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All Journal Media Statistika Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Matematika: MANTIK BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Ilmiah Pendidikan dan Pembelajaran Jambura Journal of Mathematics Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Jurnal E-Komtek Jurnal Statistika dan Matematika (Statmat) Tematik : Jurnal Teknologi Informasi Komunikasi REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Indo-MathEdu Intellectuals Journal Formosa Journal of Science and Technology (FJST) Indonesian Journal of Contemporary Multidisciplinary Research Innovative: Journal Of Social Science Research Indonesian Journal of Advanced Research (IJAR) Euclid Asian Journal of Applied Education BEGIBUNG: Jurnal Penelitian Multidisiplin MARAS : Jurnal Penelitian Multidisplin Parameter: Jurnal Matematika, Statistika dan Terapannya Future Academia : The Journal of Multidisciplinary Research on Scientific and Advanced PRIMER: Jurnal Ilmiah Multidisiplin

Gumgum Darmawan

Departemen Statistika, Fakultas MIPA, Universitas Padjadjaran

Author-ID : 250042

Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Perancangan Arsitektur Sistem Informasi Absensi dan Penggajian Menggunakan Framework Zachman: Studi Kasus: PT. XYZ Realty Sastradipraja, Cecep Kurnia; Darmawan, Gumgum; Hadi, Juandi
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 4 No 1 (2020)
Publisher : Politeknik Dharma Patria Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v4i1.139

Penelitian ini bertujuan untuk merancang arsitektur sistem informasi dengan pendekatan model enterprise architecture menggunakan metode Framework Zachman dengan mengadopsi 4 baris (Planner, Owner, Designer, Builder) dan 5 kolom (What, How, Where, Who, When) di PT. XYZ Realty Kabupaten Sukabumi. Teknik dan sumber pengumpulan data adalah melalui proses observasi, wawancara, dan penyebaran angket terhadap pihak-pihak terkait pada PT. XYZ Realty. Hal ini dilakukan berdasarkan hasil temuan yang menunjukan bahwa kondisi saat ini khususnya pada bagian keuangan, dalam pengelolaan absensi dan penggajian karyawan masih menggunakan alat bantu aplikasi Ms. Excel, serta belum terintegrasinya antara data absensi dan data penggajian. Dari penelitian ini menghasilkan prototipe aplikasi dengan hasil analisis dan pengujian korelasi yang menunjukan bahwa penerapan metode Framework Zachman yang diimplementasikan pada sistem informasi absensi dan penggajian berbasis web memilki korelasi yang sangat kuat.

Hybrid Model of Singular Spectrum Analysis and ARIMA for Seasonal Time Series Data Darmawan, Gumgum; Rosadi, Dedi; Ruchjana, Budi N
CAUCHY Vol 7, No 2 (2022): CAUCHY: Jurnal Matematika Murni dan Aplikasi (May 2022) (Issue in Progress)
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i2.14136

Hybrid models between Singular Spectrum Analysis (SSA) and Autoregressive Integrated Moving Average (ARIMA) have been developed by several researchers. In the SSA-ARIMA hybrid model, SSA is used in the decomposition and reconstruction process, while forecasting is done through the ARIMA model. In this paper, hybrid SSA-ARIMA uses two auto grouping models. The first model, namely the Alexandrov method and the second method, is alternative auto grouping with a long memory approach. The two-hybrid models were tested for two types of seasonal patterns, multiplicative and additive seasonal time series data. The analysis results using both methods give accurate results; as seen from the MAPE generated the 12 observations for the future, the value is below 5%. The hybrid SSA-ARIMA method with Alexandrov auto grouping is more accurate for an additive seasonal pattern, but the hybrid SSA-ARIMA with alternative auto grouping is more accurate for a multiplicative seasonal pattern.

Aplikasi Metode Singular Spectral Analysis (SSA) dalam Peramalan Pertumbuhan Ekonomi Indonesia Tahun 2017 Rina Sri Kalsum Siregar; Dina Prariesa; Gumgum Darmawan
Jurnal Matematika MANTIK Vol. 3 No. 1 (2017): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

The purpose of this study was to look at seasonal patterns in the data of Gross Domestic Product (GDP) quarterly in the year 2000-2016 and the implementation of Singular Spectral Analysis (SSA) in the data of GDP to predict the data of GDP in 2017. The SSA method used is the method of recurrent forecasting with bootstrap confidence interval to look at its beliefs of the interval. The source of data derived from the data of GDP in 2000-2016 with the base year in 2000 compiled by the Central Statistics Agency (CSA). The results showed that the SSA method can be used as a reliable method and can be valid that view from the value of MAPE size is 0.82 and the size of the tracking signal at -4.00.

Perbandingan Keakuratan Hasil Peramalan Produksi Bawang Merah Metode Holt-Winters dengan Singular Spectrum Analysis (SSA) Yogo Aryo Jatmiko; Rini Luciani Rahayu; Gumgum Darmawan
Jurnal Matematika MANTIK Vol. 3 No. 1 (2017): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

The Holt-Winters method is used to model data with seasonal patterns, whether trends or not. There are two methods of forecasting in Singular Spectrum Analysis (SSA), namely recurrent method (R-forecasting) and vector method (V-forecasting). The recurrent method performs continuous continuation (with the help of LRF), whereas the vector method corresponds to the L-continuation. Different methods of course make a difference in the accuracy of forecast results. To see the difference between the three methods is done by looking at the comparison of accuracy and reliability of forecast results. To measure the accuracy of forecasting used Mean Absolute Percentage Error (MAPE) and to measure the reliability of forecasting results is done by tracking signal. Applications are done on Indonesian red onion production from January 2006 to December 2015. Forecasting of both methods in SSA uses window length L = 39 and grouping r = 8. With α = 0.1, β = 0.001 and γ = 0.5, Holt-Winters additive method gives better result with MAPE 13,469% than SSA method. Keywords:

Peramalan Indeks Harga Konsumen dengan Metode Singular Spectral Analysis (SSA) dan Seasonal Autoregressive Integrated Moving Average (SARIMA) Deltha Airuzsh Lubis; Muhamad Budiman Johra; Gumgum Darmawan
Jurnal Matematika MANTIK Vol. 3 No. 2 (2017): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Consumer Price Index (CPI) are the indicators used to measure the inflation and deflation of a group of goods and services in general. Forecasting CPI to be important as early detection in facing price hikes. This study uses the SSA and SARIMA. SARIMA a parametric model that requires various assumptions while SSA is a nonparametric technique that is free from a variety of assumptions, but both methods require seasonal patterns in the data. Based on the research results, methods of SSA with length window(L) of 24 and a grouping of 4 (1 group of seasonal and 3 groups of trends) and SARIMA models of order (0,1,1), (0,1,1) 6 is the most accurate and reliable models in forecasting CPI to the value Padang Sidempuan City. Forecasting CPI Padang Sidempuan City for the next 5 months with SSA method and SARIMA (0,1,1), (0,1,1) 6 shows the pattern of a trend is likely to increase but forecasting the 5th month with SSA method showed a surge in the value of CPI high or high inflation will occur.

Perbandingan Metode Peramalan ARIMA dan ARFIMA pada Data Long Memory GUMGUM DARMAWAN
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 9, No 2 (2009)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v9i2.1000

Pada makalah ini akan di bandingkan dua metode peramalan dari data long memory. Metodepertama menggunakan metode peramalan ARIMA, dimana sebelumnya data dilakukan pembedaan(differencing) dengan nilai pembeda yang telah ditentukan. Metode kedua menggunakan metodeperamalan ARFIMA langsung. Model ARFIMA yang dikaji adalah Model ARFIMA(1,d,0), ModelARFIMA(0,d,1) dan Model ARFIMA(1,d,1). Perbedaan dari kedua metode ini ditentukan berdasarkannilai dari MSE (Mean Square Error). Software yang digunakan pada penelitian ini adalah Software R(OSSR)

IDENTIFIKASI PERUBAHAN POLA CURAH HUJAN MELALUI PERIODOGRAM STANDAR Gumgum Darmawan; Budhi Handoko; Zulhanif Zulhanif
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 9 No 1 (2017): Jurnal Ilmiah Matematika dan Pendidikan Matematika
Publisher : Jurusan Matematika FMIPA Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2017.9.1.2859

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).

PEMBUATAN APLIKASI PRAKTIS DESAIN BLOK ACAK LENGKAP MENGGUNAKAN BAHASA PEMROGRAMAN LAZARUS Budhi Handoko; Yeny Krista Franty; Gumgum Darmawan
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 9 No 1 (2017): Jurnal Ilmiah Matematika dan Pendidikan Matematika
Publisher : Jurusan Matematika FMIPA Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2017.9.1.2852

Completely Randomized Block Design is one type of design experiments in Statistics which is used to conduct experiment involving treatments and blocks. Blocking is used to make experimental unit become homogen in each blok. Analysis to the reults of the experiment using the design usually perform using licensed software such as SPSS or Minitab. In order to use those softwares, we have to buy the license. In addition, if use them, we have to arrange the data into column form which require knowledge about orthogonality principle. This research will develop an application to analyze the experimental data easily, because the input in the form of table that suitable with experimental data format. In this research we used Lazarus Visual Programming which is one of open source software. The result of this research is a practical application that can be used to analyze experimental result produced using completely randomized block design which is bery easy to use. Experimental data from table of experimental result can be directly copied into the cells of the program thus data entry can be fastened. This program is an executable file (*.exe) so it is not necessary to be installed and the size of file is small enough which is 19 megabyte.

FORECASTING COVID-19 IN INDONESIA WITH VARIOUS TIME SERIES MODELS Gumgum Darmawan; Dedi Rosadi; Budi Nurani Ruchjana; Resa Septiani Pontoh; Asrirawan Asrirawan; Wirawan Setialaksana
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.83-93

In this study, Covid-19 modeling in Indonesia is carried out using a time series model. The time series model used is the time series model for discrete data. These models consist of Feedforward Neural Network (FFNN), Error, Trend, and Seasonal (ETS), Singular Spectrum Analysis (SSA), Fuzzy Time Series (FTS), Generalized Autoregression Moving Average (GARMA), and Bayesian Time Series. Based on the results of forecast accuracy calculation using MAPE (Mean Absolute Percentage Error) as model evaluation for confirmed data, the most accurate case models is the bayesian model of 0.04%, while all recovered cases yield MAPE 0.05%, except for FTS = 0.06%. For data for death cases SSA and Bayesian Models, the best with MAPE is 0.07%.

Analisis Intervensi dalam Model SARIMA untuk Memprediksi Laju Inflasi di Kota Tasikmalaya Pian Widianingsih; Gumgum Darmawan; Neneng Sunengsih
Formosa Journal of Science and Technology Vol. 1 No. 4 (2022): August 2022
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v1i4.1030

Pengendalian inflasi merupakan sasaran akhir dari kebijakan moneter yang dilakukan oleh Bank Indonesia, dengan bantuan Badan Pusat Statistika dalam melakukan pencatatan dan perhitungan inflasi. Tingginya harga minyak mentah dunia mengakibatkan kenaikan bahan bakar kendaraan bermotor dan bahan pokok masyarakat sejak Maret 2022. Tingkat inflasi tertinggi di Jawa Barat terjadi di Kota Tasikmalaya sebesar 1,04 persen dan berlanjut pada bulan berikutnya, sehingga mempengaruhi laju perekonomian daerah. Upaya untuk mengatasi masalah tersebut adalah dengan melakukan prediksi laju inflasi di Kota Tasikmalaya pada periode selanjutnya sebagai acuan memperoleh strategi yang optimal dalam menstabilkan perekonomian daerah. Metode yang digunakan dalam penelitian ini adalah Analisis Intervensi Fungsi Step dalam Model SARIMA karena dapat mengatasi perubahan pola pada data yang diakibatkan oleh kejadian intervensi. Berdasarkan hasil analisis, diperoleh model terbaik yaitu ARIMA (0,1,1)(1,1,0)12 dengan nilai MAPE sebesar 11,3%.

Co-Authors Achmad Bachrudin Akbar, Muhammad Faizal Alamanda Putri, Fariza Aldi Anugerah Sitepu Alfarisi, Widi Wildani Alifia, Wanda Aliya Auliyazhafira, Shabira Amanah Dwiadi, Qurnia Angga Pratama Anindya Apriliyanti Pravitasari Apriliana, Linda Aribah, Rana Asrirawan Aurilia Pratiwi, Dhanti Azka Larissa Rahayu Bertho Tantular Budhi Handoko Budi Nurani Ruchjana Budianti, Laila Clarissa Clorinda, Chrysentia Dedi Rosadi Defi Yusti Faidah Deltha Airuzsh Lubis Dina Prariesa Eko Yulian eko yulian, eko Ery Sadewo, Ery Fajar Indrayatna Farhan Bagus Prakoso Ferdian Agustiana Fitriani Azuri, Dila Hadi, Juandi Haura, Zhafira Hirlan Khaeri I Gede Nyoman Mindra Jaya Indriani , Ayu Intan Nurma Yulita Ismatilah, Nuzila Janatin, Janatin Karin, Nabila Khaeri, Hirlan Kiki Amelia, Kiki Kusuma Putri, Aisha Muhamad Budiman Johra Muhammad Faizal Akbar Mulya Nurmansyah Ardisasmita Mulya, Callista Audrey Najwa, Sandrina Neneng Sunengsih Neneng Sunengsih Novianti Indah Putri Nurhapilah, Hani Nurul Gusriani Pian Widianingsih Puteri, Dian Islamiaty Putri Syallya, Najma Rafifah Putri, Salma Azzahra Rafidah, Raihanah Rahman Al Madan, Aulia Resa Septiani Pontoh Restu Arisanti Rhafi Ahdian, Muhammad Rina Sri Kalsum Siregar Rini Luciani Rahayu Rizal Amegia Saputra Ruchjana, Budi N Ruslan Ruslan Samaria Nauli, Theresia Sangrila, Ayu Sastradipraja, C K Setialaksana, Wirawan - Sitepu, Aldi Anugerah Sitohang, Yosep Oktavianus Sri Sutjiningtyas Sri Winarni Sri Yuliana Sudartianto, Sudartianto Talakua, Andrew Hosea Tri Wulanda Fitri Triyani Hendrawati Utami, Yosi Febria Widiantoro, Carissa Egytia Widodo, Valeno Glenedias Wildani Alfarisi, Widi Yasyfi Avicenna, Muhammad Yeny Krista Franty Yogo Aryo Jatmiko Yosep Oktavianus Sitohang Yunizar, Mahdayani Putri Yusep Suparman Yuyun Hidayat Zen Munawar Zulhanif Zulhanif

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