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EDUMATIC: Jurnal Pendidikan Informatika
Published by Universitas Hamzanwadi
ISSN : -     EISSN : 25497472     DOI : 10.29408
Core Subject : Science, Education,
Computer Science & IT Education
EDUMATIC: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) adalah jurnal ilmiah bidang pendidikan informatika yang diterbitkan oleh Universitas Hamzanwadi dua kali setahun yaitu pada bulan Juni dan Desember. Adapun fokus dan skup jurnal ini adalah (1) Komputer dan Informatika dalam Pendidikan; (2) Model Pembelajaran dan Model TIK; (3) Pengembangan Media Pembelajaran Berbasis Teknologi Informatika; (4) Interaksi Manusia dan Komputer; (5) Sistem Informasi dan Teknologi Informasi.
Arjuna Subject : -
Articles 439 Documents
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Pemetaan Lintasan Karir Alumni Berdasarkan Analisis Cluster: Kombinasi K-Means dan Reduksi Dimensi Autoencoder Prasetyawan, Daru; Mulyanto, Agus; Gatra, Rahmadhan
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29713

Abstract

Alumni career mapping is a crucial aspect of evaluating and developing higher education programs. Cluster analysis, particularly the integration of k-means and autoencoder methods, has emerged as an effective solution for grouping complex and multi-dimensional alumni career data. This study aims to implement and assess the combination of k-means and autoencoder algorithms in alumni career mapping based on GPA, study duration, waiting time, job type, salary, job level, and field of study suitability. The autoencoder is employed to reduce dimensions, while k-means clusters alumni into groups based on the similarity of their career profiles. The data used in the cluster analysis is sourced from the tracer study. Pre-processing of the tracer study data is conducted through several stages, including cleaning, encoding, and normalization. The evaluation results indicate that the combination of k-means and autoencoder yields superior Silhouette and DBI scores. The Silhouette score with the autoencoder achieved 0.6112, while without it, the score was only 0.3956. The DBI value with the autoencoder is 0.566, whereas without it, the DBI reached 1.022. This cluster analysis effectively grouped the tracer study data into six clusters based on similarities in career profiles. The clustering results suggest that the formed clusters are more influenced by the alumni's job type and duration of study.
Analisis Sentimen Program Makan Siang Gratis di Twitter/X menggunakan Metode BI-LSTM Attaulah, Dimas Thaqif; Soyusiawaty, Dewi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29725

Abstract

The free lunch program became a widely discussed topic on social media, reflecting public opinion towards the policy. This research aims to analyze public sentiment towards free lunch program to evaluate the policy's effectiveness and understand public perception. Data was collected through web crawling techniques on the Twitter/X platform, resulting in 7,441 data. Processing stages include preprocessing, sentiment labeling using VADER, keyword visualization with wordcloud, and application of word embedding using Word2Vec. The oversampling technique is used to overcome data imbalance. Sentiment classification was developed using Bi-LSTM and evaluated with accuracy, precision, recall, and F1-score. The developed Bi-LSTM model achieved 88.75% accuracy, with 88.9% precision, 88.8% recall, and 88.8% F1-score. Analysis results show that the majority of public responses are positive or neutral, although there were negative sentiments that highlighted potential problems such as corruption and increasing national debt. These results provide insight into public opinion on the free lunch policy and demonstrate the effectiveness of the Bi-LSTM model in social media sentiment classification.
Sistem Pendukung Keputusan berbasis Vikor untuk Penyaluran Gas Lpg 3 Kg Bersubsidi Rahmadani, Nadhilla; Fauziah, Rizky; Mardalius, Mardalius
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29751

Abstract

PT. Citra Gas Nusantara always has difficulty in determining priority bases and has not done so objectively or still does so manually. So that in this condition it has an impact on uneven distribution that does not match the needs of the community. The purpose of this study is to create a decision support system for selecting priority LPG Gas bases based on VIKOR. The research method used is the waterfall method, which consists of the analysis, planning, implementation, and testing processes. Only 10 alternatives out of 31 alternative choices are used in the calculation, because only 10 alternatives are included in the priority base criteria based on four criteria: cylinder ownership, customers, discipline, and accuracy of Brimola transactions. The results of our findings are in the form of a decision support system for selecting priority bases for the distribution of 3 Kg LPG Gas which is realized in the form of a website. The results of the VIKOR technique calculation for the Aminuddin base obtained the lowest score of 0, so in this case, the Aminuddin Base is the base that must be prioritized for distribution. In VIKOR, the lowest value is the best compromise solution or the best alternative that is ranked first. The results of the black box test starting from login, the menu on the main page and logout also show that the system can run as expected and will also have an impact on increasing efficiency and effectiveness in the distribution of subsidized 3 Kg LPG Gas
Pendekatan Multi-Input dalam Deteksi Kanker Kulit: Implementasi EfficientNetV2-B2 dan LightGBM Ibad, M. Azka Khoirul; Winarno, Sri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29771

Abstract

Skin cancer is one of the types of cancer with a high prevalence rate, so early detection is very important to increase the chances of recovery. This study aims to develop a skin cancer detection model that combines image data and tabular data using EfficientNetV2-B2 for image feature extraction and LightGBM for tabular data prediction estimation. The ISIC 2024 dataset used consists of 401,059 images of skin lesions with tabular features, including age, gender, location, diameter, and shape of the lesions. Tabular data is processed with normalization and encoding to avoid bias. Image data is also processed with augmentation techniques from kerascv. This multi-input model combines image and tabular features using concatenation techniques, with a dense layer as the final output. Our findings show that the model's accuracy and AUC value reached 96% and 98%, with success in handling class imbalance using undersampling and oversampling techniques. This study shows that the combination of images and tabular data increases the accuracy of skin cancer detection by 2%, compared to conventional CNN models, which only achieve an accuracy of around 94%. Moreover, this model offers better computational efficiency compared to conventional CNN models. The main contribution of this research is the use of multi-input that complements visual information with clinical data for more accurate and efficient skin cancer detection.
X-RayVision-Net: CBAM-Infused YOLOv8 for Rapid Pulmonary Disease Recognition Wijaya, Louis Mario; Wonohadihjojo, Daniel Martomanggolo
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.32784

Abstract

Tuberculosis and pneumonia continue to pose major global health challenges, particularly in regions with limited radiological resources, where overlapping chest X-ray patterns often complicate differential diagnosis. This study proposes X-RayVision-Net, a hybrid deep learning framework that integrates the Convolutional Block Attention Module (CBAM) into the YOLOv8 architecture to enhance pulmonary disease classification. A quantitative experimental design was employed using 10,056 chest X-ray images categorized as normal, pneumonia, or tuberculosis, collected from multiple public datasets. Image preprocessing involved Contrast Limited Adaptive Histogram Equalization (CLAHE) and balanced data augmentation to improve visual consistency and address class imbalance. The proposed model was trained for 100 epochs and evaluated against a standard YOLOv8 baseline using accuracy, precision, recall, and F1-score. Experimental results demonstrate that the CBAM-enhanced YOLOv8 model achieved an accuracy of 98.99%, outperforming the baseline model (97.37%) and yielding consistent improvements across all performance metrics. The findings confirm that the incorporation of channel and spatial attention mechanisms effectively refines pulmonary feature representation, facilitating more accurate discrimination between tuberculosis and pneumonia. This framework presents a rapid and reliable computer-aided diagnostic approach suitable for deployment in clinical environments with constrained radiology expertise.
Machine Learning Approaches for Export Trend Classification: Evidence from Leading Commodities in Indonesia Muslimah, Virasanty; Rezki, Rezki; Jabar, Wildan Abdul
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.32949

Abstract

Sorong City holds a strategic position in the export economy of Papua Barat Daya; however, its export performance remains volatile due to global price fluctuations, logistical constraints, and shifts in international demand. To address these challenges, this study applies machine learning-based classification to analyze and predict export trend dynamics of Sorong’s leading commodities. Specifically, the study compares the performance of Naïve Bayes and Random Forest classifiers within a quantitative experimental framework. The dataset comprises 874 export records (2023–2025), including HS Codes, export values, destination countries, exporters, and export types. The methodological workflow encompasses data preprocessing, trend labeling, normalization, label encoding, class balancing using SMOTE, and model evaluation via 80:20 train-test split and 10-fold cross-validation. Performance metrics include accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results reveal that Random Forest outperforms Naïve Bayes, achieving 74% accuracy compared to 57%, and more effectively captures nonlinear feature relationships. Despite a reduction in ROC-AUC during cross-validation, Random Forest demonstrates greater robustness in export trend prediction. Overall, the findings highlight the potential of machine learning to enhance regional trade forecasting, inform evidence-based policy formulation, and strengthen data-driven export management in emerging regional economies.
Security Maturity Assessment of Indonesian Android Mobile Banking Apps using MobSF and OWASP Faozi, Rizal Aglal; Majid, Nuur Wachid Abdul; Widodo, Suprih
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33285

Abstract

The rapid expansion of mobile banking in emerging economies has increased exposure to client-side security risks, while MASVS-based security maturity benchmarking of conventional banking applications remains underrepresented in the literature. This study conducts a standard-based comparative security maturity assessment of two major Indonesian Android banking applications, BRImo and myBCA. APK files obtained from the Google Play Store were analysed using Static Application Security Testing with the Mobile Security Framework (MobSF) and evaluated against OWASP MASVS Level 2 and MASVS-R. MobSF scores were interpreted as relative indicators of security maturity based on severity-weighted findings across multiple domains. The results reveal a clear divergence in maturity levels. Although both applications demonstrate strong network-layer protection, BRImo exhibits structural weaknesses in storage, cryptography, platform interaction, and resilience domains, indicating fragmented defence-in-depth implementation. In contrast, myBCA shows more consistent cross-domain control integration. This study contributes an MASVS-based security maturity benchmarking approach and provides conceptual evidence that formal regulatory compliance may coexist with inconsistent client-side technical implementation. The findings offer analytically transferable insights for developers, security auditors, and regulators in rapidly digitalising financial ecosystems.
Optimizing XGBoost Performance through Recursive Feature Elimination for Methanol Conversion Prediction Kurniawan, Ibnu Richo; Akrom, Muhamad Febrian; Hidayat, Novianto Nur; Naufal, Muhammad
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33509

Abstract

The strong nonlinear interaction between catalytic properties and operating conditions complicates accurate space time yield modeling in thermocatalytic carbon dioxide hydrogenation, especially when redundant descriptors are included. Although XGBoost is widely used for predictive tasks, the influence of feature redundancy on generalization and interpretability in carbon dioxide to methanol systems remains insufficiently examined. This study investigates the integration of Recursive Feature Elimination with XGBoost using 639 experimental observations derived from copper based catalysts. Reducing the feature set from fifteen to eight variables improves generalization performance, as indicated by lower prediction error and higher explained variance. The retained variables correspond to key catalytic and operational parameters, including reaction temperature, pressure, and copper content, aligning with established kinetic and mechanistic principles. These results show that eliminating redundant descriptors stabilizes cross validated performance and reduces training complexity without sacrificing predictive accuracy. The reduced model concentrates predictive weight on kinetically relevant variables, providing a clearer quantitative representation of the parameters that govern space time yield in carbon dioxide hydrogenation.
An Efficient Two Stage Detection Segmentation Framework for Automated Road Crack Assessment Hujaya, Alvin; Pribadi, Muhammad Rizky
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33699

Abstract

Road cracks significantly degrade infrastructure quality and pose a threat to traffic safety. To minimize manual inspection inefficiencies, this study investigates a segmentation model integrating MobileNetV3-Small as a backbone for the U-Net architecture to reduce processing time. The performance of the proposed MobileNetV3-Small-U-Net is benchmarked against a standard U-Net using three public datasets: DeepCrack (537 images), CFD (118 images), and Crack500 (3368 images) sourced from GitHub and Kaggle. This research explores the influence of optimization algorithms on evaluation results across these diverse datasets. Specifically, the study evaluates Adam, RMSprop, and SGD optimizers at an image resolution of 224 x 224 pixels, with a 0.001 learning rate and 0.9 momentum. On-the-fly augmentation techniques, including horizontal flips and brightness adjustments (0.8 to 1.2), were implemented during training. Experimental results demonstrate that MobileNetV3-Small-U-Net enhances computational efficiency by achieving a 9 ms inference time, which is 2 ms faster than the standard U-Net. These findings confirm that a MobileNetV3-Small backbone accelerates inference, despite a slight trade-off in evaluation metrics. Additionally, results reveal that the SGD optimizer is unsuitable for these segmentation tasks due to high error rates and the lack of an adaptive learning rate.

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