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Programming and Algorithm Fundamentals
Published by Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia
ISSN : -     EISSN : 3123979X     DOI : 10.66472
Core Subject :
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 3 Documents
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Search results for , issue "vol. 1 no. 2 (2026): april: programming and algorithm fundamentals" : 3 Documents clear
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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