purpose of this research is to predict the amount of teasales production at PT. Rumpun Sari Medini Year 2017. Based on the results ofthis study, it was found that the highest tea production value occurred inDecember 2017 of 226,670 quintals, while the lowest production in January2017 was 226,603 quintals.Keyword: ARIMA, Peramalan Produksi Teh, PT. Rumpun Sari Medini]]>
<![CDATA[PERAMALAN JUMLAH TAMU DAN PENGUNJUNG DINNER HOTEL MEGA BINTANG SWEET KABUPATEN BLORA DENGAN PENDEKATAN ARIMA ]]>
<![CDATA[Irfana Maulana Ismail]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Semarang ]]>
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<![CDATA[Mega Bintang Sweet Hotel is one of the hotels in Blora Regency. Article is forecasting comparative analysis of number of guess room occupancy at Mega Bintang Sweet Hotel, Blora, Central Java using forecasting methods: Autoregressive Integrated Moving Average (ARIMA). Article used 60 data from January 2012 to December 2016, and the results of research using forecasting method suggest that the right model is ARIMA (0,1,1) model with the following equation : Zt = -0,6840Zt-1 – 0,4813t-2 + at + 1,0135at-1 and the smallest MSE is 7754. Keywords : forecasting, guess room occupancy, MSE.]]>
<![CDATA[PENJUALAN SEPATU MEREK ‘NIKE’ DENGAN METODE AUTOREGRESSIVE INTREGATED MOVING AVERAGE (ARIMA) ]]>
<![CDATA[Rizal Ripal Rifana]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Semarang ]]>
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<![CDATA[Sales are the prevalent income in the company and are the gross amount that is levied on the customer for goods and services, In the global trading era. Therole of Industry becomes very important, especially in maintaining fair businesscompetition. The data used is from Kavernosa Sport Shop from 2012 until 2016.ARIMA method is a model formation approach that is strong enough for timeseries analysis. In this analysis we get the best model to predict the ARIMAmodel (0,0,1) With model equation: √ (Y_t) = 32,8 + e_t + 0,4153e_ (t-1).From the equation model can be predicted that the highest number of salesoccurred in January in 2017 with a prediction of sales of as many as 45 pairs ofshoes.Keywords: Toko Kavernosa Sport Majalengka West Java, Model Shoes Brand 'Nike', ARIMA.]]>
<![CDATA[PERAMALAN HARGA MINYAK MENTAH STANDARWEST TEXAS INTERMEDIATEDENGAN PENDEKATAN METODEARIMA ]]>
<![CDATA[Syahril Faozi]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Semarang ]]>
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<![CDATA[Crude Oil is an important commodity. Because crude oil is a much needed source of energy all over the world. So that changes of oil prices will greatlyaffect the state of a country's economy. The price of crude oil in certainconditions has a significant increase and decrease. Rising crude oil prices willhave an impact on both exporting and importing countries in terms of inflation,stock prices and interest rates. Thus, statistical techniques that can be used toforecast time series data types are ARIMA (Autoregressive Integrated MovingAverage). Based on the above description of the objectives to be achieved is toforecast the price of crude oil on June 23 - July 3, 2016. From the forecastingresults with Box-Jenkins method, the best ARIMA model obtained is ARIMA (1,1,1) with forecasting model: Keywords: ARIMA, Oil Price, Forecast]]>
<![CDATA[PERBANDINGAN METODE AUTOREGRESI DAN AUTOKORELASI SERTA SINGLE EXPONENTIAL SMOOTHING ]]>
<![CDATA[Aprilia Ummi Mujahidah]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Semarang ]]>
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<![CDATA[Saripetojo Ice factory was founded in 1923 and produce ice to meet household needs as well as the preservation of fish, of which most is Cilacap Regency sea. This research aims to predict sales of ice in an ice Factory Saripetojo Cilacap in 2017. By using the method of forecasting method Autoregresi and Autocorrelation and Single Exponential Smoothing. The data used in this study as many as 84 of data from January 2010 until December 2016. From the results of research using the method of Autoregresi and Autocorrelation within one period producing the equation Autoregresi is better than Single Exponential Smoothing method with a value of 11568614 MSE. Based on the Autoregresi equation is obtained the highest occurring forecasting in December that is of low beams and 23524 occurred in January, namely of 18770 beams.Keywords: Autoregresi and autocorrelation, MSE, Single Exponential Smoothing]]>
<![CDATA[ANALISIS PERAMALAN JUMLAH PERMINTAAN DARAH DI UNIT TRANFUSI DARAH (UTD) KOTA SEMARANG ]]>
<![CDATA[Hendrani Ismanto]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Semarang ]]>
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<![CDATA[Blood is an important component that plays a role in bringing nutrients and oxygen to all organs of the body. The demand for blood supply is increasing every year as population increases are no exception in Semarang. To anticipate the amount of blood demand that is uncertain, a model is needed that can predict the amount of blood demand. The method used is the Box-Jenkins ARIMA method, the object to be studied is the demand for blood types at the Indonesian Red Cross Blood Transfusion Unit (UTD) in the city of Semarang in 2011-2016. The results obtained are blood demand data can be predicted using the ARIMA Box-Jenkins method with the ARIMA model (1,1,0). Keywords: Darah, ARIMA Box-Jenkins, Peramalan, Semarang]]>
<![CDATA[Peramalan Hasil Panen Mangga dengan Pendekatan Seasonal Autoregresif Integrated Moving Average Method ]]>
<![CDATA[Willy Estuhardini Ersa Muthahar]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Semarang ]]>
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<![CDATA[Mango is one of the agricultural products in Indramayu large enough.This arises from a number of farmers who cultivate this fruit, and the public interestis high enough to consume this fruit. The data studied from 2003 until 2015.Given the plot of data generated, the data has a seasonal pattern of eachperiod. This research is done by using SARIMA method (SeasonalAutoregressive Moving Average). From the results of forecasting methodsSARIMA models Box-Jenkins ARIMA produce (1,1,1) (1,1,0) =42) with the equation.From this research obtained by forecasting yields all kinds of mangoes in 20162017 in Indramayu district with the highest prediction results obtained inOctober-December 2017 and the lowest padabulan January-March 2016 Keywords: Forecasting, Harvest Mango, SARIMA]]>
<![CDATA[Perbandingan Metode Fuzzy Time Series dengan Metode Box-Jenkins untuk Memprediksi Jumlah Kunjungan Pasien Rawat Inap (Studi Kasus: Puskesmas Geyer Satu) ]]>
<![CDATA[S Susilowat]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ Prosiding University Research Colloquium Proceeding of The 7th University Research Colloquium 2018: Mahasiswa (student paper presentation) ]]>
Publisher : <![CDATA[Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY ]]>
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<![CDATA[Puskesmas merupakan unit kesehatan masyarakat yang bergerak dibidang kesehatan yang melayani masyarakat secara paripurna.Puskesmas Geyer satu adalah satu-satunya puskesmas di kecamatanGeyer yang sudah terakreditasi dan satu-satunya puskesmas yangmenyediakan unit rawat inap. Permasalahan yang dihadapi olehpuskesmas Geyer satu adalah tidak sebanding dengan sumber dayayang ada dengan pasien rawat inap yang harus dilayani. Dalampenelitian ini diusulkan metode peramalan fuzzy time series denganmetode peramalan Box-Jenkins. Tujuan penelitian ini adalah untukmembandingkan metode peramalan fuzzy time series dengan metodeperamalan Box-Jenkins dengan melihat ukuran kesalahan peramalanmenggunakan Mean Square Error (MSE) serta meramalkan jumlahkunjungan pasien rawat inap menggunakan metode terbaik. Metodefuzzy time series adalah metode peramalan menggunakan himpunanfuzzy dalam proses peramalannya sementara metode Box-Jenkins ataubiasa disebut ARIMA adalah metode konvesional yang menggunakanhimpunan tegas dalam proses peramalannya. Nilai Ketepatan metodefuzzy time series diperoleh nilai MSE = 38499.98. Metode Box-Jenkins dengan model ARIMA (1,1,1) dengan nilai MSE = 91556. Haltersebut menunjukkan bahwa metode fuzzy time series memiliki nilaiketetapan yang lebih kecil daripada metode Box-Jenkins. Dari hasilpenelitian ini disimpulkan bahwa metode fuzzy time series adalahmetode yang lebih baik untuk meramalkan jumlah kunjungan pasienrawat inap daripada metode Box-Jenkins.]]>
<![CDATA[Peramalan Kunjungan Wisatawan Mancanegara Melalui Bandara Adi Sucipto Menggunakan Support Vector Machine ]]>
<![CDATA[Rizal Abdul Aziz]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ Prosiding University Research Colloquium Proceeding of The 7th University Research Colloquium 2018: Mahasiswa (student paper presentation) ]]>
Publisher : <![CDATA[Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY ]]>
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<![CDATA[Pada tahun 2017 akibat dari jatuhnya harga migas dan batubarapariwisata menduduki posisi kedua di bawah Crude Palm Oil (CPO)sebagai penghasil devisa tertinggi bagi Indonesia. Wisatawanmancanegara (wisman) diharapkan menjadi penghasil devisa tertinggimaka peramalan kunjungan wisman sangat penting bagi pemerintahdan industri, karena peramalan menjadi dasar dalam perencanaankebijakan yang efektif. Dalam penelitian ini diusulkan metode SupportVector Machine (SVM) untuk meramalkan kunjungan wismanberdasarkan pintu masuk di Bandara Adi Sucipto. SVM memilikikelebihan yaitu dapat menangani permasalahan linier dan non-linier.Sehingga dapat dilakukan untuk melakukan peramalan data timeseries dengan berbagai macam pola yang ada. Selain itu dapatmemprediksi permasalahan non-linier serta menawarkan akurasi yangcukup baik. Penelitian ini menggunakan data dari Januari 2010sampai Oktober 2017 sebanyak 94 data yang terbagi menjadi 66 datatraining dan 28 data testing. Dari hasil penelitian didapat nilai MeanSquare Error (MSE) untuk data latih sebesar 0.05 dan nilai MSE ujisebesar 0.0715. Nilai MSE tersebut kecil sehingga dapat digunakanmeramalkan jumlah wisatawan mancanegara tahun 2018 maka dapatmanjadi sumber informasi dan bahan pertimbangan dalam mengambilsuatu keputusan yang tepat oleh department pariwisata Yogyakartadan management bandara Adi Sucipto.]]>
<![CDATA[Perbaikan Peramalan Produksi Padi di Kabupaten Kendal dengan Menggunakan Metode Support Vector Machine (SVM) ]]>
<![CDATA[Ayu Andita]]>;
<![CDATA[Wellie Sulistijanti]]>
<![CDATA[ Prosiding University Research Colloquium Proceeding of The 7th University Research Colloquium 2018: Mahasiswa (student paper presentation) ]]>
Publisher : <![CDATA[Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY ]]>
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<![CDATA[Sektor pertanian memegang peranan penting bagi perekonomiandaerah sebagai peningkatan produksi bahan pangan khususnya bahanmakanan pokok bagi kehidupan manusia, tentunya masyarakat diKabupaten Kendal. Pada Tahun 2015, produksi padi di KabupatenKendal menempati peringkat 18 se-Jawa Tengah, Dinas PertanianKendal sangat memperhatikan perkembangan produksi padi sehinggamemerlukan perencanaan dalam peramalan yang harus dilandasidengan kekuatan model dan parameter yang signifikan. Penelitian inimenggunakan data dari bulan Januari 2013- Januari 2016, 70% dari48 data sebagai data training dan 30% data testing. Peramalan yangbaik memiliki nilai Mean Square Error (MSE) yang kecil. Peramalanproduksi padi dengan metode Seasonal Autoregressive IntegratedMoving Average (SARIMA) diperoleh MSE training sebesar 0.043918dan MSE testing 77.118.361,62, terlihat bahwa nilai MSE antarakeduanya sangat jauh berbeda. Dengan metode Support VektorMachine (SVM) diperoleh MSE training sebesar 0,14 dan MSE testingsebesar 0,57. Terlihat bahwa nilai MSE yang dihasilkan sangat dekattidak jauh berbeda dengan keduanya. Inilah kelebihan metode yangtelah diusulkan oleh peneliti dengan menggunakan Metode SVMmerupakan metode yang baik untuk meramalkan produksi padi diKabupaten Kendal, karena nilai MSE training dan MSE testing yangkecil, tidak memerhatikan parameter yang signifikan dan dapatmenanggani permasalahan linier ataupun non linier tanpamemerhatikan pola data.]]>
