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All Journal JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Computer Networks, Architecture and High Performance Computing Yayasan Cita Cendikiawan Al Khwarizmi Journal Of Science Education And Management Business The Indonesian Journal of Computer Science
Aldi, Febri
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Sistem Informasi Penerimaan Murid Baru Berbasis Web Menggunakan Metode Waterfall Aldi, Febri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 1 (2021): Article Research Volume 6 Issue 1: January 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i1.11242

Abstract

The current development of information technology on the activities of people's daily lives is very influential. Where in doing an activity that takes a lot of time can be shortened to complete it. One of the things that we can see at this time is in the field of education. Education service technology is feasible if the government, education service providers, and the community all work together. The problem is how to build information system technology that can provide educational service processes without intervening in existing operational standards. The New Student Admissions Information System is a website-based platform designed to enable public access to information about schools and the new student registration process. The waterfall approach was used to construct this information system, together with the PHP programming language, the Codeigniter 3 framework, the MVC pattern, and the MySQL database. The results obtained in the form of a new student admission information system website that provides services related to registration, and processing of registration data by the school administration. With this research, it is expected to facilitate new student admission officers at Al Azhar Islamic Elementary School 32 Padang to process data well, and help parents to register their children as new students at Al Azhar Islamic Elementary School 32 Padang.
Comparison of Drug Type Classification Performance Using KNN Algorithm Aldi, Febri; Nozomi, Irohito; Soeheri, Soeheri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11487

Abstract

The error of decommissioning is a serious problem that is often faced in medicine. In the face of these problems, information technology has a very important role. One of the information technologies that can be used is to use the machine learning classification algorithm K-Nearest Neighbor KNN. KNN is a type of machine learning algorithm that can be applied to problems with classification and regression prediction. The classification of types of drugs for patients greatly affects the health of the patient. The patient data is processed and transformed to numbers, which are then divided into training data and test data from 90:10, 80:20, 70:30 and using the Cross Validation model. KNN works through the nearest neighboring value with a value of k = 3 calculated by the calculation of Euclidean Distance, and then evaluated using the Confusion Matrix. The performance of the KNN algorithm resulted in the highest Accuracy value of 98.33%, a Precision value of 98.8%, a Recall value of 96.2%, and an F-measure value of 97.48%. The performance is obtained from the sharing of training data and 90:10 test data. The data share results in high performance compared to other data shares, including using the Cross Validation model. And the lower the k value, the higher the value of the resulting performance. The results show that the performance of the KNN algorithm is working well.
Machine Learning to Identify Monkey Pox Disease Aldi, Febri; Nozomi, Irohito; Sentosa, Rio Bayu; Junaidi, Ahmad
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.v8i3.12524

Abstract

In May 2022, it has received by WHO reports from non-endemic countries on cases of monkey pox disease. Monkey pox is a rare zoonotic disease caused by infection with the monkeypox virus that belongs to the genus orthopoxvirus and the family poxviridae, and also the variola virus. This study aims to classify patients who have contracted the monkey pox virus. We modeled an analysis of monkey pox disease and conducted comparisons utilizing a dataset from Kaggle consisting of a CSV file with records for 25,000 patients. The monkey pox dataset was analyzed using the correlation coefficient and the number of target variables. Machine learning (ML) methods are used for classification by utilizing the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) algorithms. This study resulted in the highest classifier Gradient Boosting (GB) algorithm with an accuracy value of 71%. then the accuracy obtained by Support Vector Machine (SVM) is 69%, Random Forest (RF) accuracy is 68%, and finally K-Nearest Neighbor (KNN) obtains 63% accuracy. This ML method is expected to analyze monkey pox disease so that it helps the country and government, especially the health field in assessing, identifying, and being able to take appropriate action against monkey pox disease.
Extraction of Shape and Texture Features of Dermoscopy Image for Skin Cancer Identification Aldi, Febri; Sumijan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13557

Abstract

Skin diseases are increasing and becoming a very serious problem. Skin cancer in general there are 2, namely melanoma and non-melanoma. Cases that are often encountered are in non-melanoma types. A critical factor in the treatment of skin cancer is early diagnosis. Doctors usually use the biopsy method to detect skin cancer. Computer-based technology provides convenient, cheaper, and faster diagnosis of skin cancer symptoms. This study aims to identify the type of skin cancer. The data used in the study were 6 types of skin cancer, namely Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus image, Pigmented Benign Keratosis image, or Vascular Lesion, with a total of 60 dermoscopy images obtained from the Kaggle site. Dermoscopy image processing begins with a pre-processing process, which converts RGB images to LAB. After that, segmentation is carried out to separate objects from the background. The method of extracting shape and texture features is used to obtain the characteristics of dermoscopy images. As many as 2 types of shape features, namely eccentricity and metric, and 4 types of texture features, namely contrast, correlation, energy, and homogeneity. The result of this study is that it can identify the type of skin cancer based on image features that have been extracted using a program from the Matlab application. The technique of extracting shape and texture features is proven to work well in identifying the type of skin cancer. In the future it is expected to use more data, and add color features in identifying dermoscopy images.
Penggunaan SIPAGA Dalam Pengusulan Rencana Kebutuhan Barang Milik Daerah (RKBMD) Kepada Pemerintahan Kota Padang Wadisman, Cendra; Putra, Yeviki Maisyah; Aldi, Febri
Dedikasi Sains dan Teknologi (DST) Vol. 3 No. 2 (2023): Dedikasi Sains dan Teknologi : Volume 3 Nomor 2, Nopember 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v3i2.3209

Abstract

Sistem Informasi dapat diterapkan di berbagai bidang, termasuk bisnis, pemerintahan, pendidikan, kesehatan, dan lain-lain. Tujuannya adalah untuk meningkatkan efisiensi, efektivitas, dan kualitas pengambilan keputusan dengan menyediakan informasi yang akurat, relevan, dan tepat waktu. SIPAGA merupakan Sistem Informasi Perencanaan dan Harga yang digunakan oleh Pemerintahan Provinsi Sumatera Barat dalam mengelola Rencana Kebutuhan Barang Milik Daerah (RKBMD). SIPAGA menaungi 2 aplikasi, yaitu Sistem Informasi Standar Harga Barang dan Jasa (SISHBJ) dan Sistem Informasi Perencanaan Barang Milik Daerah (SIPBMD). Organisasi Perangkat Daerah (OPD) di Provinsi Sumatera Barat saat ini sangat terbantu dalam mengusulkan Standar Harga melalui SISHBJ dan RKBMD melalui SIPBMD. SISHBJ menghasilkan standar harga yang akan digunakan oleh OPD dalam menyusun rencana belanja OPD melalui Sistem Informasi Perencanaan Daerah (SIPD) dan RKBMD digunakan sebagai dasar dalam penyusunan rencana belanja OPD. sehingga bagian Pengelola BMD saat ini yang ada pada Bidang Pengelolaan BMD pada Badan Pengelolaan Keuangan dan Aset Daerah (BPKAD) dapat meningkatkan kinerja serta pencatatan riwayat usulan dari OPD.
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.
Skin Cancer Segmentation On Dermoscopy Images Using Fuzzy C-Means Algorithm Aldi, Febri; Sumijan
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3797

Abstract

Millions of people around the world suffer from skin cancer, a common and sometimes fatal disease. Dermoscopy has become an effective diagnostic technique for skin cancer. Precise segmentation is essential for skin cancer diagnosis. Segmentation allows more precise analysis of dermoscopic images by defining the boundaries of the lesion and separating it from surrounding healthy tissue. Dermoscopy images served as a source of research data, and Fuzzy C-Means (FCM) segmentation techniques were used. FCM is a promising method and has received a lot of attention lately. FCM is able to distinguish the various components within the lesion and effectively separate the lesion from the surrounding area. As a result, the distribution of membership degree values of each pixel in the image for each cluster represents the segmentation results obtained through FCM. The FCM technique for segmenting dermoscopic images is expected to significantly improve the precision and effectiveness of skin cancer diagnosis.
Comparative Analysis of YOLOv11 with Previous YOLO in the Detection of Human Bone Fractures Aldi, Febri; Nozomi, Irohito; Hafizh, M.; Novita, Triana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6051

Abstract

Accurate and rapid detection of bone fractures is an important challenge in the medical world, particularly in the field of radiology. This study aims to analyze and compare the performance of the YOLOv11 model with several previous versions of YOLO, namely YOLOv5, YOLOv8, and YOLOv10 in the task of detecting human bone fractures on X-ray and MRI images. The dataset used is the Human Bone Fractures Multi-modal Image Dataset (HBFMID) which consists of 641 raw images (510 X-rays and 131 MRIs). The four models were trained using the HBFMID dataset that had gone through a manual augmentation and annotation process, then tested using evaluation metrics such as precision, recall, mAP50, and mAP50-95. The training results showed that YOLOv11 has the most stable and consistent loss curve, with a fast convergence process. In terms of evaluation, YOLOv11 recorded a precision of 99.87%, a recall of 100%, a 99.49% mAP50, and an 84.13% increase in the number of mAP-95s, which generally outperformed other models. In addition, the visual prediction results show that YOLOv11 can detect fracture areas with the right bounding box and a balanced confidence score, without showing symptoms of overconfidence or inconsistency. When compared to approaches from previous studies, YOLOv11 also showed a significant improvement in detection accuracy. Thus, YOLOv11 is rated as the most optimal and reliable model in deep learning-based automatic bone fracture detection. This model has great potential to be applied in medical diagnosis support systems to improve the efficiency and accuracy of digital fracture identification.
Pengaruh Electronic Word Of Mouth Dan Harga Terhadap Keputusan Pembelian Dengan Brand Trust Sebagai Variabel Intervening Pada Produk MS Glow Muhammad, Hammar Faris; Yulasmi; Aldi, Febri
Journal of Science Education and Management Business Vol. 4 No. 2 (2025): JOSEMB (Journal Of Science Education And Management Business)
Publisher : Riset Sinergi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/joseamb.v4i2.506

Abstract

This research aims to find out how much the Influence of Electrinic Word Of Mouth and Price on Purchase Decisions with Brand Trust as an Intervening Variable in Ms Glow Products. The sample withdrawal method used was non-probality sampling, namely Ms. Glow consumers at Upi Yptk Padang students who happened to meet as many as 100 respondents. The method used is Stucture Equation Modeling (SEM) with Partial Least Square (PLS) 3.0. The results of hypothesis testing show that (1) Electronic word of mouth has a non-positive and insignificant effect on Brand Trust (2) Pricing decisions do not have a significant effect on Brand Trust (3) Word Of Mouth decisions have a positive and significant effect on Purchase Decisions (4) Price Decision has a positive and significant effect on Purchase Decision (5) Brand Trust decisions have a positive and significant effect on Purchase Decisions (6) Word Of Mouth Decision Intervening the relationship between Purchase Decision and Brand Trust has a positive and significant effect (7) Intervening Price Decisions The relationship between Purchase Decisions and Brand Trust has a positive and significant effect
Identifikasi Kanker Darah pada Gambar Apusan Darah Perifer (PBS) Menggunakan Ekstraksi Fitur HSV Nozomi, Irohito; Aldi, Febri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4177

Abstract

Blood cancer is a category of diseases that have an impact on the development and operation of blood cells. Due to the complexity and diversity of these diseases, proper diagnosis is required before starting treatment. Medical imaging techniques have undergone significant advances in recent years, especially in peripheral blood smear (PBS) image processing. The aim of this study was to uncover how important the extraction of PBS image features is for the diagnosis of blood cancer. Feature extraction is essential to detect anomalies in blood cells in terms of blood cancer detection. The method used is feature extraction based on hue and saturation values (HSV) and uses Machine Support Vector Machine (SVM) machine learning algorithms in classifying malignant and benign PBS images. PBS image data used in this study was 100 images, consisting of 50 malignant PBS images, and 50 benign PBS images. Through the application of HSV feature extraction techniques and PBS image analysis, SVM algorithms can uncover latent indicators of blood cancer and facilitate timely and precise diagnosis. With the SVM technique, classification accuracy can be achieved by 92%. These results demonstrate the potential effectiveness of this feature extraction method. Extraction of HSV features may alter the diagnosis of blood cancer with additional research and application in clinical settings.
Co-Authors Ahmad Junaidi Hafizh, M. Muhammad, Hammar Faris Novita, Triana nozomi, irohito Putra, Yeviki Maisyah Sentosa, Rio Bayu Soeheri, Soeheri Sumijan Surmayanti, Surmayanti Tajuddin, Muhammad Wadisman, Cendra Yuhandri Yuhandri, Yuhandri Yulasmi