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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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INVESTMENT GOLD DURING THE COVID-19 PANDEMIC WITH LINEAR REGRESSION, NONLINEAR REGRESSION AND ARIMA Tarigan, Enita Dewi; Yanti, Maulida; Hasibuan, Citra Dewi; Siringoringo, Yan Batara Putra; Erwin, Erwin
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6832

Abstract

As a result of the COVID-19 pandemic, many individuals have turned to low-risk investment options. Common choices include gold, stocks, deposits, and foreign currencies, with gold emerging as a particularly popular investment. This study aims to forecast gold prices using linear regression, nonlinear regression, and ARIMA models, with the most accurate model determined by the lowest Mean Absolute Percentage Error (MAPE). Gold price data was sourced from www.kitco.com. The MAPE for the Linear Regression model was 4.362, the Nonlinear Regression model 3.3428, and the Time Series (ARIMA) model 2.727. Consequently, the ARIMA model demonstrated superior accuracy in forecasting gold prices compared to the Linear and Nonlinear Regression models.
Child Stunting Classification using the LightGBM Method: A Case Study in the Rowosari District of Kendal, Central Java Galuh Syafa Azahra; Mujiati Dwi Kartikasari
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6865

Abstract

Stunting in children under five is a major health problem in Indonesia, significantly affecting physical, mental, and cognitive development, which can ultimately lower their quality of life in the future. Early identification of children at risk of stunting remains a challenge due to limited resources and the effectiveness of existing prediction methods. Machine learning techniques offer a promising approach to improving stunting classification and risk prediction. This study aims to develop an accurate and efficient classification model for identifying stunting status using the Light Gradient Boosting Machine (LightGBM) method. The study was conducted on children in Rowosari District, Kendal, Central Java, utilizing seven key variables: gender, age (months), birth weight, birth length, current weight, current height, and stunting status. The results indicate that the LightGBM model achieved 97% accuracy with an AUC value of 0.99, demonstrating a high ability to distinguish stunting status. Furthermore, the model successfully identified the key risk factors contributing to stunting. These results show that the LightGBM method has the potential to make very accurate predictions and also help people come up with more timely and targeted intervention strategies that are based on data. By leveraging machine learning, health practitioners and policymakers can improve stunting prevention efforts, optimize resource allocation, and implement more effective public health strategies to reduce stunting prevalence in Indonesia.
Modeling Geographically Weighted Negative Binomial Regression on The Incidence of Adolescent Smoking in Indonesia Patasik, Ghadytha Marie Lucia; Sanusi, Wahidah; Tampa, Alimuddin
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7262

Abstract

This study aims to determine the significant factors affecting adolescent smoking cases in Indonesia with the Geographically Weighted Negative Binomial Regression model. The research method uses a quantitative approach to identify the relationship of each variable and analyze the effect of a treatment. Data analysis was processed using the GWNBR model with the help of R-Studio. The results showed that the case of teenage smoking experienced overdispersion of data with factors that had a significant effect, namely the percentage of poor people, the level of education of parents, the price of cigarettes, the level of income and the percentage of working teenagers, with factors that had a significant effect spatially, namely the percentage of poor people and the percentage of working teenagers. So it can be concluded that the spatial pattern of smoking incidence in Indonesia based on the results of the spatial heterogeneity test shows significant variation across provinces, so that the spatial pattern of the incidence of adolescent smoking is heterogeneous.
Implementation of DBSCAN for Earthquake Clustering in Indonesia with Potential Surface Damage Hardianti Hafid; Rahmat Hidayat; Rahmat H. S.
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7318

Abstract

Indonesia is one of the countries with the highest seismic activity in the world due to its location at the convergence of three major tectonic plates. Understanding earthquake distribution patterns is crucial for disaster mitigation efforts and policy planning. This study applies the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster earthquake data in Indonesia based on magnitude and depth. The data used is secondary data from the Meteorology, Climatology, and Geophysics Agency (BMKG) for the period of January–December 2023. The research stages include data collection and preprocessing, applying the DBSCAN algorithm with the selection of Eps and MinPts parameters, and evaluating the clustering results using the silhouette coefficient and Davies-Bouldin Index (DBI). The results show that the combination of Eps = 0.5 and MinPts = 5 produces clusters with a silhouette coefficient of 0.3959 and a DBI of 0.7384, indicating a fairly good cluster structure. Visualization results reveal high-density clusters in active seismic zones and several smaller clusters representing specific earthquake characteristics. This study provides insights into the earthquake distribution patterns in Indonesia and demonstrates that DBSCAN effectively identifies complex cluster structures. The findings can serve as a reference for seismological studies and support earthquake disaster mitigation efforts.
Unveiling the Connection: The Impact of Poverty Rate on Human Development Index in the Special Region of Yogyakarta Province Using the Almon Lag Model Vatin, Kayyis; Kartikasari, Mujiati Dwi
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7348

Abstract

The Human Development Index (HDI) is established as one of the main indicators included in the fundamental framework of regional development. Ideally, HDI, which serves as a benchmark for regional development, has a negative correlation with poverty conditions. Historical poverty can influence the current HDI based on elements of health, education, and a decent standard of living. The Special Region of Yogyakarta is the province with the second-highest HDI in Indonesia but has had the highest poverty rate in Java in recent years. The Almon Lag Model can analyze the impact of poverty on HDI by considering the distributed lag effect. This study aims to analyze the impact of the poverty rate on HDI over the past twenty-seven years. Based on the analysis, the best model utilizes a lag length of three years and a polynomial degree of two. The model has a Mean Absolute Percentage Error (MAPE) of 0.73%, indicating that the applied Almon Lag Model can make accurate predictions.
Distance Function Analysis on Fuzzy C-Means for Clustering Aquaculture Production in North Sumatra hafni, Nur; Rangkuti, Yulita Molliq; Karo, Ichwanul Muslim Karo
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6500

Abstract

Abstrac As a maritime country, Indonesia has considerable fisheries resource potential, including in North Sumatra. However, less than optimal management causes a gap between the demand and supply of cultivated fish in this region. This research aims to analyze the use of the Fuzzy C-Means (FCM) method in classifying aquaculture production in North Sumatra based on Regency/City and primary commodities. This research also compares three methods of measuring Euclidean, Manhattan, and Chebyshev distances to determine the best method for data clustering. The North Sumatra Central Statistics Agency (BPS) data for 2018-2022. The results showed that there were two clusters, namely cluster 1 consisting of 4 districts with high fisheries production and cluster 2 consisting of 29 districts with low fisheries production. Evaluation using the Modified Partition Coefficient shows that the Manhattan distance method has the highest MPC value of 0.922. These results indicate that of the three distance methods, the Manhattan method is the best for applying Fuzzy C-Means to classify aquaculture production in North Sumatra.
DECISION SUPPORT SYSTEM FOR DETERMINING OUTSTANDING TEACHERS AT SMK BHAKTI PERTIWI MANONJAYA, TASIKMALAYA REGENCY, USING THE SIMPLE ADDITIVE WEIGHTING METHOD Sukma, Thalia Mutiara; Sakinah , Awit Marwati
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.4574

Abstract

The most difficult thing in determining outstanding teachers is the effort to eliminate subjective factors so that every choice made is objective based on predetermined criteria. In order for the implementation of the teacher selection to run, this is what makes it necessary to have a decision support system that is able to provide assessment consistency with the application of normalization and provide convenience in the results of the values ​​that will be determined from each criterion by applying the Simple Additive Weighting (SAW) method so that it can determine the best alternative. This system is also useful for schools in determining and calculating values ​​in determining teachers who deserve to receive awards in the selection of outstanding teachers. This system has also been designed dynamically so that it can make it easier for the principal to determine the criteria according to the school year.
The Implementation of the DIANA Method to Map the Spread of Tuberculosis in North Sumatra Visualized on a Website : Mapping Tuberculosis Cases Using DIANA Clustering and Interactive Visualizations Gunawan, Rizky; Molliq Rangkuti, Yulita; Muslim Karo Karo, Ichwanul
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6590

Abstract

Tuberculosis disease has a high prevalence rate in North Sumatra, thus requiring an0 appropriate handling strategy. This article aims to map Tuberculosis cases in North Sumatra Province using the Divisive Analysis (DIANA) method visualized on a website. The DIANA method is used to cluster Tuberculosis case data based on certain characteristic patterns such as case rate, mortality rate, and cure rate. The DIANA (Divisive Analysis Clustering) clustering method starts with the entire data as one large cluster, which is then broken down gradually into smaller subclusters based on the level of dissimilarity between elements. Splitting starts from the element furthest from the cluster center, followed by evaluation of other elements to determine the corresponding cluster. This process continues until clusters are formed at the desired level of granularity. The results of the analysis show that clustering with the DIANA method produces clusters that separate areas with high, medium, and low case rates. Evaluation of clustering using the Davies Bouldin Index (DBI) showed the best value of 0.2943 in 2022. In addition, this article produces a Leafletjs-based interactive map to visualize the clustering results, so that it can be used to identify priority areas for intervention. From the mapping results, it was obtained that one district/city in North Sumatra, namely Medan City, entered the red zone, then Deli Serdang Regency was included in the orange zone, then Simalungun Regency, Binjai City, Langkat Regency, Mandailing Natal Regency, Serdang Bedagai Regency and Asahan Regency entered the yellow zone. Finally, there are 25 regencies/municipalities in the green zone consisting of Kab. West Nias, Kota Tebing Tinggi, Kab. Dairi, Kab. Pakpak Bharat, Kab. Padang Lawas Utara, Kota Tanjung Balai, Kab. Labuhan Batu Selatan, Kab. Karo, Kota Padangsidimpuan, Kab. Padang Lawas, Pematang Siantar City, Gunung Sitoli City, Sibolga City, South Nias Regency, Coal Regency, Samosir Regency, Humbang Hasundutan Regency, North Tapanuli Regency, Nias Regency, Central Tapanuli Regency, North Nias Regency, Labuhan Batu Regency, North Labuhan Batu Regency, South Tapanuli Regency, and Toba Samosir Regency.
Classification Poverty Levels in Indonesia Using Discriminant Analysis Muthahharah, Isma; Hafid, Hardianti
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6816

Abstract

Poverty is a complex global challenge affecting countries like Indonesia that seek to improve the welfare of their citizens. Although the number of Indonesia's poor has fluctuated over the past few years, the study shows a decline in 2022. Using Multivariate Discriminant Analysis, this study aims to classify poverty levels in Indonesian provinces. Previous findings highlighted the relationship between the poverty depth index and average and duration of schooling. Through the development of classification models, this research seeks to provide a better understanding of poverty factors and support more effective policymaking in combating poverty in various regions. Using secondary data from the Central Bureau of Statistics in 2022, this research is quantitative research that produces important insights for the formulation of poverty eradication policies and programs in Indonesia. The result is the low provincial group of 20 provinces only 10 provinces are correctly predicted, the remaining 10 are predicted in the high province group. The same thing happened in the high province group of 13 provinces, only 9 provinces were correctly predicted, while the remaining 4 were predicted in the low group.
Numerical Approach for Solving Fractional Integral Equations using Fixed-point Iterative Scheme Ansar, Ahmad; Fatimah, Meryta Febrilian; Renggawati, Rini
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6859

Abstract

This paper is aimed to solve fractional integral equations using fixed point iterative approach. We used D-plus iteration process with nonexpansive mapping in uniformly convex Banach spaces. We start by prove weak and strong convergence theorems related for nonexpansive mapping and compare convergence rate of some iterative scheme through numerical example. Furthermore, we use this method to prove the existence and estimate the solution of fractional integral equations. The D-plus iterative method shows the good performance in solve fractional integral equations. We also give some example to illustrate main results.

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