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Arie Vatresia
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Jurnal Pseudocode
Published by Universitas Bengkulu
ISSN : 23555920     EISSN : 26551845     DOI : https://doi.org/10.33369
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
Pseudocodeis a scientific journal in the information science family that contains the results of informatics research, scientific literature on informatics, and reviews of the development of theories, methods, and application of informatics engineering science. Pseudocode is published by the Informatics Study Program, Faculty of Engineering, University of Bengkulu. Editors invite researchers, practitioners, and students to submit article manuscripts in the field of informatics engineering. Pseudocode is published 2 (two) times a year in February and September with p-ISSN 2355-5920 e-ISSN 2655-1845. Jurnal Pseudocode is Accredited by the Ministry for Research, Technology and Higher Education (RISTEKDIKTI) in SINTA 4 No. 36/E/KPT/2019 since 13 December 2019.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 210 Documents
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Pengembangan model pengenalan huruf SIBI pada kondisi low-light berbasis convolutional neural network Francisco, Francisco; Aklani, Syaeful Anas
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.14-20

Abstract

Deaf and speech-impaired individuals in Indonesia face communication barriers due to limited public understanding of sign language. In real use, SIBI communication often occurs in dim lighting, yet recognition models are mainly evaluated under normal illumination, motivating robust low-light recognition. This study develops a CNN model based on MobileNetV2 to recognize SIBI (Indonesian Sign Language System) letter gestures under low-light conditions (50-100 lux). The dataset comprises 5,579 images of 26 SIBI letters, divided stratified 80:10:10. The methodology includes preprocessing with Bilateral Filter, CLAHE in LAB color space, and Adaptive Gamma Correction, plus transfer learning and fine-tuning with data augmentation. Evaluation results show 97.13% test accuracy, with most errors among similar letters. Real- time testing is stable within 50-100 lux, though accuracy decreases below 50 lux or with shadows. These findings indicate that the proposed preprocessing methods and MobileNetV2 CNN maintain reliable SIBI recognition in low-light environments.
Analisis Sentimen Ulasan Aplikasi Access by KAI Menggunakan Algoritma Naïve Bayes Ariansyah, Beni; Negara, Edi Surya
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.21-27

Abstract

The Access By KAI application, developed by PT Kereta Api Indonesia (Persero), allows users to purchase train tickets via mobile devices. This study aims to perform sentiment analysis on user reviews of the Access By KAI application using the naive Bayes algorithm. Data processing was carried out through stages such as case folding, cleaning, tokenizing, stopword removal, and stemming, and evaluation using metrics of accuracy, precision, recall, and F1-score showed that the naive Bayes algorithm provides satisfactory results. The study results indicate that the naive Bayes algorithm is able to classify reviews with an accuracy rate of up to 68% with a precision of 83% for the positive class, 59% for the negative class, and 79% for the neutral class; recall of 67% for the positive class, 93% for the negative class, and 42% for the neutral class. From these results, it is expected to help developers identify the aspects most complained about by users and improve service quality.
Penerapan Algoritma Random Forest Dan Support Vector Machine Untuk Deteksi Distributed Denial of Service (DDoS) Riswaya, Asep Ririh; Fadlil, Abdul; Yudhana, Anton
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.28-35

Abstract

Distributed Denial of Service (DDoS) attacks significantly threaten the availability of modern network services by overwhelming resources with malicious traffic. The increasing complexity of these attacks, including multi-vector and low-rate variants, reduces the effectiveness of traditional detection methods based on signatures and static rules. This study explores the effectiveness of Random Forest (RF) and Support Vector Machine (SVM) algorithms in detecting DDoS attacks using the CICDDoS2019 dataset, focusing on the impact of various decision threshold values on performance. The CICDDoS2019 dataset consists of 431,371 network traffic flows with 80 numerical features. Preprocessing involves eliminating null values, standardizing numerical attributes, and encoding labels into binary classifications of normal and DDoS traffic. The dataset is then divided into training and testing sets at a 70:30 ratio. Performance evaluation is done using a confusion matrix to calculate accuracy, precision, recall, and F1-score. Results show that both algorithms perform well, but Random Forest offers greater consistency, with a threshold of 0.5 achieving the best balance in metrics.
Analysis of the application of interactive learning media 3d model based on augmented reality Pratama, Jimmy; Helena, Helena; Syahputra, Bayu
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.36-43

Abstract

Technological developments have encouraged the use of Augmented Reality (AR) as an interactive learning medium in education. However, learning 3D animation using Autodesk Maya is still considered difficult by beginner students due to the complexity of the features and interface. This study aims to develop and analyze the benefits of an interactive learning media application based on markerless Augmented Reality (AR) in recognizing icons and functions of the Autodesk Maya Toolbox. The research method used is applied research with a qualitative approach and the Multimedia Development Life Cycle (MDLC) development method. The application was developed using Unity, Vuforia, and Autodesk Maya, and tested on 30 students through interviews. The results of the study show that the markerless AR application is able to help students understand and recognize the Autodesk Maya toolbox more easily through interactive and clear 3D object visualization. Thus, this application is considered useful as a supporting medium for innovative and interactive 3D animation learning.
Implementasi Sistem Verifikasi dan Pengenalan Wajah Berbasis YOLOv8n dan VGG-Face untuk Absensi Perkuliahan Listya Suhardi, Cahyaningsih; Eislam, Candra Milad Ridha; Wicaksono, Fernanda Agung
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.44-53

Abstract

This research presents the development of a facial recognition and verification system that aims to address attendance record falsification in university lectures, a persistent challenge in non-biometric attendance management. The proposed framework integrates the efficiency of YOLOv8n for facial detection with the strong feature representation capability of VGG-Face. The system applies image augmentation to create varied facial embeddings, uses Cosine Similarity for identity verification, and evaluates its performance through accuracy, precision, recall, and F1-Score metrics. Experimental evaluations on student facial datasets captured under different lighting conditions, poses, and viewing angles show that the system achieves a stable accuracy of around 90 % without augmentation, increasing to 97 % with augmentation, which enhances overall stability and reliability. These results demonstrate that the integration of YOLOv8n and VGG-Face offers an effective and dependable solution for strengthening the security and credibility of facial recognition-based attendance systems in academic settings.
Analisis komparatif efektivitas keamanan dan penerimaan pengguna pada protokol presensi digital berbasis QR code statis dan dinamis Putro, Dwi Purnomo; Solikin, Mokhamad; Suryani, Puput Eka
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.63-68

Abstract

QR code–based digital attendance systems are widely used in higher education, yet they continue to face security challenges such as attendance fraud and inaccurate time recording. This study aims to comparatively evaluate the effectiveness of static and dynamic QR code–based attendance protocols by examining system security and user acceptance. A quantitative approach was employed through a field experiment and a survey grounded in the Technology Acceptance Model (TAM), encompassing Perceived Usefulness, Perceived Ease of Use, and Behavioral Intention. Data were analyzed using independent samples t-tests and Cohen’s d effect size measures. The results show that the dynamic QR code protocol significantly outperforms the static QR code across all TAM constructs (p < 0.001), with very large effect sizes (d > 1.0). These findings indicate that enhanced security mechanisms do not reduce usability, but instead strengthen user acceptance and adoption intention.
Klasifikasi Burnout Mahasiswa menggunakan Algoritma Random Forest dengan Analisa Feature Importance yuriko, Andrean; Hadi, Abd.
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.54-62

Abstract

Prolonged stress in higher education environments can lead to emotional, physical, and mental exhaustion among students, negatively affecting concentration, academic performance, and mental health. This study aims to classify student burnout levels using the Random Forest algorithm and to analyze model performance as well as dominant contributing factors through a feature importance approach. The dataset was obtained from the Kaggle platform and consisted of 1,100 samples covering psychological, physiological, environmental, academic, and social variables. The research methodology included data preprocessing, data balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and data splitting with six scenarios: 70:30, 75:25, 78:22, 80:20, 83:17, and 87:13. Experimental results showed that all splitting scenarios produced relatively stable performance, with the 80:20 split achieving the highest accuracy of 89%, while other scenarios ranged from 85% to 87%. Feature importance analysis indicated that physiological factors, particularly blood_pressure, had the most significant contribution to student burnout classification.
Analisis Prediksi Probabilitas Otomatisasi Pekerjaan Tahun 2030 Menggunakan Algoritma Linear Regression Dan Gradient Boosting Nicholas Leonardo; Ahmad Zidane Arrasyid; Natagama, Muhammad Arif Billah; Hafidz Muhammad Dzaky; Vitri Tundjungsari
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.69-74

Abstract

The rapid development of artificial intelligence and automation is expected to significantly impact future employment. This study aims to predict job automation probability in 2030 using supervised learning methods. A public dataset containing job types, education levels, and automation probabilities was utilized. Linear Regression and XGBoost Regressor were employed to build and compare predictive models. The research process included data preprocessing, training–testing data split, model training, and performance evaluation using Root Mean Square Error (RMSE) and coefficient of determination (R²). Experimental results indicate that XGBoost outperforms Linear Regression by achieving lower RMSE and higher R² values. This study provides insights into automation risks and may support workforce skill development planning.
Implementasi Metode Topsis Pada Sistem Pendukung Keputusan Penentuan Prioritas Penerima Bantuan Mesin Untuk Sentra Ikm Berbasis Website (Studi Kasus : Dinas Perindustrian Dan Perdagangan Provinsi Bengkulu) Monica, Exca Wella; Sari, Julia Purnama; Andreswari, Desi
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.84-90

Abstract

The industrial sector is one of the economic sectors being developed in Indonesia as a driving force for the progress of other economic sectors. One form of this is the Small and Medium Industries (SMEs), which are quite dependent on government assistance, both in terms of capital and equipment. One of the government programs to advance SMEs is the provision of machinery assistance to SME centers. In practice, the selection process for machine assistance recipients at the Bengkulu Industry and Trade Office still uses Microsoft Excel, which has limitations, including the risk of human error, logical errors in formulas, differences in results due to rounding numbers, and a lack of objectivity in assessments. Therefore, a Decision Support System (DSS) is needed to minimize errors, producing more objective, transparent, and accurate decisions. This study uses the TOPSIS method to calculate the preference value of SMEs and data aggregation techniques (averages) to calculate the final value of SME centers. The resulting output is a ranking of SME centers eligible for machine assistance. Of the total of 26 comparisons between the rankings from the system and the rankings from the Industry and Trade Office, 20 rankings were obtained that were the same, so that the system accuracy reached 76.92%. Furthermore, the system's feasibility test achieved an interval score of 4.6, or 92%, which is considered very good. The final result of this research is the creation of a website-based decision support system for prioritizing recipients of machine assistance for small and medium enterprises SME centers.
Perancangan Sistem Informasi Geografis (SIG) untuk Pendataan Jaringan Kabel pada Area Distribusi Milyanto, Aldi Huseini; Sujatmika, Arif Rahman; Budiman
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.91-96

Abstract

This study focuses on the development of a Geographic Information System (GIS) designed to support the recording of cable network data in distribution areas. One of the main challenges encountered by network managers is the lack of an effective and accurate method for mapping the locations of network infrastructure. To address this issue, a web-based GIS is proposed to enable more structured processes of data recording, monitoring, and maintenance of poles and cable networks. The system development adopts a prototype-based approach, which includes requirement analysis, system modeling, implementation, and testing phases. The application is implemented using HTML, PHP, MySQL, and Leaflet.js as a digital mapping library. Spatial data are collected through field surveys and converted into geographic coordinates for visualization on an interactive map. The developed system is able to display pole locations and cable routes in real time, thereby supporting asset management and distribution network analysis. Overall, the system provides an effective solution for managing cable network infrastructure data.

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