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Fahrudin Muhtarulloh
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KUBIK: Jurnal Publikasi Ilmiah Matematika
Published by UIN Sunan Gunung Djati Bandung
ISSN : 23380896     EISSN : 26860341     DOI : 10.15575/kubik
Core Subject : Economy, Science, Education,
Computer Science & IT Economics, Econometrics & Finance Mathematics
Fuzzy Systems and its Applications Geometry Theories and its Applications Graph Theories and its Applications Real Analysis and its Applications Operation Research and its Applications Statistical Theories and its Applications Dinamical Systems and its Applications Mathematics Modeling and its Applications Discrete Mathematics and its Applications Computer Mathematics and its Applications Mathematics Actuaria and its Applications
Arjuna Subject : Matematika - Matematika Terapan
Articles 3 Documents
 1 
Search results for , issue "Vol 10 No 2 (2025): IN PRESS" : 3 Documents clear
Bellman-Ford Algorithm for Optimizing Drinking Water Distribution by Perumda Air Minum Tirta Raharja in Cicalengka Lusiani, Anie; Sartika, Euis; Nuryati, Neneng; Hedi, Hedi
KUBIK Vol 10 No 2 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Access to clean water in Cicalengka District, Bandung Regency, remains limited, with current coverage reaching only 5.08% of the population. Perumda Air Minum Tirta Raharja has set a target to expand drinking water service to 44% by 2030. To support this goal, this study investigates the optimization of the water distribution pipe network in Cicalengka. The research applies the Bellman-Ford algorithm to model the distribution network as a weighted, undirected, and connected graph, where customer houses are represented as vertices, pipe connections as edges, and pipe lengths as weights. Using data from the existing network and customer locations, the algorithm was implemented to identify optimal distribution paths. The results yielded two shortest path alternatives between the specified source and destination nodes. These findings demonstrate the potential of graph-based optimization in improving distribution planning and can serve as a reference for the development and management of future water supply infrastructure.
Statistical Optimization of Experimental Conditions for Enhanced Removal of Heavy Metals from Wastewater Sylvi, Pismia
KUBIK Vol 10 No 2 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v10i2.49983

Abstract

Increasing concentrations of heavy metals in industrial liquid waste demand the development of more efficient treatment strategies to minimize the impact on the environment and public health. This study aims to statistically optimize experimental conditions to improve the efficiency of heavy metal removal (especially Pb²⁺, Cd²⁺, and Cr⁶⁺) from synthetic liquid waste through the batch adsorption process. A well-planned experimental design was implemented using the Response Surface Methodology (RSM) approach with Central Composite Design (CCD) to evaluate the individual influences as well as interactions of four key variables: the initial concentration of the metal, the pH of the solution, the adsorbent dose, and the contact time. The results of the experiment were modeled in the form of second-order polynomial regression, and model validation was carried out strictly through variety analysis (ANOVA), determination coefficients (adjusted R² and R²), and lack-of-fit tests. The optimization process successfully identified a combination of operating parameters that significantly improved the elimination efficiency, reaching a level above 95% at the optimized conditions that had been validated. The residue analysis showed the fulfillment of the assumptions of normality, homogeneity of variance, and error independence, thus confirming the predictive reliability of the model. These findings confirm the effectiveness of RSM-based optimization approaches in wastewater treatment research, as well as the importance of statistical-based experimental planning in maximizing process efficiency. This approach provides a robust framework for advanced applications in industrial waste management and sustainable environmental engineering.
Logistic Regression Analysis to Determine Factors Influencing Career Choices of Undergraduate Alumni: Analisis Regresi Logistik untuk Menentukan Faktor-Faktor yang Mempengaruhi Pilihan Karir Lulusan Sarjana Perguruan Tinggi Febrian, Didi; Harliana, Putri; Farhana, Nurul Ain
KUBIK Vol 10 No 2 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v10i2.45050

Abstract

An undergraduate alumni has three career choices, namely work, postgraduate study or entrepreneurship. The success of an alumni's career is not only influenced by academic knowledge, but also by other factors that reflect the individual's personal and professional abilities. These factors are certainly obtained from the experiences that the alumni have gone through while studying for their undergraduate program. The respondents of this study were 100 undergraduate alumni of Universitas Negeri Medan in 2021 and 2022. This study aims to determine the effect of GPA (X1), study period (X2), number of achievements (X3), organizational activity (X4), work intensity during undergraduate (X5), and TOEFL score (X6) on alumni career choices (Y). In logistic regression analysis, the independent variable must have at least two categories so that the alumni's career choices (Y) are working (0) and postgraduate study (1). The results of the study showed that only the work intensity factor during S1 (X5) and TOEFL score (X6) had a significant influence on alumni career choices. The Logistic Regression Model based on the influential variables was . The percentage of model accuracy can predict correctly by 72.0%.

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