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All Journal Media Statistika Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Journal of Natural Science and Integration JURNAL EKSAKTA PENDIDIKAN (JEP) Eksakta : Berkala Ilmiah Bidang MIPA Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Leibniz: Jurnal Matematika Journal of Mathematics UNP UNP Journal of Statistics and Data Science Indonesian Journal of Statistics and Its Applications
Yenni Kurniawati
Jurusan Matematika, FMIPA Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Other

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Implementation of an Artificial Neural Network Based on the Backpropagation Algorithm in Forecasting the Closing Price of the Jakarta Composite Index (IHSG) Muhammad Fadhil Aditya Aditya; Zilrahmi; Yenni Kurniawati; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss1/137

Abstract

Investing is highly common in Indonesia. Continuous investment activities carried out by the community will increase economic activity and employment opportunities, increase national income, and increase the level of prosperity of the community. In carrying out share buying and selling transactions, there is a means for companies to obtain funds from official financiers or investors, which is called the capital market. One of the indices issued by the IDX is the Jakarta Composite Index (IHSG). Statistics can be used to help investors, the government, or related institutions to predict the value of the IHSG. One method that can be used to predict data is an Artificial Neural Network (ANN). Backpropagation method is a multi-layer ANN method that works in a supervised learning. The idea of the Backpropagation algorithm is that the input of the neural network is evaluated against the desired output results. The purpose of this research is to give forecasting values with high accuracy to describe the movement of IHSG close price values using the ANN method based on the Backpropagation algorithm. The research showed that the BP (4,6,1) model produced an RMSE value of 28,24024 and a MAPE value of 0.00342%. Based on the results of this research, an Artificial Neural Network model based on the Backpropagation Algorithm can be applied to predict the IHSG Closing Price value.
Sentiment Analysis of DANA Application Reviews on Google Play Store Using Naïve Bayes Classifier Algorithm Based on Information Gain Cindy Caterine Yolanda; Syafriandi Syafriandi; Yenni Kurniawati; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss1/147

Abstract

DANA is a digital payment platform that provides various features to make it easier for users to make payments, transfers, and balance replenishment online. DANA application users provide a variety of reviews that include both constructive and critical opinions, which can be valuable input for DANA application developers. The purpose of this research is to evaluate the results of sentiment classification of DANA application user reviews on the Google Play Store service using the Naïve Bayes Classifier method and Information Gain feature selection. In addition, this study aims to assess the effect of applying IG feature selection on the performance of the resulting model. In this study, reviews are divided into two categories, namely positive and negative based on lexicon-based labeling. Furthermore, data weighting, feature selection, and data division are carried out with a proportion of 80% train data and 20% test data before model building. There are two models, namely a model without feature selection (NBC model) and a model with feature selection (NBC-IG model). The evaluation results showed that the NBC model with 1106 features performed well, with 82.91% accuracy, 83.96% precision, and 90.23% recall. Meanwhile, the NBC-IG model with 536 features showed higher performance, with 85.09% accuracy, 85.79% precision, and 92.09% recall. The application of IG feature selection with the IG value limit parameter > 0.01 in the NBC model successfully reduced the number of features by 570, and improved model performance with an increase in accuracy by 2.18%, precision by 1.83%, and recall by 1.86%.
Artificial Neural Network Model for Estimating the Poor Population in Indonesia as an Effort to Alleviate Poverty Febi Febiola Putri; Atus Amadi Putra; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/154

Abstract

Forecasting the poverty rate in Indonesia is one of the activities that is considered to be able to help various parties, such as being able to help the government in planning more effective and efficient poverty alleviation programs. In this study, forecasting the poverty rate in Indonesia was carried out using the backpropagation artificial neural network method. The purpose of this research is to model and predict the poverty rate using the backpropagation artificial neural network model, and to determine the accuracy of the forecasting results produced by this method. This research is an applied researc. The data used is annual data on proverty in Indonesia from 2917-2021. The data is then divided into two parts, namely training data and test data. The results show that the best artificial network model is BP (7,7,2) with 7 neurons in the input layer, 7 neurons in the hidden layer, and 2 neurons in the output layer. The accuracy of this model is good with a MAPE value of 0.07633%. The forecasting results in the next period show that the highest number of poor people is East Java province with a value of 3604.1698 thousand people in the first semester (March) of 2022 and has increased in the second semester period (September) of 2022 with a value of 3698.822 thousand people
Perbandingan Algoritma C4.5 dan C5.0 Dalam Klasifikasi Status Gizi Balita Stunting dhea afrila harelvi; Admi Salma; Yenni Kurniawati; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol2-iss2/172

Abstract

Stunting is one of the health conditions that reflect aspects of nutrition and child growth, allowing us to observe the nutritional status of toddlers. The aim of this study is to determine the classification results of the C4.5 and C5.0 algorithms in cases of stunted toddler nutritional status and to compare the results between the C4.5 and C5.0 algorithms in classifying stunted toddler nutritional status using k-fold cross-validation. The data in this study are secondary data. Which is collected from Puskesmas IV Pesisir Selatan Regency. The research variables are divided into two, namely the response variable Y, which is Toddler Nutritional Status, and predictor variables X including Age, Toddler Gender, Toddler Weight, and Toddler Height. The result of the study obtain the algorithm C5.0 produse accuracy value of the C5.0 algorithm is higher than that of the C4.5 algorithm. The C5.0 algorithm provides an average accuracy result of 83% while the C4.5 algorithm provides an accuracy result of 79%. Thus, it can be concluded that the C5.0 algorithm is better at classifying stunted toddler nutritional status.
ANALISIS KEMISKINAN DI INDONESIA MENGGUNAKAN LOCAL INDICATOR OF SPATIAL ASSOCIATION DAN SPATIAL ERROR MODEL Khairani, Putri Rahmatun; Kurniawati, Yenni; Amalita, Nonong; Mukhti, Tessy Octavia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v6i1.966

Abstract

Poverty in Indonesia remains a significant socio-economic challenge with notable regional disparities. The eastern provinces, particularly Papua, Maluku, and East Nusa Tenggara, experience persistently high poverty rates, suggesting a strong spatial influence. This study examines the spatial distribution of poverty using the Local Indicators of Spatial Association and the Spatial Error Model with 2024 data from the Indonesian Central Statistics Agency (BPS) for 38 provinces. The analysis employs a K-Nearest Neighbors weighting matrix (k = 10) for spatial dependencies. The LISA results identify High-High poverty clusters in Papua, Maluku, and East Nusa Tenggara. In contrast, Low-Low clusters are concentrated in Java and Bali, indicating a strong spatial pattern (Moran’s I = 0.4448). SEM findings reveal that the Gini index (β = 29.97) and population density (β = 0.016) significantly influence poverty, whereas inflation and total population do not. The model explains 76.1% of poverty variance (R² = 0.760966), highlighting its superiority over traditional regression models. These findings underscore the need for spatially adaptive policies to address poverty effectively. Policymakers should prioritize equitable economic development, regional investment, and infrastructure improvements, particularly in high-poverty clusters. Integrating spatial econometric models with KNN provides deeper insights into interregional disparities, supporting more precise and inclusive development strategies
Mapping Area of Nagari Tanjung Gadang Sijunjung Regency Kurniawati, Yenni; Fitria, Dina; Salma, Admi
Pelita Eksakta Vol 8 No 1 (2025): Pelita Eksakta, Vol. 8, No. 1
Publisher : Fakultas MIPA Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/pelitaeksakta/vol8-iss01/281

Abstract

Developing a digital village as a government point of view supports Nagari Tanjung Gadang as one of Sicantik (a village loving statistics). The village and server got the up to date data about the village and its sub-village. The problem for the village is presenting and analysing the data to publish as it is used. They also found difficulties in writing it into a publication format. The server gave an assistance to write Lumbuang Data Nagari Tanjung Gadang. The result is a book which explains the demographic condition of the village.
Comparison of The Singular Spectrum Analysis and SARIMA for Forecasting Rainfall in Padang Panjang City Putri, Fadhira Vitasha; Fitri, Fadhilah; Kurniawati, Yenni; Zilrahmi, Zilrahmi
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p61-74

Abstract

Indonesia is an area with a tropical climate, so it has two seasons, namely the rainy season and the dry season. The rainy season lasts from November to March and during this period rainfall tends to be high in several areas. Padang Panjang City is one of the cities with the smallest area in West Sumatra Province, which has the nickname Rain City. This is because the city of Padang Panjang has cool air with a maximum air temperature of 26.1 °C and a minimum of 21.8 °C, so this city has a fairly high level of rainfall with an average of 300 to 400 mm/year. This article discusses rainfall forecasting for Padang Panjang City by comparing the Singular Spectrum Analysis and Seasonal Autoregressive Integrated Moving Average methods. The data used spans 8 years, from January 2016 to December 2023. Forecasting results are obtained from the best method selected based on the smallest Mean Absolute Percentage Error value. The Singular Spectrum Analysis method has a Mean Absolute Percentage Error value of 5.59% and Singular Spectrum Analysis and Seasonal Autoregressive Integrated Moving Average  has a value 7.43%. The best forecasting method is obtained by the Singular Spectrum Analysis method.
Classification of Rice Growth Phase Using Regression Logistic Multinomial Model and K-Nearest Neighbors Imputation on Satellite Data Ghaly, Fayyadh; Kurniawati, Yenni; Amalita, Nonong; Fitria, Dina
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p1-9

Abstract

One of the efforts made by the government to maintain food security is to provide statistical data on rice production through accurate calculation of harvest areas using the area sampling framework approach. Although area sampling framework surveys produce accurate estimates, the costs required are quite high when applying this method. To overcome this problem, one solution that can be applied is to utilize satellite imagery to monitor the greenness index of plants using the enhanced vegetation index. However, in real conditions, the Landsat-8 optical satellite is susceptible to cloud cover, which results in missing data. This study aims to model the phase of rice plants using the regression logistic multinomial model by utilizing Landsat-8 satellites and k-nearest neighbors imputation handling to overcome missing data. The results showed that the model had varying performance in each phase, with an average balanced accuracy of 66.45%. This figure shows that the model can classify the area sampling framework data imputed using the k-nearest neighbors imputation method well. The model shows optimal performance in the late vegetative and generative phases but is less effective in detecting the harvest, puso, and non-rice paddy phases.
Application of Singular Spectrum Analysis in Predicting Rupiah Exchange Yuan Hendrawan, Muhammad; Zilrahmi, Zilrahmi; Kurniawati, Yenni; Fitria, Dina
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p75-85

Abstract

The exchange rate between two countries is the price of the currency used by residents of these countries to trade with each other, the relationship between the Rupiah exchange rate and the Yuan is one of the important aspects in the dynamics of international trade. Therefore, forecasting the exchange rate is important as an effort to predict the exchange rate of Rupiah against Yuan in the future. The method used for forecasting is Singular Spectrum Analysis, namely decomposition and reconstruction. The accuracy of the resulting forecast is measured using the Mean Absolute Percentage Error criterion. The exploration results obtained are forecasting accuracy based on the Mean Absolute Percentage Error value of 2.15% with a window length of 23 which identifies that the forecasting results are accurate and effective. Forecasting is said to be accurate if the Mean Absolute Percentage Error value is lower than 10% and close to 10%
Peramalan Curah Hujan Sebagai Upaya Mitigasi Bencana Menggunakan Seasonal Autoregressive Integrated Moving Average Fayyadh Ghaly; Amelia Susrifalah; Yenni Kurniawati
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 1 (2025): VOLUME 13 NO 1 TAHUN 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i1.55289

Abstract

Rainfall prediction is important in disaster mitigation to reduce impacts such as drought, flood, and landslide. Rainfall data that has a seasonal pattern requires an appropriate forecasting method, one of which is SARIMA. This study predicts rainfall at the Deli Serdang Climatology Station, North Sumatra, based on monthly observation data for 2018–2023, showing a seasonal pattern with a 12-month cycle. The best model obtained is SARIMA (0,0,1) (0,0,1)12 with a MAPE of 19.5%, indicating a prediction accuracy of 80.5%. The forecasting results indicate a decrease in rainfall in the first semester of 2024, which is in the medium rainfall category. These findings can support disaster risk mitigation strategies and natural resource management planning related to climate change. The SARIMA model also has the potential to be applied in further climatology studies.
Co-Authors Afifa Lufti Insani AL Rezki Ivansyah Amelia Susrifalah Anang Kurnia Andre Marvero Anggi Adrian Danis Anjelisni, Nining Annisa Rizki Amalia Arnellis Arnellis Atus Amadi Putra Aulia, Yuke Aurumnisva Faturrahmi Cindy Caterine Yolanda Deska Warita Dewi Murni dhea afrila harelvi Dina Fitria Dina Fitria Dina Fitria, Dina Disti Harlin Dodi Vionanda Dony Permana Dwi Sulistiowati, Dwi Elfiani Sarian Bur Elfin Innaka Hamidah Elza Vinora Fadhilah Fitri Fahmi Amri Fashihullisan Fayyadh Ghaly Fayza Annisa Febrianti Febi Febiola Putri Fitri, Fadhilah Ghaly, Fayyadh Hadiyanti Riskha Hary Merdeka Helma Helma Helma Helma Hendrawan, Muhammad Ihsan Dermawan Irwan Irwan Khairani, Putri Rahmatun Khairisa Putri, Nadya Kusman Sadik Lutfian Almash M Fathoni Arnas Manja Danova Putri Meira Parma Dewi Minora Longgom Nasution Muhammad Fadhil Aditya Aditya Muhammad Fadhil Irsyad NA Mentacem Nofriadi, Berliana Nonong Amalita Oktaviani, Bernadita Permana, Dony Prida Nova Sari Putri, Fadhira Vitasha rahmad revi fadillah Salma, Admi Siskha Maulana Basrul Syafriandi Syafriandi Syafriandi Syafriandi Tessy Octavia Mukhti Tessy Octavia Mukhti Wimmi Sartika Windi Dwi Saputra Yunistika Ilanda Zamahsary Martha Zilrahmi, Zilrahmi