Article Per Year (5 Year)
p-Index From 2021 - 2026
6.694
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL DINAMIKA EKONOMI PEMBANGUNAN Jurnal Ilmu Dasar SAINSMAT IPTEK Journal of Proceedings Series KONSELI: Jurnal Bimbingan dan Konseling (E-Journal) Journal of Regional and City Planning Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JTERA (Jurnal Teknologi Rekayasa) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Penelitian dan Pengembangan Pelayanan Kesehatan International Journal of Pedagogy and Teacher Education J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika METIK JURNAL Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Perencanaan Pembangunan Journal of Education and Learning Mathematics Research (JELMaR) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) TheJournalish: Social and Government The Journal of Indonesia Sustainable Development Planning (JISDeP) JoMMiT : Jurnal Multi Media dan IT Jurnal Riset Ilmu Ekonomi Jurnal Algoritma East Java Economic Journal SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Jurnal Mahasiswa Ilmu Komputer PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Multiscale Materials Informatics Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial
Harun Al Azies
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 56 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 56 Documents
Search

 2   3   4   5   6 
Integration of Ensemble Stacking in Machine Learning for Thermal Stability Prediction of Metal-Organic Frameworks (MOF) Pratama, Ananta Surya; Umam, Taufiqul; Irnanda, Muhammad Diva; Nugroho, Dandy Prasetyo; Azies, Harun Al
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142759682025

Abstract

This study aims to develop a predictive model for the thermal stability of Zinc-based Metal-Organic Frameworks (Zn-MOFs), which are crucial in high-temperature applications. The approach used is stacking ensemble learning, which integrates several base models, including Ridge Regression, Lasso Regression, K-Nearest Neighbors (KNN) Regression, Support Vector Regression (SVR), Linear Regression, RANSAC (Random Sample Consensus), Huber Regression, and Gaussian Process Regression, with the meta-model TheilSenRegressor. Experimental results indicate that the stacking model delivers high-accuracy predictions, evidenced by a Root Mean Squared Error (RMSE) of 0.0025 and a coefficient of determination (R²) of 0.9993 on the training data, and an RMSE of 0.0023 and an R² of 0.9994 on the test data, demonstrating the model's excellent generalization capability. A comparison with the Robust Regression model shows that the stacking model is more stable and consistent in providing accurate predictions for both the training and test sets. These findings suggest that the machine learning-based stacking ensemble learning approach can serve as a more efficient and faster alternative to conventional experimental methods in predicting the thermal stability of Zn-MOFs.
Menumbuhkan Literasi Teknologi Melalui Pengenalan Aplikasi Computer Vision Di Kalangan Pelajar Muhammad Naufal; Harun Al Azies
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 1 No. 3 (2024): Agustus : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v1i3.356

Abstract

The purpose of this community service project is to provide education to enhance technological literacy by introducing the basics of Computer Vision applications among students. The activities included a seminar attended by 170 students. An analysis using the Wilcoxon statistical test on pre-post test results showed a significant improvement in participants' understanding of Computer Vision applications. The test results indicated a significant difference before and after the activity with a value of 0.011. Through this community service, participants have successfully grasped the material presented to enhance literacy as change agents in the digital era, positively impacting societal progress through improved understanding of technology in the field of Computer Vision.
Enhanced Brain Tumor Classification through Gamma Correction in Deep Learning Naufal, Muhammad; Al Azies, Harun; Brilianto, Rivaldo Mersis
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4474

Abstract

Classification of brain tumors is a problem in computer-aided diagnosis (CAD). This study classifies three classes of brain tumors: gliomas, meningiomas, and pituitary tumors. Image enhancement is useful for improving the quality of images to be recognized by Computer-Aided Diagnosis (CAD) systems. Gamma correction is one spatial method aimed at manipulating contrast. This method operates with a spatial approach and has relatively low computational time but yields satisfactory results in certain cases. This research compares Gamma Correction with Convolutional Neural Network (CNN) in the classification of brain tumor types. The CNN method without Gamma Correction achieves an accuracy of 86.52%, precision of 83.63%, sensitivity of 86.11%, and specificity of 93.27%. The application of Gamma Correction at 1.5 results in improved performance with an accuracy of 88.80%, precision of 86.49%, sensitivity of 88.06%, and specificity of 94.50%. Meanwhile, Gamma Correction at 0.5 shows an accuracy of 88.59%, precision of 87.59%, sensitivity of 86.68%, and specificity of 94.17%. Overall, the implementation of Gamma Correction in the classification of brain tumor types successfully enhances the CNN classification performance in terms of precision, sensitivity, and specificity compared to without its use.
GWRPCA ALGORITHMIC FRAMEWORK: ANALYZING SPATIAL DYNAMICS OF POVERTY IN EAST JAVA PROVINCE Al Azies, Harun; Ariyanto, Noval
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i1.3945

Abstract

This study employs Regression Principal Component Analysis (RPCA) and Geographically Weighted Regression Principal Component Analysis (GWRPCA) algorithms to analyze poverty in East Java Province, using data from Statistics Indonesia (BPS). The research investigates regency/city-level poverty percentages and identifies influential factors such as education, literacy rates, housing conditions, and economic indicators. The results reveal that GWRPCA, with an 85.10% R2 value, outperforms RPCA, highlighting its effectiveness in capturing spatial diversity and providing a nuanced portrayal of poverty characteristics across regencies/cities in East Java. In conclusion, GWRPCA emerges as a powerful algorithmic tool for informing targeted poverty alleviation policies, offering insights into spatial variations. The study suggests future research directions to explore evolving spatial patterns and consider additional variables for a more comprehensive analysis. The findings emphasize the significance of spatial considerations in devising effective, context-specific strategies for each regency/city in East Java
THE RELATIONSHIP BETWEEN PUBLIC INFORMATION OPENNESS AND ICT DEVELOPMENT Al Azies, Harun; Dikaputra, Ishak Bintang
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 2 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i2.4238

Abstract

The relationship between Information and Communication Technology (ICT) development and the level of Public Information Openness (KIP) holds significant implications for inclusive and sustainable societal development. This study employs statistical analysis, including Pearson correlation, to examine this relationship across Indonesian provinces in 2022. Findings indicate a positive correlation between ICT development and KIP. Access to ICT infrastructure and ICT usage show significant correlations with IKIP levels across various provinces. Provinces with better ICT development generally exhibit higher KIP levels. However, the relationship with ICT skills is comparatively weaker, indicating other influencing factors on ICT literacy within the community. The conclusion drawn from this research is that ICT development positively contributes to enhancing Public Information Transparency in Indonesia. Therefore, further efforts are needed to support equitable ICT development, enhance digital literacy, and strengthen public information transparency, enabling the population to effectively harness information and communication technology
TOWARDS OPTIMIZATION: A DATA-DRIVEN APPROACH USING K-MEDOIDS CLUSTERING ALGORITHM FOR REGIONAL EDUCATION QUALITY ASSESSMENT Al Azies, Harun; Rohmatullah, Fawwaz Atha; Rochmanto, Hani Brilianti; Isnarwaty, Devi Putri
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4862

Abstract

This study applies the k-medoids clustering machine learning approach to assess regional clustering in Indonesia based on educational quality. Data on the quality of education, including indicators of school enrollment rate (APS), gross enrollment rate (APK), and pure participation rate (APM), is gathered and processed from all provinces in Indonesia. The k-medoids clustering technique is used to carry out the clustering process, while metrics like Dunn's index, connection coefficient, and silhouette score are used to evaluate the results. The study's findings indicate that three clusters are the ideal amount, with a silhouette score of 0.2388, a connectivity coefficient of 7.1405, and a Dunn's index value of 0.1651. Cluster homogeneity is likewise moderate, despite the regions' moderate distances from one another. This assessment offers a thorough understanding of Indonesia's educational quality clustering pattern, which can serve as a foundation for developing education strategies in different areas
Explainable Machine Learning-Based Decision Tree Model for Early Detection of Hypertension Risk Sofiani, Hilda Ayu; Maulana, Isa Iant; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Rizqa, Ifan; Santoso, Dewi Agustini; Nugraini, Siti Hadiati
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15585

Abstract

Hypertension is one of the leading causes of cardiovascular disease and is often referred to as a “silent killer” because it typically remains asymptomatic until serious complications, such as stroke or kidney failure, occur. Early detection of hypertension risk is therefore essential to enable timely intervention and prevention. This study aims to develop an explainable machine learning–based Decision Tree model for early detection of hypertension risk using clinical and lifestyle data. The balanced dataset includes variables such as age, body mass index (BMI), blood pressure, family history, smoking habits, stress levels, and sleep duration. The dataset used in this study was obtained from the “Hypertension Risk Prediction Dataset” available on the Kaggle platform, consisting of 1,985 patient records and 11 main features covering variables such as age, body mass index (BMI), systolic and diastolic blood pressure, family history, smoking habits, stress level, physical activity, and sleep duration. The dataset is balanced between the hypertension and normal categories, enhancing the reliability of the classification results. The model was constructed using a Decision Tree Classifier implemented in Scikit-learn and validated through cross-validation to minimize overfitting. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results indicate that the model achieved an accuracy of 96% and an AUC of 0.9645, demonstrating excellent classification performance. The motivation behind this research lies in the growing need for interpretable artificial intelligence models in healthcare, where transparency and explainability are critical for clinical trust and ethical decision-making. Unlike black-box models, the Decision Tree approach allows clinicians to trace each prediction path, understand contributing variables, and apply insights in real-world medical settings. The primary advantage of this model lies in its transparency, as each prediction can be interpreted through explicit decision rules. Overall, this explainable and high-performing model shows strong potential as a clinical decision support tool for early hypertension screening and prevention programs.
Comprehensive Benchmark of Yolov11n, SSD MobileNet, CenterFace, Yunet, FastMtCnn, HaarCascade, and LBP for Face Detection in Video Based Driver Drowsiness Go, Agnestia Agustine Djoenaidi; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Winarno, Sri; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Maulana, Isa Iant; Arif, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8678

Abstract

Face detection is a critical foundation of video-based drowsiness monitoring systems because all downstream tasks such as eye-closure estimation, yawning detection, and head movement analysis depend entirely on correctly identifying the face region. Many previous studies rely on detector-generated outputs as ground truth, which can introduce bias and inflate model performance . To avoid this limitation, I manually constructed a ground truth dataset using 1,229 frames extracted from 129 yawning and microsleep videos in the NITYMED dataset. Ten representative frames were sampled from each video using a face-guided extraction script, and all frames were manually annotated in Roboflow following the COCO format to ensure accurate bounding box labeling under varying lighting, head poses, and facial deformation. Using this manually annotated dataset, I conducted a comprehensive benchmark of seven face-detection algorithms: YOLOv11n, SSD MobileNet, CenterFace, YuNet, FastMtCnn, HaarCascade, and LBP. The evaluation focused on localization quality using Intersection over Union (IoU ≥ 0.5) and Dice Similarity, allowing each algorithm’s predicted bounding box to be directly compared against human defined ground truth. The results show that HaarCascade achieved the highest IoU and Dice scores, particularly in frontal and well-lit frames. FastMtCnn also produced strong alignment with a high number of correctly matched frames. CenterFace and SSD MobileNet demonstrated smooth bounding box fitting with competitive Dice scores, while YOLOv11n and YuNet delivered moderate but stable performance across most samples. LBP showed the weakest results, mainly due to its sensitivity to lighting variations and soft-texture regions. Overall, this benchmark provides an unbiased and comprehensive comparison of modern and classical face-detection algorithms for video-based driver-drowsiness applications.
Dampak Penggunaan Data Augmentasi Terhadap Akurasi MobileNetV2 Dalam Deteksi Mikrosleep Berbasis Rasio Aspek Mata Maulana, Isa Iant; Riadi, Muhammad Fatah Abiyyu; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Pramunendar, Ricardus Anggi; Basuki, Ruri Suko
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8719

Abstract

Detecting microsleep is important in preventing accidents caused by decreased alertness, especially in activities that require high concentration such as driving. This study aims to develop an image-based microsleep detection model using the MediaPipe FaceMesh. The EAR value is only used for the tagging process that forms the basis for dataset creation. The main problem investigated is how to produce a classification model that can accurately distinguish between normal eye conditions and microsleep conditions using image data taken from eye area snippets. To address this issue, this study applies a series of stages, starting from dataset formation, initial processing in the form of image size adjustment, normalization, and quality improvement through data augmentation, to model training using the MobileNetV2 architecture with transfer learning and fine-tuning techniques. The results of the experiment show that the use of data augmentation strategies has a significant effect on improving model performance, with the best configuration producing a test accuracy of 87.54 percent, with other high performance metrics, namely Precision of 88.64 percent, Recall (Sensitivity) of 87.14 percent, and F1-Score of 87.34 percent. These findings prove that an eye area image-based approach combined with a convolutional neural network model is capable of providing promising performance in detecting microsleep conditions. These findings prove that an approach based on eye area images combined with a convolutional neural network model can deliver promising performance in detecting microsleep. This research is expected to form the basis for the development of a more effective microsleep detection system that can be implemented in real world environments.
Building an Intelligent System for Food Distribution Empowered by AI: Tackling Surplus–Deficit Inequality in the Barlingmascakeb Agglomeration Al Azies, Harun; Pratama, Ananta Surya; Umam, Taufiqul; Irnanda, Muhammad Diva
Jurnal Dinamika Ekonomi Pembangunan Vol 8 (2025): Special Issue: Call for Paper Pusaka Jateng
Publisher : Fakultas Ekonomika dan Bisnis, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jdep.8.0.19-40

Abstract

Food distribution disparities remain a persistent challenge in the Barlingmascakeb region (Banyumas, Cilacap, Purbalingga, Banjarnegara, and Kebumen), where socio-economic and infrastructural factors drive regional inequalities. This study applies a machine learning–based classification approach to identify sub-districts categorised as food surplus or deficit. The dataset, initially imbalanced, was balanced using the Synthetic Minority Oversampling Technique (SMOTE), followed by training and evaluating four ensemble algorithms: AdaBoost, Gradient Boosting, XGBoost, and CatBoost. Among the tested models, AdaBoost demonstrated the best overall performance with an accuracy of 0.9565, precision of 1.00, recall of 0.8333, and F1-score of 0.9091. Gradient Boosting achieved a more balanced recall (0.8333) than XGBoost and CatBoost, although with lower precision. Based on the Gradient Boosting model, Feature importance analysis identified the Food Security Index as the most critical determinant of food status, followed by clean water access, morbidity rate, health workforce availability, and poverty levels. This study offers a novel contribution by providing a high-resolution, sub-district-level classification of food surplus and deficit conditions using interpretable ensemble machine learning models integrated with multidimensional socio-economic and health indicators. Practically, the model supports targeted and data-driven food distribution policies; theoretically, it reinforces the multifaceted nature of food security beyond production alone; and for future research, it opens opportunities to extend the framework to spatio-temporal and optimization-based food distribution models.
Co-Authors Achmad Wahid Kurniawan Achmad Wahid Kurniawan Adhitya Nugraha Agus Suharsono Akrom, Muhamad Al zami, Farrikh Alfa Trisnapradika, Gustina Alzami, Farrikh Ananda, Imanuel Khrisna Andrean, Muhammad Niko Anwar Efendi Nasution Aprilyani Nur Safitri Ardytha Luthfiarta Ariyanto, Noval Ayu Febriana Dwi Rositawati Ayu Pertiwi Ayu Pertiwi Bambang Widjanarko Otok Brilianti Rochmanto, Hani Brilianto, Rivaldo Mersis Budi, Setyo Chasanah, Annisa Himatul Dea Trishnanti Dea Trishnanti Devi Putri Isnarwaty Dewi Agustini Santoso Dikaputra, Ishak Bintang Elvira Mustikawati P.H Fahmi Amiq Fawwaz Atha Rohmatullah Firmansyah, Gustian Angga Fitriani, Fenny Gangga Anuraga Ganiswari, Syuhra Putri Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Gustina Alfa Trisnapradika Hani Brilianti Rochmanto Hani Brilianti Rochmanto Herawati, Wise Herowati, Wise Hidayat, Novianto Hidayat, Novianto Nur Ifan Rizqa Irnanda, Muhammad Diva Ishak Bintang Dikaputra Isnarwaty, Devi Putri ISWAHYUDI ISWAHYUDI Junta Zeniarja Kharisma, Ni Made Kirei Maulana, Isa Iant Megantara, Rama Aria Moch Anjas Aprihartha Mohammad Arif Muhamad Akrom Muhammad Naufal Muhammad Naufal, Muhammad Muljono Muljono Noor Ageng Setiyanto, Noor Ageng Noval Ariyanto Novianto Hidayat Nugraini, Siti Hadiati Nugroho, Dandy Prasetyo Nur Safitri, Aprilyani Prabowo, Wahyu Aji Eko Pratama, Ananta Surya Pravesti, Cindy Asli Pulung Nurtantio Andono Purhadi Purhadi Putra, Permana Langgeng Wicaksono Ellwid Rahman, Irfan Fauzia Rahmawati Erma Standsyah Ramadhan Rakhmat Sani Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Rohmatullah, Fawwaz Atha Ruri Suko Basuki Safitri, Aprilyani Nur Sari Ayu Wulandari Setyo Budi Shafwah, Shifatush Sofiani, Hilda Ayu Sri Winarno Sri Winarno Sudibyo, Usman Supriadi Rustad Trishnanti, Dea Trisnapradika, Gustina Alfa Umam, Taufiqul Usman Sudibyo Vivi Mentari Dewi Wahyu Wisnu Wardana Wise Herawati Wise Herowati Zahro, Azzula Cerliana Zain, Affa Fahmi Zami, Farrikh Al