cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Devni Prima Sari
Contact Email
devniprimasari@fmipa.unp.ac.id
Phone
+6285868648474
Journal Mail Official
mjomaf@ppj.unp.ac.id
Editorial Address
Data Analytics, Mathematical Modelling, and Forecasting (DMF) Research Group Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Negeri Padang Jalan Prof. Dr. Hamka, Air Tawar Padang, Sumatera Barat Web: mjomaf.ppj.unp.ac.id Email: mjomaf@ppj.unp.ac.id
Location
Kota padang,
Sumatera barat
INDONESIA
Mathematical Journal of Modelling and Forecasting
Published by Universitas Negeri Padang
ISSN : -     EISSN : 29881013     DOI : https://doi.org/10.24036/mjmf.v1i2
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
The Mathematical Journal of Modelling and Forecasting are scientific journals in the fields of mathematics, statistics, actuarial, financial mathematics, computational mathematics, and applied mathematics. This journal is published twice a year, precisely in June and December in an online version. All publications are available in full text and free to download.
Arjuna Subject : Matematika - Matematika Terapan
Articles 44 Documents
 1   2   3   4   5 
Analysis of the Application of Queue Theory in Ambacang Community Health Centers Padang Sukra Hamna; Nadiyatul Ghina
Mathematical Journal of Modelling and Forecasting Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v4i1.53

Abstract

Community health centers (Puskesmas) play a crucial role in providing primary healthcare services. However, long patient queues often reduce service efficiency and patient satisfaction. This study analyzes queue performance at a Puskesmas outpatient clinic using the M/M/1 queuing model to evaluate waiting time and service effectiveness. Data were collected through seven days of field observations, recording patient arrival patterns and service times during operational hours. The results show that the patient arrival rate is  patients per minute, while the service rate is  patients per minute. The system operates under a stable condition with a utilization factor of . The average waiting time in the queue is  minutes, whereas the total time spent in the system is  minutes, indicating that a significant proportion of patient time is spent waiting for service. The originality of this study lies in its empirical application of the M/M/1 queuing model to a real outpatient healthcare system in a Puskesmas setting, providing practical performance indicators that can support operational decision-making in primary healthcare services. However, the study is limited by a relatively short observation period of seven days and data collected from a single healthcare facility, meaning the findings reflect local conditions and cannot be generalized to other healthcare centers with different operational characteristics. The study recommends improving workforce management and implementing a structured patient scheduling system to reduce waiting time and enhance service efficiency.
Application of Support Vector Regression (SVR) with a Radial Basis Function (RBF) Kernel for Predicting the Global Happiness Index Aji Pandu Winata; Nala Kamila Azizy; Wina Ayu Lestari; Riwi Dyah Pangesti
Mathematical Journal of Modelling and Forecasting Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v4i1.54

Abstract

The Global Happiness Index is widely used to measure countries' well-being across social, economic, and health-related dimensions. The complex and non-linear relationships among these dimensions often limit the predictive performance of conventional linear regression models. This study aims to evaluate the effectiveness of Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel in predicting Global Happiness Index scores. The study used data from 158 countries obtained from Kaggle, including GDP per capita, social support, healthy life expectancy, freedom, trust in government, and generosity as predictor variables. Data preprocessing was performed before splitting the dataset into training and testing sets, and the optimal SVR parameters were determined using Grid Search with K-fold cross-validation. The optimal SVR-RBF model produced an RMSE of 0.4462 and an MAE of 0.3829 on the testing data. In addition, the model achieved an R² value of 0.8328, indicating that it explained 83.28% of the variation in Global Happiness Index scores. These results suggest that SVR with an RBF kernel is an effective approach for modeling complex nonlinear relationships and can be used as a reliable tool for predicting national happiness levels.
Optimizing the Distribution of Cow Skin Crackers at UMKM Putra-Putri Agli Using the Min-Plus Algebra Method for Shortest Route Determination Marliana; Andika Ellena Saufika Hakim Maharani; Muhammad Rijal Alfian
Mathematical Journal of Modelling and Forecasting Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v4i1.57

Abstract

This study applies Min–Plus Algebra to model and analyze the distribution network of UMKM Putra-Putri Agli, a small enterprise in West Lombok engaged in the production and distribution of cowhide crackers. The objective of this research is to analyze the distribution network and identify shortest-path relationships between distribution locations based on actual distance data. The distribution system is represented as an undirected weighted graph, where nodes correspond to distribution locations and edge weights represent the distances between locations obtained from Google Maps. The analysis is conducted by constructing a distance matrix and applying Min–Plus Algebra operations to compute successive matrix powers, leading to the formation of the closure matrix . The results show that the closure matrix successfully identifies the minimum distances between all pairs of distribution locations and provides the basis for deriving a distribution route with a total distance of 88.1 km. The findings also indicate that the network's structure and connectivity significantly influence route formation, as some locations can only be reached via intermediate nodes. The novelty of this study lies in the application of Min–Plus Algebra to an UMKM distribution network using actual field data and a network structure characterized by limited connectivity. The results demonstrate that Min–Plus Algebra provides a systematic algebraic framework for analyzing shortest-path relationships in small-scale distribution networks. However, the model is limited to static distance-based analysis and does not consider dynamic factors such as traffic conditions, travel time variations, or vehicle capacity constraints.
Application of the Lee Fuzzy Time Series Method in Forecasting Retail Rice Prices in West Sumatra Zarli Irsalina; Riry Sriningsih
Mathematical Journal of Modelling and Forecasting Vol. 4 No. 1 (2026): June 2026
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v4i1.59

Abstract

Retail rice prices are one of the important food commodities that affect economic stability in West Sumatra Province. Fluctuations in rice prices occurring in each period require forecasting to support decision-making related to food policies. This study aims to forecast retail rice prices in West Sumatra using the Lee Fuzzy Time Series method and to measure the forecasting accuracy. One of the advantages of the Lee Fuzzy Time Series (FTS) method is that it does not require stationarity assumption testing. The Lee Fuzzy Time Series method is designed for short-term forecasting and can be applied to both stationary and non-stationary data. In addition, this method can handle uncertainty and fluctuations in the data. Therefore, the Lee Fuzzy Time Series method is suitable for forecasting rice prices, as rice price data are classified as time series data. The data used were monthly retail rice price data from January 2020 to December 2024 obtained from the Central Statistics Agency (BPS). The forecasting stages included determining the universe of discourse, interval formation, fuzzification, establishing Fuzzy Logical Relationships (FLR), forming Fuzzy Logical Relationship Groups (FLRG), and defuzzification. Forecasting accuracy was measured using Mean Absolute Percentage Error (MAPE). The results showed that the Lee Fuzzy Time Series method produced a MAPE value of 1.27%, which is categorized as very good. The forecasting result for January 2025 indicated that retail rice prices were predicted to increase compared to the previous period.

Page 5 of 5 | Total Record : 44

 1   2   3   4   5 

Filter by Year

2023 2026


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2026): June 2026 Vol. 3 No. 2 (2025): December 2025 Vol. 3 No. 1 (2025): June 2025 Vol. 2 No. 2 (2024): December 2024 Vol. 2 No. 1 (2024): June 2024 Vol. 1 No. 2 (2023): December 2023 Vol. 1 No. 1 (2023): June 2023