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Pengaruh Luas Kebakaran Hutan dan Lahan Terhadap Tingkat Pencemar Udara PM10 Dwi Maharani; Dewi Arafa Azra; Sarah Aprilia; Uzzi Ziqma; Resa Septiani Pontoh
Jurnal Ekologi, Masyarakat dan Sains Vol 2 No 2 (2021): Jul-Des 2021
Publisher : ECOTAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55448/ems.v2i2.14

Land and forest fires that took place in the Indonesian Province will produce some substances that can pollute the air. The air pollution is monitored by the Air Pollution Standard Index or ISPU which consist of a concentration value of 5 kinds of substances as parameters, such as particulate (PM10), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3). The causes of land and forest fires vary, it could be due to nature and humans. Forest fires caused by nature can occur due to a prolonged dry season, while those caused by humans can occur due to the burning of forests to open land for new plantations. Land and forest fires can also be detected, as mentioned above, using the air pollution standard index (ISPU). This research examines the effect of forest and land fires on the level of air pollution, which focused on examining the particulate (PM10). The method used is simple linear regression, which is an equation that can describe the relationship between dependent variables and independent variables. The results of simple linear regression analysis are Y=26,605+8,99E-5X, and with a confidence level of 95%, the area of Forest and Land Fires (Ha) Province in Indonesia in 2019 (X) has a significant effect on the average particulate matter (PM10) of Province in Indonesia in 2019 (Y).

PENERAPAN CONFIGURAL FREQUENCY ANALYSIS (CFA) UNTUK MENENTUKAN KARAKTERISTIK USER DAN NON USER MOTOR X DI KOTA CIREBON (Studi Kasus PT. XYZ) Muhamad Iqbal Mawardi; Bertho Tantular; Resa Septiani Pontoh
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 9 No 2 (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.2017.9.2.2868

ABSTRACT. PT. XYZ has a role as the main dealer of motor X in West Java with marketing area in three cities: Bandung, Bekasi and Cirebon. The company survey always held every year about Brand Awareness & Image to users and non user motor X to know the characteristics of consumers. Based on the company report survey there was a decrease in the indicators of Top Of Mind Unit in Cirebon City for the user of 30.8% and for non users of 9.8%. This study aims to determine the characteristics that cause the decline of the Top Of Mind Unit based on the configuration of user characteristics and non user motor X in Cirebon City by using Configural Frequency Analysis (CFA). From the results of data analysis can be seen that there are three configurations of user characteristics and non users that deviate from the base model is formed. These three configurations can be considered in determining the marketing strategy to maintain and improve the Top Of Mind Unit in Cirebon City.

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%.

PENERAPAN CONFIGURAL FREQUENCY ANALYSIS (CFA) UNTUK MENENTUKAN KARAKTERISTIK USER DAN NON USER MOTOR X DI KOTA CIREBON (Studi Kasus PT. XYZ) Muhamad Iqbal Mawardi; Bertho Tantular; Resa Septiani Pontoh
Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Vol 9 No 2 (2017): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2017.9.2.2868

ABSTRACT. PT. XYZ has a role as the main dealer of motor X in West Java with marketing area in three cities: Bandung, Bekasi and Cirebon. The company survey always held every year about Brand Awareness & Image to users and non user motor X to know the characteristics of consumers. Based on the company report survey there was a decrease in the indicators of Top Of Mind Unit in Cirebon City for the user of 30.8% and for non users of 9.8%. This study aims to determine the characteristics that cause the decline of the Top Of Mind Unit based on the configuration of user characteristics and non user motor X in Cirebon City by using Configural Frequency Analysis (CFA). From the results of data analysis can be seen that there are three configurations of user characteristics and non users that deviate from the base model is formed. These three configurations can be considered in determining the marketing strategy to maintain and improve the Top Of Mind Unit in Cirebon City.

Association Between Smoking Behavior and Tuberculosis in Indonesia : A Meta-Analysis Oinike, Aritonang Keshia; Pontoh, Resa Septiani; Tantular, Bertho
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2019: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (672.42 KB)

According to WHO, Pulmonary TB is still one of the most deadly diseases and the death rate has reached millions of lives throughout the world including Indonesia. Smoking is one of the main causes of someone contracting pulmonary TB. The goal of this study is to find association between Pulmonary TB and smoking habits in Indonesia using published study. Published studies that reproting Pulmonary TB and smoking habits was complemented by manual searching. Thirty-six studies were selected that covering 12 province in Indonesia. Meta-analysis was conducted using Random effect model. 46641 patients were included in the analysis, and there were 1698 patients that suffering from Pulmonary TB and 44943 patients that not suffering. The association between Pulmonary TB and smoking habits is statistically significant with <0.0001 in p-value and 4.94 in z-score. The pooled odds ratio estimate for smokersvsnon smokers was of 1.8697dan 95% - CI [1.4583; 2.3972] or patients who smoked 1.86 times at risk of pulmonary TB than non-smoking patients. Smoking habits is significantly associated with the risk of Pulmonary TB in Indonesia.

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

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

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.

ANALYTICAL APPROACH OF GENERALIZED LINEAR MODELS FOR HANDLING OVERDISPERSION IN POVERTY DATA OF INDONESIA Arisanti, Restu; Pontoh, Resa Septiani; Winarni, Sri; Wibowo, Fellita Odelia; Khairunnisa, Hanifah; Pratama, Raissheva Andika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1575-1586

Poverty is one of the complex phenomena that occurs in Indonesia. Various socio-economic variables in Indonesia influence poverty, which we can mathematically model using the Generalized Linear Model (GLM) framework. In this study, we modeled data on the number of poor people per province in 2023 taken from the Badan Pusat Statistik of Indonesia website. The response variable in this study was initially assumed to exhibit equidispersion, where the variance equals the mean. However, the observed variance exceeded the mean, indicating overdispersion. Consequently, Negative Binomial Regression, an extension of the GLM that introduces an additional dispersion parameter, was applied to account for this overdispersion. This approach accommodates overdispersed count data by incorporating a gamma-distributed latent variable. The aim of this study is to determine the best model using Negative Binomial Regression in handling overdispersion in Indonesia's poverty data. This model was chosen for its robustness in capturing increased data variability, enabling the identification of factors that influence poverty. The results of this study offer a mathematically rigorous approach to better understand the underlying dynamics of poverty across provinces in Indonesia.

Identification and Modelling Tuberculosis Incidence Risk Factors in West Java with Negative Binomial Mixed Model Random Forest Arisanti, Restu; Pontoh, Resa Septiani; Winarni, Sri; Putri, Nisa Akbarilah; Maurin, Stefany
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): 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.v10i2.29750

Tuberculosis (TB) remains a major public health problem in many parts of the world, including in West Java Province, Indonesia. By guiding targeted medication, an accurate assessment of TB risk factors can enhance overall efforts to control tuberculosis. This study introduces modelling by integrating Negative Binomial Mixed Models (NBMM) and Random Forest (RF) called the Negative binomial mixed model random forest (NBMMRF) model. This model is used to identify and assess risk factors associated with the incidence of tuberculosis. First, utilized NBMM to add fixed effects and random effects in the model and compensate for overdispersion. Modelling count data with overdispersion is a crucial problem in epidemiological studies, and the NBMM component in this model provides a flexible. Afterward, we include a Random Forest component in the model, which helps us detect relevant predictive features and change model weights accordingly. The resulting Negative Binomial Mixed Model Random Forest (NBMMRF) has a high accuracy value of up to 0.915. In contrast to simpler models, the NBMMRF model can capture complex and nonlinear interactions between predictors and outcomes.

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