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All Journal Jurnal Masyarakat Informatika Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi INTERACTION: Jurnal Pendidikan Bahasa Jurnal Teknologi Informasi dan Terapan (J-TIT) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Mahasiswa Ilmu Komputer JAMU : Jurnal Abdi Masyarakat UMUS SmartComp Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika: Jurnal Pengembangan IT Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Muhammad Naufal
Dian Nuswantoro University, Semarang
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Journal : Building of Informatics, Technology and Science

 1 
Implementasi Grid Search CV KNN dengan Preprocessing Z-Score Outlier Removal untuk Sistem Prediksi Risiko Kehamilan Anggita, Ivan Maulana; Naufal, Muhammad; Zami, Farrikh 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.v7i2.8206

Abstract

This study aims to optimize the K-Nearest Neighbors (KNN) algorithm in predicting pregnancy risk levels using the “maternal health risk” dataset from the UCI Machine Learning Repository. The methodology includes data preprocessing through outlier detection and removal using Z-score, normalization with Standard Scaling, and categorical encoding on the target labels. Hyperparameter tuning is performed using GridSearchCV to identify the optimal combination of KNN parameters (number of neighbors, distance weight, and distance metric). The results show that the unoptimized KNN model achieved an accuracy of only 69.46%, whereas the optimized model reached an accuracy of 82.00%, with macro average precision of 81.91%, recall of 82.89%, and F1-score of 82.23%. Evaluation using a confusion matrix also revealed significant performance improvement, especially in the high-risk category. The optimized model was deployed as a web application using the Flask framework and Docker via Hugging Face Spaces, enabling real-time and efficient online pregnancy prediction. These findings indicate that combining KNN with GridSearchCV and data normalization significantly enhances prediction performance and offers practical application in healthcare decision support systems.
Optimalisasi Arsitektur LSTM dengan Pendekatan Bidirectional untuk Deteksi Kantuk Pengemudi Berbasis Fitur Wajah Hartono, Andhika Rhaifahrizal; Naufal, Muhammad; Alzami, Farrikh
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.v7i2.8219

Abstract

Traffic accidents caused by driver fatigue and drowsiness remain a serious safety concern in many countries, including Indonesia. Various image-based drowsiness detection systems have been developed, yet many still rely on single-frame analysis and lack the ability to capture complete temporal context. To address this issue, a system capable of accurately and real-time detecting signs of drowsiness is required. This study aims to evaluate and compare the performance of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) algorithms for a facial-feature-based drowsiness detection system. The dataset used is YawDD, which consists of videos of drivers yawning and in neutral conditions. Each video was decomposed into frames and analyzed using MediaPipe to extract facial landmarks. Two main features, Eye Aspect Ratio (EAR) and Mouth Opening Ratio (MOR), were utilized. Due to class imbalance, the SMOTE technique was applied to the minority class in the training data. Both LSTM and BiLSTM models were compared under similar architecture configurations. The results show that BiLSTM outperformed LSTM with an accuracy of 94,74% and an F1- score 94,82%, compared to 92,98% accuracy and 93,22% F1-score achieved by LSTM. These findings demonstrate that bidirectional sequential processing in BiLSTM is more effective in capturing the temporal patterns of drowsiness symptoms. This study contributes to the development of accurate and efficient computer vision-based drowsiness detection systems.
Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women Nazella, Desvita Dian; Hadi, Heru Pramono; Al Zami, Farrikh; Ashari, Ayu; Kusumawati, Yupie; Suharnawi, Suharnawi; Megantara, Rama Aria; Naufal, Muhammad
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.v7i3.8415

Abstract

Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values ​​indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
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 Achmad Akrom, Muhamad Akrom, Muhamad Febrian Al Fahreza, Muhammad Daffa Al zami, Farrikh Al-Azies, Harun Alzami, Farrikh Amanda Cahyadewi, Felicia Amron, Azmi Jalaluddin Andrean, Muhammad Niko Anggi Pramunendar, Ricardus Anggita, Ivan Maulana Ardytha Luthfiarta ARIYANTO, MUHAMMAD Arofi, Muhammad Labib Zaenal Ashari, Ayu Ayu Pertiwi Azizi, Husin Fadhil Brilianto, Rivaldo Mersis Dairoh Dairoh Danar Cahyo Prakoso Dega Surono Wibowo Denta Saputra, Fahrizal Dewi Agustini Santoso Dwi Puji Prabowo, Dwi Puji Eko Purnomo Bayu Aji Erika Devi Udayanti Erwin Yudi Hidayat Fadlullah, Rizal Fahmi Amiq Firmansyah, Gustian Angga Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hadi, Heru Pramono Handayani, Ni Made Kirei Kharisma Harisa, Ardiawan Bagus Hartono, Andhika Rhaifahrizal Harun Al Azies Harun Al Azies Heni Indrayani Hepatika Zidny Ilmadina Hidayat, Novianto Nur Ifan Rizqa Indra Gamayanto Indrawan, Michael Iswahyudi ISWAHYUDI ISWAHYUDI Kharisma, Ni Made Kirei Khoirunnisa, Emila Kurniawan Aji Saputra Kurniawan, Defri Kurniawan, Ibnu Richo Kusumawati, Yupie Liya Umaroh Liya Umaroh Liya Umaroh, Liya Malim, Nurul Hashimah Ahmad Hassain Maulana, Isa Iant Megantara, Rama Aria Moch Anjas Aprihartha Mohammad Arif Mukaromah Mukaromah MUKAROMAH MUKAROMAH Muslih Muslih Nazella, Desvita Dian Ningrum, Novita Kurnia Noor Ageng Setiyanto, Noor Ageng Novianto Nur Hidayat Nugraini, Siti Hadiati Paramita, Cinantya Pergiwati, Dewi Prabowo, Wahyu Aji Eko Puspita, Rahayuning Febriyanti Putra, Permana Langgeng Wicaksono Ellwid Rafid, Muhammad Ramadhan Rakhmat Sani Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Richo Kurniawan, Ibnu Ruri Suko Basuki Safitri, Aprilyani Nur Sofiani, Hilda Ayu Sri Winarno Sudibyo, Usman Suharnawi Suharnawi Trisnapradika, Gustina Alfa Umar Fakhrizal, Irsyad Very Kurnia Bakti, Very Kurnia Widyatmoko Karis Yosep Teguh Sulistyono, Marcelinus Zahro, Azzula Cerliana Zami, Farrikh Al