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All Journal The IJICS (International Journal of Informatics and Computer Science) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika
Rachmadini, Haliza Suci
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Mathematics

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Determination of Critical Node in The Java Sumatra Kalimantan Submarine Cable Communication System Rachmadini, Haliza Suci; Aman, Amril; Paruhum Silalahi, Bib
Mathline : Jurnal Matematika dan Pendidikan Matematika Vol. 8 No. 2 (2023): Mathline: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/mathline.v8i2.422

Abstract

Disruption of the Java Sumatra Kalimantan (Jasuka) submarine cable communication system significantly impacted the smooth flow of communications. To reduce the impact, the detection of critical nodes in the network uses the critical node detection method to identify the most important nodes in the Jasuka network. This study aims to apply the critical node detection method as integer linear programming on the Jasuka network to obtain critical nodes by minimizing the number of paired connections on the nodes. The data in this research comes from the Jasuka network, represented as nodes and edges, and then analyzed using Python 3.11 software. The results showed that the critical node of the Jasuka submarine cable communication system is located at index 5 and 14 or the landing point Dumai, Riau and Palembang Jambi. The critical node on the Jasuka network can be a reference for Telkom Indonesia to pay special attention to the landing point because the damage will impact the entire network.
Integrating Random Forest And Forward-Chaining Inference For Automated Coffee Quality Classification Using Sensory Standards sari, ika yusnita; Khairunnisa, Khairunnisa; Rahmi, Elvika; Rangkuti, Siti Rafiah; Rachmadini, Haliza Suci
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9585

Abstract

The increasing consumption of coffee has driven the need for a fast and consistent coffee quality assessment process. The quality of specialty coffee is generally determined through cupping tests based on sensory attributes; however, this method still relies heavily on panelist subjectivity and requires considerable time and cost. This study aims to develop an automated system for specialty coffee quality classification by integrating the Random Forest algorithm and Forward Chaining inference logic. Random Forest is employed to perform initial classification and identify the importance level of sensory attributes, while Forward Chaining functions as a rule-based system to validate and explain the classification results. The study utilizes 207 coffee sensory profile data samples with 11 attributes based on the Specialty Coffee Association (SCA) cupping standards. The experimental results show that the Random Forest model achieves optimal performance with 100% accuracy, precision, recall, and F1-score, with Total Cup Points identified as the most dominant attribute. The integration of these two methods produces an accurate, consistent, and explainable coffee quality classification system in accordance with SCA standards.
Co-Authors Amril Aman Bib Paruhum Silalahi Ika Yusnita Sari Khairunnisa Khairunnisa Rahmi, Elvika Rangkuti, Siti Rafiah