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All Journal International Journal of Advances in Intelligent Informatics Jurnal Sistem dan Manajemen Industri Journal of Robotics and Control (JRC) The Indonesian Journal of Computer Science
Leuveano, Raden Achmad Chairdino
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Journal : International Journal of Advances in Intelligent Informatics

 1 
A genetic algorithm approach to green vehicle routing: Optimizing vehicle allocation and route planning for perishable products Asih, Hayati Mukti; Leuveano, Raden Achmad Chairdino; Dharmawan, Dhimas Arief; Ardiansyah, Ardiansyah
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1784

Abstract

This paper introduces a novel approach to the Green Vehicle Routing Problem (GVRP) by integrating multiple trips, heterogeneous vehicles, and time windows, specifically applied to the distribution of bakery products. The primary objective of the proposed model is to optimize route planning and vehicle allocation, aiming to minimize transportation costs and carbon emissions while maximizing product quality upon delivery to retailers. Utilizing a Genetic Algorithm (GA), the model demonstrates its effectiveness in achieving near-optimal solutions that balance economic, environmental, and quality-focused goals. Empirical results reveal a total transportation cost of Rp. 856,458.12, carbon emissions of 365.43 kgCO2e, and an impressive average product quality of 99.90% across all vehicle trips. These findings underscore the capability of the model to efficiently navigate the complexities of real-world logistics while maintaining high standards of product delivery. The proposed GVRP model serves as a valuable tool for industries seeking sustainable and cost-effective distribution strategies, with implications for broader advancements in supply chain management.
Optimizing LPG distribution: A hybrid particle swarm optimization and genetic algorithm for efficient vehicle routing and cost minimization Indrianti, Nur; Leuveano, Raden Achmad Chairdino; Abdul-Rashid, Salwa Hanim; Kuncoro, Andreas Mahendro; Liestyana, Yuli
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1837

Abstract

This paper aims to develop an optimized solution for the Vehicle Routing Problem (VRP), tailored explicitly for Liquid Petroleum Gas (LPG) distribution, with a focus on minimizing transportation costs and enhancing delivery reliability. The critical role of LPG as an essential public infrastructure commodity, widely utilized for cooking and heating, makes its efficient and reliable distribution a significant logistical challenge due to the strict adherence to delivery time windows, heterogeneous fleets, multi-trip scenarios, and intricate loading and unloading requirements. To address these complexities, this study proposes a novel hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) that uniquely integrates multi-trip routing, time windows, and heterogeneous vehicle fleet management into a single optimization framework. The dual-phase optimization strategy leverages the exploratory capability of PSO and the solution-refining power of GA, resulting in high-quality, feasible solutions. Validation against real-world data involving VRP instances with 88 and 40 stations demonstrates the model’s practical impact, achieving reductions of up to 4.56% in transportation costs compared to existing operational routes. This research makes a significant contribution to interdisciplinary domains, including logistics optimization, sustainability, and energy distribution, by offering a robust and scalable model that comprehensively addresses complex, real-world VRP constraints.
Non-destructive classification of sugarcane milling feasibility using deep learning: A comparative study of VGG19 and ResNet50 Indrianti, Nur; Leuveano, Raden Achmad Chairdino; Rustamaji, Heru Cahya; Ferriyan, Andrey; Mulyono, Panut; Wijaya, Bayu Prasetya
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

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

Abstract

Assessing sugarcane quality is crucial for ensuring both economic value and processing efficiency in sugar production. Conventional approaches, such as refractometer-based Brix measurements, are destructive, labor-intensive, and unsuitable for large-scale or rapid field evaluations. This highlights the need for non-destructive, automated solutions that can deliver accurate and scalable assessments. This study proposes a deep learning framework for classifying sugarcane internodes into two quality categories based on Brix values: unsuitable for milling (<16 °Brix) and suitable for milling (≥16 °Brix) using image-based analysis. The dataset consists of two configurations: Luar1 (single internode) and Luar2 (a split internode with two outer sides placed side by side), each photographed against white and black backgrounds. Preprocessing, data augmentation, and transfer learning were applied using VGG19 and ResNet50 under a two-phase strategy. Phase 1 involved freezing the backbone layers (50 epochs), and Phase 2 involved fine-tuning (100 epochs). The results demonstrate that fine-tuning significantly enhanced model performance. VGG19 achieved accuracies between 72.12% and 75.06%, while ResNet50 consistently outperformed it, reaching 78.85% with the Luar2_Putih dataset. Confusion matrix analysis further confirmed ResNet50’s superior ability to minimize misclassification, particularly for high-quality canes that are crucial for milling feasibility. These findings advance non-destructive quality assessment in sugarcane and support the United Nations Sustainable Development Goals (SDG 2, SDG 9, and SDG 12) by strengthening food security through improved crop utilization, fostering innovation in agricultural technologies, and promoting sustainable production practices in the sugar industry.
Co-Authors Abdul-Rashid, Salwa Hanim Andiko Putro Suryotomo Andrey Ferriyan, Andrey ARDIANSYAH ARDIANSYAH Asih, Hayati Mukti Dharmawan, Dhimas Arief Heru Cahya Rustamaji Kuncoro, Andreas M Nur Indrianti Panut Mulyono Ridho, Muhammad Ihsan Sani, Sayang Tahya, Michel Pierce Wijaya, Bayu Prasetya Yuli Liestyana