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Journal of Mathematics, Computation and Statistics (JMATHCOS)
Published by Universitas Negeri Makassar
ISSN : 24769487     EISSN : 27210863     DOI : https://doi.org/10.35580/jmathcos
Core Subject : Education,
Mathematics
Fokus yang didasarkan tidak hanya untuk penelitian dan juga teori-teori pengetahuan yang tidak menerbitkan plagiarism. Ruang lingkup jurnal ini adalah teori matematika, matematika terapan, program perhitungan, perhitungan matematika, statistik, dan statistik matematika.
Arjuna Subject : Matematika - Matematika Terapan
Articles 210 Documents
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Logistik Regression Analysis of Factor Influencing Drung Abuse Cases for Inmates in Class IIA Parepare Prison Ale Miftahulhaer, Ale Miftahulhaer; Wahidah Alwi; Adnan Sauddin; Khalilah Nurfadilah
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9448

Abstract

Drugs are addictive substances that can have a negative impact on the body, especially on the central nervous system. Drug abuse can be caused by various factors including parental influence, knowledge, attitudes, family structure, peer pressure, and community environment. The purpose of this study was to identify the factors asociated with drug abuse cases in Class IIA Parepare Prison. The sample consisted of 85 respondebts from cases related to drug abuse in the prison. Logistic regression analysis was ued, with drug abuse status (using drugs/not using drugs) as the dependent variable and gender, age, knowledge, family, peers, and community environment as independent variables. The results of this study indicate that a high level of knowledge has a regression odds ratio of 13.6489. This indicate that inmates with higher knowledge about drugs had a significantly greater likelihood of avoiding drug abuse compared to those with lower knowledge.
Implementation of Random Forest Algorithm for Shallot Price Forecasting in Makassar City Hardianti Hafid; Arwini Arisandi; Reski Wahyu Yanti
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9477

Abstract

This study aims to implement the Random Forest algorithm for forecasting shallot prices in Makassar City using monthly historical data from January 2018 to December 2024, obtained from the Statistics Indonesia (Badan Pusat Statistik) of South Sulawesi Province. The analysis begins with identifying significant lags through the Partial Autocorrelation Function (PACF) plot, resulting in seven input variable schemes. Each scheme was tested using training and testing datasets. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that Scheme 1 (Lag 1) achieved the best performance with a MAPE value of 13.08%, which falls into the “good” category. Price forecasts for January–December 2025 using the best scheme indicate a price range of IDR 23,200 – 24,300 per kilogram, with peak prices in March, July, and November, and the lowest prices in April, August, and December. Although the model successfully captures historical price patterns, real-world fluctuations driven by seasonal factors, supply disruptions, and distribution costs may cause prediction deviations. This study recommends integrating exogenous variables and real-time data to improve forecasting accuracy and support local food price stabilization policies.
Predictive and Etiologic Analysis of Typhoid Fever Using Multivariate Logit Regression and GPT Data Analyst Wiyanti, Wiwik; Fadlulloh, Naufal
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9483

Abstract

Typhoid fever is one of the dangerous diseases and many sufferers. This disease attacks humans regardless of age and in principle can be cured. The chance of a patient's recovery can be known, among other things, when someone has historical information or the patient's history during the time of treatment. Historical data from various typhoid patients can be used to predict the recovery of other typhoid patients, if they are in similar conditions. This study aims to apply multivariate logistic regression from the concept of prediction and etiology to examine the recovery factors of typhoid patients. This study's etiological notion is restricted to using a single exposure variable. The data analysis uses quantitative research. The results of the predictive concept show that the regression equation model is y=0.465 -1.174Age(2)-0.646Age(3)-0.888Age(4)+0.211Hemoglobin(1)+0.317Platelet(1)+0.308Calcium(1)-0.330Current_Medication(1) +0.500Current_Medication(2). The chance of a typhoid patient recovering is a maximum of 75%, which can occur when the patient's platelet and calcium conditions are normal. Meanwhile, the lowest chance of patient recovery is 23%, which can occur when the patient is 31-40 years old and the treatment applied is ceftriaxone. From an etiologic concept, two best models were found, namely a model where age is the exposure variable, current medication is the confounder, and treatment_outcome is the dependent variable, and the second model where age is the exposure variable, current medication is the confounder, and treatment_outcome is the dependent variable. From the etiological concept, it can be seen that the variables that have the most influence on patient recovery are age and the treatment used. In addition, the use of GPT Data Analyst was concluded to be unable to directly analyze logistic regression data for typhus cases, but it can be used to help simplify data analysis for researchers by using logistic regression coding.
Forecasting PT Pertamina Geothermal Energy TBK (PGEO) Share Prices using the Arch-Garch Model Ramadhan, Ramadhan; Yurinanda, Sherli; Sarmada, Sarmada
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9493

Abstract

This study focuses on forecasting the daily closing price of PT Pertamina Geothermal Energy Tbk (PGEO) stocks, recognizing the non-stationary and volatile nature of financial time series data. Traditional forecasting methods, such as the ARIMA (Autoregressive Integrated Moving Average) model, are often insufficient for such data because they rely on the assumption of homoscedasticity, or constant variance in the residuals. An analysis of PGEO's daily stock prices from November 2023 to July 2024 revealed significant fluctuations, indicating the presence of heteroscedasticity, where the variance of the residuals is not constant. In tackling this problem, the study utilized the ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) frameworks, purpose-built to identify and model the phenomenon of volatility clustering within financial datasets. By integrating the ARIMA model with GARCH, the study aimed to create a more robust forecasting tool. After testing various combinations, the MA(1)–GARCH(1,1) model was identified as the most suitable for predicting PGEO's stock prices. This model successfully captured the fluctuating volatility and produced a highly accurate forecast, as evidenced by a Mean Absolute Percentage Error (MAPE) of just 2.97%. A MAPE value below 10% is generally considered to represent a very high level of forecasting accuracy, confirming the effectiveness of the chosen model in providing reliable short-term predictions for stock market movements. Keywords: ARCH-GARCH, Stock price forecasting, ARIMA
Optimization E-Commerce Consumer Segmentation Based On K-Means Clustering And Machine Learning Sakinah, Awit; Awaliyah, Dewi Syifa
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9548

Abstract

The rapid growth of e-commerce in Indonesia has driven the need for more targeted marketing strategies. Consumer segmentation is an effective approach to understanding purchasing behavior. This study implements the K-Means Clustering algorithm, an unsupervised machine learning method, to perform consumer segmentation based on e-commerce product data. The dataset was obtained from the Kaggle platform, with key features including product ratings, prices, and sales volume. The number of clusters is determined automatically using the Silhouette Score method to achieve optimal segmentation. The segmentation results are visualized through a web-based application using Streamlit, allowing users to easily explore the characteristics of each cluster. Each cluster is analyzed to provide insights into consumer behavior and potential marketing strategies. This study demonstrates that a data-driven approach using machine learning can be effectively applied to support business decision-making in the e-commerce domain
Stability Analysis and Estimation of the Basic Reproductive Ratio Using a SEITA-Type Model of HIV/AIDS Spread in Cilegon City Mahuda, Isnaini; Sholihin, Miftahus; Sonda, Atia; Sari, Putri Dina; Asshiddieqie, Rafi Ramadhan; Udiansyah, Naufal Arrafi
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9586

Abstract

HIV/AIDS remains a critical global health issue that requires a multidisciplinary approach to reduce its transmission. Understanding the transmission dynamics through mathematical models can assist in formulating effective intervention strategies. This study aims to analyze the stability of HIV/AIDS transmission model in Cilegon City using five compartments, namely Susceptible, Educated, Infected, Treatment, and AIDS or SEITA-type model. Subsequently, the basic reproductive ratio (R0) is estimated by constructing the Next Generation Matrix (NGM) and the dynamic simulation of the model is carried out using parameters calibrated based on HIV/AIDS data from Cilegon City. The Analysis of stability equilibrium points show that the disease-free equilibrium point is locally asymptotically stable when R0<1 and when R0>1 then endemic equilibrium point is locally asymptotically stable. Furthermore, the numeric simulation results indicate that the increasing parameter transition rate from the susceptible subpopulation to the educated subpopulation, the ARV treatment rate applied to the infected subpopulation and decreasing parameter transition rate from the educated subpopulation to the susceptible subpopulation, could suppress the basic reproduction number, thereby enabling effective control of the HIV/AIDS spread in Cilegon City.
Long-term Evolution of Maternal and Child Health Indicators in Indonesia: Evidence from 28 Years of National Health Data Ayu Rahayu; Abar, Nur Rahmah Yunita
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9605

Abstract

Indonesia has undergone significant healthcare system transformations over the past three decades, including the implementation of universal health coverage in 2014. However, comprehensive long-term analyses of maternal and child health (MCH) trends spanning nearly three decades remain limited. This study examines the evolution of key MCH indicators in Indonesia from 1995 to 2023 to assess progress, identify patterns, and inform future policy directions. We conducted a longitudinal trend analysis using data from the Indonesian National Health Survey (Riset Kesehatan Dasar/Riskesdas) covering the period from 1995 to 2023. Nine key indicators were analyzed: prevalence of health complaints, skilled birth attendance, childhood immunization coverage (BCG, DPT, polio, measles), and breastfeeding practices. Statistical methods included linear trend analysis, Bayesian changepoint detection, correlation analysis, and segmented regression to assess policy impacts. Healthcare utilization patterns, including outpatient care, inpatient care, self-medication, and traditional medicine use, were examined as contextual indicators. Substantial improvements were observed across most MCH indicators over the 28 years. Skilled birth attendance showed the most dramatic progress, increasing from 46.1% (1995) to 95.7% (2023), representing an annual improvement rate of 2.55%. Childhood immunization coverage achieved high levels (>85%) for most vaccines by the 2000s, though measles vaccination remained variable (54-80% range). Breastfeeding patterns showed a structural break in 2015 due to methodological changes, which complicated trend interpretation. Healthcare utilization evolved significantly, with outpatient care increasing from 20% to >50% before declining to 35% by 2023, while self-medication practices rose substantially to 80%. Correlation analysis revealed alignment between health needs and service utilization (r = 0.48 for outpatient care). Changepoint analysis identified accelerated improvements around 2000-2005 and 2014-2015, coinciding with healthcare decentralization and universal coverage implementation respectively. Indonesia achieved remarkable progress in maternal and child health over 28 years, with skilled birth attendance approaching universal coverage and immunization programs maintaining high performance. The implementation of universal health coverage in 2014 coincided with continued improvements, though some recent declines in vaccination coverage warrant attention. The evolution from traditional medicine to modern healthcare services, alongside increasing self-medication practices, reflects maturing health systems requiring adaptive policy responses. Indonesia's experience demonstrates that sustained MCH improvements are achievable in large middle-income countries through systematic health system strengthening, though maintaining momentum requires continuous adaptation to emerging challenges. These findings provide valuable insights for other countries pursuing similar health system transformation goals.
Stunting Risk Factor Analysis Using Seemingly Unrelated Regression (SUR) Integrated with Machine Learning Wiyanti, Wiwik; Zenklinov, Amanatullah Pandu; Seleki, Jacob Stevy; Ramadhani, Sausan; Jimy, Valensius; Arifin, Samsul
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9630

Abstract

Stunting is a disorder in children caused by chronic nutritional problems. Stunting is usually characterized by a child’s failure to grow and develop optimally. In Indonesia, the government is focusing on addressing of stunting case, as it aims to develop the superior of human resources. In Tangerang regency is currently holding a “Gebrak Tegas” program to address the problem of stunting. The causes of stunting required scientific study to help the government understand the scientific factors that causing stunting. Therefore, this study aims to analyze the factors causing stunting the children in the Tangerang regency. The data analysis method in this study use the Seemingly Unrelated Regression (SUR), which is integrated with a machine learning, namely Random Forest. The data used in this study were obtained from primary data through questionnaires. The subjects of this study were parents of stunting and non-stunting children who were at “Posyandu” under of “Kelapa dua” and “Binong” health centers. Sampling method in this study is purposive-random sampling. The results of the data analysis showed that the five variables from the factors measured had the most significant influence, namely nutritional, socio-economi, and pregnancy and childbirth history factors. Five variables that influence children stunting are animal protein, which has the highest probability of 87.5% when the children consumes protein once or twice a week. The children consume vitamin A twice has a 97% probability. The source of income for the parents, whether from the private or self-employment, has a probability of over 90%. Furthermore, the consumption of iron-boosting tablets by mother during pregnancy and the amount of income from the parents have a probability of 84%.
Implementation of the Bayesian Spatial Model for Mapping the Relative Risk of HIV Cases in Makassar City Aisyah Putri , Siti Choirotun; Aprilia Wardani Syam , Dewi; Aswi, Aswi; Hidayat , Rahmat
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9770

Abstract

Human Immunodeficiency Virus (HIV) remains a major public health challenge in Indonesia, including Makassar City. This study aims to estimate and map the relative risk (RR) of HIV cases in Makassar City using the Bayesian spatial Conditional Autoregressive (CAR) Leroux model. The dataset comprises the number of HIV cases and the population of each district, with covariates including distance to the city center and population density. Results of Moran's I test indicated significant spatial autocorrelation in HIV cases across Makassar City. Model selection based on the Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) identified the optimal model as the CAR Leroux with an Invers-Gamma (IG) hyperprior (0.5;0.0005) and distance as a covariate, yielding the lowest DIC and WAIC values. The estimation results demonstrated that distance is negatively associated with HIV incidence. The highest RR was observed in Ujung Pandang district, while the lowest was in Biringkanaya District. These findings may provide a basis for identifying priority intervention areas and support the development of more targeted and effective HIV elimination strategies.
Knowledge Discovery Through Sentiment Analysis and Topic Modeling of BCA Mobile and MyBCA Putri, Salsa Anindya; Tania, Ken Ditha; Naretha Kawadha Pasemah Gumay
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9782

Abstract

The swift adoption of mobile banking in Indonesia highlights the growing demand for secure and innovative digital financial services. PT Bank Central Asia Tbk (BCA) offers two primary applications, BCA Mobile and myBCA, catering to millions of users. Gaining insight into user perceptions is crucial for enhancing service quality and building trust. This research uses sentiment analysis and topic modeling on Google Play Store reviews for both applications to facilitate knowledge discovery. Reviews were labeled using IndoBERT, and seven classification models were assessed, including five machine learning methods and two deep learning techniques. The Gated Recurrent Unit (GRU) model demonstrated the highest performance, achieving an accuracy of 89.70%. In the realm of topic modeling, a comparison between Latent Dirichlet Allocation (LDA) and BERTopic revealed that BERTopic delivered the highest coherence score of 0.6244, identifying eight significant negative topics. The findings indicate that BCA Mobile users frequently reported issues such as login failures, unexplained balance deductions, and missing features, while myBCA users encountered problems like post-update errors, login difficulties, and challenges with face verification. This research aligns with Sustainable Development Goal (SDG) 9 by showing how knowledge discovery from user reviews can promote innovation and enhance resilient, user-centered digital banking infrastructures.

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