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All Journal Mathematical Journal of Modelling and Forecasting Codeverse: Journal of Emerging Digital Realities
Muhammad Rijal Alfian
University of Mataram
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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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.
Comparison of Apple Inc Stock Forecasting Accuracy Using Hybrid TSR Linear-ARIMA Model and ARIMA Model Aulia Padhila Ersu; Muhammad Rijal Alfian; Nur Asmita Purnamasari
Codeverse: Journal of Emerging Digital Realities Vol. 1 No. 2 (2025): CODEVERSE: Journal of Emerging Digital Realities
Publisher : Codeverse: Journal of Emerging Digital Realities

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to compare the accuracy of Apple Inc. stock price forcasting using two time series models, namely the hybrid TSR Linear-ARIMA model and the ARIMA model. The background of this research is the need for more accurate forcasting methods in a dynamic stock market, especially for technology stocks such as Apple which have high volatility. The research methodology uses the quantitative approach with daily Apple stock price time series data for the period 2023. The hybrid TSR Linear-ARIMA model incorporates trend and residual components, while the ARIMA model uses the Box-Jenkins approach. Both models were implemented using statistical software R Studio and Minitab. The results that the ARIMA model provided better forcasting accuracy compared to the hybrid TSR Linear-ARIMA model. Comparative analysis using the MAPE shows the ARIMA model has a lowwer error rate. Specifically, the ARIMA model produces a MAPE of 2.909%, while the hybrid TSR Linear-ARIMA model produces a MAPE of 3.780%. in conclusion, the ARIMA model proved to be more effective in forecasting the stock price of Apple Inc. compared to the hybrid TSR Linear-ARIMA model. This research contributes to the development of forecasting techniques in finance and investment, especially for technology stock.
Co-Authors Andika Ellena Saufika Hakim Maharani Aulia Padhila Ersu Marliana Nur Asmita Purnamasari