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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Data Sciences Jurnal AbdiMas Nusa Mandiri Vivabio : Jurnal Pengabdian Multidisiplin Computer Science (CO-SCIENCE) Journal Medical Informatics Technology SWARNA: Jurnal Pengabdian Kepada Masyarakat
Aziz, Faruq
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Penerapan Konsep Finite State Automata Dalam Proses Pendaftaran Kelas Kursus Bahasa Inggris Pada Tempat Kursus Aziz, Faruq; Said, Fadillah; Sudrajat, Adjat
MATICS Vol 12, No 2 (2020): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v12i2.9330

Abstract

Research on website-based test applications for digital course registration in order to make it easier for course institutions to determine classes that are consistent and effective and efficient. This research provides alternative solutions for course institutions for class selection or course program that is suitable for users who will study at the place of the course which is cost-effective and time-consuming too. In this study a course registration application is designed by integrating a website and database to retrieve data after the user has tested. It is intended that users who want to learn English can receive a choice of classes or programs in accordance with their abilities and initial knowledge. This minimizing test instructor errors in determining class and program choices that will be obtained by the user. In this study using the Finite State Automata (FSA) method to discuss the NFA type FSA model can be implemented in the registration process to the user (member) where the course is expected as needed and can better understand how the process of class or program selection is effective and right on target
Segmentation of Mango Fruit Image Using Fuzzy C-Means Marlinda, Linda; Fatchan, Muhamad; Widiyawati , Widiyawati; Aziz, Faruq; Indrarti, Wahyu
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2 (2021): Article Research Volume 5 Number 2, April 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i2.10933

Abstract

Mango contains about 20 vitamins and minerals such as iron, copper, potassium, phosphorus, zinc, and calcium. The freshness of the ripe mango will taste sweet. The level of ripeness of the mango fruit can be seen from the texture of the skin and skin color. Ripe mangoes have a bright, fragrant color and a smooth skin texture. The problem found in mango segmentation is that the image of the mango fruit is influenced by several factors, such as noise and environmental objects. In measuring the maturity of mangoes traditionally, it can be seen from image analysis based on skin color. The mango peel segmentation process is needed so that the classification or pattern recognition process can be carried out better. The segmented mango image will read the feature extraction value of an object that has been separated from the background. The procedure on the image that has been analyzed will analyze the pattern recognition process. In this process, the segmented image is divided into several parts according to the desired object acquisition. Clustering is a technique for segmenting images by grouping data according to class and partitioning the data into mango datasets. This study uses the Fuzzy C Means method to produce optimal results in determining the clustering-based image segmentation. The final result of Fuzzy C-based mango segmentation processing means that the available feature extraction value or equal to the maximum number of iterations (MaxIter) is 31 iterations, error (x) = 0.00000001, and the image computation testing time is 2444.913636
Expert System Detects Laptop Damage Using Naive Bayes Method Marlinda, Linda; Aziz, Faruq; Widiyawati, Widiyawati; Widyasih, Ajeng Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11379

Abstract

During the Covid-19 pandemic, teachers and students carried out the online teaching and learning process from home. Distance learning has several problems including the limitations of teachers and students in the world of information and communication technology, the facilities and infrastructure they have, and environmental conditions that are less supportive. The use of laptops and the internet every day are used by students and teachers as the main means of the online teaching and learning process. Continuous use without proper maintenance and lack of knowledge in overcoming the problem of damage to laptops makes teachers and students unable to identify the location of the damage and how to deal with it. Therefore, this expert system application was created to assist teachers and students in detecting the symptoms of laptop damage experienced and solutions to overcome the damage. In the development of this expert system using the Naive Bayes method, this method only requires a small amount of training data to determine parameter estimates during the classification process. The results of the application of the nave Bayes method produce appropriate calculations based on the symptoms of damage and a predetermined list of damage so that it can make it easier for users when analyzing the beginning by using existing symptoms with a system that has been built with very efficient time and has an accuracy rate of 100%.
Decision Tree Algorithm to Measure Employee Performance Discipline Marlinda, Linda; Fitri , Evita; Nugraha , Siti Nurhasanah; Aziz, Faruq; Setiawan , Santoso
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11796

Abstract

Performance appraisal is done to measure the performance of an employee on the work done. The company conducts performance appraisals on employees at least every six months, involving all employees. This study uses the Absenteeism_at_work dataset. The purpose of this research is to analyze the performance of the Decision Tree algorithm in the classification process. Classification will be grouped into two, namely: disciplined and undisciplined The classification process will be carried out using K-Nime. Algorithm performance measurement using Knime Analytics Platform is open-source software for creating data science models. Knime builds data understanding and designs data science workflows and reusable components using accuracy, recall, and precision parameters. From the research conducted, the results of the Decision Tree algorithm have an accuracy rate of 94.6% while the label No. 5.4%. Based on the nineteen attributes proposed, it can be concluded that the Decision Tree algorithm has better performance.
Logistic Regression with Hyper Parameter Tuning Optimization for Heart Failure Prediction Herwanto, Teguh; Kodri, Wan Ahmad Gazali; Aziz, Faruq; Hewiz, Alya Shafira; Riana, Dwiza
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.3

Abstract

Heart failure is a major public health concern that causes a substantial number of deaths worldwide. Risk factor analysis is required to diagnose and treat patients with heart failure. The logistic regression with hyper parameter tuning optimization is presented in this research, with ejection fraction, high blood pressure, age, and  serum creatinine as relevant risk factors. This study indicates that better data preparation utilizing Deep Learning with hyper parameter adjustment be used to determine the best parameter that has a substantial influence as a risk factor for heart failure. The experiments employed data from the Faisalabad Institute of Cardiology and Allied  Hospital in Faisalabad (Punjab, Pakistan), which included 299 samples. The experimental findings reveal that the proposed approach obtains a recall of 63.16% greater than related works.
Enhancing Skin Cancer Classification Using Optimized InceptionV3 Model Daniati Uki Eka Saputri; Nurul Khasanah; Aziz, Faruq; Taopik Hidayat
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.14

Abstract

Skin cancer is a disease that starts in skin cells characterized by uncontrolled growth that can attack skin tissue. Although it has a high cure rate if treated in a timely manner, a delay in diagnosis can have serious consequences. The use of computer technology, especially Artificial Intelligence (AI), has played an important role in improving health services, including in the context of skin cancer. New innovations in the classification and detection of skin cancer using artificial neural networks have led to significant improvements in diagnosis and treatment. One promising approach is using the InceptionV3 algorithm, which has high accuracy and is capable of processing high-resolution images. This study aims to implement InceptionV3 to classify two types of skin cancer, namely malignant and benign, with an emphasis on improving accuracy performance. With the pre-processing process, namely augmentation and the addition of several features, this study aims to provide accurate and efficient results in skin cancer classification. The results of this study can have a positive impact in increasing the accuracy of early detection of skin cancer, especially by future researchers.
Relevance of e-Health Needs and Usage in Indonesia Chairul, Yasrizal; Aziz, Faruq; Hadianti, Sri
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.20

Abstract

The eHealth application can be used for healthcare, supervision, literature, education, and research. It is a cost-efficient and secure application based on information and communication technology for the health and medical fields. The use of Information and Communication Technology (ICT) as an infrastructure or medium that connects hospitals and health centers using the eHealth electronic health application is the key problem facing the implementation of eHealth on a worldwide scale. eHealth is an ICT-based application for the healthcare industry and one of the Action Plans of the World Summit on the Information Society (WSIS) Geneva 2003. The goal of using the eHealth app is to increase patient access, medical process efficiency, effectiveness, and process quality. This covers the administration of medical services provided by hospitals, clinics, health centers, medical professionals (including therapists and doctors), laboratories, pharmacies, and insurance
Tubercolusis Segmentation Based on X-ray Images Priyono, Eko; Fatah, Teddy Al; Ma’mun, Sukrul; Aziz, Faruq
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.22

Abstract

Tuberculosis or TB is an infectious disease caused by the bacteria Mycobacterium tubercolusis. This disease usually attacks the lungs, but can also affect other organs such as the kidneys, bones and brain. TB is highly contagious, and can spread through the air when someone who is infected coughs or sneezes. Risk factors that can increase a person's chances of developing TB include a weak immune system, such as people with AIDS, diabetes, or people taking immunosuppressant drugs. And people who live or work in environments with high rates of TB transmission are also at risk of infection. Symptoms of TB are usually a cough that lasts more than three weeks, unexplained weight loss, fever, night sweats and persistent fatigue. In more severe cases, TB can cause coughing up blood, chest pain and difficulty breathing. One of the examination tools that can be used to detect TB disease is x-rays. Which produces X-Rays to help and confirm the diagnosis of TB disease, to see the chest part of the body which is used as medical record documentation. In X-ray photos, random dark and light spots of noise are often found which are caused by several factors. Based on the facts above, image segmentation is an important task for doctors in diagnosing disease. Automatic detection or segmentation of lung images from chest x-ray images is the initial stage of the diagnosis process. This research aims to implement a segmentation method to determine edge detection in clearer images using several segmentation methods, namely the Canny Edge Detection method, Sobel reading chest x-ray results for tuberculosis. And canny edge detection with segmented RGB image (otsu's thresholding) produces the highest value, namely 230,466.0 pixels and a lesion volume of 14,818.625 mm3.
Optimising Cataract Detection in Fundus Images through EfficientNet-Based Classification Ibrahim, Andi; Sabara, Edi; Dirsam, Winarlin; Aziz, Faruq
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.25

Abstract

Turbidity of the lens of the eyeball that causes blindness or loss of vision is known as a cataract. By diagnosing the causes and symptoms of cataracts, early detection helps patients in prevention and treatment. The purpose of the research was to classify the image of the fundus into two classes: normal and cataract. The study also looked at how the optimizers for stochastic gradient descent, adaptive moment estimation, root mean square propagation, adaptive gradient algorithm, adaptive delta, and Nesterov-accelerated adaptive moment estimation stacked up against each other. We used the EfficientNet architecture in CNN and preprocessed the normal fundus and cataract fundus images by dividing each into training data (N = 80) and validation data (N = 20) from the Kaggle repository. We added test data from the normal fondus image (N =20) to see the accuracy of the results. We get 100% accuracy of training data, 87% and 77% validation data, and 100% and 95% test data.
Identification of Potato Plant Pests Using the Convolutional Neural Network VGG16 Method Hadianti, Sri; Aziz, Faruq; Nur Sulistyowati, Daning; Riana, Dwiza; Saputra, Ridwan; Kurniawantoro
Journal Medical Informatics Technology Volume 2 No. 2, June 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i2.37

Abstract

Pests are one of the main challenges in potato cultivation that can significantly reduce crop yields. Therefore, quick and accurate pest identification is crucial for effective pest control. This research aims to develop a pest identification system for potato plants using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The dataset used consists of images of pests commonly found on potato plants. After the labeling process, these images were used to train the CNN VGG16 model. The research results show that the CNN VGG16 method can identify types of pests with an accuracy rate of 73%. The results serve as a reference to help farmers and agricultural practitioners detect the presence of pests earlier and take the necessary actions to reduce crop losses.
Co-Authors Adjat Sudrajat Andi Ibrahim Andi Saryoko Andini, Siwi Putri Chairul, Yasrizal Daniati Uki Eka Saputri Daryati, Elisabeth Isti Dirsam, Winarlin Dwiyogo, Santos Winarso Dwiza Riana Eka Herdit Juningsih Eka Saputri , Daniati Uki Eko Priyono Eliyana, Instianti Erna, Maria Agustina Fatah, Teddy Al Fitri , Evita Fitri, Evita Handayani, Popon Herwanto, Teguh Hewiz, Alya Shafira Ida Ayu Putu Sri Widnyani Ida Zuniarti, Ida Instianti Elyana, Instianti Irmawati Irmawati Kodri, Wan Ahmad Gazali Kurniawantoro Kurniyawantoro, Kurniyawantoro Lawrence Adi Supriyono Linda Marlinda Mayangky, Nissa Almira Ma’mun, Sukrul Muhamad Fatchan Nita Merlina, Nita Nugraha , Siti Nurhasanah Nugraha, Siti Nurhasanah Nur Sulistyowati, Daning Nurmalasari Nurmalasari Nurul Khasanah Nurul Khasanah, Nurul Panca Saputra, Elin Prasetio, Arfhan Pratiwi, Risca Lusiana Ramadhan, Aditya Rian Sabara, Edi Saeda, Ida Said, Fadillah Santoso Setiawan Saputra, Bagas Eka Saputra, Ridwan Sarah Khairunnisa Budiyanto Septian, Rendi Siti Masturoh Sri Hadianti Sulistyowati, Daning Nur Sutisna, Rian Taopik Hidayat Veithzal Rivai Zainal Wadu, Novita M Kana Wahyu Indrarti Widiyawati , Widiyawati Widiyawati Widiyawati Widyasih, Ajeng Sri YANTO YANTO