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<![CDATA[PENERAPAN JARINGAN SARAF TIRUAN PADA DATA GEMPA BUMI DI PROVINSI BENGKULU ]]>
<![CDATA[Winalia Agwil]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang ]]>
Publisher : <![CDATA[Department Statistics, Faculty Mathematics and Natural Science, UNIMUS ]]>
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DOI: 10.26714/jsunimus.8.2.2020.152-158
<![CDATA[Provinsi Bengkulu merupakan wilayah yang sangat dekat dengan subduksi lempeng Eurasia dan Indo-Australia, hal ini mengakibatkan provinsi Bengkulu menjadi daerah yang rawan terjadinya bencana gempa bumi. Prediksi mengenai banyak kejadian dan rata-rata magnitudo gempa sangat menarik untuk di teliti. Penelitian mengenai analisis gempa bumi telah banyak dilakukan salah satunya dengan metode data mining yaitu Jaringan Saraf Tiruan. Tujuan dari penelitian ini adalah memperoleh arsitektur jaringan terbaik yang diterapkan pada data frekuensi kejadian dan rata-rata magnitudo gempa bumi per bulan di Provinsi Bengkulu. Kriteria pemilihan arsitektur jaringan terbaik dilakukan dengan membandingkan nilai RMSE dan MAE setiap kemungkinan arsitektur yang terbentuk. Hasil prediksi rata-rata magnitudo per bulan yang dimodelkan dengan arsitektur 1-3-1 lebih baik dibandingkan dengan arsitektur 12-3-1.]]>
<![CDATA[PERAMALAN INFLOW UANG KARTAL BANK INDONESIA KPW TASIKMALAYA JAWA BARAT DENGAN METODE KLASIK DAN MODERN ]]>
<![CDATA[Nur Silviyah Rahmi]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika ]]>
Publisher : <![CDATA[Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham ]]>
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DOI: 10.26714/jsunimus.8.2.2020.166-174
<![CDATA[Ketersediaan uang kartal di Bank Indonesia (BI) dapat ditinjau melalui arus keluar masuknya uang kartal yang disebut dengan istilah inflow. Banyaknya uang yang beredar di masyarakat akan berpengaruh pada kondisi perekonomian suatu negara, sehingga Bank Indonesia (BI) menyusun perencanaan kebutuhan uang rupiah. Penelitian ini bertujuan untuk meramalkan inflow uang kartal di KPw Bank Indonesia (BI) Tasikmalaya dengan menggunakan pemodelan ARIMA, ARIMAX, Metode Dekomposisi, Metode Winter’s, MLP (Multilayer Perceptron) atau FFNN (Feed Forward Neural Network), Regresi Time Series, Metode Naïve dan Model Hybrid. Dari delapan metode runtun waktu tersebut baik klasik maupun modern akan dicari metode mana yang memberikan hasil akurasi ramalan yang terbaik dengan kriteria RMSE, MAPE dan MAD. Kesimpulan yang dihasilkan yaitu Hybrid ARIMA-NN yang merupakan gabungan dari model ARIMA dengan neural network tidak menjamin kinerja hasil peramalan yang lebih baik. Seperti yang disebutkan dalam hasil M3 Competition, semakin kompleks metode yang digunakan belum tentu metode tersebut menghasilkan akurasi yang lebih baik dibandingkan metode sederhana (klasik). Pada ramalan data inflow KPw BI Tasikmalaya Jawa Barat ini, menghasilkan kesimpulan bahwa metode regresi time series memiliki nilai kriteria pemodelan paling kecil dibandingkan dengan metode lainnya.]]>
<![CDATA[Metode Quick Count dan Analisis Autokorelasi Spasial Menggunakan Indeks Moran (Studi Kasus: Pemilihan Presiden Indonesia Tahun 2019 di Kalimantan Timur) ]]>
<![CDATA[Riska Veronika]]>;
<![CDATA[Memi Nor Hayati]]>;
<![CDATA[Ika Purnamasari]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika ]]>
Publisher : <![CDATA[Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham ]]>
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DOI: 10.26714/jsunimus.8.2.2020.121-126
<![CDATA[Quick count is a quick caculation method based on sampling that is used to show the results of the temporary vote before the official election results are published. Votes can be influenced by party bases in various regions, so the linkage of the results of vote acquisition between regions needs to be taken into account. Spatial autocorrelation is the correlation between variables and themselves based on space or region. This research has a goal to determine the difference between the results of the estimated vote acquisition using the quick count method with the results of the KPU vote and spatial autocorrelation using the Moran index to determine whether or not there is a spatial autocorrelation of the results of the vote acquisition in the presidential election. The data used is the vote acquisition data of each pair of presidential candidates in the 2019 Indonesian presidential election in East Kalimantan Province using stratified random sampling. The results of the difference between the estimated votes obtained by the quick count method and the KPU calculation is relatively small at 0.01% and from the results of the spatial autocorrelation test hypothesis it is known that there is no spatial autocorrelation of the results of the vote acquisition for each pair of Indonesian presidential candidates in 10 districts/cities in East Kalimantan in 2019. ]]>
<![CDATA[PEMODELAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DAN JAKARTA ISLAMIC INDEX (JII) MENGGUNAKAN REGRESI BIRESPON SPLINE TRUNCATED BERBASIS GUI R ]]>
<![CDATA[Dhea Dewanti]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Alan Prahutama]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika ]]>
Publisher : <![CDATA[Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham ]]>
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DOI: 10.26714/jsunimus.8.2.2020.134-143
<![CDATA[The capital market is one of the economic drivers and representations for assessing the condition of companies in a country. Indonesia Stock Exchange (IDX) as one of the institutions in the capital market has 24 types of indexes that can be used as main indicators that reflect the performance of capital market, two of them are the Composite Stock Price Index (CSPI) and the Jakarta Islamic Index (JII). CSPI and JII movements are influenced by several factors, both from domestic and from foreign, such as inflation and the Dow Jones Industrial Average (DJIA). Modeling of CSPI and JII in this study was carried out using biresponses spline truncated nonparametric regression methods using Graphical User Interface (GUI) R with the intention of facilitating the analysis process. This method is used because there is a correlation between CSPI and JII and there is no specific relationship pattern between the response variable (CSPI and JII) and the predictor variable (inflation and DJIA). The best biresponses spline truncated model is determined by the order, number and location of the knots seen based on minimum GCV criteria. By using monthly data from January 2016 to December 2019, the best biresponses spline truncated model is obtained when the model for CSPI is in order 2 and the model for JII is in order 3 with 2 knots for each predictor variable. This model has a coefficient of determination of 85,54437% and MAPE of 2,65595% so that it has a very good ability in forecasting.]]>
<![CDATA[PERBANDINGAN MODEL JARINGAN SYARAF TIRUAN DENGAN ALGORITMA LEVENBERG-MARQUADT DAN POWELL-BEALE CONJUGATE GRADIENTPADA KECEPATAN ANGIN RATA-RATA DI KOTA SEMARANG ]]>
<![CDATA[Dwi Ispriyanti]]>;
<![CDATA[Alan Prahutama]]>;
<![CDATA[Tarno Tarno]]>;
<![CDATA[Budi Warsito]]>;
<![CDATA[Hasbi Yasin]]>;
<![CDATA[Pandu Anggara]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang ]]>
Publisher : <![CDATA[Department Statistics, Faculty Mathematics and Natural Science, UNIMUS ]]>
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DOI: 10.26714/jsunimus.8.2.2020.127-133
<![CDATA[Wind is one of the most important weather components. Wind is defined as the dynamics of horizontal air mass displacement measured in two parameters, namely speed and direction. Wind speed and direction depend on the air pressure conditions around the place. High wind speed intensity can cause high sea water waves. To estimate wind speed intensity required a study of wind speed prediction. One of method that can be used is Artificial Neural Network (ANN). In ANN there are several models, one of which is backpropagation. Thepurpose of this researchis to compare between backpropagation model with Levenberg-Marquadt and Powell-Beale Conjugate Gradient algorithms. The results of this researchshowing that Powell-Beale Conjugate Gradient better than Levenberg-Marquadtalgorithms. The best model architecture obtained is a network with two input layer neurons, six hidden layer neurons, and one output layer neuron. The activation function used are the logistic sigmoid in the hidden layer and linear in the output layer. MAPE value based on the chosen model is 0,0136% in training process and 0,0088% in testing process.]]>
<![CDATA[Perancangan Multidimensional Scalling Metrik dengan GUI PYTHON 3.8 untuk Klasifikasi Program Keluarga Berencana ]]>
<![CDATA[Bramesa Winanda Nugraha]]>;
<![CDATA[Tatik Widiharih]]>;
<![CDATA[Puspita Kartikasari]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang ]]>
Publisher : <![CDATA[Department Statistics, Faculty Mathematics and Natural Science, UNIMUS ]]>
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DOI: 10.26714/jsunimus.8.2.2020.114-120
<![CDATA[Program KB (Keluarga Berencana) merupakan suatu bentuk upaya yang dilakukan oleh pemerintah untuk mengendalikan banyaknya kuota penduduk. Program KB selalu dikaitkan dengan alat kontrasepsi sebagai kendaraan untuk menyukseskan program tersebut. Metode kontrasepsi dibagi menjadi dua yaitu jangka panjang (MKJP) yang meliputi Intra Uterine Device, Implan, Metode Operasi Wanita dan Metode Operasi Pria dan jangka pendek (Non MKJP) yang meliputi Suntik, Kondom, dan Pil. Penelitian ini bertujuan untuk memetakan Kabupaten/Kota di Jawa Tengah berdasarkan metode kontrasepsi yang digunakan oleh peserta KB dalam dua dimensi. Metode pemetaan yang digunakan adalah Multidimensional Scaling Metrik dengan membangun suatu program berbasis Graphical User Interface (GUI) Python. Hasil penelitian ini memvisualisasikan karakteristik dari peserta KB pada Kabupaten/Kota berdasarkan jenis dan metode kontrasepsi yang digunakan. Pada kuadran I memiliki karakteristik penggunaan Non MKJP. Kuadran II memiliki karakteristik penggunaan PIL. Kuadran III merupakan kelompok dengan tingkat penggunaan kontrasepsi yang rendah baik MKJP maupun Non MKJP. Kuadran IV memiliki karakteristik penggunaan IUD. Dengan kriteria perceptual map yang dihasilkan sempurna, ditunjukan oleh nilai stress sebesar 0.4%.]]>
<![CDATA[METODE SERVQUAL, KUADRAN IPA, DAN INDEKS PGCV UNTUK MENGANALISIS KUALITAS PELAYANAN RUMAH SAKIT X ]]>
<![CDATA[Ulfi Nur Alifah]]>;
<![CDATA[Alan Prahutama]]>;
<![CDATA[Agus Rusgiyono]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang ]]>
Publisher : <![CDATA[Department Statistics, Faculty Mathematics and Natural Science, UNIMUS ]]>
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DOI: 10.26714/jsunimus.8.2.2020.144-151
<![CDATA[The quality of service provided by the hospital is very important because it can be used as a reference in determining customer satisfaction. Service quality can be perceived as good and successful if the customer is satisfied with the services and suitable with what customers expect. However, if the services are not suitable with customer expectations, the service quality will be perceived as bad. This study aims to analyze the service quality of X Hospital based on five dimensions of service quality. The data was collected by distributing questionnaires to 64 selected respondents who were patients from Hospital X. Then, the data were calculated the value of the gap between customer expectations and perceptions. Then analyzed using the Importance Performance Analysis method and the Potential Gain Customer Value Index to determine the priority of service quality improvement. Based on the research results, there are only 5 indicators that have a positive gap score, which means that the service quality is suitable with customer expectations. From the Importance Performance Analysis quadrant, the indicators of service quality are spread across four quadrants. From the PGCV index, the indicator of service quality that becomes the first priority for improvement is the ease of access to purchase necessities for patients.]]>
<![CDATA[ANALISIS SENTIMEN PT TIKI JALUR NUGRAHA EKAKURIR (PT TIKI JNE) PADA MEDIA SOSIAL TWITTER MENGGUNAKAN MODEL FEED FORWARD NEURAL NETWORK ]]>
<![CDATA[Salma Farah Aliyah]]>;
<![CDATA[Hasbi Yasin]]>;
<![CDATA[Suparti Suparti]]>;
<![CDATA[Budi Warsito]]>;
<![CDATA[Tatik Widiharih]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang ]]>
Publisher : <![CDATA[Department Statistics, Faculty Mathematics and Natural Science, UNIMUS ]]>
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DOI: 10.26714/jsunimus.8.2.2020.103-113
<![CDATA[In the 2000s until now, e-commerce systems have continued to develop throughout the world and even in Indonesia. PT Tiki Jalur Nugraha Ekakurir (PT Tiki JNE) is a freight forwarding company that provides convenience for the public in carrying out online shopping activities, and shipping other goods. The large volume of shipments makes PT Tiki JNE have several problems in service that have led to several kinds of responses from users. Sentiment analysis on Twitter social media can be an option to see how PT Tiki JNE’s users respond to services that have been provided. These responses are classified into positive sentiments and negative sentiments. In this research data processing is performed using text mining as the initial source of numerical data from document data which will later be classified using the Artificial Neural Network model with the Resilient Backpropagation algorithm. Data labeling is done manually and sentiment scoring. The test results show that the best model obtained is FFNN 867-7-1 by using the evaluation model 10-Fold Cross Validation to get an overall accuracy performance of 80.27%, kappa accuracy of 39.13%, precision of 69.04%, recall of 70.56%, and f-measure of 69.8% which can be interpreted that the model used is quite good. Analysis of the results using wordcloud shows the tendency of opinion sentiment categories depending on the words used in the tweet.]]>
<![CDATA[KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019) ]]>
<![CDATA[Amanah Saeroni]]>;
<![CDATA[Memi Nor Hayati]]>;
<![CDATA[Rito Goejantoro]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang ]]>
Publisher : <![CDATA[Department Statistics, Faculty Mathematics and Natural Science, UNIMUS ]]>
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DOI: 10.26714/jsunimus.8.2.2020.88-94
<![CDATA[Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analysis and comparing the accuracy of the K-NN method classification and Fisher discriminant analysis on the insurance customer premium payment status. The data used is the insurance customer data of PT. Prudential Life Samarinda in 2019 with current premium payment status or non-current premium payment status and four independent variables are age, duration of premium payment, income and premium payment amount. The results of the comparative measurement of accuracy from the two analyzes show that the K-NN method has a higher level of accuracy than Fisher discriminant analysis for the classification of insurance customers premium payment status. The results of misclassification using the APER (Apparent Error Rate) in K-NN method is 15% while in Fisher discriminant analysis is 30%.]]>
<![CDATA[Pendugaan Kemiskinan Menggunakan Small area Estimation dengan Pendekatan Emperical Best Linear Unbiased Prediction (EBLUP) ]]>
<![CDATA[Dini Gartina]]>;
<![CDATA[Laelatul Khikmah]]>
<![CDATA[ Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang ]]>
Publisher : <![CDATA[Department Statistics, Faculty Mathematics and Natural Science, UNIMUS ]]>
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DOI: 10.26714/jsunimus.8.2.2020.159-165
<![CDATA[Kemiskinan merupakan permasalahan yang berkaitan dengan berbagai aspek kehidupan manusia. Selama ini kemiskinan diduga menggunakan data Susenas yang diukur melalui pendekatan pengeluaran perkapita. Faktanya, objek yang disurvei pada Susenas ini hanyalah rumah tangga yang melakukan kegiatan ekonomi, sehingga memungkinkan jumlah sampel tidak mewakili karakteristik dari keseluruhan populasi. Jika data tersebut digunakan untuk menduga kemiskinan akan menghasilkan pendugaan yang bias dan varians yang besar karena jumlah sampel kecil kurang representatif untuk mewakili data. Upaya yang dapat dilakukan untuk menduga pada area kecil dengan menambah sampel, namun hal ini membutuhkan biaya yang banyak sehingga untuk mengatasi masalah tersebut yaitu dengan mengoptimalkan data yang tersedia dengan menggunakan small area estimation (SAE). Salah satu pendekatan yang dapat digunakan pada pendugaan area kecil yaitu dengan menggunakan pendekatan Emperical Best Linear Unbiased Prediction (EBLUP). Pada penelitian ini keakuratan dari penduga EBLUP dapat dievaluasi dengan Mean Square Error (MSE). Hasil penelitiannya penduga Emperical Best Linear Unbiased Prediction (EBLUP) lebih baik dibandingkan dengan pendugaan langsung. MSE penduga langsung lebih besar daripada MSE penduga tidak langsung. Nilai rata-rata MSE penduga langsung sebesar 0.005729 dan rata-rata MSE penduga EBLUP sebesar 0.002873.]]>
