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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Informasi dan Pendidikan Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Infortech Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat SmartComp
Yuhandri Yuhandri, Yuhandri
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Aerospace Engineering Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Other

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Penerapan Natural Language Processing Pada Sistem Chatbot Sebagai Helpdesk Obyek Wisata Menggunakan Metode Naïve Bayes Yuhandri, Yuhandri; Sovia, Rini; Syaiffullah, Afif; Yenila, Firna; Permana, Randy
Jurnal Infortech Vol 5, No 2 (2023): Desember 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v5i2.20911

Abstract

Keberhasilan pengembangan sektor pariwisata tidak hanya bergantung pada daya tarik wisata semata. Terdapat banyak faktor dalam penghambat perkembangan sektor tersebut. Salah satu faktor tersebut adalah kurangnya perkembangan dalam pola periklanan dan sistem pengelolaan informasi pariwisata. Sebagai akibatnya, sasaran pariwisata tidak terdefinisi dengan baik, dan wisatawan mungkin tidak memilih destinasi tersebut. Bukitinggi sebagai salah satu destinasi wisata yang terdapat di Sumatera Barat juga tidak lepas dari permasalahan tersebut. Kurang tersedianya informasi lengkap tentang potensi pariwisata di Kota Bukittinggi membuat wisatawan mengandalkan sumber-sumber seperti media sosial, internet, dan sumber lainnya untuk mendapatkan informasi. Namun, informasi yang ada belum mencakup seluruh aspek pariwisata, sehingga menyebabkan ketidakpastian bagi calon wisatawan. Untuk mengatasi permasalahan tersebut, dihadirkan sebuah sistem respons obrolan otomatis atau disebut dengan  Chatbot. Teknologi Chatbot merupakan salah satu bentuk sistem Natural Language Processing (NLP) dalam kecerdasan buatan. Chatbot berperan sebagai agen percakapan yang dapat berfungsi sebagai meja bantuan. Dalam konteks ini, helpdesk menjadi elemen penting yang menangani berbagai keluhan dari berbagai pihak dengan menyediakan informasi dan solusi. Dalam penelitian ini, dikembangkan sistem Chatbot menggunakan algoritma Naive Bayes untuk menjawab pertanyaan umum (FAQ) mengenai informasi pariwisata di Kota Bukittinggi.
Catfish Fry Detection and Counting Using YOLO Algorithm Takyudin, Takyudin; Fitri, Iskandar; Yuhandri, Yuhandri
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6746

Abstract

The development of computer vision technology is growing very fast and penetrating all sectors, including fisheries. This research focuses on detecting and counting catfish fry. This research aims to apply deep learning in detecting catfish fry objects and counting accurately so as to help farmers and buyers reduce the risk of loss. The detection system in this research uses digital image processing techniques as a way to obtain information from the detection object. The research method uses YOLO Object Detection which has a very fast ability to identify objects. The object detected is a catfish puppy object that is given a bounding box and the detection label displays the class name and precision value. The dataset amounted to 321 images of catfish puppies from internet and photography sources that were trained to produce a new digital image model. The number of split training, validation and testing datasets is worth 831 annotation images, 83 validation images and 83 images for the testing process. The value of the training model mAP 50.39 %, Precision 61.17 % and Recall 58 % Detection test results based on the YOLO method obtained an accuracy rate of 65.7%. The avg loss value in the final model built with YOLO is 4.6%. Based on the results of tests carried out with the number of objects 50 to 500 tail size 2-8 cm using video, objects in the image are successfully recognized with an accuracy of 63% to 70%. Calculations using the YOLO algorithm show quite good results.
Development and modification Sobel edge detection in tuberculosis X-ray images Devita, Retno; Fitri, Iskandar; Yuhandri, Yuhandri; Yani, Finny Fitry
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1191-1200

Abstract

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.
Development of image extraction using the centerline method in the identification of appendicitis in ultrasonography Rizki, Syafrika Deni; Yuhandri, Yuhandri; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1750-1758

Abstract

Appendicitis is a disease that refers to inflammation of the appendix caused by obstruction, or blockage, in the lumen of the appendix. We investigated that this disease can be detected early through medical imaging such as ultrasonography (USG). However, the role of ultrasound in these cases is still limited due to the low visualization rate of the visible appendix. Based on this, this research aims to develop an image extraction process using the Centerline method in the process of identifying appendicitis in ultrasound images. The development of the extraction process is presented in the performance of the centerline and boundary extraction (CBE) algorithm which can represent image objects as boundaries that limit and separate one area from other areas. The research dataset used was 2097 ultrasound images sourced from 90 patients at the West Sumatra Lung Hospital. Based on the tests that have been carried out, it has been proven that it can reduce the width of the image object iteratively until the object is represented as a center line or the thinnest representation. The performance of the CBE algorithm in the identification process is sufficient to provide accuracy results of 92%. These results can be a new extraction concept that can provide accuracy in the identification process.
Enhanced U-Net Architecture for Glottis Segmentation with VGG-16 Aldi, Febri; Yuhandri, Yuhandri; Tajuddin, Muhammad
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3088

Abstract

Laryngeal endoscopic image analysis with segmentation techniques has great potential in detecting various diseases in the glottic area, which is essential for early diagnosis and proper treatment. This study proposes developing the U-Net architecture by integrating the VGG-16 model, aiming to improve the accuracy in detecting glottic areas. VGG-16 is applied to the encoder and bridge sections so that the model can take advantage of previously learned knowledge. This modification is expected to improve segmentation performance compared to standard U-Net, especially in handling variations in laryngeal image complexity. The dataset used consisted of 1,200 images taken randomly from the BAGLS website, a collection of laryngeal endoscopic image data rich in variation. The training results show that the standard U-Net produces an accuracy of 0.9995, IoU 0.6744, and DSC 0.7814. The improved U-Net showed a significant performance improvement, with an accuracy of 0.9998, an IoU of 0.8223, and a DSC of 0.9153. This improvement confirms that modifying the U-Net architecture using VGG-16 provides superior results in detecting glottic areas precisely. VGG-16 also helps model performance in overcoming the problem of smaller datasets. In addition, both models were tested using relevant evaluation metrics, and the test results showed that the improved U-Net consistently outperformed other CNN-based segmentation methods. These advantages show that the proposed approach improves accuracy and contributes significantly to developing glottic disease detection methods through laryngeal endoscopic image analysis, which can ultimately support clinical practice in detecting abnormalities in glottis more effectively.
Optimizing the gallstone detection process with feature selection statistical analysis algorithm Yanto, Musli; Yuhandri, Yuhandri; Tajuddin, Muhammad; Septiana, Vina Tri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1183-1191

Abstract

Early detection is one form of early anticipation in treating gallstone disease patients using medical images. However, the problem that exists is that there are still many shortcomings in medical images, such as noise in the image that causes the detection process to not run optimally. Based on this, this study aims to carry out the process of detecting gallstone objects in magnetic resonance cholangiopancreatography (MRCP) images by optimizing the performance of extraction techniques for feature selection. Optimization of extraction techniques in feature selection is carried out using the performance of the feature selection statistics analysis (FSSA) algorithm. The performance of the FSSA algorithm can provide improvements in the feature selection process by excelling in the performance of classification methods such as k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), and the Pearson correlation (PC) method. Based on the tests that have been carried out, the performance of the FSSA algorithm in the detection process provides an accuracy level of 95.69%, a sensitivity of 89.65%, and a specificity of 98.43%. Overall, this study can contribute to the development of extraction and provide a significant technical impact on optimizing the gallstone detection process.
Development of character extraction techniques to detect chicken gender based on egg shape Setiawan, Adil; Yuhandri, Yuhandri; Tajuddin, Muhammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1851-1861

Abstract

This research investigates the differentiation of chicken sex based on egg shape images by developing an innovative eccentricity shape feature extraction method. The goal is to determine the sex of chickens before hatching, by identifying the sex of the egg prior to incubation. Images of eggs are captured using a smartphone camera, creating a dataset of 150 images each of male and female eggs, with expert assistance. The research aims to accurately identify male and female eggs, aiding breeders in sorting them. The research introduces a unique method to expand the eccentricity value range, enhancing the precision of egg shape analysis. Characteristic extraction results include: area = 1290194, eccentricity = 6.56, contrast = 0.03, correlation = 0.99, energy = 0.44, and homogeneity = 0.98, with a previous value of 0.72. For Feature Selection, the values obtained are: eccentricity = 0.901188049, Area = 0.73, Energy = 0.03, Contrast = 0.01, Homogeneity = 0.01, and Correlation = 0.01. These findings demonstrate significant improvements in differentiating chicken sex from egg images, showcasing the effectiveness of the newly developed eccentricity shape feature extraction method.
Development of Euclidean Distance Algorithm for ANFIS Optimization in IoT-based Pond Water Quality Prediction Dahria, Muhammad; Defit, Sarjon; Yuhandri, Yuhandri
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26497

Abstract

Pond water quality is a pivotal factor that influences the productivity and health of biota in aquaculture systems. The monitoring and prediction of water quality parameters, including temperature, pH, and dissolved oxygen (DO) levels, are imperative for maintaining optimal environmental conditions. The objective of this research is to develop the Euclidean Distance algorithm as an optimization method in adaptive neuro-fuzzy inference system (ANFIS) modeling to enhance the accuracy of internet of things (IoT)-based pond water quality prediction. Water quality parameter data is collected in real-time using IoT sensors connected to an ESP32 microcontroller and transmitted to a cloud storage platform for analysis. Subsequently, the data undergoes a series of processing steps, including min-max normalization and feature selection based on Euclidean distance. This process aims to generate a more representative and relevant subset of data for the subsequent model training process. The ANFIS model was trained using the optimized data and evaluated using MSE, MAD, MRSE and MAPE metrics. The training process involving four data sharing scenarios demonstrated a reduction in error when compared to the model that lacked optimization, specifically: The following proportions were determined: 50% versus 50% (0.11824 versus 0.15536), 70% versus 30% (0.18666 versus 0.19454), 80% versus 20% (0.17843 versus 0.18833), and 90% versus 10% (0.22477 versus 0.22859). The findings indicate that the incorporation of the Weighted Euclidean Distance algorithm within the IoT-based prediction system can markedly enhance the efficiency and precision of the ANFIS model.
Application of Convolutional Neural Networks for Automated Iris Edge Detection in Sleepiness Monitoring during Blended Learning Tukino, Tukino; Yuhandri, Yuhandri; Sumijan, Sumijan
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.882

Abstract

This study introduces a novel lightweight Convolutional Neural Network (CNN) model, T-Net, designed for real-time drowsiness detection based on eye closure patterns. The model was developed to address the prevalent issue of student fatigue in resource-constrained environments, such as during prolonged online learning or blended learning sessions. Unlike traditional deep learning models, T-Net prioritizes efficiency while maintaining high accuracy, making it suitable for deployment on devices with limited computational resources. The model uses a 68-point facial landmark detection technique to extract the eye region and accurately classify eyelid states (open or closed). Evaluated on two benchmark datasets, Dataset-1 (342 eye images) and Dataset-2 (1,510 eye images), T-Net demonstrated superior performance, achieving classification accuracies of 99.33% and 99.27%, respectively, outperforming other pre-trained models such as VGG19, ResNet50, and MobileNetV2. Usability testing revealed a high acceptance rate, with a System Usability Scale (SUS) score of 84.5, indicating the system’s practicality for real-world use. Additionally, statistical analysis showed a significant correlation (r = 0.67, p 0.01) between prolonged screen time and the emergence of visual fatigue symptoms. This study highlights the effectiveness of a lightweight CNN approach for real-time fatigue monitoring, offering a balance between performance and computational efficiency. The results suggest that T-Net can be effectively integrated into student monitoring systems to ensure alertness during learning sessions. Future research will focus on expanding the dataset, integrating infrared imaging for low-light environments, and incorporating additional fatigue indicators such as yawning and head pose.
Penerapan Metode Monte Carlo Dalam Memprediksi Jumlah Antrian Pasien Yang Berobat Ardiyan, Destio; Yuhandri, Yuhandri; Sumijan, Sumijan
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 4 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i4.8498

Abstract

Abstrak: Prediksi jumlah antrian pasien yang berobat merupakan suatu pendekatan dalam analisis data untuk memperkirakan jumlah pasien yang akan datang ke fasilitas kesehatan dalam periode tertentu. Prediksi ini dapat membantu rumah sakit atau klinik dalam mengoptimalkan sumber daya, seperti tenaga medis, ruang tunggu, dan waktu pelayanan, sehingga dapat meningkatkan efisiensi dan kualitas layanan. Jumlah kunjungan pasien yang terlalu banyak terkadang berpotensi menimbulkan rasa tidak puas menunggu menyebabkan keterlambatan pelayanan, ketidakpuasan pasien, dan beban kerja staf medis yang tidak merata. Penelitian ini bertujuan untuk memprediksi jumlah antrian pasien yang berobat kedepannya dengan lebih efesien. Metode yang digunakan dalam penelitian ini adalah Metode Monte Carlo. Metode ini memiliki tahapan, Menentukan Distribusi Probabilitas, Distribusi Probabilitas Kumulatif, Menetapkan Interval Angka Acak, Membangkitkan Angka Acak, Percobaan Simulasi Monte Carlo Antrian Kedatangan Pasien. Dataset yang diolah dalam penelitian ini bersumber dari bagian Administrasi diklit RSU Aisyiyah Padang. Dataset terdiri dari data reservasi 1 periode dengan ketentuan dataset mingguan dari minggu 1 sampai minggu 52. Hasil penelitian ini dapat menghitung presentase jumlah antrian pasien yang berobat dengan Tingkat akurasi 83%. Penelitian ini dapat menjadi acuan dalam memprediksi jumlah antrian pasien yang berobat untuk mencegah rasa tidak puas menunggu dan keterlambatan pelayanan dan beban kerja staf medis yang tidak merata.Kata Kunci – Teknologi Informasi; Simulasi; Prediksi; Antrian, Monte Carlo
Co-Authors Afifah Cahayani Adha Agus Perdana Windarto Akbar Iskandar Aldi Muharsyah Aldi, Febri Andrean, Fajri Ilhami Anita Sindar Ardiyan, Destio Arif Budiman Aulia, Allans Prima Aziz, Majid Rahman Budayawan, Khairi Chandra, Mrs Montesna Dahria, Muhammad Devita, Retno Dewi Eka Putri Dikki Handoko Dolly Indra Dwi Narulita Dwika Assrani Effendy, Geraldo Revanska Efori Buulolo Eka Praja Wiyata Mandala Esa Kurniawan Fauzan, Yuniko Febri Hadi Feri Irawan Finny Fitry Yani Firzada, Fahmi Fuad El Khair Gayatri, Satya Gemilang, Fhajri Arye Gunadi Widi Nurcahyo Hartika Zain, Ruri Hartika Hartomi, Zupri Henra Hendrick, H Idun Ariastuti Iftitah, Hasanatul Iskandar Fitri, Iskandar Jaya, Budi Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Julius Santony Julius Santony Kadrahman, Kadrahman Kurniawan, Jefdy Lidia K Simanjuntak M Ikhsan Setiawan M, Mutia Maharani Maharani, Maharani Mayola , Liga Mesran, Mesran Musli Yanto Na'am, Jufriadif Natalia Silalahi, Natalia Nelly Astuti Hasibuan Nuning Kurniasih Nurdiyanto, Heri Permana, Randy Petti Indrayati Sijabat Pohan, Yosua Ade Purnomo, Nopi Putra, Heru Rahmat Wibawa Putra, Rafi Septiawan Putri, Stefani Rahayu, Rita Rahmad Dian Rakhmad Kuswandhie Rio Andika Malik Ronda Deli Sianturi S Sumijan Sagala, Gamrina Salmiati, S Sarjon Defit Sarjon Defit Septiana, Vina Tri Setiawan, Adil Sisi Hendriani Siska, Ayu Prima Soraya Rahma Hayati Sovia, Rini Sri Dewi Stephano, Rivo Sugiarti, Sugiarti Suginam Suhaidir, Lc Granadi Sumijan Sumijan Sumijan Sumijan Sumijan, S Surya Darma Nasution Sutiksno, Dian Utami Syafrika Deni Rizki, Syafrika Deni Syafril Syafril Syaiffullah, Afif Tajuddin, Muhammad Takyudin, Takyudin Tessa Y M Sihite Tukino, Tukino Veri, Jhon Virgo, Ismail Vratiwi, Septiana Wanto, Anjar Wendi Boy Winanda, Teddy Yanto, Musli Yasmin, Nabilla Yendi Putra Yeni, Nasma Yenila, Firna Yolla Rahmadi Helmi Yudha Aditya Fiandra Zikir Risky, Muhammad Arif