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All Journal Journal Global Technology Computer Management of Information System Journal
Sanjaya, Donny
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Computer Science & IT Control & Systems Engineering Engineering

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Penerapan Metode Multi-Attributive Border Approximation Area Comparison Pada Sistem Pendukung Keputusan Penentuan Penerima Bantuan Pangan Non Tunai Sanjaya, Donny; Nababan, Arif Hamied; Sinuhaji, Nirwan; Siregar, Dini Rizqi Dwikunti; Danur, Surizar Rahmi
Journal Global Technology Computer Vol 4 No 2 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jogtc.v4i2.7370

Abstract

Non-Cash Food Assistance (BPNT) is a type of assistance managed by the Ministry of Social Affairs. The problem that often occurs when selecting recipients of non-cash food assistance previously was choosing families receiving non-cash food assistance without complying with the specified requirements or criteria. In the selection of families receiving non-cash food assistance, there is still a family attitude such as the village head and his staff who choose families receiving non-cash food assistance. This is certainly very bad, resulting in poor people not getting assistance and will also cause social jealousy among residents and not produce community welfare. A decision support system (DSS) is a system that is able to provide problem-solving capabilities and communication capabilities for problems with semi-structured and unstructured conditions. In the decision support system, the MABAC method can be applied which is able to produce the best decisions and several inputted alternatives. The results obtained from the process carried out were that A4 was the selected alternative with the highest value, namely 0.602
Algoritma Data Mining Menggunakan Metode Decision Tree Untuk Memprediksi Pola Penjualan Produk Springbed Mengggunakan Algoritma C4.5 Sanjaya, Donny; Saleh, Amir; Novida Sari, Sri; Rahmi Danur, Surizar
Management of Information System Journal Vol 4 No 2: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i2.2567

Abstract

Problems that often occur in the world of spring bed sales business are frequent ups and downs in predictions, the difficulty of detecting patterns in what can increase sales from buyers makes spring bed sales business people often experience losses, this also happens because business people don't know the strategy. Certainly in increasing sales, it is necessary to make predictions with a high level of accuracy, one of which is with the help of the application of computer science data mining using the C4.5 method. The C4.5 method used in this research is able to produce an optimal decision tree, with the ability to sort out the most relevant attributes in predicting springbed sales. The use of this data mining algorithm is expected to provide insight to springbed business players in making strategic decisions, such as stock management, production planning and more effective marketing campaigns. The experimental steps in this research include collecting springbed sales data. Experimental results show that the Decision Tree algorithm using the C4.5 method is able to provide spring bed sales predictions with an adequate level of accuracy. This model can help Springbed sales business players in planning more appropriate business strategies based on estimated market demand to increase the ups and downs of sales
Co-Authors Amir Saleh Danur, Surizar Rahmi Nababan, Arif Hamied Novida Sari, Sri Rahmi Danur, Surizar Sinuhaji, Nirwan Siregar, Dini Rizqi Dwikunti