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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Analytical Uncertainty
Irvanizam Irvanizam
Universitas Syiah Kuala
Computer Science & IT Engineering Mathematics

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Multiple-Attribute Decision-Making Based on AHP-TOPSIS for Gas Station Site Selection Problem Muhammad Nabil Maulana; Kikye Martiwi Sukiakhy; Rini Deviani; Sri Azizah Nazhifah; Husaini Husaini; Irvanizam Irvanizam
Journal of Analytical Uncertainty Vol. 1 No. 1 (2025): JAU: December 2025
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i1.270

Abstract

The continuous growth of private vehicle usage in Indonesia has led to a significant increase in fuel demand, making the strategic placement of gas stations a critical issue for transportation infrastructure planning. However, inappropriate site selection may result in uneven service coverage, affecting increased operational costs and reduced accessibility for road users. Therefore, a systematic and objective decision-making approach is required to support gas station location planning. Motivated by this challenge, this study develops an integrated decision-support framework to evaluate and select strategic gas station sites based on multiple criteria. The framework combines the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The AHP method is employed in the first stage to determine the relative importance weights of the evaluation criteria based on expert judgments. In the second stage, the TOPSIS method is implemented to rank candidate locations and identify the alternative closest to the ideal solution. To validate the proposed framework, a case study involving multiple candidate locations is experimented with. Experimental results demonstrate that the proposed AHP–TOPSIS approach is a practical tool for selecting gas station site location, with location L3 identified as the most strategic site for gas station construction.
AN INTELLIGENT LEARNING-DRIVEN FOR DYNAMIC WASTE COLLECTION ROUTING USING LSTM AND EVOLUTIONARY CVRP OPTIMIZATION Muhammad Amin; Muhammad Iqbal; Irvanizam Irvanizam
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8357

Abstract

Inefficient waste collection routes result in significant operational costs and environmental impacts. Traditional static routes based on historical averages often deviate substantially from actual requirements. This study proposes an intelligent framework integrating Long Short-Term Memory (LSTM) networks for dynamic time-series forecasting with an Evolutionary Capacitated Vehicle Routing Problem (CVRP) optimizer. The LSTM model captures temporal waste generation patterns using a 7-day sliding window; these patterns are fed into a metaheuristic optimizer that minimizes travel distance to disposal sites while eliminating redundant trips. Experimental results demonstrate high prediction accuracy, with the Mean Squared Error (MSE) converging at 0.0001 during the validation phase. Furthermore, the optimization process achieved a 10.95% reduction (22.5 km/day) in average travel distance compared to the baseline model. In high-density scenarios, the framework improved route efficiency by up to 15.99%. The study concludes that combining deep learning memory capabilities with evolutionary optimization provides a reliable decision-support system for smart city waste management..
Co-Authors Amin, Muhammad Husaini Husaini Kikye Martiwi Sukiakhy Muhammad Iqbal Muhammad Nabil Maulana Rini Deviani Sri Azizah Nazhifah