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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) 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 RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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 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 BERNAS: Jurnal Pengabdian Kepada Masyarakat 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
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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.
Optimasi Hyperparameter Model Ensemble untuk Klasifikasi Sentimen Ulasan OVO Chasanah, Annisa Himatul; Al Azies, Harun
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 2: Desember 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.95-104

Abstract

Pertumbuhan layanan dompet digital di Indonesia mendorong meningkatnya jumlah ulasan pengguna yang mengandung opini penting terkait kualitas layanan. Analisis sentimen menjadi penting untuk memahami persepsi pengguna terhadap aplikasi OVO. Penelitian ini menganalisis 10.644 ulasan dari Google Playstore yang dikumpulkan melalui teknik web scraping. Ulasan tersebut diproses melalui tahapan text preprocessing, representasi fitur menggunakan Word2Vec, serta penyeimbangan kelas menggunakan Synthetic Minority Oversampling Technique (SMOTE). Tiga algoritma ensemble learning yaitu Random Forest, XGBoost, dan LightGBM diterapkan dan dioptimasi melalui Grid Search dan Randomized Search, dengan evaluasi menggunakan 10-Fold Cross-Validation serta uji statistik paired t-test. Hasil menunjukkan bahwa meskipun XGBoost dan LightGBM memperoleh nilai cross-validation yang lebih tinggi, performa terbaik pada data uji dicapai oleh Random Forest. Model tersebut mencapai akurasi 89,90% dan ROC-AUC Macro 91,11% pada skema Grid Search, serta akurasi 89,76% dan ROC-AUC Macro 91,19% pada skema Randomized Search. Temuan ini menunjukkan bahwa Random Forest memiliki kemampuan generalisasi paling stabil terhadap data ulasan OVO dibandingkan dua model boosting. Penelitian ini memberikan kontribusi pada pengembangan analisis sentimen berbahasa Indonesia melalui integrasi Word2Vec, SMOTE, dan optimasi hyperparameter, serta membuka peluang eksplorasi lanjutan menggunakan contextual embedding dan teknik penyeimbangan data yang lebih adaptif.
Komparasi SVM dan IndoBERT dalam Klasifikasi Sentimen Program Makanan Bergizi Gratis Shafwah, Shifatush; Al Azies, Harun
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 2: Desember 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.105-112

Abstract

Program Makanan Bergizi Gratis (MBG) memunculkan beragam respons masyarakat di media sosial, khususnya pada platform X (Twitter). Analisis sentimen diperlukan untuk memahami kecenderungan opini publik terhadap program tersebut. Penelitian ini membandingkan kinerja Support Vector Machine (SVM) dan IndoBERT dalam mengklasifikasikan sentimen positif dan negatif pada 2.674 tweet terkait MBG. Data diperoleh melalui web scraping dan diproses melalui tahapan cleaning, normalisasi teks, tokenisasi, serta pelabelan menjadi dua kelas sentimen. Ketidakseimbangan data ditangani menggunakan Synthetic Minority Oversampling Technique (SMOTE). Model SVM dilatih menggunakan representasi fitur TF-IDF, sedangkan IndoBERT dilatih melalui fine-tuning sebagai model transformer. Evaluasi performa dilakukan menggunakan 10-Fold Cross-Validation, confusion matrix, ROC-AUC, dan uji statistik paired t-test. Hasil penelitian menunjukkan bahwa SVM memperoleh akurasi 94,64% dan F1-Score 94,63%, sedangkan IndoBERT mencapai akurasi 90,11% dan F1-Score 89,92%. Meskipun IndoBERT mencatat nilai AUC sedikit lebih tinggi, kinerja keseluruhan SVM lebih unggul secara konsisten pada data yang telah diseimbangkan dengan SMOTE. Uji paired t-test menghasilkan nilai p < 0,05, yang menunjukkan bahwa perbedaan performa kedua model bersifat signifikan. SVM lebih efektif digunakan untuk klasifikasi sentimen dua kelas pada dataset MBG yang relatif kecil dan bersifat informal.
Klasifikasi Opini Publik terhadap Kenaikan PPN 12% di Platform X menggunakan Multinomial Naïve Bayes Hani Brilianti Rochmanto; Harun Al Azies
UJMC (Unisda Journal of Mathematics and Computer Science) Vol 10 No 2 (2024): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department, Faculty of Sciences and Technology Unisda Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/ujmc.v10i2.9120

Abstract

The increase in Value-Added Tax to 12% in 2025 has sparked diverse public opinions on the social media platform X (Twitter). This study aims to classify public sentiment toward the policy using Multinomial Naïve Bayes with a Term Frequency-Inverse Document Frequency (TF-IDF) approach. Multinomial Naïve Bayes is a probabilistic classification algorithm that assumes feature independence. Data were collected through web crawling using the keyword "ppn 12%" and underwent pre-processing, including text normalization, stopword removal, and stemming. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The best-performing model was obtained by tuning the alpha hyperparameter to 0.01, achieving an average accuracy of 83.37%, precision of 83.32%, recall of 83.38%, and an F1-score of 82.99% using 10-fold cross-validation. The findings indicate that Multinomial Naïve Bayes, combined with SMOTE and hyperparameter tuning, effectively classifies public sentiment and provides insights into public responses regarding the Value-Added Tax policy.
Evaluation of Histogram-Based Image Enhancement Methods for Facial Images in Drowsy Driver Using No-Reference Metrics Naufal, Muhammad; Al Azies, Harun; Alzami, Farrikh; Brilianto, Rivaldo Mersis
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12055

Abstract

Low-light facial images suffer significant quality degradation, leading to performance degradation in surveillance and face recognition systems, where conventional enhancement methods often produce over-enhancement or unnatural noise artifacts. This study compares three histogram equalization methods, namely HE, AHE, and CLAHE, for low-light facial image enhancement, with evaluation using no-reference quality assessment metrics, including NIQE, LOE, and Entropy, as well as visual analysis and histogram distribution. The results showed that AHE produced the lowest NIQE (4.96 ± 1.38) and the highest entropy (7.86 ± 0.11) but had significant noise artifacts, HE produced an overly even distribution with NIQE of 6.34 ± 1.41, while CLAHE showed the most balanced performance with the lowest LOE (0.07 ± 0.02) and the best visual quality when using the optimal clip limit in the range of 1.2-2.0, providing an optimal trade-off between contrast enhancement, naturalness preservation, and artifact minimization with computational efficiency below 1 ms.
Co-Authors Achmad Wahid Kurniawan Achmad Wahid Kurniawan Adhitya Nugraha Agus Suharsono Alfa Trisnapradika, Gustina Alzami, Farrikh Ananda, Imanuel Khrisna Ananta Surya Pratama Andrean, Muhammad Niko Annisa Himatul Chasanah 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 De Rosal Ignatius Moses Setiadi Dea Trishnanti Dea Trishnanti Devi Putri Isnarwaty Dewi Agustini Santoso Dikaputra, Ishak Bintang Elvira Mustikawati P.H Fahmi Amiq Farrikh Al Zami Fatma Ahardika Nurfaizal 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 Herawati, Wise Herowati, Wise Hidayat, Novianto Hidayat, Novianto Nur Ifan Rizqa Ika Puspitasari Indah Putianik 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 Diva Irnanda Muhammad Naufal Muhammad Naufal, Muhammad Muljono Muljono Naufal Malik Herlambang 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 Rivaldo Mersis Brilianto Rohmatullah, Fawwaz Atha Ruri Suko Basuki Safitri, Aprilyani Nur Sari Ayu Wulandari Setyo Budi Shafwah, Shifatush Shifatush Shafwah Sofiani, Hilda Ayu Sri Winarno Sri Winarno Sudibyo, Usman Supriadi Rustad T. Sutojo Taufiqul Umam Trishnanti, Dea Trisnapradika, Gustina Alfa Umam, Taufiqul Usman Sudibyo Vivi Mentari Dewi Wachid Zufar Ramadhan Wahyu Aji Eko Prabowo Wahyu Wisnu Wardana Wise Herawati Wise Herowati Zahro, Azzula Cerliana Zain, Affa Fahmi Zami, Farrikh Al