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Application of ARIMA to Curly Red Chili Prices in Bengkulu City Melda Juliza; Puce Angreni
INSOLOGI: Jurnal Sains dan Teknologi Vol. 2 No. 2 (2023): April 2023
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v2i2.1871

Abstract

Curly red chili in Bengkulu City often experiences price fluctuations from time to time. These price fluctuations are sometimes very extreme, causing public unrest both for food processing industry entrepreneurs and for daily household needs. Therefore, this study uses time series techniques to predict the price of curly red chili in Bengkulu City. This study discusses chili price forecasting using the Box-Jenkins ARIMA model based on curly red chili price data in Bengkulu City from 03 October 2022 to 28 February 2023. This research aims to look at the accuracy of the best model for curly red chili prices in Bengkulu city for the ARIMA model based on ACF & PACF criteria with autocorrelation coefficient values, and the smallest AIC criteria with the auto.arima function in R software. Next, forecast the price of curly red chili in Bengkulu City for the next period with the ARIMA model based on the best criteria obtained. Based on the ADF test, it can be seen that the data is not stationary so the data differencing process is carried out. The analysis results show that the best ARIMA model for curly red chili price data in Bengkulu City is the automatic ARIMA model with the smallest AIC criteria using the auto.arima function with the value of RMSE is 4197.7. The ARIMA model that is formed is the ARIMA (1,1,1) model. Next, the results of forecasting the price of curly red chili for Bengkulu City obtained based on the ARIMA (1,1,1) on 01 March 2023 is Rp 41,700.
Comparison of Methods ARIMA and MAR Models with MODWT Decomposition on Non-Stationary Data Angreni, Puce; Melda Juliza
INSOLOGI: Jurnal Sains dan Teknologi Vol. 2 No. 2 (2023): April 2023
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v2i2.1888

Abstract

The forecasting methods used in this study are Autoregressive Integrated Moving Average (ARIMA) and Multiscale Autoregressive (MAR). The ARIMA model does not include predictor variables in the model. The MAR model is a model that performs the transformation process using wavelets. The MAR model adopts an autoregressive time series (AR) model with wavelet coefficients and scale coefficients as predictors. The wavelet coefficient and scale are obtained by decomposition using Maximal Overlap Discrete Wavelet Transformation (MODWT). MODWT functions to describe data based on the level of each wavelet filter. This study aims to determine the best forecasting model using ARIMA and MAR models. The time series data used in this study is data on the rupiah exchange rate against the US dollar. Data on the rupiah exchange rate against the US Dollar for 2019-2020 is non-stationary data, so the ARIMA and MAR models can be used in this study.
Evaluasi Metode Inisialisasi pada Model Pemulusan Eksponensial melalui Data IPM Banyumas Raya Melda Juliza; Novita Eka Chandra; Felinda Arumningtyas; Puce Angreni
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13519

Abstract

The forecasting accuracy of exponential smoothing models is significantly influenced by the determination of initial values (initialization). This study aims to evaluate the performance of initialization methods for Brown’s Double Exponential Smoothing model using Human Development Index (HDI) data from the Banyumas Raya region for the period 2010-2025. The research stages included identifying data patterns, constructing models using both simple initialization and optimal initialization with numerical optimization, performing the Ljung-Box test for residual diagnostics, and comparing model accuracy. Evaluation results indicate that the Brown model using the optimal initialization method effectively captures trend patterns. The application of optimal initialization consistently improved model accuracy across all regencies. The highest error improvement was observed in Banyumas Regency (28.65%), followed by Cilacap (28.29%), Banjarnegara (24.77%), and Purbalingga (22.89%). Based on these results, the optimal initialization model was used to project HDI values for the next three periods, revealing a sustained upward trend. In conclusion, determining initial values is a crucial component that alongside smoothing parameter optimization must be seriously considered when developing forecasting models.
Perbandingan Model Klasifikasi Multikelas Tingkat Depresi Mahasiswa dengan Skor PHQ-9 Felinda Arumningtyas; Puce Angreni; Lutfiah Maharani Siniwi
UJMC (Unisda Journal of Mathematics and Computer Science) Vol. 12 No. 1 (2026): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v12i1.13601

Abstract

Depression is one of the most common mental health disorders among university students and may adversely affect academic performance and social functioning. The severity of depression can be assessed using the Patient Health Questionnaire-9 (PHQ-9), which classifies individuals into several levels of depression severity. This study aims to compare several machine learning models for multiclass classification of student depression levels based on PHQ-9 scores. The study employed the PHQ-9 Student Depression Dataset consisting of 682 student records. Predictor variables included age, gender, the nine PHQ-9 items, sleep quality, study pressure, and financial pressure. The models evaluated were Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and XGBoost. Model performance was assessed using accuracy, precision, recall, and F1-score metrics. The results indicate that XGBoost achieved the best performance, with an accuracy of 78,10%, macro precision of 0,77, macro recall of 0,77, and macro F1-score of 0,77. These findings demonstrate that XGBoost provides relatively good performance in the multiclass classification of student depression levels. This study suggests that machine learning approaches have the potential to support the identification of depression severity among university students.
Comparison of Geographically Weighted Regression with Adaptive Gaussian and Bisquare Kernel on Open Unemployment Rate in Riau Islands Widya Reza; Febrya Christin Handayani Buan; Puce Angreni
Leibniz: Jurnal Matematika Vol. 6 No. 01 (2026): Leibniz: Jurnal Matematika
Publisher : Program Studi Matematika - Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/leibniz.v6i01.705

Abstract

Regression analysis is an analysis to determine the relationship and influence of independent variables on the dependent variable. If the data has a spatial relationship, this analysis has the potential to produce a less accurate model because the regression analysis ignores the influence of the location. One of the data indicated to have a spatial relationship is the open unemployment rate. One spatial analysis that can be used to accommodate spatial relationships is the Geographically Weighted Regression (GWR) model. In the GWR model, a spatial weighting matrix is required whose size depends on the proximity between locations. In this study, two spatial weighting matrix were used: Adaptive Gaussian Kernel and Adaptive Bisquare Kernel. Based on the results of the analysis, it is known that the factors influencing the open unemployment rate in the Riau Islands in 2024 at several locations are the human development index, Economic Growth, and Minimum Wages by Regency/City. Based on the R2 value and AIC value, the best spatial weight matrix produced is the Adaptive Bisquare Kernel weighting function with an R2 value of 93.32% and an AIC value of 15.2835.
Co-Authors Febrya Christin Handayani Buan Felinda Arumningtyas Lutfiah Maharani Siniwi Melda Juliza Novita Eka Chandra Widya Reza