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All Journal Jurnal Informatika dan Rekayasa Perangkat Lunak Scientific Journal of Informatics
Sutikno
Universitas Diponegoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Vehicle Routing Problem (VRP) Approaches for Waste Collection Optimization: A Systematic Literature Review Munengsih Bunga; Mochamad Agung Wibowo; Sutikno
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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Abstract

An effective route is necessary because waste transport is an important part of the urban waste management system. The most popular technique is the Vehicle Routing Problem (VRP) to maximize fleet movement while reducing risk, time, cost, and energy. To identify developments in VRP models in the context of waste transportation, this study used a Systematic Literature Review (SLR), conducted in accordance with PRISMA guidelines, to review 96 articles. The SLR results indicate that VRP models have evolved from basic models such as CVRP and VRPTW to more constraint-rich models such as MTVRP, ARP, risk-aware VRP, EVRP, and multi-objective VRP. Hybrid and metaheuristic algorithms such as ALNS, GA, ACO, and SA have become the most popular in solving this problem due to their ability to handle large problem sizes and high operational complexity. Route planning can now utilize real-time data thanks to the integration of IoT, WSN, and GIS technologies. Overall, these results indicate that VRP research in waste transport is moving towards smarter, more adaptive, and sustainable approaches. These results also enable the development of more contextual models and algorithms in the future.
Optimization of Mineral Fuel Export Forecasting Using Attention-based Long Short-Term Memory Ananda Prasetya; Jatmiko Endro Suseno; Sutikno
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.38381

Abstract

Purpose: This study aims to optimize the forecasting of the Net Value of Indonesia's mineral fuel exports using the Attention-based Long Short-Term Memory (LSTM) model, supported by Dropout and Recurrent Dropout techniques that are combined to produce an optimal model. Methods: Modeling uses an LSTM architecture equipped with an Attention mechanism, as well as Dropout and Recurrent Dropout. The research procedure uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. The research material used is the Indonesian mineral fuel export dataset with HS code 27 from 2014 to 2025. Model was built using the Random Search method to optimize hyperparameters such as the number of neurons (units), activation functions (Tanh, ReLu), and optimizers (Adam, Nadam, RMSprop). Result: The Attention-based LSTM model with Dropout and Recurrent Dropout techniques achieved a MAPE of 7.76%, which was better than the other models tested. Attention analysis shows that lag 12 has the greatest dominance, while lags 11 to 10 also contribute significantly, indicating an annual seasonal pattern. Projections for the next 12 months show a moderate decline in Net Value, in line with seasonal trends and historical data. Novelty: The main contribution of this research is the optimization of an Attention-based LSTM model using a combination of Dropout and Recurrent Dropout techniques, which is effective in forecasting Indonesia's mineral fuel export values because it is able to capture annual seasonal patterns, thereby improving the accuracy and stability of the forecast results.
Co-Authors Ananda Prasetya Jatmiko Endro Suseno Mochamad Agung Wibowo Munengsih Bunga