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All Journal Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pseudocode International Journal of Advances in Intelligent Informatics Journal of Development Research ILKOM Jurnal Ilmiah Abdimasku : Jurnal Pengabdian Masyarakat Journal of Applied Data Sciences Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Indonesia Sosial Teknologi Jikom: Jurnal Informatika dan Komputer Jurnal Publikasi Ilmu Manajemen Journal of Computing Theories and Applications Influence: International Journal of Science Review Jurnal Ekonomi dan Bisnis Scientific Journal of Informatics International Journal of Accounting, Management, and Economics Research : Ijamer
Abdul Syukur
Universitas Dian Nuswantoro
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Promoting MSME Financing Decisions The Influence of Financial Behavior and the Role of Risk Taking Behavior Abdul Syukur; Amalia Nur Chasanah; Fery Riyanto
Jurnal Ekonomi dan Bisnis Vol. 5 No. 2 (2026): Juni 2026
Publisher : Faculty of Economics and Business Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jekobs.v5i2.16522

Abstract

This study aims to examine the effect of financial behavior on credit decision making among MSME owners in Central Java and to investigate the moderating role of risk-taking behavior. A quantitative approach was employed involving 185 MSME owners selected through purposive sampling. Data were collected using questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that financial behavior has a positive and significant effect on credit decision making. This finding suggests that MSME owners with better financial behavior are more likely to make rational and effective credit decisions. However, risk-taking behavior does not moderate the relationship between financial behavior and credit decision making. The findings highlight that the quality of credit decisions is primarily influenced by financial management capabilities rather than the willingness to take business risks.   Penelitian ini bertujuan untuk menganalisis pengaruh financial behavior terhadap credit decision making pada pelaku UMKM di Jawa Tengah serta menguji peran risk taking behavior sebagai variabel moderasi. Penelitian menggunakan pendekatan kuantitatif dengan melibatkan 185 pemilik atau pengelola UMKM yang dipilih melalui teknik purposive sampling. Data dikumpulkan menggunakan kuesioner dan dianalisis dengan Partial Least Squares Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa financial behavior berpengaruh positif dan signifikan terhadap credit decision making. Temuan ini mengindikasikan bahwa pelaku UMKM yang memiliki perilaku keuangan yang baik cenderung mampu mengambil keputusan kredit yang lebih rasional dan berkualitas. Sementara itu, risk taking behavior tidak terbukti memoderasi hubungan antara financial behavior dan credit decision making. Hasil penelitian menegaskan bahwa kualitas keputusan kredit lebih dipengaruhi oleh kemampuan pengelolaan keuangan dibandingkan keberanian dalam menghadapi risiko usaha.
Classification of Types of Dates Using Extraction of Shape and Texture Features with K-Nearest Neighbors Method Muhammad Ichsan; Abdul Syukur; Affandy; Moch. Arief Soeleman
INFLUENCE: INTERNATIONAL JOURNAL OF SCIENCE REVIEW Vol. 4 No. 1 (2022): INFLUENCE: International Journal of Science Review
Publisher : Global Writing Academica Researching and Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/influencejournal.v4i1.333

Abstract

Dates are one type of plant that comes from palm trees, dates can also be called in Latin arecaceae. There are several types of dates available in the market making it difficult for buyers (consumers) to recognize the types. The purpose of this study was to determine the accuracy of the results of dates from ajwa, grape and mejol dates using the classification of texture and shape features using the K-NN (K-Nearest Neighbors) method. The stages of image processing, namely data processing, segmentation, and extraction using the K-Nearest neighbors method, from this research it is known that the accuracy value is 96.33%.
An integration of quantum systems using BB84 for enhanced security in aeroponic smart farming Christy Atika Sari; Purwanto Purwanto; Eko Hari Rachmawanto; Abdul Syukur
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26450

Abstract

Modern aeroponic systems leverage internet of things (IoT) technology for automated control of climate, lighting, and nutrient delivery, rendering them susceptible to unauthorized access and network attacks. Such disruptions can lead to financial losses and impair agricultural productivity by altering essential growth conditions. To mitigate these risks, robust security measures including encryption and firewalls are essential, alongside continuous monitoring and updates to combat evolving threats. Addressing cyber threats in urban aeroponic systems, implementing quantum encryption emerges as a promising solution. Quantum key distribution (QKD) ensures highly secure encryption keys using quantum states that change upon eavesdropping, thereby thwarting intrusion attempts effectively. Integrating quantum encryption in aeroponic control systems safeguards data integrity and operational continuity against cyber threats, bolstering urban agriculture resilience. Our findings demonstrate the efficacy of quantum BB84 protocol integrated with API for Eve’s security. Quantum bit error rate (QBER) measurements revealed minimal interference (0.015) for Alice and Bob, contrasting with higher initial QBER (up to 1.0) for Eve, indicative of intrusion attempts. Histogram analysis further underscored quantum security’s effectiveness in identifying and mitigating breaches. For future research, enhancing quantum encryption protocols and integrating advanced detection mechanisms will be essential.
Contrast-Limited Adaptive Histogram Equalization for Enhancing YOLOv8-Based Industrial Bolt Defect Detection Muhammad Nurbaitullah; Abdul Syukur; Ahmad Zainul Fanani
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.41185

Abstract

Purpose: Defect detection in industrial bolts is crucial for ensuring product reliability, production safety, and consistent quality control in modern industrial environments. However, visual inspection of metal bolts remains challenging due to low contrast, uneven lighting, and reflective surfaces that often hide subtle defect patterns and reduce detection accuracy. Most existing YOLO-based approaches focus on architectural modifications to improve performance, which may increase model complexity and limit real-time applicability. Methods: This study integrates Contrast-Limited Adaptive Histogram Equalization (CLAHE) with YOLOv8 to improve defect visibility prior to detection. CLAHE enhances local contrast by redistributing pixel intensities while suppressing noise amplification, thereby strengthening feature representation for deep learning-based detection. Experiments were conducted on a publicly available industrial bolt dataset annotated via Roboflow, using a 3-fold cross-validation strategy. Performance was assessed with Precision, Recall, mAP@50, mAP@50–95, FPS, and FLOPs to evaluate accuracy and real-time feasibility. Result: Experimental results based on a 3-fold cross-validation scheme indicate that the proposed CLAHE–YOLOv8 model achieves consistent performance improvements over the baseline YOLOv8 configuration. The method obtains an average Precision of 0.9495±0.0068, Recall of 0.9028±0.0235, mAP@50 of 0.9364±0.0156, and mAP@50–95 of 0.7121±0.0037, while maintaining real-time inference performance at 29.79 FPS. These results demonstrate that contrast-based preprocessing contributes positively to detection stability and localization consistency without increasing model complexity. Novelty: The novelty of this research lies in demonstrating that data-level contrast enhancement using CLAHE effectively improve industrial bolt defect detection performance without architectural modification, offering a practical and computationally efficient solution for real-time industrial inspection systems.
Optimization of CNN Architectures through Fine-tuning for SIBI Classification Nur Hilmi Insan Muhammad; Abdul Syukur; Pujiono Pujiono
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.2830.378-392

Abstract

This research addresses the computational optimization of convolutional neural network (CNN) architectures for the classification of Indonesian Sign Language System (Sistem Isyarat Bahasa Indonesia, SIBI) static alphabet imagery to enhance digital communication accessibility. Utilizing a domain-specific dataset comprising 1,165 images across 26 alphabet classes, this study tackles the prominent challenges of limited sample sizes and severe class imbalance. We evaluate five state-of-the-art CNN architectures MobileNetV2, DenseNet121, Xception, InceptionV3, and ResNet50V2 under four distinct training data paradigms before and after adaptive fine-tuning. To eliminate predictive bias without pixel-level distortion, oversampling is operationalized via Latent Space SMOTE on flattened vector embeddings, combined with dynamistic runtime image augmentation. The experimental results reveal that MobileNetV2, when optimized through partial layer-freezing (locking 150 baseline layers) under the integrated augmentation and oversampling combination scenario, achieved the highest macro-classification accuracy of 98.30%. This architecture also demonstrated superior efficiency, reducing the computational training latency to 0.53 minutes. The findings underscore the strategic advantage of leveraging optimized lightweight networks like MobileNetV2 for domain-specific visual recognition tasks.
Behavioral Financial Factors and Bank Managers Financial Performance: Evidence from Semarang Abdul Syukur; Awanis Linati Haziroh; Maria Safitri
Jurnal Telekomunikasi dan Informatika Lbh. 4 Àir. 1 (2026): International Journal Of Accounting, Management, And Economics Research (IJAME
Publisher : Fakultas Ekonomi dan Bisnis Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56696/ijamer.v4i1.188

Abstract

This study aims to examine the effects of financial self-efficacy, financial literacy, and financial decision-making on the financial performance of bank managers in Semarang City. A quantitative approach was employed with 125 respondents selected through purposive sampling from both state-owned and private banks. Data were collected using a 1–7 Likert scale questionnaire and analyzed using PLS-SEM with SmartPLS 4. The results indicate that financial decision-making and financial self-efficacy have positive and significant effects on financial performance, while financial literacy has no significant effect. These findings highlight the importance of behavioral factors and decision-making quality in improving the financial performance of bank managers..
Computer-Aided Diagnosis (CAD) of Stroke in The Brain CT-Scan Images Using Integration of Grey Level Co-Occurrence Matrix (GLCM) Texture Feature Extraction And K-Nearest-Neighbour (KNN) Classification Casidi Casidi; Abdul Syukur; M. Arief Soeleman; Aris Nurhindarto
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.646

Abstract

This study presents an advanced and efficient computer-aided diagnosis (CAD) system for stroke detection using brain CT images, integrating Grey Level Co-Occurrence Matrix (GLCM) feature extraction and K-Nearest Neighbour (KNN) classification. The objective is to enhance stroke detection accuracy and efficiency in clinical settings. A dataset of 400 brain CT images, divided into 300 for training and 100 for testing with equal normal and stroke classes, was used to evaluate performance. The GLCM texture features significantly differentiated between normal and stroke images. The optimized KNN model demonstrated high performance, achieving 99% classification accuracy, 100% sensitivity, 98% specificity, 97% precision, a 99% F1 score, 100% positive predictive value, and 98% negative predictive value. The average computation time per image was 3.2 seconds, indicating feasibility for real-time application. In conclusion, the GLCM-KNN integrated CAD system proves to be an accurate and efficient method for stroke diagnosis on brain CT scans, offering a potential solution for early stroke detection in resource-limited healthcare facilities.
A Bi-LSTM Prediction Model Integrated with GIS for Spatiotemporal Malaria Endemicity Mapping and Early Warning in Indonesia Wellie Sulistijanti; Safaat Yulianto; Abdul Syukur; Ngatimin Ngatimin
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1483

Abstract

Malaria continues to be a major public health challenge in Indonesia, particularly in eastern provinces where transmission patterns are influenced by climatic variability, geographical heterogeneity, and historical incidence trends. This study proposes an integrated spatio-temporal malaria forecasting and early warning framework by combining Bidirectional Long Short-Term Memory (Bi-LSTM), Geographic Information Systems (GIS), and SHapley Additive exPlanations (SHAP). Monthly malaria incidence, climate variables, population data, and provincial spatial data from 12 endemic provinces in Indonesia during 2014–2025 were used. The data were preprocessed through incidence-rate conversion, outlier handling, log transformation, Min-Max normalization, and six-month sliding window segmentation. The proposed Bi-LSTM model was assesed using RMSE, sMAPE, and R², and compared againts Naive Forecasting, SARIMA, and Simple LSTM baselines. The model attained optimol global performance, with an RMSE of 0.0522, sMAPE of 18.39%, and R² of 0.9553. The provincial analysis shows good performance throughout most regions, including high-burden areas like Papua and West Papua, however a decline in relative accuracy was observed in West Nusa Tenggara due to near-zero incidence rates. SHAP analysis revealed that historical malaria incidence was the primary predictor, whereas rainfall emerged as the most significant climatic variable. GIS-based forecasting showed spatial patterns aligned with malaria epidemiology in Indonesia, with Papua exhibiting the gratest predicted incidence in December 2025. These findings demonstrate that the Bi-LSTM–GIS–SHAP framework can support malaria endemicity mapping, interpretable forecasting, and province-level early warning for targeted public health interventions.
Boundary-Aware Learning for Glioma Detection in MRI Using YOLOv8 Segmentation Supervision Muhammad Nurbaitullah; Abdul Syukur; Ahmad Zainul Fanani
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.42307

Abstract

Purpose: Since glioma is the most aggressive and infiltrative type of brain tumor, its detection in magnetic resonance imaging (MRI) is especially difficult. Despite the excellent overall accuracy for brain tumor detection with YOLOv8-based object detection, the glioma-specific performance is limited owing to ambiguity of tumor boundaries. This work seeks to elucidate if boundary-aware learning can enhance glioma detection beyond typical bounding box–based approaches. Methods: This study focuses exclusively on glioma detection using the Cheng brain tumor MRI dataset. YOLOv8 is used as the baseline detector, and boundary-aware learning is implemented through segmentation supervision using YOLOv8-Seg by leveraging pixel-level tumor masks. All the experiments are done in a standardized training environment to allow fair and unbiased comparison. Result: Experimental evaluation shows that YOLOv8-Seg achieved a detection precision of 0.899, recall of 0.905, and mAP@50 of 0.940, while segmentation results achieved a mask precision of 0.900, recall of 0.904, and mAP@50 of 0.943. For glioma-specific analysis, the model achieved a box mAP@50 of 0.875 and a mask mAP@50 of 0.877. These results indicate that segmentation supervision improves spatial boundary representation even though improvements in conventional detection metrics remain marginal. Novelty: Unlike the other works that are based on augmentation of the data and performance of better detection, this work has devised a glioma-centric design, and shows bounding box-based detection is insufficient. This work highlights the need for considering boundary aware learning applying the supervision of segmentation in the automated glioma detection system, which can improve the reliability and interpretability of the system.
Channel-spatial dual-attention for plant disease detection: CBAM-ECA integrated CNN models with visual explainability Anton Anton; Supriadi Rustad; Guruh Fajar Shidik; Abdul Syukur
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2377

Abstract

Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and channel-wise feature refinement, particularly in multi-crop classification settings. This study proposes a redundancy-aware dual-attention architecture, termed ATSA-DenseNet, which integrates the spatial branch of the Convolutional Block Attention Module (CBAM-Spatial) with Efficient Channel Attention (ECA) within a DenseNet121 backbone. Unlike prior dual-attention frameworks that retain full CBAM and introduce channel-level redundancy, the proposed design isolates complementary spatial and channel mechanisms to improve representational efficiency without increasing computational complexity. The framework is evaluated on controlled multi-crop PlantVillage-derived datasets comprising tomato, potato, pepper, and maize. Across both 3-crop and 4-crop configuration, ATSA-DenseNet consistently outperforms baseline DenseNet121 and single-attention variants, achieving 99.94% accuracy and 0.9994 macro-F1 on the 4-crop setting while maintaining a lightweight footprint (6.96M parameters, 2.87G FLOPs). Grad-CAM visualizations indicate improved localization of disease-relevant regions compared to the baseline. While results are obtained under controlled imaging conditions, the findings demonstrate that redundancy-aware dual-attention enhances feature discrimination efficiency in multi-class agricultural classification tasks. Future work will extend validation to real-field datasets with natural variability.
Co-Authors Abdul Hamid Adhe Irham Thoriq Affandy Affandy Affandy Ahmad Zainul Fanani Ahmad Zainul Fanani Alzami, Farrikh Amalia Nur Chasanah Anton Anton Arief Sulaiman Aris Nurhindarto Aris Marjuni Awanis Linati Haziroh Ayu Pertiwi Casidi Casidi Christy Atika Sari De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Febrianur Ibnu Fitroh Sukono Putra Ferda Ernawan Fery Riyanto Fery Riyanto Guruh Fajar Shidik Ika Novita Dewi Irvan Muzakkir Lu'luul Maknun M. Arief Soeleman Maria Safitri Moch Arief Soeleman Moch. Arief Soeleman Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muhammad Haris Zuhri Muhammad Ichsan Muhammad Nurbaitullah Muslih Muslih Ngatimin Ngatimin Nur Hilmi Insan Muhammad Nurhindarto, Aris Pujiono Pujiono Purwanto Purwanto R. Daniel Hartanto Rifal Richard Pangemanan S. Kholidah Rahayu Maharani Safaat Yulianto SUPRIADI RUSTAD Supriyanto Titien Suhartini Wellie Sulistijanti Yohannes Vianney Ario Nugroho