Article Per Year (5 Year)
p-Index From 2021 - 2026
6.306
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) 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 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 : 53 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Building of Informatics, Technology and Science

 1 
Stacking Machine Learning Approach for Predicting Thermal Stability of Zinc–Metal Organic Frameworks (Zn-MOF) Pratama, Ananta Surya; Irnanda, Muhammad Diva; Umam, Taufiqul; Nugroho, Dandy Prasetyo; Azies, Harun Al
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8329

Abstract

Thermal stability is a fundamental parameter that determines the feasibility of Metal Organic Frameworks (MOF) for high-temperature industrial applications, including catalysis, gas purification, and energy storage. Experimental evaluation of thermal stability, while accurate, is often costly and time-consuming, highlighting the need for computational prediction models that are both efficient and dependable. This study develops a Quantitative Structure Property Relationship (QSPR) model using a stacking ensemble regression framework to predict the thermal stability of Zn-MOFs. The stacking approach combines Linear Regression, Lasso Regression, and Huber Regression as base learners, with Linear Regression serving as the meta-model, thereby leveraging the complementary strengths of individual algorithms. Results demonstrate that the stacking ensemble consistently outperformed all single models, delivering highly reliable predictions that remained stable across multiple validation scenarios. Furthermore, external validation with experimental data confirmed the model’s robustness and its ability to generalize beyond the training dataset. These findings underline the reliability of stacking as not only a tool for improving accuracy but also for ensuring predictive stability and reproducibility. The study highlights the potential of machine learning, particularly ensemble methods, as a powerful and trustworthy predictive framework for the rational design of thermally stable MOFs, offering both scientific and industrial significance in sustainable energy applications.
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.
Co-Authors Achmad Wahid Kurniawan Achmad Wahid Kurniawan Adhitya Nugraha Agus Suharsono Akrom, Muhamad 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