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Programming and Algorithm Fundamentals
Published by Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia
ISSN : -     EISSN : 3123979X     DOI : 10.66472
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Aims This journal aims to disseminate fundamental and applied research in programming, algorithm design, and computational problem-solving that form the foundation of modern computing systems. Scope Algorithm design and complexity analysis Data structures and optimization techniques Programming paradigms and languages Computational thinking and problem-solving Parallel and distributed algorithms Algorithmic foundations of software systems Programming education and curriculum studies
Arjuna Subject : -
Articles 8 Documents
 1 
Design and Analysis of a Novel Parallel Algorithm for Large Scale Graph Optimization with Dynamic Load Balancing in Heterogeneous Computing Environments Dedy Tri Cahyono; Jaja Miharja
Programming and Algorithm Fundamentals Vol. 1 No. 1 (2026): January: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i1.16

Abstract

This research focuses on the design and evaluation of a novel parallel graph optimization algorithm incorporating dynamic load balancing (DLB) to address inefficiencies in heterogeneous computing environments. Large-scale graph optimization problems, such as those in social networks, bioinformatics, and transportation systems, often suffer from computational imbalances when using traditional static load balancing approaches, leading to underutilized resources and prolonged execution times. The primary objective of this research is to develop an algorithm that can dynamically adjust workload distribution across processors, enhancing computational efficiency and scalability. The proposed method combines heuristic techniques, including region expansion and multilevel partitioning, with diffusive load balancing strategies to minimize inter-processor communication overhead. Experimental results demonstrate that the proposed algorithm reduces execution time by up to 40% compared to static methods, with optimized resource utilization and more balanced workload distribution. The scalability of the algorithm is also evident, as it adapts effectively to increasing problem sizes and processor counts. These findings suggest that dynamic load balancing is crucial for improving parallel graph optimization in real-world applications. Future work will focus on further enhancing the algorithm’s responsiveness to rapidly changing workloads and expanding its applicability to additional domains.
Integrating Computational Thinking and Adaptive Curriculum Frameworks to Enhance Problem Solving Skills in Undergraduate Programming Education Across Diverse Learning Contexts Nicodemus Rahanra; Ahmad Ashifuddin Aqham; Eko Siswanto
Programming and Algorithm Fundamentals Vol. 1 No. 1 (2026): January: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i1.17

Abstract

This study investigates the integration of computational thinking (CT) principles with adaptive curricula to enhance problem-solving skills in undergraduate programming education. Traditional programming curricula often emphasize syntax and basic concepts, neglecting critical problem-solving strategies. The adaptive curriculum framework used in this study combines CT skills such as decomposition, pattern recognition, abstraction, and algorithmic thinking with personalized learning experiences. A mixed-method approach, combining qualitative and quantitative research, was employed to assess the effectiveness of this integrated approach. The results show significant improvements in students' problem-solving abilities, conceptual understanding, and engagement compared to a control group following a traditional curriculum. Students in the experimental group, which received the adaptive curriculum, demonstrated better performance in applying algorithms and debugging code. Additionally, students expressed higher levels of engagement and motivation, suggesting that the personalized learning environment fostered greater academic involvement. The study highlights the importance of integrating CT principles with adaptive learning frameworks to create a more inclusive and effective learning environment that accommodates diverse learning needs. The findings suggest that adaptive curricula can bridge gaps in traditional education by providing personalized support and ensuring that students progress at their own pace. This approach is especially beneficial for programming education, where both conceptual understanding and practical problem-solving skills are critical for success. Future research should explore the long-term impact of adaptive learning frameworks and investigate how these technologies can be integrated with traditional teaching methods to maximize their effectiveness.
A Hybrid Data Structure and Algorithmic Approach for Efficient Memory Management and Query Processing in High Performance Software Systems Zulfikar Zulfikar; Febri Adi Prasetya; Marsiska Ariesta Putri
Programming and Algorithm Fundamentals Vol. 1 No. 1 (2026): January: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i1.20

Abstract

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.
Comparative Evaluation of Functional, Object Oriented, and Declarative Programming Paradigms for Scalability and Maintainability in Distributed Data Processing Applications Simon Simarmata; Panser Karo karo
Programming and Algorithm Fundamentals Vol. 1 No. 1 (2026): January: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i1.23

Abstract

This study compares the scalability and maintainability of three prominent programming paradigms-functional programming (FP), object-oriented programming (OOP), and declarative programming (DP)-in the context of distributed data processing systems. The research aims to evaluate how each paradigm performs under increased data volume and its ability to handle complex operations, while also assessing the ease of maintenance through code readability, modularity, and the flexibility of updating and debugging. The study employs a comparative experimental design, implementing identical data processing tasks, such as data aggregation, filtering, and transformation, across each paradigm. Key findings indicate that FP and DP outperform OOP in terms of scalability, with their stateless nature and high-level abstractions enabling efficient parallel processing and task distribution. FP, with its emphasis on immutability and concurrency, and DP, with its focus on describing desired outcomes rather than implementation specifics, both demonstrate superior performance in handling large datasets. However, while OOP excels in modularity and flexibility, its reliance on mutable state and shared resources hampers its scalability in distributed environments. In terms of maintainability, both FP and DP offer clearer, more maintainable code due to their abstraction levels, making them easier to update and extend. OOP, while modular, presents challenges in managing mutable state, complicating maintenance. This paper concludes with practical recommendations for developers on when to use each paradigm based on system requirements and suggests areas for future research, such as hybrid paradigms and long-term maintainability studies in real-world applications.
Complexity Analysis of Adaptive Scheduling Algorithms for Real Time Parallel Processing in Cloud Computing Platforms with Fault Tolerance Mechanisms Warto Warto; Iif Alfiatul Mukaromah
Programming and Algorithm Fundamentals Vol. 1 No. 1 (2026): January: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i1.25

Abstract

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.
Predicting Final CGPA of University Students Using Machine Learning : A Comparative Study of XGBoost, Random Forest, Decision Tree, and Linear Regression Aina Mawardah Oktaviani
Programming and Algorithm Fundamentals Vol. 1 No. 2 (2026): April: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i2.505

Abstract

Predicting student academic performance is a critical task for educational institutions to identify at-risk students and improve educational outcomes. This study develops a machine learning-based predictive model for forecasting final Cumulative Grade Point Average (CGPA) of university students using demographic, academic, and lifestyle-related features. The dataset comprises 5,000 student records with ten attributes including gender, age, major, attendance percentage, study hours per day, previous CGPA, sleep hours, social hours per week, and final CGPA as the target variable. Four regression algorithms were implemented and compared: Linear Regression, Decision Tree Regressor, Random Forest Regressor, and XGBoost Regressor. Exploratory Data Analysis (EDA) revealed that Previous CGPA exhibits the strongest positive correlation with Final CGPA (r = 0.88), followed by Attendance Percentage (r = 0.30) and Study Hours Per Day (r = 0.23). Sleep Hours showed a weak negative correlation (r = -0.01). Among the evaluated models, XGBoost achieved the highest predictive performance with an R² score of 0.949, RMSE of 0.119, and MAE of 0.098. This study shows that the ensemble method, especially XGBoost, far outperforms simple linear models in capturing complex non-linear relationships on students' academic performance prediction tasks. These findings provide valuable insights for designing early intervention strategies and personalized academic support systems in colleges.
Comparison of Random Forest and Support Vector Machine Algorithms for Heart Disease Prediction Using SMOTE and Hyperparameter Tuning Techniques Randi Trinanda
Programming and Algorithm Fundamentals Vol. 1 No. 2 (2026): April: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i2.537

Abstract

Heart disease remains one of the leading causes of death worldwide, making early and accurate detection a critical priority in healthcare. However, building an effective predictive model is challenged by the presence of class imbalance in medical datasets, where the number of healthy patients often exceeds disease cases, potentially biasing model performance toward the majority class. This study aims to compare the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms for heart disease prediction, incorporating Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance and Hyperparameter Tuning to optimize each model's configuration. The dataset used consists of 2,026 patient records with 13 clinical features including age, blood pressure, cholesterol, EKG results, and thallium test results, with heart disease (Absence/Presence) as the target variable. Experiments were conducted in two scenarios: without SMOTE and Tuning as baseline, and with SMOTE and Hyperparameter Tuning as the optimized scenario. Results show that both models improved after optimization, with Random Forest achieving the best overall performance with an accuracy of 87.44%, recall of 0.8634, F1-Score of 0.8610, and AUC-ROC of 0.9449, outperforming SVM which reached an accuracy of 86.45% and AUC-ROC of 0.9396. These findings conclude that Random Forest combined with SMOTE and Hyperparameter Tuning is the most suitable model for heart disease prediction, particularly in minimizing false negatives which are clinically critical in medical diagnosis.
Graph Convolutional Network with TF-IDF Embeddings for Public Sentiment Classification on Indonesia's Free Nutritious Meal Program Zhulfani Faisal Adam
Programming and Algorithm Fundamentals Vol. 1 No. 2 (2026): April: Programming and Algorithm Fundamentals
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/paf.v1i2.542

Abstract

The Free Nutritious Meal Program, implemented as a national priority policy, has generated a wide array of public responses on social media, reflecting society's perception of the initiative. However, accurately classifying sentiment from short-text content such as tweets remains a challenging task due to limited context, informal language, and high lexical variability. Traditional machine learning models often fall short in capturing the complex semantic and structural information embedded in such data. To address this issue, this study proposes a sentiment classification model based on the Graph Convolutional Network (GCN) architecture, utilizing TF-IDF embeddings as the text representation method. A dataset comprising 5,979 labeled tweets was collected from the social media platform X. A document graph was constructed by computing cosine similarity between TF-IDF vector representations of the tweets, and this graph served as input to the GCN model. The proposed method was evaluated using accuracy, precision, recall, and F1-score. Experimental results show that the TF-IDF-based GCN model achieved an accuracy of 82.71% and an F1-score of 82.65%, outperforming conventional classifiers such as Logistic Regression, Support Vector Machine, and Naive Bayes. These findings demonstrate the effectiveness of integrating semantic-rich embeddings and graph-based learning in classifying public sentiment toward policy-related topics on social media.

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