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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
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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Penerapan Metode Grey-Markov(1,1) Untuk Peramalan Penerimaan di Kantor Pengawasan dan Pelayanan Bea Cukai Tipe Madya Pabean Cikarang Mulya, Callista Audrey; Darmawan , Gumgum; Yusti Faidah, Defi; Ahdika, Atina
Indo-MathEdu Intellectuals Journal Vol. 4 No. 3 (2023): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v4i3.431

Abstract

The Customs Supervision and Service Office is given a revenue target that must be achieved annually. However, revenue at the Customs Supervision and Service Office tends to fluctuate because it is strongly influenced by various external factors that are difficult to predict. Projections need to be done to see if the given revenue target can be achieved. This study aims to conduct forecasting so that it can be estimated how much revenue will be at the end of the year (December 2023). Research is conducted using the Grey(1,1) and Grey-Markov(1,1) models. The analysis results show that the Grey-Markov(1,1) model provides better forecasting accuracy compared to the Grey(1,1) model with a MAPE value of 5.390541% and a Posterior Error Ratio of 0.190644. These results show that the Grey Markov(1,1) model is more accurate than the Markov(1,1) mode, and that this method (Grey Markov(1,1)) is very good for forecasting with little data.
Extreme Gradent Boosting Method Forecasting Rainfall in Lembang District, West Java Province Putri, Salma Azzahra; Darmawan , Gumgum; Arisanti, Restu; Clarissa Clorinda, Chrysentia
Indo-MathEdu Intellectuals Journal Vol. 4 No. 3 (2023): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v4i3.452

Abstract

Lembang is a notable regional tourism destination that bears considerable significance within the urban area of Bandung. Lembang is widely recognized for its flourishing agricultural sector, which supports a significant community of farmers engaged in the cultivation of fruits, vegetables, and ornamental plants, in addition to its intrinsic scenic beauty. Therefore, the acquisition of precipitation data is of considerable significance for individuals live in the area to maintain their economic endeavors. This study employs daily historical data from the period of 2018 to 2021, wherein approximately 70% of the data is categorized as sparse. This discourse aims to examine the utilization of the Extreme Gradient Boosting (XGboost) technique for predicting rainfall in the Lembang region, specifically emphasizing its effectiveness in handling limited data. The findings indicate that the model, when trained and tested using a 7:3 data split ratio, achieved a mean absolute error (MAE) of 1.834 for training and 4.473 for testing. Additionally, the root mean square error (RMSE) was calculated to be 3.319 for training and 7.637 for testing. The optimal hyperparameters consist of a learning rate of 0.005, a max_depth value of 10, and the utilization of 300 decision trees as n_estimators. The model effectively captures the pattern of sparse time series data and non-rainy days data, as evidenced by its low error metrics. However, it slightly underestimates the rainfall rate on the days with intense precipitation
PERAMALAN PENDAPATAN ASLI DAERAH MENGGUNAKAN METODE EXPONENTIAL SMOOTHING DENGAN PENDEKATAN STATE SPACE gumgum, Gumgum Darmawan
BEGIBUNG: Jurnal Penelitian Multidisiplin Vol. 1 No. 2 (2023): Vol. 1 No. 2 (2023): BEGIBUNG : Jurnal Penelitian Multidisiplin, November 2023
Publisher : Lembaga Berugak Baca

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62667/begibung.v1i2.17

Abstract

Pendapatan Asli Daerah (PAD) merupakan dana yang diperoleh daerah antara lain dari hasil pajak, retribusi, pengelolaan kekayaan daerah, dan pendapatan daerah lain yang sah. PAD berfungsi untuk melihat gambaran keberhasilan pembangunan daerah dan secara langsung akan digunakan kembali untuk membiayai pembangunan daerah. Pada tahun 2021, Bappeda Kabupaten Cianjur menyusun draft RPJMD yaitu rancangan rencana pembangunan daerah selama 5 tahun ke depan yang di dalamnya berisi proyeksi anggaran dana Kabupaten Cianjur. Pada draft tersebut angka proyeksi PAD sangat tinggi sehingga menimbulkan kekhawatiran akan terlaksananya kegiatan pembangunan yang sudah direncanakan. Maka dilakukan peramalan PAD dengan metode statistika untuk membantu menyusun perencanaan yang tepat. Dalam melakukan peramalan, diperlukan metode yang sesuai sehingga dapat memberikan peramalan yang akurat. Pada penelitian ini ukuran data penelitian tidak banyak (n=29) dan data mengandung pola. Oleh karena itu penelitian ini menggunakan metode exponential smoothing dengan pendekatan model state space yang mana merupakan hasil dari pengembangan dari metode pemulusan eksponensial standar dengan menggunakan pendekatan model state space yang memperhatikan aspek level, tren, musiman, serta error sehingga model yang dihasilkan untuk melakukan peramalan lebih sesuai dengan data penelitian menghasilkan nilai akurasi prediksi yang lebih baik. Berdasarkan hasil analisis diperoleh model terbaik yang digunakan adalah model ETS(M,A,N) dan dihasilkan nilai MAPE sebesar 12,0119%.
Peramalan Konsumsi Gas Alam Amerika Serikat dengan Double Seasonality menggunakan Singular Spectrum Analysis (SSA) Amanah Dwiadi, Qurnia; Indriani , Ayu; Samaria Nauli, Theresia; Nurhapilah, Hani; Darmawan , Gumgum
Innovative: Journal Of Social Science Research Vol. 3 No. 6 (2023): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

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Abstract

Gas alam memainkan peran penting dalam memenuhi kebutuhan energi Amerika Serikat, yang merupakan salah satu konsumen terbesar di dunia. Konsumsi gas alam terus meningkat sejak implementasi proyek "An America First Energy Plan" pada tahun 2017. Peramalan yang akurat tentang jumlah konsumsi gas alam sangat dibutuhkan. Dalam penelitian ini, kita akan menggunakan model Singular Spectrum Analysis (SSA). Metode ini tidak memerlukan pemenuhan asumsi parametrik dan diterapkan dengan baik pada data musiman. SSA dapat menggambarkan pola tren dan komponen lainnya dengan struktur sederhana. Konsep utamanya adalah ‘pemisahan’ yang mengkarakterisasi seberapa baik komponen berbeda dapat dipisahkan satu sama lain. SSA terdiri dari dua tahap yang saling melengkapi, yaitu tahap dekomposisi dan tahap rekonstruksi.Dari hasil pengujian, data konsumsi gas alam yang digunakan dalam penelitian ini memiliki pola musiman. Berdasarkan analisis model tersebut, yang memberikan nilai MAPE sebesar 1,62 % , dengan hasil peramalan yang ralatif konstan setiap tahunnya.
Model Peramalan Double Seasonal Pada Data Konsumsi Gas Alam Amerika Serikat Dengan Pendekatan Analisis Spektral Fitriani Azuri, Dila; Putri Syallya, Najma Rafifah; Najwa, Sandrina; Alifia, Wanda; Darmawan , Gumgum
Innovative: Journal Of Social Science Research Vol. 3 No. 6 (2023): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

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Abstract

Gas alam merupakan sumber energi utama di seluruh dunia dan saat ini banyak digunakan untuk berbagai keperluan di Amerika Serikat. Konsumsi gas alam di Amerika Serikat memiliki pola double seasonal yang terjadi karena faktor iklim. Tujuan dari penelitian ini adalah untuk menemukan periode tersembunyi pada data dan meramalkan konsumsi gas alam dengan menggunakan analisis spektral dan metode Double Seasonal ARIMA. Hasil analisis spektral menunjukkan bahwa terdapat double seasonal dengan periode 12 bulan dan 6 bulan, yang berarti karakteristik perubahan konsumsi gas alam di Amerika Serikat cenderung meningkat atau menurun setiap 12 bulan dan 6 bulan. Model yang terpilih, SARIMA(0,1,0)(1,1,4)6(1,1,1)12, menunjukkan keefektifannya dalam memprediksi pola konsumsi di masa mendatang dengan MAPE sebesar 2,61% yang mengindikasikan keandalan model yang tinggi.
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

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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 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