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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 13 Documents
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Search results for , issue "Vol. 8 No. 1 (2025)" : 13 Documents clear
Design of a Greenhouse System for Vegetable Plants Based on the Internet of Things Janne Deivy Ticoh; Junaedy Alexandrya Kaengke; Ridwan, Ridwan
Jurnal Sistem Cerdas Vol. 8 No. 1 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i1.501

Abstract

This research aims to overcome the problem of lack of efficiency in Green House management in North Sulawesi. Where management is still done manually. The main problem in this research is the high cost of procuring a control system and farmers' lack of understanding regarding the Internet of Things (IoT). To solve this problem, an IoT based Green House control system was developed which integrates various sensors to monitor and control temperature, humidity and light intensity automatically. This system uses an ESP32 microcontroller and the Blynk application to facilitate remote monitoring. Trial results shows that this system is able to maintain optimal environmental conditions for plant growth, increase productivity, and reduce the risk of damage due to climate change. By implementing this system, it is hoped that farmers in North Sulawesi can optimize the use of resources and increase the quality and quantity of harvests make an important contribution to development of sustainable agriculture in the tropic
Analysis of Reverse Logistics Implementation in Improving Operational Efficiency Arzety, Zahra; Pulansari, Farida
Jurnal Sistem Cerdas Vol. 8 No. 1 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i1.502

Abstract

In the era of globalization and increasingly fierce competition, some industrial companies are required to focus not only on product and distribution efficiency but also on post-consumer product management. Supply chain issues need to be considered because they are related to the productivity of the company, if the supply chain management of a company runs well, the company's goals will be achieved, and vice versa. In addition to helping implement the principle of reducing, reusing, and recycling, reverse logistics also acts as feedback from customers on the product (finished or unfinished), including the sustainability aspect of the product. The frequent return of kWh (kilowatt-hour) meters in households thatoccur returns/defects and are returned from the reporting of consumers or customers that still exist and also the handling of companies that have not met the standards and strategies for the future. The results of the analysis of the maturity level of the implementation of reverse logistics in these three companies can be categorized at level 3 (Developed Level) with the results of measurements using MSI (Method of Successive Interval) successively are 2.549; 2.292; 2.459; 2.932; 2.677. With the calculation of scores on IFE and EFE in the IE matrix, it is in quadrant II (medium-high).
Customer Segmentation Analysis Through RFM-D Model and K-Means Algorithm Refri Martiansah; Siti Monalisa; Fitriani Muttakin; Mona Fronita
Jurnal Sistem Cerdas Vol. 8 No. 1 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i1.504

Abstract

This research analyzes customer segmentation through the RFM-D (Recency, Frequency, Monetary, and Diversity) model and the K-Means algorithm. The data comes from sales transactions at Café Z from January 2023 to February 2024, with 10,212 entries. The applied methodology includes several stages: data pre-processing, cleaning, transformation, normalization, and clustering. Clustering validation was carried out using the Davies-Bouldin Index (DBI) to ensure the quality of the clusters formed. The analysis results identified three customer clusters based on purchasing behavior, indicating that the K-Means algorithm effectively groups customers. These findings provide insight for companies to design marketing strategies that are more focused and appropriate to the characteristics of each customer segment. Companies can improve operational efficiency, increase customer satisfaction, and maximize profitability by utilizing this segmentation. This research contributes to optimizing resource allocation and personalizing marketing approaches, ultimately strengthening customer relationships.

Page 2 of 2 | Total Record : 13

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