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All Journal Building of Informatics, Technology and Science Indonesian Journal of Informatic Research and Software Engineering PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Mubarak, Haykal Alya
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Penerapan Algoritma K-Means dan K-Medoids dalam Pengelompokkan Data Inventaris Rig: Application Of K-Means And K-Medoid Algorithm In Rig Inventory Data Grouping Mubarak, Haykal Alya
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 3 No. 2 (2023): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v3i2.965

Abstract

Perusahaan yang bergerak pada pengeboran minyak dan gas merupakan suatu instansi dengan berfokus pada kegiatan pengeboran sumur migas demi mendapatkan sumber daya dari bawah tanah. Dikarenakan pada perusahaan migas bumi merupakan industri yang memiliki kompleksitas data tinggi, terutama dalam hal pengelolaan inventaris rig atau peralatan yang digunakan untuk pengeboran minyak dan gas. Data inventaris rig mencakup berbagai informasi, seperti kapasitas rig, jenis peralatan, kategori peralatan, dan lain sebagainya. Dengan begitu banyaknya data yang terkumpul, pengelompokan data menjadi kritis untuk memahami pola dan hubungan antar data dalam inventaris rig. Pada penelitian ini, dengan memanfaatkan dua algoritma ini, data kelompok akan ditetapkan oleh inti data masing-masing cluster lalu dihitung mengonsumsi formula K-means dan K-medoids sehingga  membentuk ketetapan dari tiap algoritma, selanjutnya bisa dilakukan perbandingan dengan kelompok yang dapat dianalisis berdasarkan karakter pembantunya. Berdasarkan hasil pengkajian ini, K-Medoids lebih baik dibanding K-Means atas nilai DBI terbaik yaitu 0,073 pada nilai uji K=2.    
Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data Rizalde, Alika Rahmarsyarah; Mubarak, Haykal Alya; Ramadhan, Gilang; Fatan, Mohd. Adzka
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.1106

Abstract

The hallmarks of Obsessive-Compulsive Disorder (OCD) are intrusive, anxiety-inducing thoughts (called obsessions) and associated repeated activities (called compulsions). To understand the patterns and relationships between OCD data that have been obtained, data will be grouped (clustering). In clustering using several clustering algorithms, namely K-Means, BIRCH, In this work, hierarchical clustering was used to identify the optimal cluster value comparison, and the Davies Bouldin Index (DBI) was used to confirm the results. Then the results of the best cluster value in processing OCD data are using the BIRCH algorithm in the K10 experiment which gets a value of 1.3. While the K-Means algorithm obtained the best cluster at K10 with a value obtained of 1.36 and the Hierarchical clustering algorithm also at the K10 value of 2.03. Thus in this study, the comparison results of the application of 3 clustering algorithms obtained results, namely the BIRCH algorithm shows the value of the resulting cluster is the best in clustering OCD data. This means that the BIRCH algorithm can be used to cluster OCD data more accurately and efficiently.
A Deep Learning Approach Bassed on Classification to Detect Facial Skin Defect Rizalde, Alika Rahmarsyarah; Mubarak, Haykal Alya; Khairunnisa, Batrisia; Fatan, Mohd. Adzka
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1558

Abstract

As people are more active, facial skin is often neglected, which can lead to acne, eye bags, and redness. In this study, deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Networks (GANs) are used to classify facial skin damage. DenseNet201 and MobileNetV2 architectures were also used to evaluate the models in this study. The dataset used consists of facial skin disease photos collected from the Kaggle database. The model was trained and tested to classify the types of skin damage after going through data collection and preprocessing stages. The results showed that the GANs model and the DenseNet201 and MobileNetV2 architectures were the best models, with test accuracy values of 89% for the GANs model, 88% for the DenseNet201 architecture, and 89% for the MobileNetV2 architecture. These results show that deep learning approach techniques can help classify and find facial skin problems well. and it is expected that it will be a great progress in the field of dermatology and skin health.
Klasifikasi Citra X-Ray Tuberkulosis Menggunakan Convolutional Neural Networks Mubarak, Haykal Alya; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6515

Abstract

Tuberculosis (TB) is a serious infectious disease that is still one of the main causes of death in the world, especially in developing countries. X-ray image analysis is an important step in controlling this disease. This research aims to classify X-ray images of tuberculosis using a deep learning approach with three Convolutional Neural Networks (CNN) architectures: DenseNet201, Xception, and MobileNetV2. The dataset used consists of 3,000 X-ray images, divided into two categories: normal and TBC, obtained from Kaggle, which are then processed through normalization, augmentation, and data division using the hold-out method with a ratio of 70:30, 80:20 , and 90:10. The research results show that DenseNet201 with the Nadam optimizer at 90:10 data division produces the highest accuracy of 100%, making it the best combination for TBC X-ray image classification. Xception achieved the best accuracy of 96.66% with the Nadam optimizer at a data split of 80:20. MobileNetV2 shows an optimal accuracy of 98.69% using the Adam optimizer at a 90:10 data split. This research proves that DenseNet201 with the Nadam optimizer is very effective in handling medical image classification, especially for tuberculosis. These results provide an important contribution to the development of deep learning-based technology to improve the accuracy of tuberculosis diagnosis.
Lung Disease Risk Prediction Using Machine Learning Algorithms Aulia, Ananda Putri; Adelia, Qaula; Mubarak, Haykal Alya; Fatan, Mohd. Adzka; Sudarno, Sudarno
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

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

Abstract

Lung diseases, including lung cancer, are one of the leading causes of death in the world. Early detection is essential to increase patients' chances of recovery and reduce healthcare costs. The utilization of machine learning algorithms can be used to solve this problem. This study evaluates five machine learning algorithms, namely K-Nearest Neighbors (K-NN), Naïve Bayes Classifier (NBC), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), for lung disease prediction using a dataset of 30,000 data with 11 attributes from Kaggle. The dataset was processed through data preprocessing and divided into training and test data with a ratio of 70%:30% and 80%:20%. The algorithm performance was evaluated using precision, recall, F1-score, and accuracy metrics. The results show that RF, SVM, and DT algorithms have the highest performance, with accuracy reaching 94.72% at 70%:30% ratio. The DT algorithm, which previously showed low performance in heart disease classification, provided competitive results in lung disease prediction. This research focuses on the importance of proper algorithm selection and data organization to improve the effectiveness of disease prediction. The findings contribute to the development of artificial intelligence technology for medical applications, particularly in supporting early diagnosis of lung diseases.
Co-Authors Adelia, Qaula Aulia, Ananda Putri Fatan, Mohd. Adzka Gilang Ramadhan Khairunnisa, Batrisia Rice Novita Rizalde, Alika Rahmarsyarah Sudarno Sudarno