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Deli River Water Quality Control In Medan City Using Statistical Methods Quality Control Rizkina, Manisyah; Siregar, Machrani Adi Putri; Batubara, Ana Uzla
Jurnal Pijar Mipa Vol. 19 No. 2 (2024): March 2024
Publisher : Department of Mathematics and Science Education, Faculty of Teacher Training and Education, University of Mataram. Jurnal Pijar MIPA colaborates with Perkumpulan Pendidik IPA Indonesia Wilayah Nusa Tenggara Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpm.v19i2.6591

Abstract

Clean water is a type of water-based resource that is of good quality and is commonly used by humans for consumption or in carrying out daily activities. Clean water quality that meets standards/quality is very difficult to obtain because river water quality has been polluted by various kinds of waste from various human activities, so that the potential impact on river quality decreases both in terms of quantity and quality. 70% of the Deli River pollution is solid and liquid waste, domestic waste, industrial waste, and along the Deli River it has affected the quality of the river water. To quantitatively identify air quality, the Statistical Quality Control method can be used. Statistical quality control is a collection of strategies, techniques, and actions taken to ensure that they produce a quality product. The aim of this research is to determine the control of the water quality of the Deli River in Medan City using Statistical Quality Control. Based on the data obtained, the water quality standards of the Deli River in the Sumatra II River Basin Agency from January 2023 - December 2023 have not been statistically controlled because there are several data samples that are out of control. Then, Deli River water quality control was carried out based on graphic control using SQC (Statistical Quality Control). The results of quality control using SQC show that SQC provides different controls, because in SQC TDS control on data on controlled 3 times, while the and R data on DO, the and R data on and and R data for fatty oil are controlled.
Fuzzy Clustering-Based Grouping for Mapping the Distribution of Student Success Data Mustakim, Mustakim; Aini, Delvi Nur; Batubara, Ana Uzla; Erkamim, Moh.; Legito, Legito
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.1227

Abstract

Learning activities are the main activity in the overall teaching and learning process in schools. This is because whether or not the achievement of educational goals depends on how the learning process is carried out by students. The uneven level of student success in learning is one of the problems in the school's efforts to realize the vision and mission of SMKN 5 Pekanbaru in preparing skilled graduates to be able to work in certain sectors by the public interest and the industrial world. In this study, mapping and grouping student grade data was carried out using the Fuzzy C-Means algorithm to provide information to the school in making the right decisions and optimizing the learning process. Furthermore, clustering was carried out in several experiments K=3 to K=7, and obtained the best validity value tested with the Silhouette Index of 0.4277 located at K=5. Then the distribution of cluster 5 on student score data was obtained with details, namely cluster 1 with a capacity of 1 student, cluster 2 with a capacity of 27 students, cluster 3 with a capacity of 1 student, cluster 4 with a capacity of 10 students, cluster 5 with a capacity of 23 students.
Mathematical Physics and the Study of Complex Quantum Systems Batubara, Ana Uzla; Ali, Zainab; Vandika, Arnes Yuli
Research of Scientia Naturalis Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i6.1585

Abstract

The study of complex quantum systems is a fundamental aspect of modern physics, providing insights into the behavior of matter at microscopic scales. Mathematical physics plays a crucial role in developing the theoretical frameworks necessary for understanding these systems, yet challenges remain in applying these concepts to real-world scenarios. This research aims to investigate the application of mathematical techniques in analyzing complex quantum systems. The focus is on identifying effective mathematical models and methods that can enhance our understanding of quantum phenomena. A comprehensive literature review was conducted, analyzing various mathematical approaches utilized in quantum mechanics, including perturbation theory, group theory, and numerical simulations. Case studies were examined to illustrate the successful application of these methods in real-world quantum systems. Findings indicate that advanced mathematical techniques significantly improve the modeling and analysis of complex quantum systems. The application of perturbation theory and numerical simulations provided deeper insights into system behaviors, while group theory facilitated a better understanding of symmetry properties. This research highlights the indispensable role of mathematical physics in the study of complex quantum systems. By emphasizing the integration of mathematical techniques, the study contributes to the advancement of theoretical physics and offers pathways for future research in quantum mechanics.