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KOMPUTASI GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION BERBASIS GRAPHICAL USER INTERFACE (GUI) Yasin, Hasbi; Warsito, Budi; Ispriyanti, Dwi; Suparti, Suparti; Hakim, Arief Rachman
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 1 (2018)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1575.013 KB)

Abstract

Geographically and Temporally Weighted Regression (GTWR) merupakan salah satu metode spatio temporal yang dikembangkan pada model regresi linier. Pengembangan dilakukan dengan menambahkan unsur spasial yang direpresentasikan dengan lokasi geografis dan penambahan unsur temporal yang diwakili oleh waktu pengamatan.  Dengan metode GTWR akan diperoleh parameter bersifat lokal menurut lokasi dan waktu pengamatan. Perkembangan teknologi telah memunculkan berbagai alat bantu dalam proses analisis data. Salah satunya berkembangnya software statistik yang berbasis antarmuka berupa Graphical User Interface (GUI) untuk memudahkan pengguna. Hasil penelitian ini adalah sebuah sistem komputasi untuk proses analisis data menggunakan model GTWR baik estimasi parameter maupun inferensinya. Hasil penelitian menunjukkan bahwa dengan dengan menggunakan GUI GTWR pengguna akan sangat dimudahkan dalam proses analisis data spasial menggunakan metode GTWR. Hasil penelitian menunjukkan bahwa model spatio temporal GTWR lebih baik digunakan untuk pemodelan Indeks Standar Pencemar Udara (ISPU) dengan pembobot Bisquare karena mempunyai nilai R2 terbesar dengan MSE dan AIC yang terkecil bila dibandingkan dengan pembobot yang lain. Kata kunci :  Antar Muka Grafis, ISPU, GTWR, Spasial, Temporal.
PENGELOLAAN LIMBAH BATIK CAIR SECARA BIOLOGIS PADA UKM BATIK MUTIARA HASTA DAN KATUN UNGU SEMARANG Warsito, Budi
WARTA WARTA LPM, Vol. 21, No. 2, September 2018
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.742 KB) | DOI: 10.23917/warta.v21i2.5602

Abstract

Batik merupakan salah satu peninggalan budaya nenek moyang bangsa Indonesia yang perlu terus dipelihara dan dilestarikan. Salah satu persoalan yang muncul dari usaha pembuatan batik adalah limbah yang dihasilkan. Limbah yang langsung dibuang tanpa proses penyaringan terlebih dahulu sehingga dapat membahayakan bagi lingkungan. Oleh karena itu perlu dilakukan kegiatan pendampingan bagi para pengusaha batik agar melakukan pengolahan lebih dahulu limbah yang dihasilkan sebelum dibuang ke lingkungan. Kegiatan pengabdian masyarakat ini dilakukan melalui tahapan penyuluhan bagi UKM batik Mutiara Hasta dan Katun Ungu di Kota Semarang serta pembuatan bak pengolah limbah secara biologis dengan media meliputi kerikil, kerakal, bioball dan batu zeolit. Kadar polutan pada limbah yang dihasilkan dari kedua UKM tersebut jauh melebihi ambang batas yang diijinkan. Melalui pengolahan limbah secara biologis diharapkan limbah yang dibuang tidak lagi membahayakan lingkungan dan UKM dapat melakukan pengolahan secara berkelanjutan karena metode ini tidak memerlukan biaya yang besar.
Kombinasi Synthetic Minority Oversampling Technique (SMOTE) dan Neural Network Backpropagation untuk menangani data tidak seimbang pada prediksi pemakaian alat kontrasepsi implan Mustaqim, Mustaqim; Warsito, Budi; Surarso, Bayu
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 2 (2019): July-December
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1531.069 KB) | DOI: 10.26594/register.v5i2.1705

Abstract

Combination of Synthetic Minority Oversampling Technique (SMOTE) and Backpropagation Neural Network to handle imbalanced class in predicting the use of contraceptive implants  Kegagalan akibat pemakaian alat kontrasepsi implan merupakan terjadinya kehamilan pada wanita saat menggunakan alat kontrasepsi secara benar. Kegagalan pemakaian kontrasepsi implan tahun 2018 secara nasional sejumlah 1.852 pengguna atau 4% dari 41.947 pengguna. Rasio angka kegagalan dan keberhasilan pemakaian kontrasepsi implan yang cenderung tidak seimbang (imbalance class) membuatnya sulit diprediksi. Ketidakseimbangan data terjadi jika jumlah data suatu kelas lebih banyak dari data lain. Kelas mayor merupakan jumlah data yang lebih banyak, sedangkan kelas minor jumlahnya lebih sedikit. Algoritma klasifikasi akan mengalami penurunan performa jika menghadapi kelas yang tidak seimbang. Synthetic Minority Oversampling Technique (SMOTE) digunakan untuk menyeimbangkan data kegagalan pemakaian kontrasepsi implan. SMOTE menghasilkan akurasi yang baik dan efektif daripada metode oversampling lainnya dalam menangani imbalance class karena mengurangi overfitting. Data yang sudah seimbang kemudian diprediksi dengan Neural Network Backpropagation. Sistem prediksi ini digunakan untuk mendeteksi apakah seorang wanita mengalami kehamilan atau tidak jika menggunakan kontrasepsi implan. Penelitian ini menggunakan 300 data, terdiri dari 285 data mayor (tidak hamil) dan 15 data minor (hamil). Dari 300 data dibagi menjadi dua bagian, 270 data latih dan 30 data uji. Dari 270 data latih, terdapat 13 data latih minor dan 257 data latih mayor. Data latih minor pada data latih diduplikasi sebanyak data pada kelas mayor sehingga jumlah data latih menjadi 514, terdiri dari 257 data mayor, 13 data minor asli, dan 244 data minor buatan. Sistem prediksi menghasilkan nilai akurasi sebesar 96,1% pada epoch ke-500 dan 1.000. Implementasi kombinasi SMOTE dan Neural Network Backpropagation terbukti mampu memprediksi pada imbalance class dengan hasil prediksi yang baik.  The failed contraceptive implant is one of the sources of unintended pregnancy in women. The number of users experiencing contraceptive-implant failure in 2018 was 1,852 nationally or 4% out of 41,947 users. The ratio between failure and success rates of contraceptive implant, which tended to be unbalanced (imbalance class), made it difficult to predict. Imbalance class will occur if the amount of data in one class is bigger than that in other classes. Major classes represent a bigger amount of data, while minor classes are smaller ones. The imbalance class will decrease the performance of the classification algorithm. The Synthetic Minority Oversampling Technique (SMOTE) was used to balance the data of the contraceptive implant failures. SMOTE resulted in better and more effective accuracy than other oversampling methods in handling the imbalance class because it reduced overfitting. The balanced data were then predicted using backpropagation neural networks. The prediction system was used to detect if a woman using a contraceptive implant was pregnant or not. This study used 300 data, consisting of 285 major data (not pregnant) and 15 minor data (pregnant). Of 300 data, two groups of data were formed: 270 training data and 30 testing data. Of 270 training data, 13 were minor training data and 257 were major training data. The minor training data in the training data were duplicated as much as the number of data in major classes so that the total training data became 514, consisting of 257 major data, 13 original minor data, and 244 artificial minor data. The prediction system resulted in an accuracy of 96.1% on the 500th and 1,000th epochs. The combination of SMOTE and Backpropagation Neural Network was proven to be able to make a good prediction result in imbalance class.
CLUSTERING DATA PENCEMARAN UDARA SEKTOR INDUSTRI DI JAWA TENGAH DENGAN KOHONEN NEURAL NETWORK Warsito, Budi; Ispriyanti, Dwi; Widayanti, Henny
Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Vol 4, No 1 (2008): Vol 4, No 1 (2008)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (45.724 KB) | DOI: 10.14710/presipitasi.v4i1.17-22

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Industrial clustering in Central Java based on polutan yielded to be intended in order to obtaine an industrial group as information in development wisdom specially at Central Java Province. The  method that  is  selected  in  industrial  clustering  is  Kohonen  Artificial  Neural  Network.  An Artificial Neural Network is configured for a specific application, such as pattern recognition or data classification, through a learning process. Kohonen Neural Network can be used in data clustering through unsupervised learning. This network will divide the input pattern into some cluster, based on trained weight. Then this weight will be updated until it can classified itself into the class needed. This paper will present the result of the air contamination data clustering at industrial sector in Central Java at the year 2006 using Kohonen Neural Network. The result of this clustering is industrial clustering, based on polutan yielded, become three clusters.
PREDIKSI CURAH HUJAN KOTA SEMARANG DENGAN FEEDFORWARD NEURAL NETWORK MENGGUNAKAN ALGORITMA QUASI NEWTON BFGS DAN LEVENBERG-MARQUARDT Warsito, Budi; Sumiyati, Sri
Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Vol 3, No 2 (2007): Vol 3, No 2 (2007)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (120.768 KB) | DOI: 10.14710/presipitasi.v3i2.46-52

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This paper study the rainfall prediction at Semarang City as time series data with Feed Forward Neural Network  (FFNN)  model.  The  learning  algorithm  that  be  used  are  the  Quasi  Newton BFGS and Levenberg-Marquardt algorithm. The input unit is determined based on the best of ARIMA model. The computation is done with use  Matlab 7.1 program with 1000 epoch, five unit of hidden layer, 100 replication  and use  input at lag  variabel  1,  12  and 13, respectively. The result shows that the prediction is good in relatively, where Quasi Newton BFGS algorithm result the Mean Square Error (MSE) that more accurate.
PEMODELAN GENERAL REGRESSION NEURAL NETWORK UNTUK PREDIKSI TINGKAT PENCEMARAN UDARA KOTA SEMARANG Warsito, Budi; Rusgiyono, Agus; Amirillah, M. Afif
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (86.16 KB) | DOI: 10.14710/medstat.1.1.43-51

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This paper is discuss about General Regression Neural Network (GRNN) modelling to predict time series data, i.e. the air pollution rate in Semarang City comprises the floating dust, carbon monoxide (CO) and nitrogen monoxide (NO). The GRNN model have four processing layer that are input layer, pattern layer, summation layer and output layer. The input variable is determined by the ARIMA model. The result of GRNN modelling shows that the network have a good performance both at predict in sample and predict out of sample, that can be seen from the mean square error.   Keywords: GRNN, predict, air pollution  
Rancangan D-Optimal Model Gompertz dengan Maple Widiharih, Tatik; Warsito, Budi
MEDIA STATISTIKA Vol 10, No 1 (2017): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (470.103 KB) | DOI: 10.14710/medstat.10.1.1-12

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Gompertz model is used in many areas including biological growth studies, animal and husbandry, chemistry, and agricultural. Locally D-optimal designs for Gompertz models with three parameters is investigated. We used the Generalized Equivalence Theorem of Kiefer and Wolvowitz to determine D-optimality criteria. Tchebysheff system is used to decide that the D-optimal design is minimally supported design or nonminimally supported design. The result, D-optimal design for Gompertz model is minimally supported design with uniform weight on its support.Keywords:D-optimal, Generalized Equivalence Theorem, Tchebysheff System,  Minimally Supported, Uniform Weight.
PREDIKSI TERJANGKITNYA PENYAKIT JANTUNG DENGAN METODE LEARNING VECTOR QUANTIZATION Hidayati, Nurul; Warsito, Budi
MEDIA STATISTIKA Vol 3, No 1 (2010): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (235.618 KB) | DOI: 10.14710/medstat.3.1.21-30

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Learning Vector Quantization (LVQ) is a method that train the competitives layer with supervised. The competitives layer will learn automatically to classify the input vector given. If some input vectors has the short distance then the input vector will be grouped into the same class. The LVQ method can be used to classify the data into some classes or categories. At this paper, the LVQ method will be applied to classify if someone is suffer potenciate of heart desease or not. The data that be trained are 268 data of heart desease patient from UCI (University of California at Irvine) with 10 variables that are factors influence that infected of heart desease. From some trials showed that the learning rate (α) = 0.25, decrease of learning rate (Decα) = 0.1, and the minimum learning rate (Minα) = 0.001 are values that give a good prediction with level of accuracy is about 66.79 %.   Keywords: Learning Vector Quantization, Classify, Heart Desease
PEMODELAN JARINGAN SYARAF TIRUAN DENGAN ALGORITMA ONE STEP SECANT BACKPROPAGATION DALAM RETURN KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT Najwa, Maulida; Warsito, Budi; Ispriyanti, Dwi
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.388 KB) | DOI: 10.14710/j.gauss.v6i1.14768

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Exchange rate is the currency value of a country that is expressed by the value of another country's currency. Changes in exchange rates indicate risks or uncertainties that would return obtained by investors. With the predicted value of return, investors can make informed decisions when to sell or buy foreign currency to gain an advantage. Forecasting of return values can be using artificial neural network with backpropagation. In backpropagation procedure, data is divided into two pairs, namely training data for training process and testing data for testing process. In the training process, the network is trained to minimize the MSE. One of optimization method that can minimize the MSE is one step secant backpropagation. In this research, the data used is the return of the exchange rate of rupiah against US dollar in the period of January 1st, 2015 until December 31st, 2015. The results were obtained architecture best model neural network that was built from 8 neurons in the hidden layer, 1 unit of input layer with input xt-1 and 1 unit of output layer. The activation function used in the hidden layer and output layer are bipolar sigmoid and linear, respectively. The architecture chosen based on the smallest MSE of testing data is 0.0014. After obtaining the best model, data is foreseen in the period of November 2016 produce MAPE=153.23%.Keyword : Artificial Neural Network, Backpropagation, One Step Secant, Time Series, Exchange Rate.
PEMODELAN B-SPLINE UNTUK MENGESTIMASI KURVA YIELD OBLIGASI PEMERINTAH KODE FIXED RATE Nurcahyanti, Tri Meida; Widiharih, Tatik; Warsito, Budi
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (853.178 KB) | DOI: 10.14710/j.gauss.v8i2.26669

Abstract

Bond is a medium-long term loan agreement that can be handed over, it contains a promise from the issuer to pay rewards in the form of interest on a particular period and paying off the principal debt on the time that has been appointed to the bond buyer. A method to find out the relationship between yield and time to maturity for a type of bond at any given time is illustrated through the yield curve. One of the methods for estimating yield curve is B-spline. The data that used to estimate the yield curve with B-spline model are sourced from Indonesia Stock Exchange, namely Government Bond Trading Report with code FR (Fixed Rate). The data periods used are 9, 16, and 23 November 2018. The best model for estimating the yield curve at any period of the data is linear B-spline model with 6 knots but the knot position is different for every data period. Based on the calculation of MAPE, the ability of the model to predict is very good. Investment with maximum profit based on the estimation of yield curve using B-spline linear model with 6 knot is FR0071.Keywords: bond, yield, yield curve, Government Bond, B-spline
Co-Authors Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Agus Rusgiyono Alan Prahutama Anindita Nur Safira Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Aris Sugiharto Arsyil Hendra Saputra Atmaja, Dinul Darma Atur Ekharisma Dewi Aurum Anisa Salsabela Bagus Dwi Saputra Bayu Surarso Bimastyaji Surya Ramadhan Cintika Oktavia Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Dian Mariana L Manullang Dinar Mutiara Kusumo Nugraheni Dwi Ispriyanti Ekky Rosita Singgih Wigati Faisal Fikri Utama Fath Ezzati Kavabilla Fatiya Nur Umma Fiqria Devi Ariyani Gayuh Kresnawati Ghifar Rahman Hanif Kusumasasmita Hasbi Yasin Henny Widayanti, Henny Hizkia Christian Putra Setiadi Indra Jaya Infan Nur Kharismawan Intan Monica Hanmastiana Kiswanto Kiswanto M. Afif Amirillah M. Andang Novianta Maryono Maryono Maulida Najwa, Maulida Moch. Abdul Mukid Mochamad Arief Budihardjo mohamad jamil muhammad shodiq Munji Hanafi Mustafid Mustafid Mustaqim Mustaqim, Mustaqim Nisa Afida Izati Nitami Lestari Putri Nur Fitriyah Nurcahyanti, Tri Meida Nurul Hidayati Pandu Anggara Puspita Kartikasari Rachmat Gernowo Rachmat Gernowo Rahmat Gernowo Rahmatul Akbar Ratna Kencana Putri Rini Nuraini Rita Rahmawati Rita Rahmawati Riza Rizqi Robbi Arisandi Rukun Santoso Salma Farah Aliyah Sang Nur Cahya Widiutama Shahnilna Fitrasha Bayastura Silvia Elsa Suryana Siti Fadhilla Femadiyanti Sri Endah Moelya Artha Sri Sumiyati Sudarno Sudarno Sudarno Sudarno Sugito Sugito Suparti Suparti Syafrudin Syafrudin Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Tri Yani Elisabeth Nababan Ummayah, Putri Qodar Vincensius Gunawan Slamet Kadarrisman Wahyul Amien Syafei Winahyu Handayani Yundari, Yundari