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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Natural A Jurnal Tata Kota dan Daerah International Journal of Advances in Intelligent Informatics Prosiding Seminar Matematika dan Pendidikan Matematik ELINVO (Electronics, Informatics, and Vocational Education) QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Islamic Monetary Economics and Finance JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) Musamus Journal of Physical Education and Sport (MJPES) Infotekmesin Inferensi Infotek : Jurnal Informatika dan Teknologi Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Proceeding ISETH (International Summit on Science, Technology, and Humanity) Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Sewagati PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Riyahuna: Jurnal Pendidikan Bahasa Arab Jurnal Terapan Pendidikan Dasar dan Menengah

Heri Kuswanto

Departemen Statistika, Institut Teknologi Sepuluh Nopember (ITS)

Author-ID : 303601

Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Analisis Penyebaran Properti Reservoir Pada Petrophysical Modelling Di Lokasi ”X” Papua Barat Dengan Metode Universal Kriging Mohammad Hatta Rafsanjani; Heri Kuswanto; Sutikno Sutikno
Jurnal Sains dan Seni ITS Vol 1, No 1 (2012): Jurnal Sains dan Seni ITS (ISSN 2301-928X)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Salah satu penelitian Upstream Technology Center (UTC) Direktorat hulu PT. Pertamina (Persero) adalah pada Lokasi ”X” Papua Barat. Analisis Geologi pada penelitian di Lokasi ”X” menerapkan metode Geostatistika pada petrophy-sical modelling. Penelitian ini mengaplikasikan metode Uni-versal Kriging pada Petrophysical Modelling. Metode ini dapat memberikan analisis yang baik secara geologi karena interpolasi properti reservoir primer dilakukan dengan memasukkan trend jenis batuan (facies) sebagai kontrol sehingga penyebaran yang dilakukan memiliki interpretasi yang kuat secara geologi. Pro-perti reservoir yang digunakan adalah porositas dan Net to Gross (NTG). Analisis semivariogram eksperimental dilakukan agar didapatkan semivariogram teoritis porositas dan NTG untuk masing-masing zona. Kesimpulan yang didapatkan adalah Zona 1A dan 1B merupakan target reservoir yang prospektif karena berdasarkan analisas statistika deskriptif dan univeral kriging didapatkan hasil penyebaran porositas dan NTG tertinggi daripada lokasi zona yang lainnya.

Penerapan Model Dsarfima Untuk Peramalan Beban Konsumsi Listrik Jangka Pendek Di Jawa Timur Dan Bali Pramono Dwi Utomo; Heri Kuswanto; Suhartono Suhartono
Jurnal Sains dan Seni ITS Vol 1, No 1 (2012): Jurnal Sains dan Seni ITS (ISSN 2301-928X)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

PT. PLN sebagaipemasokutamatenagalistrikdituntutharusmenyediakanpasokanlistrik yang sesuaidengankebutuhanlistrikpadasuatuwilayah, sehinggatidakterjadikerugianbaikpadakonsumenmaupun PT. PLN sendiri.Penelitianiniakanmengkajimetode yang tepatuntukmeramalkankebutuhanbebankonsumsilistrikjangkapendek di wilayahJawaTimurdan Bali yang mengandungpolamusimanganda. Penelusuranlebihlanjutmenunjukkanbahwa data memilikisifatlong memory, yang berartibahwa data memilikiketergantunganjangkapanjang.Metode ARFIMA adalahmetode yang tepatuntukmemodelkan data yang bersifatlong memory.Padapenelitianinidilakukanperbandinganantara model DSARIMA ([15],1,[1,3,4,5]) (0,1,1)48 (0,1,1)336dan model DSARFIMA ([1,2,3,7,13,17],d,[1]) (0,1,1)48(0,1,1)336dengand=0,7347281 untukmeramalkanbebankonsumsilistrikpadaperiodesatuminggu, duaminggu, tigaminggu, danempatminggukedepan. Perhitungankriteriaout-sampleuntukkebaikan model menunjukkanbahwa model DSARFIMA dapatmenghasilkanramalan yang lebihakuratdaripada model DSARIMA.

Estimasi Value at Risk pada Portofolio Nilai Tukar Mata Uang dengan Pendekatan Copula Farida Ariany; Heri Kuswanto; Suhartono Suhartono
Jurnal Sains dan Seni ITS Vol 1, No 1 (2012): Jurnal Sains dan Seni ITS (ISSN 2301-928X)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Interaksi kurs, saham, dan suku bunga memiliki hu-bungan sangat besar dengan pasar uang. Resiko investasi tidak hanya pada portofolio saham saja, namun pada portofolio kurs. Stabilitas terhadap nilai tukar mata uang suatu negara merupakan hal yang penting dan berdampak pada tingkat perekonomian negara. Penelitian ini mengestimasi Value at Risk (VaR) portofolio kurs menggunakan Copula- Generalized Auto-regresive ConditionalHeteroskedaritic (GARCH) serta simulasi Monte Carlo, hal ini bertujuan agar investasi yang dilakukan memberikan resiko yang minimal dan return yang didapatkan optimal. Sebagai studi kasus digunakan nilai tukar mata uang the euro (EURO), the United States dollar (USD), the pound sterling (GBP), dan the Malaysian ringgit(MYR). Apabila me-lakukan investasi dalam keempat mata uang secara merata maka akan didapatkan VaRatau kerugian maksimum sebesar 4,507% dengan tingkat kepercayaan 95% dan tingkat ke-percayaan 99%, kerugian maksimum yang ditanggung investor sebesar 6,501%.

Pengaruh Aggregasi terhadap Parameter Long Memory Time Series Moch. Koesniawanto; Heri Kuswanto
Jurnal Sains dan Seni ITS Vol 2, No 1 (2013)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Proses identifikasi terhadap fenomena Long Memory tidaklah mudah. Berbagai alat identifikasi seperti plot ACF dan berbagai statistik uji lain masih sangat lemah. Beberapa penelitian mengungkapkan bahwa beberapa model nonlinear dapat dengan mudah teridentifikasi sebagai Long Memory yang sering dikenal sebagai Spurious Long Memory. Oleh karena itu, dalam tugas akhir ini akan disimulasikan pengaruh flow aggregation dan stock aggregation sebagai alternatif cara untuk mendeteksi Long Memory. Saham digunakan sebagai studi kasus karena proses pencatatannya sama dengan penerapan dari stock aggregation dan beberapa penelitian menyatakan bahwa harga mutlak dari return saham sering tertangkap sebagai fenomena Long Memory, namun tidak sedikit penelitian yang memodelkan return saham dengan model nonlinear, contohnya seperti ESTAR, sehingga simulasi dibangun dengan membangkitkan data Long Memory dan ESTAR sebagai Spurious Model dengan ukuran sampel 2000 dan 5000, lalu diaggregasi masing-masing dengan kedua jenis aggregasi hingga 10 level aggregasi. Hasil simulasi menunjukkan bahwa temporal aggregation terbukti dapat mendeteksi Long Memory dan membedakannya dengan ESTAR dari pola parameter integrasinya. Pada data ESTAR, kedua aggregasi menunjukkan bahwa nilai parameternya tidak berpola atau random seiring naiknya level aggregasi, sedangkan untuk Long Memory memiliki pola khusus untuk setiap jenis aggregasi. Tiga saham yang dijadikan studi kasus yaitu BMRI, BBNI, dan BBRI lebih baik dimodelkan dengan ARFIMA daripada ESTAR karena menghasilkan forecast yang akurasinya lebih baik

Model Components Selection in Bayesian Model Averaging Using Occam's Window for Microarray Data Ani Budi Astuti; Nur Iriawan; irhamah Irhamah; Heri Kuswanto
Journal of Natural A Vol 1, No 2 (2014)
Publisher : Fakultas MIPA Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (614.073 KB)

Microarray is an analysis for monitoring gene expression activity simultaneously. Microarray data are generated from microarray experiments having characteristics of very few number of samples where the shape of distribution is very complex and the number of measured variables is very large. Due to this specific characteristics, it requires special method to overcome this. Bayesian Model Averaging (BMA) is a Bayesian solution method that is capable to handle microarray data with a best single model constructed by combining all possible models in which the posterior distribution of all the best models will be averaged. There are several method that can be used to select the model components in Bayesian Model Averaging (BMA). One of the method that can be used is the Occam's Window method. The purpose of this study is to measure the performance of Occam's Window method in the selection of the best model components in the Bayesian Model Averaging (BMA). The data used in this study are some of the gene expression data as a result of microarray experiments used in the study of Sebastiani, Xie and Ramoni in 2006. The results showed that the Occam's Window method can reduce a number of models that may be formed as much as 65.7% so that the formation of a single model with Bayesian Model Averaging method (BMA) only involves the model of 34.3%. Keywords— Bayesian Model Averaging, Microarray Data, Model Components Selection, Occam's Window Method.

Hybrid SSA-TSR-ARIMA for water demand forecasting Suhartono Suhartono; Salafiyah Isnawati; Novi Ajeng Salehah; Dedy Dwi Prastyo; Heri Kuswanto; Muhammad Hisyam Lee
International Journal of Advances in Intelligent Informatics Vol 4, No 3 (2018): November 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i3.275

Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA components and for aggregate SSA components. TSR was used for modeling aggregate trend component and Automatic ARIMA for modeling aggregate seasonal and noise components separately. Firstly, simulation study was conducted for evaluating the performance of the proposed methods. Then, the best hybrid method was applied to real data sample. The simulation showed that hybrid SSA-TSR-ARIMA for aggregate components yielded more accurate forecast than other hybrid methods. Moreover, the comparison of forecast accuracy in real data also showed that hybrid SSA-TSR-ARIMA for aggregate components could improve the forecast accuracy of ARIMA model and yielded better forecast than other hybrid methods. In general, it could be concluded that the hybrid model tends to give more accurate forecast than the individual methods. Thus, this research in line with the third result of the M3 competition that stated the accuracy of hybrid method outperformed, on average, the individual methods being combined and did very well in comparison to other methods.

Hybrid Double Seasonal ARIMA and Support Vector Regression in Short-Term Electricity Load Forecasting Kinanti Hanugera Gusti; Irhamah Irhamah; Heri Kuswanto
IPTEK Journal of Proceedings Series No 6 (2020): 6th International Seminar on Science and Technology 2020 (ISST 2020)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2020i6.11117

Forecasting is the main purpose of time series modelling. In short-term forecast, data can be predicted for a half hour-ahead. A half hour-ahead prediction faced with overlapping data series patterns risk. On the other hand, time series model can be analyzed with a linier or nonlinier approach. In this paper, we proposed the combination (hybrid) liner and nonlinier model for modelling the short-term electricity load in East Java. A half-hour electricity load forecasting is needed for real time controlling and short-term maintenance schedulling. However, the main problem of modelling time series data is determining linier or nonlinier time patterns. In short-term electricity load forecast, it depend on the moment of time (i.e weekdays, weekend, public holidays, joint holidays or religious holiday, etc) and the electricity load classification. In this analysis, we developed the Double Seasonal ARIMA (DSARIMA), Support Vector Regression (SVR), and hybrid DSARIMA-SVR. The DSARIMA model belong to linier model based on a well-known Box-Jenkins methodology. The SVR model belong to nonlinier model and the hybrid model is a mixing of linier and nonlinier models. The models are evaluated using Root Mean Square Error (RMSE) and Symmetric Mean Absolute Percentage Error (MAPE). The result shows that the accuracy of hybrid DSARIMA-SVR models are superior to the other individual models.

Quantile Regression Neural Network Model For Forecasting Consumer Price Index In Indonesia Dwi Rantini; Made Ayu Dwi Octavanny; Rumaisa Kruba; Heri Kuswanto; Kartika Fithriasari
Inferensi Vol 1, No 1 (2018): Inferensi
Publisher : Department of Statistics ITS

The main purpose of time series analysis is to obtain the forecasting result from an observation for future values. Quantile Regression Neural Network is a statistical method that can model data with non-homogeneous variance with artificial neural network approach that can capture nonlinear patterns in the data. Real data that allegedly have such characteristics is Consumer Price Index (CPI). CPI forecasting is important to assess price changes associated with cost of living as well as identifying periods of inflation or deflation. The purpose of this research is to compare several method of forecasting CPI in Indonesia. The data used in this study during January 2007 until April 2018 period. QRNN method will be compared with Neural Network with RMSE evaluation criteria. The result is QRNN is the best method for forecasting CPI with RMSE 0.95.

PERAMALAN LANGSUNG DAN TIDAK LANGSUNG MARKET SHARE MOBIL MENGGUNAKAN ARIMAX DENGAN EFEK VARIASI KALENDER Dea Astri Titi; Heri Kuswanto; Suhartono Suhartono
MEDIA STATISTIKA Vol 13, No 1 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on BPS data, the transportation industry sector contributed to about 8.01% of Indonesia's economic growth. The rapid growth of the transportation industry is also followed by the development of the automotive industry in Indonesia. The Exclusive Lisencee Agent of the Astra International group won a market share of 57% in April 2017. PT. Astra Daihatsu Motor, which is one of its subsidiaries, has a very rapid sales increase of 15% every year until Daihatsu's market share rises to 17.3%. Data from the Gabungan Industri Kendaraan Bermotor Indonesia (Gaikindo) shows an upward trend in car sales a month before Idul Fitri. This study carried out Daihatsu's direct and indirect market share forecasting using ARIMAX with a variety of calendar effects consisting of trends, monthly seasonal effects and Idul Fitri effects. The results indicated that indirect forecasting through forecasting the car sales for each brand and total market using ARIMAX outperforms the others and is able to capture the pattern of the testing data. The resulting SMAPE value of ARIMAX is smaller than direct forecasting and indirect forecasting using ARIMA.

Pengenalan Analisis Statistika untuk Meningkatkan Penelitian dan Publikasi Fungsional Statistisi di Jawa Timur Kartika Fithriasari; Nur Iriawan; Adatul Mukarromah; Irhamah; Heri Kuswanto; Wiwiek Setya Winahju; Ulfa Siti Nuraini
Sewagati Vol 5 No 3 (2021)
Publisher : Pusat Publikasi ITS

Jabatan fungsional statistisi memiliki tugas utama yaitu melakukan kegiatan statistik. Kegiatan statistik ini termasuk penyediaan data dan informasi statistik serta analisis dan pengembangan statistik. Jabatan fungsional statistisi dalam menjalankan tugasnya, perlunya peningkatan kemampuan dengan mengikuti pelatihan di bidang statistika. Pelatihan ini juga perlu keluaran yang sesuai dengan yang dibutuhkan fungsional statistisi. Oleh karena itu, pengabdian ini bertujuan untuk membantu meningkatkan kompetensi fungsional statistisi pada berbagai instansi di Jawa Timur dalam mengolah data dan publikasi, diharapkan dengan adanya pelatihan ini bisa meningkatkan kebergunaan informasi yang diperoleh agar dapat tersalurkan dengan baik. Materi yang disampaikan yaitu Statistika Data Driven, Visual dan Analisis Data, serta Karya Ilmiah dan Publikasi. Setelah adanya pelatihan, dilanjutkan dengan pendampingan terhadap fungsional statistisi dalam pengolahan data dan pembuatan karya ilmiah yang disusun oleh fungsional statistisi. Selain itu, manfaat yang dapat diperoleh yaitu terjalinnya kerja sama yang baik antara fungsional statistisi di Jawa Timur dan Departemen Statistika di Institut Teknologi Sepuluh Nopember.

Co-Authors A.A. Ketut Agung Cahyawan W Adatul Mukarromah Ahmad Fathoni Ahmad Shofiyuddin Ichsan Ani Budi Astuti Ary Miftakhul Huda Bani Unggul, Didik Beben Sutara, Beben Dea Astri Titi Dedy Dwi Prastyo Dwi Rantini Effendi, Magdalena Eka Supriatna Ekarini, Fitria Faiqah, Elok Farida Ariany Fatul, Fitasari Fithriasari, Kartika Fitri Ayu Kusumawati, Fitri Ayu Hady Suryono Hariadi Hariadi Hia, Supriadi Idrus Syahzaqi Indah Idamawarti, Itamara Irhamah Irhamah - Jamaluddin Jamaluddin Kinanti Hanugera Gusti Koesniawanto, Moch. Made Ayu Dwi Octavanny Marsono Marsono Moch. Koesniawanto Mohammad Hatta Rafsanjani Muhammad Hisyam Lee Munjahid Nitivijaya, Maulidiah Novi Ajeng Salehah Nur Iriawan Paramita, NLP Satyaning Pradnya Pramono Dwi Utomo Puspita Kartikasari Qori’atunnadyah, Marita Raditya Sukmana Rahmawati, Hasri Rouhan, Asva Abadila Rumaisa Kruba S Suhartono, S Salafiyah Isnawati Sediono, Sediono Setiawan Setiawan Sri Aryani, Sri Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Suryansah Susila, Muktar Redy Sutikno Sutikno Ulfa Siti Nuraini Widhianingsih, Tintrim Dwi Ary Wiwiek Setya Winahju

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