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PENGUATAN KOMPETENSI DALAM PEMBELAJARAN PHP DAN MYSQL BAGI SISWA JURUSAN MULTIMEDIA SMK NEGERI 1 SIANTAR Damanik, Abdi Rahim; Hartama, Dedy; Saputra, Widodo; Tata Hardinata, Jaya
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 5, No 2 (2022): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v5i2.802-807

Pengembangan ilmi serta teknologi sekarang semakin kedepan akan semakin maju karena sangat dibutuhkannya pengetahuan berbasis website. Pelatihan pembuatan website dengan PHP dan MySQL yang dilakukan pada SMK Negeri 1 Siantar dikarenakan pada SMK Negeri 1 tersebut dirasa belum maksimal dalam pengetahuan teknologi informasi yang telah diajarkan dikarenakan kondisi pandemi dan belajar daring, maka sebab itu pengabdian ini dilakukan. Adapun peserta dalam pengabdian ini adalah Siswa SMK Negeri 1 Siantar . Dalam kegiatan pengabdian ini Â yaitu memberikan suatu pelatihan dan pendampingan untuk pembuatan Website dengan menggunakan PHP dan MySQL kepada para siswa dan siswi di SMK Negeri 1 Siantar. Adapun manfaat dari pelatihan ini adalah diharapkan dapat meningkatkan kemampuan para siswa-siswi SMK Negeri 1 Siantar dalam membuat website perpustakaan berbasis database mysql. Pentingnya pelatihan ini bagi SMK Negeri 1 Siantar karena dengan melihat perkembangan zaman tentang teknologi yang canggih oleh sebab itu siswa-siswi SMK Negeri 1 Santar juga dituntut untuk dapat menggunakan teknologi tersebut agar dalam proses ujian kompetensi keahlian nantinya mendapatkan nilai yang baik

OPTIMIZING SHUFFLENET WITH GRIDSEARCHCV FOR GEOSPATIAL DISASTER MAPPING IN INDONESIA Ahmad, Abdullah; Hartama, Dedy; Solikhun, Solikhun; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6747

Accurate classification of natural disasters is crucial for timely response and effective mitigation. However, conventional approaches often suffer from inefficiency and limited reliability, highlighting the need for automated deep learning solutions. This study proposes an optimized Convolutional Neural Network (CNN) based on the lightweight ShuffleNet architecture, enhanced through GridSearchCV for systematic hyperparameter tuning. Using a geospatial dataset of 3,667 images representing earthquake, flood, and wind-related disasters in Indonesia, the optimized ShuffleNet model achieved a peak accuracy of 99.97%, outperforming baseline CNNs such as MobileNet, GoogleNet, ResNet, DenseNet, and standard ShuffleNet. While these results demonstrate the potential of combining lightweight architectures with automated optimization, the exceptionally high performance also indicates possible risks of overfitting and dataset bias due to limited variability. Therefore, future research should validate this approach using larger, multi-source datasets to ensure robustness and real-world applicability

QUANTUM-ASSISTED FEATURE SELECTION FOR IMPROVING PREDICTION MODEL ACCURACY ON LARGE AND IMBALANCED DATASETS Safii, Safii; Wahyudi, Mochamad; Hartama, Dedy
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7040

One of the biggest obstacles to creating precise machine learning models is choosing representative and pertinent characteristics from big, unbalanced datasets. While too many features raise the risk of overfitting and computational expense, class imbalance frequently results in decreased accuracy and bias. The Simulated Annealing technique is used in this study to tackle a Quadratic Unconstrained Binary Optimization (QUBO) problem that is formulated as a quantum-assisted feature selection method to handle these problems. The technique seeks to reduce inter-feature redundancy and the number of selected features. There are 102,487 samples in the majority class and 11,239 in the minority class, totaling 28 characteristics in the experimental dataset. Nine ideal features were found during the feature selection method (12, 14, 15, 22, 23, 24, 25, 27, and 28). Ten-fold cross-validation was used to assess a Random Forest Classifier that was trained using an 80:20 split. With precision, recall, f1-score, and accuracy all hitting 1.00, the suggested QUBO+SMOTE method demonstrated exceptional performance. Comparatively, QUBO without SMOTE performed worse with accuracy 0.95 and minority-class f1-score of only 0.71, whereas a traditional Recursive Feature Elimination (RFE) approach obtained accuracy 0.97 with minority-class f1-score of 0.94. These findings indicate that QUBO can reduce dimensionality and address class imbalance which requires its integration with SMOTE. This study demonstrates how quantum computing can enhance the effectiveness and efficiency of machine learning, especially for large-scale imbalanced datasets

OPTIMIZATION OF EFFICIENTNET-B0 ARCHITECTURE TO IMPROVE THE ACCURACY OF GLAUCOMA DISEASE CLASSIFICATION Akbari, Imam; Hartama, Dedy; Wanto, Anjar
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7140

Glaucoma is a chronic eye disease that can potentially cause permanent blindness if not detected early. This study aims to improve the generalization capability and reliability of glaucoma classification by optimizing the EfficientNetB0 architecture based on a Convolutional Neural Network (CNN). Optimization was carried out by applying double dropout (0.4 and 0.3) and adding a Dense layer with 128 ReLU-activated neurons to reduce overfitting and strengthen non-linear feature representation. The dataset used consists of 1,450 fundus images (899 glaucoma and 551 normal) obtained from IEEE DataPort. Model performance evaluation was performed using accuracy, precision, recall (sensitivity), specificity, F1 score, and Area Under the Curve (AUC) metrics, complemented by confusion matrix analysis to assess overall classification performance. The results showed that the optimized EfficientNetB0 model consistently outperformed the baseline comparison model with the highest accuracy, precision, recall (sensitivity), specificity, F1 score, and AUC values of 95%. Based on the system performance results obtained, the Proposed model can be used as an aid for medical personnel in classifying glaucoma conditions so that they can provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.

Multiclass Skin Lesion Classification Algorithm using Attention-Based Vision Transformer with Metadata Fusion Furqan, Mhd.; Katuk, Norliza; Hartama, Dedy
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1017

Early and accurate classification of skin lesions is essential for timely diagnosis and treatment of skin cancer. This study presents a novel multiclass classification framework that integrates dermoscopic images with clinical metadata using an attention-based Vision Transformer (ViT) architecture. The proposed model incorporates a mutual-attention fusion mechanism to jointly learn from visual and tabular inputs, augmented by a class-aware metadata encoder and imbalance-sensitive loss function. Training was conducted using the HAM10000 dataset over 30 epochs with a batch size of 32, utilizing the Adam optimizer and a learning rate of 0.0001. The model demonstrated superior performance compared to a ViT Baseline, achieving 93.4% accuracy, 92.2% F1-score, 0.95 AUC, and significant reductions in MAE and RMSE. Additionally, Grad-CAM visualizations confirmed the model’s ability to focus on diagnostically relevant regions, enhancing interpretability. These findings suggest that the integration of structured clinical information with transformer-based visual analysis can significantly improve classification robustness, particularly in underrepresented lesion types. However, the model’s current performance is evaluated only on the HAM10000 dataset, and its generalizability to other clinical or non-dermoscopic image sources remains to be validated. Future studies should therefore explore multi-institutional datasets and real-world deployment scenarios to assess robustness and scalability. The proposed framework offers a practical, interpretable solution for AI-assisted skin lesion diagnosis and demonstrates strong potential for clinical deployment.

Algoritma K-Means Menggunakan Metode Elbow Untuk Mengelompokkan Kinerja Performance Index Berdasarkan Dataset Absensi Pegawai Sabila, Bunga; Sari, Juwita Permata; Kholis, Mu’Ammar Nur; Harefa, Darman Sahputra; Hartama, Dedy
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2119

Attendance is quantitative data that reflects the level of employee discipline and has the potential to be used as a basis for performance evaluation. However, in many agencies, attendance data has not been optimally utilized in making decisions related to employee management. This study aims to classify employee performance based on attendance data using the K-Means Clustering algorithm with the Elbow Method approach. The data analyzed consisted of 57 employees with attributes of the number of tardiness, absence, and blank absences. The research process starts from data collection, pre-processing, to clustering process. Determination of the optimal number of clusters is done with the Elbow Method using Python, which shows the best K value is four. Clustering was done using RapidMiner software, by applying a visual-based workflow to group employees based on similarity in attendance patterns. The final results show the formation of four clusters with different Key Performance Index (KPI) characteristics: excellent, good, fair, and poor. These clusters reflect the level of employee discipline that can be used as a basis for objective managerial evaluation and decision-making. This research shows that the application of data mining-based clustering methods can be a tool in analyzing employee performance in an organizational environment.

OPTIMIZING SHUFFLENET WITH GRIDSEARCHCV FOR GEOSPATIAL DISASTER MAPPING IN INDONESIA Ahmad, Abdullah; Hartama, Dedy; Solikhun, Solikhun; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6747

Accurate classification of natural disasters is crucial for timely response and effective mitigation. However, conventional approaches often suffer from inefficiency and limited reliability, highlighting the need for automated deep learning solutions. This study proposes an optimized Convolutional Neural Network (CNN) based on the lightweight ShuffleNet architecture, enhanced through GridSearchCV for systematic hyperparameter tuning. Using a geospatial dataset of 3,667 images representing earthquake, flood, and wind-related disasters in Indonesia, the optimized ShuffleNet model achieved a peak accuracy of 99.97%, outperforming baseline CNNs such as MobileNet, GoogleNet, ResNet, DenseNet, and standard ShuffleNet. While these results demonstrate the potential of combining lightweight architectures with automated optimization, the exceptionally high performance also indicates possible risks of overfitting and dataset bias due to limited variability. Therefore, future research should validate this approach using larger, multi-source datasets to ensure robustness and real-world applicability

QUANTUM-ASSISTED FEATURE SELECTION FOR IMPROVING PREDICTION MODEL ACCURACY ON LARGE AND IMBALANCED DATASETS Safii, Safii; Wahyudi, Mochamad; Hartama, Dedy
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7040

One of the biggest obstacles to creating precise machine learning models is choosing representative and pertinent characteristics from big, unbalanced datasets. While too many features raise the risk of overfitting and computational expense, class imbalance frequently results in decreased accuracy and bias. The Simulated Annealing technique is used in this study to tackle a Quadratic Unconstrained Binary Optimization (QUBO) problem that is formulated as a quantum-assisted feature selection method to handle these problems. The technique seeks to reduce inter-feature redundancy and the number of selected features. There are 102,487 samples in the majority class and 11,239 in the minority class, totaling 28 characteristics in the experimental dataset. Nine ideal features were found during the feature selection method (12, 14, 15, 22, 23, 24, 25, 27, and 28). Ten-fold cross-validation was used to assess a Random Forest Classifier that was trained using an 80:20 split. With precision, recall, f1-score, and accuracy all hitting 1.00, the suggested QUBO+SMOTE method demonstrated exceptional performance. Comparatively, QUBO without SMOTE performed worse with accuracy 0.95 and minority-class f1-score of only 0.71, whereas a traditional Recursive Feature Elimination (RFE) approach obtained accuracy 0.97 with minority-class f1-score of 0.94. These findings indicate that QUBO can reduce dimensionality and address class imbalance which requires its integration with SMOTE. This study demonstrates how quantum computing can enhance the effectiveness and efficiency of machine learning, especially for large-scale imbalanced datasets

OPTIMIZATION OF EFFICIENTNET-B0 ARCHITECTURE TO IMPROVE THE ACCURACY OF GLAUCOMA DISEASE CLASSIFICATION Akbari, Imam; Hartama, Dedy; Wanto, Anjar
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7140

Glaucoma is a chronic eye disease that can potentially cause permanent blindness if not detected early. This study aims to improve the generalization capability and reliability of glaucoma classification by optimizing the EfficientNetB0 architecture based on a Convolutional Neural Network (CNN). Optimization was carried out by applying double dropout (0.4 and 0.3) and adding a Dense layer with 128 ReLU-activated neurons to reduce overfitting and strengthen non-linear feature representation. The dataset used consists of 1,450 fundus images (899 glaucoma and 551 normal) obtained from IEEE DataPort. Model performance evaluation was performed using accuracy, precision, recall (sensitivity), specificity, F1 score, and Area Under the Curve (AUC) metrics, complemented by confusion matrix analysis to assess overall classification performance. The results showed that the optimized EfficientNetB0 model consistently outperformed the baseline comparison model with the highest accuracy, precision, recall (sensitivity), specificity, F1 score, and AUC values of 95%. Based on the system performance results obtained, the Proposed model can be used as an aid for medical personnel in classifying glaucoma conditions so that they can provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.

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