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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 Jurnal Statistika dan Matematika (Statmat) Tematik : Jurnal Teknologi Informasi Komunikasi Indonesian Journal of Contemporary Multidisciplinary Research Euclid Asian Journal of Applied Education Parameter: Jurnal Matematika, Statistika dan Terapannya Future Academia : The Journal of Multidisciplinary Research on Scientific and Advanced PRIMER: Jurnal Ilmiah Multidisiplin
Gumgum Darmawan
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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

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Sampling Design for Car Survey Using Stratified Random Sampling Widodo, Valeno Glenedias; Suparman, Yusep; Darmawan, Gumgum
Indonesian Journal of Contemporary Multidisciplinary Research Vol. 2 No. 5 (2023): September 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/modern.v2i5.5872

Abstract

Perumda Pasar Juara wants to know the number of cars that enter the Kosambi Market parking area for one month. However, limited access to available data means that Perumda has to count vehicles manually. Therefore, an appropriate sampling design is needed to estimate the number of cars. The data used is the result of observations based on preliminary sampling on October 14-20, 2022 at 08:00-17:00 WIB. The variables of the data used are car arrival time, day dummy, and hour dummy. The method used is dummy regression analysis and stratified sampling design. The regression analysis results show that there are four strata where each stratum has a three-parameter Weibull distribution. Based on the results, the minimum sample size required with a 5% error rate is 116 hours per day and is allocated according to the strata
Modelling Primary Energy by Long Memory Time Series Darmawan, Gumgum; Budhi Handoko
Indonesian Journal of Contemporary Multidisciplinary Research Vol. 2 No. 6 (2023): November 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/modern.v2i6.6970

Abstract

This research employs long memory modeling techniques to analyze and forecast global energy data spanning from 1965 to 2022. Focusing on the ARFIMA (Autoregressive Fractionally Integrated Moving Average) model, the study demonstrates its efficacy in predicting energy consumption trends. The evaluation of forecasting results for the subsequent four years reveals a remarkable Mean Absolute Percentage Error (MAPE) below 5%. This outcome underscores the effectiveness of incorporating long memory components in energy modeling, offering a robust approach for accurate and reliable predictions. The findings contribute to the advancement of energy forecasting methodologies, providing valuable insights for policymakers, energy analysts, and researchers in the pursuit of sustainable and informed energy planning
Forecasting Ferry Passenger Traffic in New York City Using the Seasonal Arima (SARIMA) Model Aribah, Rana; Apriliana, Linda; Darmawan, Gumgum
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 3 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i3.54879

Abstract

This study addresses the seasonal and long-term fluctuating passenger volume patterns typical of water transportation systems such as NYC Ferry, necessitating practical forecasting methods to support operational decision-making and public transportation planning. The research aims to develop a forecasting model for NYC Ferry passenger counts using the Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology. The analysis utilizes monthly historical passenger data from January 2020 to December 2024 for training data. Key analytical steps include testing data stationarity, splitting the dataset into training and testing subsets, modeling via RStudio, forecasting, and evaluating model accuracy using Mean Absolute Percentage Error (MAPE) compared against actual observations. Results indicate that the SARIMA(1,0,0)(0,1,1)12 model outperforms other methods, yielding the lowest MAPE of 5.04%, compared to Multiplicative Winters (8.57%), SARFIMA (17.62%), and Holt-Winters (32.93%). The SARIMA model effectively captures both seasonal and monthly trends, producing accurate passenger volume predictions. These findings demonstrate SARIMA’s efficacy in monthly NYC Ferry ridership forecasting, contributing to time series literature, particularly within public transportation forecasting. Furthermore, the results offer practical insights for policymakers to strategize service capacity and enhance data-driven management of waterborne transit systems more efficiently.
Forecasting Arrival Delay at Hartsfield–Jackson Atlanta International Airport ‎Using Autoregressive Fractionally Integrated Moving Average (ARFIMA) Pratama, Angga; Amelia, Kiki; Darmawan, Gumgum
Future Academia : The Journal of Multidisciplinary Research on Scientific and Advanced Vol. 4 No. 1 (2026): Future Academia : The Journal of Multidisciplinary Research on Scientific and A
Publisher : Yayasan Sagita Akademia Maju

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61579/future.v4i1.697

Abstract

Keterlambatan penerbangan merupakan salah satu tantangan utama dalam menjaga efisiensi dan kualitas ‎layanan transportasi udara. Permasalahan ini berdampak pada peningkatan biaya operasional, gangguan ‎jadwal, serta penurunan kepuasan penumpang. Bandara Hartsfield–Jackson Atlanta International (ATL) ‎memiliki peran strategis sebagai pusat lalu lintas udara domestik di Amerika Serikat dengan volume ‎penerbangan tertinggi di dunia, sehingga memerlukan sistem peramalan yang akurat untuk mendukung ‎pengambilan keputusan operasional dan pengendalian lalu lintas udara. Penelitian ini bertujuan untuk ‎meramalkan keterlambatan kedatangan pesawat menggunakan model Autoregressive Fractionally ‎Integrated Moving Average (ARFIMA) yang mampu menangkap karakteristik long-memory pada data ‎deret waktu. Data yang digunakan berupa data bulanan keterlambatan kedatangan pesawat domestik ‎menuju ATL selama periode 2019–2023. Analisis dilakukan melalui tahapan identifikasi model, estimasi ‎parameter, diagnostik residual, serta evaluasi akurasi menggunakan ukuran Mean Absolute Scaled Error ‎‎(MASE). Hasil penelitian menunjukkan bahwa model terbaik adalah ARFIMA (1, d = 0,1821, 0) dengan ‎nilai MASE sebesar 0,876, yang menandakan tingkat akurasi peramalan yang baik. Model ini terbukti ‎efektif dalam menangkap pola fluktuasi jangka panjang dan ketergantungan temporal yang tidak dapat ‎dijelaskan oleh model ARIMA konvensional. Temuan ini menunjukkan bahwa pendekatan ARFIMA dapat ‎digunakan sebagai alat bantu dalam perencanaan operasional, pengelolaan kapasitas bandara, serta ‎pengendalian lalu lintas udara, khususnya untuk bandara besar seperti ATL yang memiliki tingkat ‎kepadatan penerbangan tinggi dan kompleksitas operasional yang besar.‎
Peramalan Kedatangan Wisatawan Macanegara ke Provinsi Bali ‎Menggunakan Metode Singular Spectrum Analysis (SSA)‎ Yuliana, Sri; Rafidah, Raihanah; Darmawan, Gumgum
Future Academia : The Journal of Multidisciplinary Research on Scientific and Advanced Vol. 4 No. 1 (2026): Future Academia : The Journal of Multidisciplinary Research on Scientific and A
Publisher : Yayasan Sagita Akademia Maju

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61579/future.v4i1.698

Abstract

Pariwisata Bali adalah sektor strategis dalam berperan penting terhadap perekonomian nasional, ‎khususnya sebagai penyumbang utama devisa negara dan lapangan kerja. Fluktuasi jumlah ‎wisatawan mancanegara sangat dipengaruhi oleh faktor musiman, dinamika ekonomi global, ‎perubahan tren pariwisata internasional, serta guncangan eksternal seperti krisis ekonomi dan ‎pandemi. Oleh karena itu, analisis peramalan wisatawan menjadi penting untuk memahami pola ‎kunjungan dan mendukung perencanaan kebijakan pariwisata yang adaptif dan berkelanjutan. Tujuan dari penelitian ini adalah melakukan peramalan terhadap jumlah wisatawan ke Bali dengan menggunakan pendekatan Singular Spectrum Analysis (SSA). Data bulanan kedatangan wisatawan (2009–2025) dianalisis dengan ‎SSA. Evaluasi akurasi dilakukan menggunakan MAPE. Model peramalan jumlah wisatawan ‎mancanegara di Provinsi Bali menghasilkan nilai MAPE sebesar 7,23%, yang termasuk kategori ‎sangat baik menurut Lewis (1982). Model berhasil menangkap pola tren utama dan fluktuasi ‎jumlah wisatawan dengan baik, dengan tingkat kesesuaian tinggi antara data aktual dan hasil ‎prediksi.‎
Enhancing Rainfall Forecasting Performance in Bandung City Using Bi-LSTM with Grid Search Optimization on Gregorian and Lunar Calendar Data Yunizar, Mahdayani Putri; Talakua, Andrew Hosea; Darmawan, Gumgum
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp595-601

Abstract

Rainfall is a climatic factor that strongly influences human activities and plays a crucial role in decision making related to water resources, mobility, and disaster preparedness. High rainfall intensity may escalate into hydrometeorological hazards, underscoring the importance of accurate rainfall forecasting to support early warning and mitigation efforts. This study aims to compare the forecasting accuracy of monthly rainfall predictions between the Gregorian and lunar calendars using the Bidirectional Long Short-Term Memory (Bi-LSTM) model optimized through a grid search approach. The method is designed to capture temporal patterns arising from the distinct structures of two asynchronous calendars. Daily rainfall data from Bandung City, Indonesia, covering the period from 2000 to 2025, were converted into monthly series in both calendar systems. The results reveal that the Gregorian calendar provides significantly better forecasting performance, achieving the lowest MAPE value of 11.60 percent at the three-month horizon. In contrast, the lunar calendar shows higher variability and reaches its best MAPE of 31.43 percent at the same horizon. These findings indicate that the Gregorian calendar offers a more stable temporal representation for rainfall forecasting in Bandung and supports improved predictive modeling for climate-related decision making.
Mathematical Modeling and Regime Dynamics of Ammonium Chloride Imports in Indonesia Using Threshold Vector Autoregressive Integrated (TVARI) Approach Widiantoro, Carissa Egytia; Arisanti, Restu; Darmawan, Gumgum
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40052

Abstract

Ammonium chloride plays an important role in Indonesia as a key raw material for Nitrogen, phosphorus, and potassium (NPK) fertilizers and various chemical industries. Despite its importance, domestic production remains limited, and the potential supply from by-product sources has not been utilized in a meaningful way. As a result, Indonesia depends heavily on imports that come mainly from a single country. This situation creates vulnerabilities in the industrial supply chain and highlights the need for a clearer understanding of how import volume and import value behave over time. This study offers a mathematical model that examines the joint movement of these two variables by using the Threshold Vector Autoregressive Integrated approach, commonly known as TVARI. The novelty of the study lies in its use of a nonlinear threshold structure to show how the system shifts between different market conditions. The model reveals that import behavior changes across regimes. When import growth is low, movements are shaped mostly by changes in import value. When growth becomes higher, the pattern reflects the stronger influence of supply conditions. A single linear model cannot fully explain these shifts. By presenting a mathematical description of these regime movements, the study enhances the understanding of the underlying dynamics of Indonesia’s industrial imports. The insights support data informed decisions in supply chain planning and relate to Sustainable Development Goal 9 (industry resilience) and Sustainable Development Goal 12 (efficient and responsible use of raw materials). Goal 8 of the Sustainable Development Agenda, which is to encourage stable and sustainable economic activity, is likewise aligned with the enhanced comprehension of market behavior.
Co-Authors Akbar, Muhammad Faizal Alfarisi, Widi Wildani Angga Pratama Apriliana, Linda Aribah, Rana Arisanti, Restu Bertho Tantular Budhi Handoko Budi Nurani Ruchjana Dedi Rosadi Eko Yulian eko yulian, eko Faidah, Defi Yusti Hadi, Juandi Hendrawati, Triyani Hirlan Khaeri Karin, Nabila Khaeri, Hirlan Kiki Amelia, Kiki Neneng Sunengsih Novianti Indah Putri Puteri, Dian Islamiaty Rafidah, Raihanah Resa Septiani Pontoh Ruchjana, Budi N Sastradipraja, C K Sitepu, Aldi Anugerah Sitohang, Yosep Oktavianus Sri Sutjiningtyas Sri Yuliana Sudartianto, Sudartianto Suparman, Yusep Talakua, Andrew Hosea Utami, Yosi Febria Widiantoro, Carissa Egytia Widodo, Valeno Glenedias Yunizar, Mahdayani Putri Zen Munawar