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All Journal International Journal Science and Technology (IJST) Jurnal Teknik dan Science (JTS)
Indra Adi Permana
Universitas Gunadarma
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Optimasi Hyperparameter Random Forest menggunakan Bayesian Optimization Untuk Prediksi Kelulusan Mahasiswa Ana Vivtia Setyawan; Indra Adi Permana
Jurnal Teknik dan Science Vol. 5 No. 2 (2026): Juni : Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v5i2.2946

Abstract

The Timely student graduation is one of the key indicators used to evaluate higher education performance. Early identification of students at risk of delayed graduation enables universities to implement appropriate academic interventions and improve student success rates. This study aims to develop a student graduation prediction model using the Random Forest algorithm and to optimize its hyperparameters through Bayesian Optimization. A synthetic dataset consisting of 750 student records was employed, incorporating demographic, socioeconomic, and academic variables. The research methodology included data preprocessing, one-hot encoding, stratified data splitting with an 80:20 ratio for training and testing sets, baseline Random Forest model development, Bayesian hyperparameter optimization using the Tree-structured Parzen Estimator (TPE) with 20 optimization trials, and model evaluation using accuracy, precision, recall, F1-score, receiver operating characteristic area under the curve (ROC-AUC), and average precision. Experimental results showed that the baseline Random Forest achieved an accuracy of 70.67%, precision of 75.00%, recall of 71.43%, F1-score of 73.17%, and ROC-AUC of 82.74%. Bayesian Optimization identified the optimal hyperparameter configuration consisting of 350 trees, a maximum tree depth of six, a minimum split size of seven samples, and a minimum leaf size of four samples. Although the optimized model produced identical accuracy and F1-score values, it improved the ROC-AUC to 83.66% and the average precision to 87.68%, indicating better probability discrimination between the two classes. Feature importance analysis revealed that the number of failed courses, first-semester GPA, cumulative GPA after two semesters, attendance rate, and repeated courses were the most influential predictors. These findings demonstrate that Bayesian Optimization enhances the probabilistic ranking capability of Random Forest and can support the development of an early warning system for identifying students at risk of delayed graduation. However, further validation using real-world academic data is required before practical implementation.
Analisis dan Perencanaan Terhadap Sistem Pelayanan Praktikum Laboratorium Teknik Informatika menggunakan Metode SWOT, Tools Data Flow Diagram Dan Entity Relational Diagram Indra Adi Permana; Amrin Fakhruddin Jauhari
Jurnal Teknik dan Science Vol. 5 No. 2 (2026): Juni : Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v5i2.2997

Abstract

Laboratorium Teknik Informatika (untuk selanjutnya disebut Lab TI) Universitas Gunadarma merupakan tempat berlangsungnya kegiatan praktikum bagi mahasiswa Universitas Gunadarma jurusan Teknik Informatika. Permasalahan yang timbul adalah sistem pelayanan praktikum yang manual sehingga tidak efektif dan efisien. Oleh karena itu digunakan metode SWOT untuk analisis sistem berjalan dan dilakukan perencanaan sistem yang baru menggunakan DFD dan ERD. Hasil analisis SWOT berada di kuadran 1, mendukung penerapan strategi agresif, yang berarti faktor strength dan opportunity dapat dimanfaatkan sebagai dasar pengembangan sistem. Dengan menentukan faktor-faktor tersebut, diharapkan bisa dijadikan dasar untuk solusi masalah yang dihadapi Lab TI
A Systematic Analysis of AI-Assisted Vibe Coding in Software Development: Opportunities, Challenges, and Risks amrin jauhari; Fahmi Fathullah; Indra Adi Permana; Robby Nugraha
International Journal Science and Technology Vol. 5 No. 2 (2026): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v5i2.3000

Abstract

The literature on vibe coding has grown rapidly; however, it remains fragmented and is largely dominated by industry reports, leaving its position relative to traditional manual programming and low-code development insufficiently examined. This gap makes it difficult for both researchers and practitioners to determine when vibe coding is appropriate and what risks should be anticipated. Purpose: This study aims to systematically map the current landscape of vibe coding, develop a comparative framework against manual and low-code software development approaches, and propose practical risk mitigation recommendations for software development practitioners. Methodology: A Systematic Literature Review (SLR) was conducted following the PRISMA protocol. Relevant publications from 2023 to 2026 were retrieved from IEEE Xplore, ACM Digital Library, Springer, ScienceDirect, and arXiv, resulting in 61 studies that were analyzed using thematic analysis. Findings: The results indicate that vibe coding can accelerate software prototyping by approximately 40–60% compared with manual development. However, it introduces a verification bottleneck by shifting developers' workload from code implementation to quality assurance and validation. Compared with low-code development, vibe coding provides greater flexibility in expressing user intent but exhibits lower output predictability. In comparison with manual development, it offers significant gains in development speed while sacrificing architectural control and code security, thereby increasing the risks of technical skill degradation, hidden security vulnerabilities, and accumulated technical debt. Implications: The findings provide practical guidance for software development teams in identifying project phases that are suitable for extensive adoption of vibe coding and those that still require manual architectural review. The study also emphasizes the importance of integrating security auditing and technical debt monitoring into AI-assisted software development workflows. Originality/Value: The novelty of this study lies in its explicit comparative framework, which systematically positions vibe coding alongside manual and low-code development across six technical dimensions, extending previous studies that have generally examined vibe coding in isolation.
Co-Authors Amrin Fakhruddin Jauhari amrin jauhari Ana Vivtia Setyawan Fahmi Fathullah Robby Nugraha