Article Per Year (5 Year)
p-Index From 2020 - 2025
1.556
P-Index
This Author published in this journals
All Journal Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Ilmiah Pendidikan dan Pembelajaran Jambura Journal of Mathematics Jurnal E-Komtek Tematik : Jurnal Teknologi Informasi Komunikasi Euclid Asian Journal of Applied Education PRIMER: Jurnal Ilmiah Multidisiplin
Gumgum Darmawan
Unknown Affiliation
Religion 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 Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
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

Abstract

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

Abstract

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.
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

Abstract

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)
PENERAPAN METODE SINGULAR SPECTRUM ANALYSIS (SSA) PADA PERAMALAN JUMLAH PENUMPANG KERETA API DI INDONESIA TAHUN 2017 Hirlan Khaeri; Eko Yulian; Gumgum Darmawan
Euclid Vol 5, No 1 (2018): EDISI JANUARI
Publisher : Universitas Swadaya Gunung Jati.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.788 KB) | DOI: 10.33603/e.v5i1.496

Abstract

Peramalan data deret waktu dengan Singular Spectrum Analysis (SSA) tengah populer beberapa tahun terakhir. Kemampuan SSA dalam menguraikan pola data deret waktu dalam bentuk yang sederhana dianggap cukup baik dalam menghasilkan data ramalan. Pada penelitian ini SSA digunakan untuk meramalkan jumlah penumpang kereta api di pulau Jawa pada tahun 2017. Penggunaan Metode Periodegram dan Tracking signal juga dibahas dalam penelitian ini. Model SSA yang paling tepat dalam kasus ini diperoleh pada windows length 19 dan jumlah grup 5 dengan MAPE 7,11 persen.
Membangun Sistem Antrian Online Untuk Bimbingan Tugas Akhir Gumgum Darmawan; Zen Munawar; Cecep Kurnia Sastradipraja; Novianti Indah Putri; Sri Sutjiningtyas
TEMATIK Vol 10 No 1 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v10i1.1266

Abstract

Penelitian ini bertujuan untuk merancang aplikasi antrian berbasis web dan WhatsApp Gateway pada program studi statistika Universitas Padjadjaran Bandung, dimana saat ini bimbingan tugas akhir (TA) mahasiswa khususnya proses penjadwalan dan bimbingan masih dilakukan secara manual. Prosedur dan mekanisme yang berjalan saat ini tetap dilaksanakan melalui kesepakatan antara dosen dengan peserta bimbingan; seorang dosen harus menjadwalkan antrian bimbingan bagi mahasiswa dengan tiga program studi dan dua jenjang studi yang berbeda, S1, dan S2. Masalah ini membuat proses bisnis kurang efektif dalam mengatur waktu, tempat, dan prosedur antrian. Berdasarkan latar belakang masalah tersebut, penulis tertarik untuk merancang konsep aplikasi antrian bimbingan tugas akhir dengan metode First Come First Service (FCFS) berbasis WhatsApp Gateway System (WAGS) dan Web. Sistem ini dapat menjawab kebutuhan program studi statistika UNPAD, dimana sistem aplikasi ini dibangun. Mahasiswa dapat menginput data peserta bimbingan, menyimpan data bimbingan, menayangkan jadwal bimbingan melalui aplikasi WhatsApp, memproses jadwal antrian, dan menampilkan jadwal antrian. Hasil implementasi aplikasi diharapkan dapat menghasilkan jadwal antrian bimbingan berdasarkan kecepatan respon pesan yang saling terkait dengan urutan jadwal bimbingan melalui penyiaran jadwal pada pesan whatsapp berdasarkan kelompok dan jenjang program studi.
PEMODELAN PRODUK DOMESTIK BRUTO (PDB) DENGAN PENDEKATAN VECTOR ERROR CORRECTION MODEL (VECM) Sitepu, Aldi Anugerah; Tantular, Bertho; Darmawan, Gumgum; Pontoh, Resa Septiani; Faidah, Defi Yusti
PRIMER : Jurnal Ilmiah Multidisiplin Vol. 1 No. 2 (2023): PRIMER : Jurnal Ilmiah Multidisiplin, April 2023
Publisher : LPPM Institut Teknologi Dan Kesehatan Aspirasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Produk Domestik Bruto (PDB) memiliki peran yang sangat penting dalam untuk mengerti kondisi perekonomian negara. Penelitian ini bertujuan untuk memodelkan variabel PDB dengan mempertimbangkan variabel RTGS (Real Time Gross Settlement). Akan tetapi, data yang digunakan dalam penelitian ini tidak memenuhi asumsi stasioner. Metode yang digunakan pada penelitian ini adalah Vector Error Correction Model (VECM) yang merupakan salah satu model multivariat runtun waktu yang merupakan bentuk Vektor Autoregresive terestriksi dengan data yang tidak stasioner namun kombinasi liniernya memiliki kointegrasi. Hasil analisis model tersebut adalah terdapat kointegrasi antara PDB dan RTGS. Parameter model diestimasi dengan hasil estimasi jangka panjang RTGS signifikan sebesar -0,8828. Dari analisis kausalitas Granger terdapat hubungan satu arah PDB dengan RTGS. Akurasi model ditunjukkan oleh nilai MAPE sebesar 0,10%.
Forecasting Electricity Sales Using the Artificial Neural Network Backpropagation Method Utami, Yosi Febria; Darmawan, Gumgum; Pontoh, Resa Septiani
Asian Journal of Applied Education (AJAE) Vol. 2 No. 4 (2023): October 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ajae.v2i4.6589

Abstract

PT PLN operates in the field of providing electrical energy and one of its goals is to meet consumer needs for electrical energy now and in the future, as well as PLN UID West Java. The initial step is to estimate how much electricity will be sold in the future. For this reason, electricity sales forecasting is carried out which can be taken into consideration by PLN UID West Java in making decisions. This research uses monthly electricity sales data in West Java for the last ten years. This data is not linear and not stationary, so an alternative method is used, namely Artificial Neural Network Backpropagation. Forecasting produces the best network architecture 12-7-1 with a MAPE of 2.965%. This architectural model is used to forecast electricity sales in West Java until August 2024.
Improving the Accuracy of Room Occupancy Forecasts with Hybrid Models Alfarisi, Widi Wildani; Darmawan, Gumgum; Tantular, Bertho
Jurnal Ilmiah Pendidikan dan Pembelajaran Vol. 9 No. 1 (2025): March
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jipp.v9i1.89464

Abstract

Conventional prediction models used so far can often not capture complex patterns influenced by various dynamic factors such as time, weather, scheduled activities, and user behaviour. This study aims to predict the occupancy rate of rooms in a popular tourist destination. The Fuzzy Time Series method was chosen because of its flexibility and ability to work without strict statistical assumptions. The addition of Markov Chains has been shown to reduce the error rate, while SSA improves the model by decomposing the data into trend, seasonal, and residual components. This study found that the hybrid FTSMC-SSA method significantly outperformed the traditional method, with a Mean Absolute Percentage Error (MAPE). This shows that the developed hybrid model has significantly improved the accuracy of room occupancy forecasting compared to a single conventional model. This model can capture complex temporal and non-linear patterns in occupancy data by combining machine learning methods such as Random Forest and Long Short-Term Memory (LSTM) and statistical approaches such as ARIMA. The implications of this study are significant for facility management and space planning in various sectors, such as offices, educational institutions, hospitals, and shopping centres. With the increased accuracy of room occupancy forecasts through hybrid models, managers can make more informed decisions regarding space usage scheduling, automatic lighting and ventilation settings, and energy savings.
ENHANCING 〖PM〗_(2.5) PREDICTION IN KEMAYORAN DISTRICT, DKI JAKARTA USING DEEP BILSTM METHOD Karin, Nabila; Darmawan, Gumgum; Hendrawati, Triyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp185-198

Abstract

Worldwide air pollution is a concern, and this is especially true in Indonesia, where most people breathe air that is more contaminated than recommended by the WHO. The concentration of presents notable health hazards. The respiratory system is the primary route of absorption for , allowing it to enter the lung alveoli and enter the bloodstream. Given the significant health risks associated with exposure, accurate forecasting methods are crucial to anticipate and mitigate its effects. Traditional forecasting methods like ARIMA have limitations in handling non-linear and complex patterns. Therefore, an accurate machine learning method is needed to improve forecasting performance. This research employs Deep Bidirectional Long-Short Term Memory (BiLSTM), a deep learning model particularly suited for time series forecasting due to its ability to capture both past and future dependencies in sequential data. To achieve accurate and precise forecasts for predicting concentration levels in Kemayoran District in November , 2023 (24 hours), this research utilized hourly concentration data from May until October , 2023, using Deep BiLSTM. The outcomes demonstrated the efficiency of the model, attaining a Mean Absolute Percentage Error (MAPE) of 17.1540% (training) and 14.2862% (testing) with an 80:20 data split. The optimal parameters, which comprised 24 timesteps, Adam optimizers with a learning rate of 0.001, 16 batch sizes, 1000 epochs, and ReLU activation functions across multiple BiLSTM layers, showcased the model’s effectiveness in forecasting the concentration in Kemayoran District, DKI Jakarta, on November , 2023.
Prediksi Harga Saham Syariah menggunakan Bidirectional Long Short Term Memory (BiLSTM) dan Algoritma Grid Search Puteri, Dian Islamiaty; Darmawan, Gumgum; Ruchjana, Budi Nurani
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23297

Abstract

Sharia stocks are one of the investment instruments in the Islamic capital market. In the capital market, it is known that stock prices are very volatile. This makes investors need to carry out a strategy for making the right decision in investing, one of which can be done by predicting stock prices. In this study, predictions were made using historical data on the closing price of Islamic shares of PT. Telkom Indonesia Tbk with the Bidirectional Long Short Term Memory (BiLSTM) method. In building the best prediction model, it is necessary to choose the right parameters and one way to do this is to use the grid search algorithm. Based on the results of the test analysis, it was found that the smallest Mean Absolute Percentage Error (MAPE) value was found in the BiLSTM model in the distribution of data with a percentage of 90% training data and 10% testing data and parameter values obtained based on parameter tuning using grid search, including the number of neurons 25, 100 epochs, 4 batches, and 0.2 dropouts. The MAPE obtained in this study was 10.83% and based on the scale on the MAPE value criteria, this shows that the resulting prediction model is accurate. As for the test results from the comparisons made on the BiLSTM and LSTM models using grid search as a tuning parameter and models without using a grid search or it can be called a trial and error approach as a tuning parameter, it is found that the model with better predictive performance is found in BiLSTM using a grid search. compared to other models.
Co-Authors Akbar, Muhammad Faizal Alfarisi, Widi Wildani Bertho Tantular Budi Nurani Ruchjana Dedi Rosadi Eko Yulian eko yulian, eko Faidah, Defi Yusti Hadi, Juandi Hendrawati, Triyani Hirlan Khaeri Karin, Nabila Khaeri, Hirlan Neneng Sunengsih Novianti Indah Putri Puteri, Dian Islamiaty Resa Septiani Pontoh Ruchjana, Budi N Sastradipraja, C K Sitepu, Aldi Anugerah Sitohang, Yosep Oktavianus Sri Sutjiningtyas Sudartianto, Sudartianto Utami, Yosi Febria Zen Munawar