Article Per Year (5 Year)
p-Index From 2020 - 2025
3.942
P-Index
This Author published in this journals
All Journal HAYATI Journal of Biosciences ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sinergitas PkM & CSR KACANEGARA Jurnal Pengabdian pada Masyarakat Building of Informatics, Technology and Science Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Robotics and Control (JRC) Jurnal Teknologi Bahan dan Barang Teknik Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun eProceedings of Engineering Jurnal Rekayasa elektrika
Bayu Erfianto
Universitas Telkom
Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

Published : 30 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 30 Documents
Search

 1   2   3 
The Application Of Multi-Sensor Data Fusion Method with Fuzzy Time Series Model to Improve Indoor Water Prediction Accuracy Quality Khoiri, Isfa' Bil; Erfianto, Bayu
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3082

Abstract

There is a lot of indoor air pollution, especially from cigarette smoke, wall paint, air fresheners and gas. With this situation, the room uses Air Box WP6003 air quality detection device by transmitting information about air quality through visualization index. This study aims to improve prediction accuracy with fuzzy time series methods processed through 2 naïve and moving average models using forecast transformers and without transformers. The level of prediction accuracy is calculated through several metrics, namely Mean Absolute Percentage Error (MAPE), Sum of Squares Error (SSE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). These results can be calculated between the actual value and the predicted value. The data used is 204584 data from 4 parameters including Temperature, TVOC, HCHO and CO2. The test results with the difference from the forecast transformer and without transformer are comparable. Temperature value obtained using naïve with transformer from RMSE of 0.158866 and naïve without transformer of 0.782397, data using moving average with transformer obtained by 0.147546 and moving average without transformer of 0.772570. This can be explained by the error analysis that was tried, where the error rate continued to increase so that the experimental results continued to be far from the actual number. From the test results it can be concluded that the accuracy of air quality prediction using naïve forecast transformer is pretty accurate.
The Anomaly Detection in Time Series Data of VOC (Volatile Organic Compound) To Generate Indoor Air Quality Alerts Nusantara, Hadi Dharma; Erfianto, Bayu
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indoor air quality is a very important factor and needs to be considered for health. Poor indoor air quality can trigger illness, reduce productivity, and disrupt the comfort of people in the space. In residential areas, hospitals, schools, nursing homes and other specialized environments, indoor air pollution can affect groups that are more vulnerable to health problems due to their health conditions or age. This research aims to predict indoor air quality using the Long Short-Term Memory (LSTM) method and provide alerts when the prediction results exceed a predetermined limit. The accuracy level is measured using Mean Absolute Percentage Error (MAPE) by calculating the difference between the original data and the prediction results. In this study, a system was created that utilizes Internet of Things (IoT) technology that can monitor the state of indoor air quality such as temperature, TVOC, CO2 and HCHO gas levels. The system uses the WP6003 Air Box Reader tool as an indoor air quality detector that is connected to the website created. This website can display data that is being recorded, download datasets that have been recorded, visualize predictions of temperature, TVOC, CO2 and HCHO and notify if any data crosses a predetermined limit. The results obtained are quite good prediction accuracy by getting a MAPE value of 0.30452, RMSE 0.023475 and the average value of the test data is 24.035 which means that if the RMSE value is close to 0, the prediction results will be more accurate. Anomalies result in values of room temperature and HCHO that are above normal limits.
White Blood Cell Detection Using Yolov8 Integration with DETR to Improve Accuracy Nugraha, Shinta Jitny Ayu; Erfianto, Bayu
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12811

Abstract

One of the body's most crucial blood cell kinds is the white blood cell. White blood cells, called leukocytes, are crucial for the body's defence mechanism and against hazardous foreign substances, tumour cells, and infectious bacteria. This paper suggests a computer-based automated system for detecting white blood cells using the YOLOV8 transformer and white blood cell analysis in digital images of blood cells. The Generate process uses Yolov8. In Generate, this will produce image processing in the form of annotation results on each type of white blood cell and dataset with COCO format. The DETR Model training conducted in this study is to increase the accuracy value of the white output of the blood cell picture formation. Test results using recall, precision, f1 score and object detection values. In the lymphocyte and basophil datasets, the number of white blood cell images used is only 10 images. Following the results of training from yolov8 using Roboflow, the results were increased relatively high, with an average increase of 0.68 in all five images of white blood cells. This test also gets an average improvement in detection results from Yolo to DETR, getting a fairly significant result of 68%, which is because YOLO cannot handle undetected objects (which are not in the training dataset; furthermore, DETR can handle multiple objects in a single image. Typically, detecting traditional objects such as YOLO requires repeatedly multiple object detection with a fixed batch size
Interpolasi Cubic Spline untuk Memetakan Distribusi Panas pada Permukaan Panel Sel Surya ERFIANTO, BAYU; SETIAWAN, ALDRY HERNANDA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 3: Published September 2020
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i3.467

Abstract

ABSTRAKPenelitian ini bertujuan untuk mengetahui sebaran atau distribusi panas yang terjadi pada permukaan panel surya atau photo voltaic (PV) dengan dengan data sebaran sensor yang diolah dengan metode interpolasi cubic spline, sehingga dapat digunakan untuk mengetahui posisi panel surya yang efektif dan ideal menerima cahaya matahari. Selain informasi panas pada panel surya juga informasi mengenai daya yang dikeluarkan oleh panel surya tersebut. Pemetaan distribusi panas pada panel PV menggunakan metode interpolasi cubic spline yang selanjutnya divisualisasikan dalam heatmap 2D. Berdasarkan heatmap hasil interpolasi dan hasil eksperimen menunjukkan panel PV pada posisi vertikal dengan kemiringin 0o pukul 12.00-14.00 menghasilkan arus tertinggi yaitu 449.2mA dengan tegangan yang dihasilkan sebesar 1.03V sehingga menghasilkan daya sebesar 0.46W. Hal ini lebih optimal dari suhu permukaan PV yang lebih panas pada pada posisi vertikal dengan kemiringin 45o ataupun posisi horizontal 0o dan 45o pada jam yang sama.Kata kunci:panel surya, heatmap, cubic spline, interpolasi  ABSTRACTThis research aims to determine the distribution of heat that is exposed on the surface of solar panels or photo voltaic (PV), where the data from distributed sensor is processed by means of cubic spline interpolation method, so that it can be used to determine the ideal and effective position of solar panel to receive sunlight radiation. In addition to heat information on the solar panel, information about the power generated by PV is also measured. Based on the heatmap generated from the interpolation method and the experimental results, it shows that the PV panel in a vertical position with 0o inclination at 12.00-14.00 produces the highest current which is 449.2mA with the output voltage of 1.03V, thus the generated power is about 0.46W. This is more optimal than the surface temperature of the PV which is hotter in the vertical position with a  45o or horizontal position with 0o and 45o at the same time.Keywords: solar panel, heatmap, cubic spline, interpolation
Time Series Classification of Badminton Pose using LSTM with Landmark Tracking Purnama, Bedy; Erfianto, Bayu; Wirawan, Ilo Raditio
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.488

Abstract

Traditional methods of analyzing badminton matches, such as video movement analysis, are time-consuming, prone to errors, and rely heavily on manual annotation. This creates challenges in accurately and efficiently classifying badminton actions and player poses. This paper aims to develop an accurate time series classification method for badminton poses using landmark tracking. The proposed method integrates Long Short-Term Memory (LSTM) networks with landmark tracking to classify badminton poses in a time series, addressing the limitations of traditional video analysis techniques. The dataset consists of 30 respondents performing three distinct activities—lob, smash, and serve—under two conditions: good and bad execution. The approach combines LSTM networks with landmark tracking data, utilizing intra-class variation from a multi-view dataset to enhance pose classification accuracy. The LSTM model achieved high accuracy in classifying badminton poses, successfully detecting serves, lobs, and smashes in real-time with over 90% accuracy. Additionally, the system improved match analysis, achieving 85% accuracy in detection and classification, demonstrating the effectiveness of combining landmark tracking with machine learning for sports analysis. This study underscores the importance of pose estimation in badminton analysis, particularly through landmark tracking, which significantly improves the accuracy of classifying player poses and contributes to the advancement of automated sports analysis.
INFRAMAP WEBGIS SEBAGAI SOLUSI PEMETAAN INFRASTRUKTUR DI KABUPATEN MAJALENGKA MENGGUNAKAN APLIKASI FITUR PETA Erfianto, Bayu; Ilhamdaniah, Ilhamdaniah; Juntriesta, Vera; Adrian, Monterico
Jurnal Sinergitas PKM & CSR Vol. 7 No. 3 (2023): DECEMBER
Publisher : Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/jspc.v7i3.7772

Abstract

Pemerintah Daerah Kabupaten Majalengka telah berusaha untuk membangun sistem informasi spasial yang menampilkan data infrastruktur di Kabupaten Majalengka. Namun hingga saat ini, pengelolaan data dan informasi spasial yang dilaksanakan pemerintah maupun swasta masih dilakukan secara parsial sesuai dengan kebutuhan dan kebijakan masing-masing. Akibatnya adalah daya guna data dan informasi spasial tersebut terbatas pada instansi masing-masing dan sekaligus membatasi pemanfaatannya bagi masyarakat atau investor swasta.  Kondisi saat ini Pemda Kabupaten Majalengka telah mempunyai portal/web yang menampilkan data dan informasi pembangunan, namun informasinya masih secara parsial.  Sebagai solusi untuk mengatasi permasalahan tersebut, beberapa staf dosen dari Telkom University dan Universitas Pendidikan bekerjasama dengan BAPPEDALITBANG Kabupaten Majalengka telah membangun InfraWeb, yaitu suatu aplikasi WebGIS untuk menampilkan informasi secara spasial tentang pengelolaan data infrastruktur dan kewilayahan Kabupaten Majalengka. InfraWeb dapat diakses melalui Internet dan terintegrasi dengan portal pemerintah daerah yang ada saat ini. InfraWeb didesain untuk mengelola dan menyajikan data dan informasi mengenai infrastruktur di Kabupaten Majalengka dengan fitur tematik seperti irigasi, jalan dan jembatan, fasilitas Pendidikan, dll. Dengan demikian, setiap dinas / instansi memungkinkan untuk memperbaharui informasi melalui peta spasial sesuai tema secara independen tanpa menggangu sistem informasi peta spasial secara keseluruhan. Data spasial yang ada juga dapat dipergunakan oleh antar instansi yang terkait untuk keperluan pendataan, pembangunan, pemeliharaan atas asset dan potensi daerah.
Feature Selection Using Pearson Correlation for Ultra-Wideband Ranging Classification Indah Hapsari, Gita; Munadi, Rendy; Erfianto, Bayu; Dyah Irawati, Indrarini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6281

Abstract

Indoor positioning plays a crucial role in various applications, including smart homes, healthcare, robotics, and asset tracking. However, achieving high positioning accuracy in indoor environments remains a significant challenge due to obstacles that introduce NLOS conditions and multipath effects. These conditions cause signal attenuation, reflection, and interference, leading to decreased localization precision. This research addresses these challenges by optimizing feature selection LOS, NLOS, and multipath classification within Ultra-Wideband (UWB) ranging systems. A systematic feature selection approach based on Pearson correlation is employed to identify the most relevant features from an open-source dataset, ensuring efficient classification while minimizing computational complexity. The selected features are used to train multiple machine-learning classifiers, including Random Forest, Ridge Classifier, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. Experimental results demonstrate that the proposed feature selection method significantly reduces model training and testing times without compromising accuracy. The Random Forest and Gradient Boosting models exhibit superior performance, maintaining classification accuracy above 90%. The reduction in computational overhead makes the proposed approach highly suitable for real-time applications, particularly in edge-computing environments where processing efficiency is critical. These findings highlight the effectiveness of Pearson correlation-based feature selection in improving UWB-based indoor positioning systems. The optimized feature set facilitates robust LOS, NLOS, and multipath classification while reducing resource consumption, making it a promising solution for scalable and real-time indoor localization applications.
Transfer Learning pada Estimasi Pose Hewan Menggunakan YoloV8 dan FineTuning Fauzi, Roki; Purnama, Bedy; Erfianto, Bayu
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kemajuan dalam teknologi pengolahan citra dan kecerdasan buatan telah membuka peluang baru dalam analisis citra, terutama dalam konteks estimasi pose hewan. Penelitian ini bertujuan menggabungkan keunggulan YOLOV8 dalam deteksi objek dengan akurasi estimasi pose hewan melalui pendekatan transfer learning. Dengan melakukan finetuning pada YOLOV8 menggunakan dataset khusus untuk estimasi pose hewan, penelitian ini berupaya meningkatkan kemampuan model dalam mengenali dan menentukan posisi berbagai bagian tubuh hewan dengan lebih tepat. Suksesnya penelitian ini diharapkan dapat memberikan kontribusi pada pengembangan estimasi pose hewan, membuka peluang dalam pengelolaan kesehatan hewan, studi perilaku hewan, dan aplikasi lain yang membutuhkan analisis citra yang kompleks. Namun, penelitian ini memiliki batasan, termasuk fokus eksklusif pada estimasi pose hewan melalui teknik transfer learning dan fine-tuningg. Kata Kunci: Stanford Dog Dataset, YOLOV8, finetuning, transfer learning.
Performance Comparison of YOLOv8 and DETR in White Blood Cell Detection Rakhmatsyah, Andrian; Abdurohman, Maman; Erfianto, Bayu; Prihatni, Delita
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6864

Abstract

Automated detection and classification of white blood cells (WBCs) from microscopic images play a vital role in supporting the diagnosis of hematological diseases. Accurate and robust object detection algorithms are essential for handling interclass similarities and imbalanced datasets. This study aims to evaluate and compare the performance of two modern object detection algorithms—Detection Transformer (DeTR) and YOLOv8—in performing multiclass WBC classification using public datasets from various sources with diverse visual characteristics. Five experimental scenarios were designed based on varying class distributions and data augmentation techniques, including horizontal/vertical flipping and random rotation. Both methods were trained and evaluated on the same dataset partitions, and their performances were assessed using the following standard metrics: precision, recall, and F1-score for each WBC class. The results show that YOLOv8 consistently achieved superior and more stable performance across all scenarios, with average F1-scores close to 1.00 even in augmented and imbalanced conditions. In contrast, DeTR performed competitively in balanced scenarios but showed lower consistency, particularly in classes such as Neutrophil and Monocyte. Data augmentation positively affected both models, although the gains were more prominent in YOLOv8. This study highlights the strong potential of YOLOv8 in real-time WBC classification tasks and presents DeTR as a viable yet less-optimized approach for this application. These findings contribute to the advancement of medical image-based object detection and offer valuable insights into the selection of appropriate algorithms for hematological image analysis
Edukasi Literasi Keamanan Digital di PAUD RA-Al-Ghiffari, Sukabirus, Dayeuhkolot, Bandung Suryani, Vera; Erfianto, Bayu; Cahyani, Niken Dwi
Jurnal Pengabdian Kepada Masyarakat (JPKM) TABIKPUN Vol. 5 No. 1 (2024)
Publisher : Faculty of Mathematics and Natural Sciences - Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jpkmt.v5i1.150

Abstract

Literasi keamanan digital merupakan kemampuan untuk mengakses, menggunakan, dan berkomunikasi secara aman dan bertanggung jawab di dunia digital. Literasi ini penting untuk diterapkan oleh murid dan orangtua murid PAUD, mengingat semakin maraknya penggunaan teknologi informasi dan komunikasi di era 4.0. TK Al-Ghiffari merupakan salah satu lembaga pendidikan yang peduli dengan literasi keamanan digital. Untuk meningkatkan literasi keamanan digital, dibutuhkan edukasi yang sesuai dengan karakteristik dan kebutuhan murid dan orangtua murid PAUD. Penyuluhan dan permainan dapat menjadi media yang efektif dan menarik untuk menyampaikan materi edukasi. Tujuan kegiatan pengabdian masyarakat ini ialah melakukan sharing knowledge mengenai pentingnya keamanan digital bagi anak-anak PAUD Al Ghiffari, SUkabirus, Bandung. Dari hasil survey yang diberikan kepada mitra PkM menunjukkan bahwa kepuasan mitra terhadap aspek kebermanfaatan sebesar 100%.
Co-Authors Adhan Mulya Rahmawan Adysti Adrianne Andrian Rahmatsyah Andrian Rahmatsyah Andrian Rakhmatsyah Angga Juliat Saputra Bedy Purnama Benedictus Augusta Vianney Cahyani, Niken Dwi Catur Wirawan Wijiutomo Dyah Irawati, Indrarini Fauzi, Roki Fazmah Arif Yulianto Gita Indah Hapsari Ilhamdaniah Ilhamdaniah Ilhamdaniah, Ilhamdaniah Indrarini Dyah Irawati Intan Muchtadi-Alamsyah Irma Yunita Masturi Juntriesta, Vera Khoiri, Isfa' Bil Maman Abdurohman Mas'ud Adhi Saputra Monterico Adrian Muhammad Hanif Abdillah Novian Anggis Suwastika Nugraha, Shinta Jitny Ayu Nusantara, Hadi Dharma Pahlevi , Rizka Reza Parman Sukarno Prihatni, Delita Ramzy Rashaun Arif Rendy Munadi Satrio Nurcahya SETIAWAN, ALDRY HERNANDA Setyorini Shandikri, Rheza Sidik Prabowo Vera Juntriesta Vera Suryani Wirawan, Ilo Raditio