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Optimization of Pertamax Fuel Distribution Using Clarke-Wright Savings, Nearest Neighbour, and Goal Programming (Case Study: Malang City) Tharisa Melani; Sobri Abusini; Marjono Marjono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.31529

Abstract

Fuel distribution optimization is crucial to meet growing demand and reduce operational costs, especially in cities like Malang, where vehicle numbers are increasing. This research addresses the distribution challenges of PT Pertamina, focusing on designing efficient routes to minimize distance, cost, and delivery time while meeting fuel demands effectively. The problem, classified as a Capacitated Vehicle Routing Problem (CVRP), is solved using the Clarke-Wright Savings (CWS), Nearest Neighbour (NN), and Goal Programming (GP). The CWS is applied to group routes efficiently by reducing travel distances, while NN determined the delivery sequence within each route. GP addressed multi-objective optimization, minimizing costs and delivery time, maximizing Pertashop demands, and optimizing vehicle use. The results show that the combination of CWS and NN algorithms reduced the total travel distance by 140 km, or 12.5% reduction. Additionally, the GP method optimized vehicle use to 13, achieving a 59.68% cost reduction and a 48.68% time savings. These findings highlight the effectiveness of combining these algorithms in fuel distribution optimization, providing a more efficient solution compared to existing routes. Moreover, this approach is adaptable to similar logistics problems, offering a foundation for further research in multi-objective optimization for distribution systems.
Analysis of Binary Logistic Regression Model on Passenger Transportation Mode Selection Between Train and Bus on Malang-Blitar Route Nabila, Nuzulul Laili; Abusini, Sobri; Sa'adah, Umu
Civil and Environmental Science Journal (CIVENSE) Vol. 8 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.civense.2024.008.01.6

Abstract

The transportation dynamics between Malang and Blitar, characterized by significant student and worker mobility, present a complex decision-making landscape for public transportation mode selection. This study employed binary logistic regression to analyze factors influencing passenger choices between trains and buses, utilizing a comprehensive survey of 100 respondents. The research revealed convenience as the most statistically significant factor in transportation mode selection, transcending traditional considerations such as ticket pricing. Despite 80 participants initially expressing a preference for trains, the predictive model suggested a potential scenario where 74% might ultimately choose buses. This counterintuitive finding highlights accessibility, service frequency, boarding ease, and overall travel comfort in transportation decision-making. By quantifying the probabilistic relationships between various variables, the study provides transportation planners with a sophisticated analytical tool for understanding passenger behavior. The findings underscore passengers' willingness to pay a premium for transportation modes offering greater flexibility and comfort, challenging conventional assumptions about cost-driven travel choices. The binary logistic regression model's insights provide valuable guidance for infrastructure development and service optimization in the Malang-Blitar transportation corridor, emphasizing the critical role of convenience in shaping transportation preferences.
Comparing Newton Raphson and Stochastic Gradient Descent Methods for Traffic Accident in Malang Fauzi, Aldi Rahmad Nur; Abusini, Sobri; Karim, Corina
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33177

Abstract

This study discusses a comparison between two optimization methods, Newton–Raphson and Stochastic Gradient Descent (SGD), in binary logistic regression modeling to analyze the severity of traffic accidents in Malang Regency. Parameter estimation was carried out using both methods to assess their effectiveness in achieving convergence and producing a well-fitted model. The results show that the Newton–Raphson method failed to achieve convergence despite its fast iteration speed, while the SGD method successfully converged, although it required a large number of iterations. Model evaluation was conducted by examining model fit through log-likelihood values and the Akaike Information Criterion (AIC). The results indicate that the SGD method produced a better-fitting model compared to Newton–Raphson. Additionally, the regression models from each method identified different predictor variables as significant, suggesting that the choice of optimization approach can influence analytical outcomes. These findings highlight the importance of selecting an appropriate optimization method in logistic regression analysis, particularly for complex and imbalanced accident data.
Ensemble Bagging in Binary Logistic Regression for Transportation Mode Selection Nabila, Nuzulul Laili; Abusini, Sobri; Sa'adah, Umu
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.32241

Abstract

This study examines train versus bus transportation mode choice on the Malang–Blitar route using binary logistic regression combined with ensemble bagging. Data from 100 respondents were analyzed using 80% for training and 20% for testing with k-fold cross-validation. Variables included travel cost differences, time, safety, comfort, and ease of access. Bagging was selected over other ensemble methods due to its effectiveness in reducing variance and overfitting with small datasets. Results showed the standard logistic regression achieved 85% accuracy on test data, while ensemble bagging with 200 replications improved accuracy to 90.83% (confidence interval: 90.379%–91.187%). McNemar’s test confirmed a statistically significant improvement (p 0.01). Under equivalent conditions, 20.6% of respondents preferred trains while 79.4% chose buses. Ease of access emerged as the primary decision factor, outweighing cost and time considerations. The optimal replication number was 200; exceeding 300 replications decreased model performance. This research contributes an optimized ensemble methodology for transportation mode prediction in developing countries, demonstrating that accessibility infrastructure significantly influences passenger preferences over traditional economic factors.
EOQ MODEL FOR DETERIORATING AND AMELIORATING ITEMS UNDER CUBIC DEMAND AND PARTIAL BACKLOGGING Laily, Hafizha Nur; Abusini, Sobri; Sa’adah, Umu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.572 KB) | DOI: 10.30598/barekengvol17iss1pp0021-0028

Abstract

The inventory model aims to determine policies in inventory control. Therefore, the availability needs to be managed as well as possible to obtain optimal performance. This study aimed to produce EOQ models for deteriorating and ameliorating products with shortage and partial backlogging policies. The traditional Economic Order Quantity (EOQ) inventory model was used to develop the model. The search algorithm of the model solution was made to get a solution from the model. In the end, a case study of the model implementation at Minimarket SATUMART, Sidoarjo, is given
THE BRANCH AND BOUND APPROACH TO A BOUNDED KNAPSACK PROBLEM (CASE STUDY: OPTIMIZING OF PENCAK SILAT MATCH SESSIONS) Ambarwati, Aditya; Abusini, Sobri; Krisnawati, Vira Hari
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2449-2458

Abstract

A method commonly employed to solve integer programming problems is the Branch and Bound. In this article, maximizing the number of matches held on the first day of pencak silat tournaments is essential because it can impact the overall dynamics and results of the competition. The model used to maximize the number of match sessions in pencak silat competitions is a variant of the Bounded Knapsack Problem (BKP), belonging to the category of integer programming models. The result obtained using the Branch and Bound method ensures that the maximum number of match sessions can be conducted. The objective value obtained using the Branch and Bound method decreases as it descends, indicating a decreasing maximum value.
A Hybrid Sweep-Nearest Neighbor-Tabu Search Approach for CVRP in FMCG Route Distribution Evary, Sikhatun Naimah; Abusini, Sobri; Muslikh, Mohamad
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35953

Abstract

This study addresses the Capacitated Vehicle Routing Problem (CVRP) in the distribution of Fast-Moving Consumer Goods (FMCG) by proposing a hybrid approach that combines the Sweep algorithm, Nearest Neighbor (NN) method, and Tabu Search (TS) algorithm. The objective is to satisfy consumer demand and vehicle capacity restrictions while minimizing the overall journey distance. The Sweep algorithm is used to cluster customers based on polar coordinates, the NN method determines initial delivery routes within each cluster, and TS refines those routes to find near-optimal solutions. Implemented on a real-world dataset of 248 stores in Malang, the proposed hybrid method achieved significant reductions in the number of clusters and total travel distance compared to conventional approaches. Results show that the Sweep algorithm successfully reduced the number of delivery clusters from 26 to 18, achieving a 30.77% reduction in grouping efficiency. Using the Nearest Neighbor method, the total route distance was 2,191.08 km. Further optimization with Tabu Search reduced the Distance to 2141.31 km. Compared to the conventional method, which is 2345.90 km, the hybrid approach resulted in an 8.72% improvement in route efficiency. These findings demonstrate that the integrated method is effective for large-scale distribution problems under capacity constraints. The hybrid method offers a practical and computationally efficient solution for large-scale FMCG distribution networks.