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All Journal Journal of Computer Science and Information Systems (JCoInS) Journal of Student Development Information System (JoSDIS)
Wijaya, Alief Achmad
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Computer Science & IT

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Analisis Keuntungan Produk Penjualan Sate Padang Menggunakan Metode Simplex Ananda, Muhammad Rizki; Wijaya, Alief Achmad; Ritonga, Akbar Pramuja; Ritonga, Irmayanti
Journal of Student Development Information System (JoSDIS) Vol 4, No 2: JoSDIS | Juli 2024
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/josdis.v4i1.5435

Abstract

This study aims to analyze the products and queues at the sale of satay padang using Simplex Method with QM for Windows application.Sate padang sellers, as food sellers, face challenges in planning and managing satay portion inventory to achieve maximum product.This study applies the Simplex Method, a mathematical programming technique, to identify the optimal combination of product inventory that can improve product and customer queues.The research steps include analysis of historical sales data, identification of decision variables, as well as the formulation of mathematical models based on optimal goals.The QM for Windows application is used as a tool to run probability and random variable distribution methods and analyze the results. The study also evaluated the impact of optimization on products, queues, inventory efficiency, and customer satisfaction.The results of this study are expected to provide practical guidance to owners of sate padang or similar businesses in managing product inventory and planning sales.In addition, this study can be a contribution to the development of optimization methods that can be applied in the context of small and medium businesses, especially in the culinary sector.
Kepatuhan Pembayaran Pajak Kendaraan Bermotor Menggunakan Algoritma Decision Tree Dan Random Forest Di Samsat Balige Wijaya, Alief Achmad; Harahap, Syaiful Zuhri; Ah, Rahma Muti; Nasution, Marnis
Journal of Computer Science and Information System(JCoInS) Vol 6, No 3: JCoInS | 2025
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v6i3.7934

Abstract

This study aims to analyze and predict the total category of Motor Vehicle Tax (PKB) payments based on payment attributes and vehicle types, which is important to improve the effectiveness of tax management and support more appropriate decision making in related agencies; within the theoretical framework, classification models such as Decision Tree and Random Forest are used to predict data categories by utilizing historical patterns in the dataset, because these algorithms are able to capture interactions between attributes and provide logical interpretations of the prediction results; the research methodology is carried out using secondary data of PKB payments for 2024 from Samsat Balige, which is divided into training data and test data for the classification process and its performance is evaluated using accuracy, precision, recall, and F1-Score metrics through the Performance operator in RapidMiner; the results of the study show that Random Forest produces a more balanced prediction distribution with 100% accuracy, while Decision Tree has 96% accuracy but tends to be biased towards the “Low” category, and analysis of important attributes such as Fines, Total Amount, and the number of Jeep and Truck type vehicles shows a significant influence on the PKB payment category; Thus, the research conclusion confirms that Random Forest is proven to be more effective and stable than Decision Tree in predicting the total PKB payment category, is able to capture complex patterns between attributes, and provides accurate predictions on relatively small datasets, making it the optimal choice for PKB data classification.
Co-Authors Ah, Rahma Muti Ananda, Muhammad Rizki Nasution, Marnis Ritonga, Akbar Pramuja Ritonga, Irmayanti Syaiful Zuhri Harahap