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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Journal of Robotics and Control (JRC) Journal of Intelligent Computing and Health Informatics (JICHI) International Journal of Robotics and Control Systems Infolitika Journal of Data Science
Dimas Chaerul Ekty Saputra
Telkom University Surabaya
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering Medicine & Pharmacology Neuroscience Public Health

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iGWO-RF: an Improved Grey Wolfed Optimization for Random Forest Hyperparameter Optimization to Identification Breast Cancer Muryadi, Elvaro Islami; Futri, Irianna; Saputra, Dimas Chaerul Ekty
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.29300

Abstract

The study focuses on improving the accuracy of breast cancer diagnosis by enhancing the predictive capabilities of a Random Forest model. This is achieved by utilizing an improved Grey Wolf Optimization algorithm for hyperparameter optimization. The main objectives are to enhance early detection, increase diagnostic precision, and reduce computational demands in clinical workflows. The work utilizes the Improved Grey Wolf Optimization (iGWO) algorithm to tune the hyperparameters of a Random Forest (RF) model, thereby improving its accuracy in diagnosing breast cancer. The methodology encompasses several steps, including data preparation, model training using iGWO-enhanced RF, performance evaluation compared to traditional methods, and validation using clinical datasets to confirm the reliability and effectiveness of the approach. The iGWO-RF model demonstrated exceptional performance in diagnosing breast cancer, achieving an accuracy of 96.4%, precision of 96.4%, recall of 98.0%, F1-score of 97.2%, and ROC-AUC of 0.988. The findings of iGWO-RF outperform those of SVM, original RF, Naive Bayes, and KNN models, indicating that iGWO-RF is effective in optimizing hyperparameters to improve prediction accuracy. The iGWO-RF model greatly enhances the accuracy and efficiency of breast cancer diagnosis, surpassing conventional models. Integrating iGWO-RF into clinical workflows is advised to improve early identification and patient outcomes. Additional investigation should focus on the utilization of this technology in various medical datasets and circumstances, highlighting its potential in a wide range of healthcare environments.
GAMA CUTE: Development of a Web-based for Gadjah Mada Caring University for Thalassemia Exit Prediction Tool by Applying Machine Learning Saputra, Dimas Chaerul Ekty; Afiahayati, Afiahayati; Ratnaningsih, Tri
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29301

Abstract

Blood disorders occur in one or several parts of the blood that affect the nature and function, and blood disorders can be acute or chronic. Blood disease consists of several types, such as anemia. Anemia is the most common hematologic disorder associated with a decrease in the number of red blood cells or hemoglobin, causing a decrease in the ability of the blood to carry oxygen throughout the body. Patients with anemia in Indonesia have increased for the age of 15-24 years. This study aimed to conduct a screening test for anemia using machine learning. It is expected to know the process of knowing the type of anemia suffered. The machine learning technique used to identify the cause of anemia is divided into four classes, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination (Beta Thalassemia Trait and Iron Deficiency Anemia or Hemoglobin E and Iron Deficiency Anemia). This study would apply the K-Nearest Neighbor (KNN) and Random Forest (RF) methods to build a model on the data collected. The evaluation results using a confusion matrix in the form of accuracy, precision, recall, and f1-score against the KNN and RF methods are 79.36%, 59.40%, 62.80%, and 62.80%. In comparison, the RF is 87.30%, 90.89%, 78.40%, and 81.00%. From the results of comparing the two methods, the Graphic User Interface (GUI) implementation using python applies the RF method. The classifier that gets the highest value among all these parameters is called the best machine learning algorithm to perform screening tests for anemia.
An Extreme Gradient Boosting for Blood Disease Classification Using Hematological Parameters: A Comparative Evaluation with Ensemble and Non-Ensemble Models Saputra, Dimas Chaerul Ekty; Oktavia, Vessa Rizky; Futri, Irianna; Pertiwi, Affifah Mutiara
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31659

Abstract

The early detection of hematological disorders remains challenging because many conditions share similar clinical characteristics and show substantial variation in laboratory measurements. Existing machine learning systems often struggle to maintain consistent accuracy in multi-class settings with imbalanced data. The research contribution is a multi-class diagnostic framework that identifies nine hematological disease categories using only routine laboratory parameters, supported by a leakage-free evaluation protocol and a comprehensive comparison across baseline classifiers. The proposed solution uses an extreme gradient boosting model as the primary classifier and evaluates it against support vector machine, random forest, and extra trees. The method includes data cleaning and numerical standardization, and class balancing with the Synthetic Minority Oversampling Technique applied only to the training subset within each fold of ten-fold cross-validation to prevent optimistic bias. Model performance is assessed using accuracy, precision, recall, and F1-score, together with computational efficiency measured through processing time and memory usage. The results show that the extreme gradient boosting model achieves the best overall performance, with an average accuracy of 98.67%, precision of 98.80%, recall of 98.67%, and an F1-score of 98.66%. It also demonstrates efficient memory usage and shorter processing time compared with the other tested methods. The competing models perform adequately but exhibit higher variability and weaker recognition for minority classes. In conclusion, these findings indicate that extreme gradient boosting provides an accurate and efficient approach for hematology-based multi-class disease classification when evaluated under a strict, leakage-free resampling protocol.
Penerapan User-Based Collaborative Filtering Algorithm Arfiani Nur Khusna; Krisvan Patra Delasano; Dimas Chaerul Ekty Saputra
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i2.1124

Abstract

Sistem online memanfaatkan website sebagai media pemasaran. Namun dengan perkembangan teknologi, pemasaran dilakukan dengan online terdapat kendala yaitu banyaknya produk yang tersedia dalam pemilihan produk. Sistem rekomendasi adalah sistem yang menyarankan informasi berguna atau menduga yang akan dilakukan user untuk mencapai tujuannya, seperti mencari teknik yang terbaik dalam memberikan rekomendasi bagi user. Menurut hasil survey yang telah dilakukan terhadap 17 orang pemakai website pemasaran produk Gadget Shield didapatkan 88,20% mengharapkan adanya penilaian user terhadap produk. Penelitian ini akan melakukan pengembangan sistem rekomendasi produk Gadget Shield pada toko Jackskins menggunakan metode User-Based Collaborative Filtering serta menggunakan Euclidean Distance untuk mengukur jarak kemiripan antar User dan Weighted Sum digunakan untuk mencari rekomendasi produk. Diharapkan dengan adanya sistem dapat memudahkan User dalam pencarian produk Gadget Shield terbaik. Guna menghasilkan produk rekomendasi,hasil nilai kemiripaan dilakukan perhitungan dengan algoritma Weighted Sum. Sistem rekomendasi Collaborative Filtering telah diuji menggunakan metode pengujian akurasi Root Mean Square Error (RMSE) dan pengujian User Acceptance Test (UAT). Hasil uji RMSE menunjukkan nilai 0,496 atau akurasinya 90,08%. Hasil pengujian UAT didapatkan 86,86% diterima. Informasi dari proses tersebutlah yang nantinya diharapkan akan bermanfaat sebagai dasar sumber rekomendasi yang akurat.
Co-Authors Afiahayati Afiahayati, Afiahayati Ahmad Watsiq Maula Alfian Ma’arif Apriliansyah, Feby Arfiani Nur Khusna Caibigan, Ritchie Natuan Dahlawy, Arriz Futri, Irianna Hizir Sofyan Khairul, Moh Krisvan Patra Delasano Kruba, Rumaisa Lismawati, Lismawati Mardalena, Selvi Muliadi Muryadi, Elvaro Islami Nurhayati, Fitria Pertiwi, Affifah Mutiara Phann, Raksmey Putri, Okhy Kharisma Raksmey Phann Sasmita, Novi Reandy Sunat, Khamron Teuku Rizky Noviandy Thinzar Aung Win Tri Ratnaningsih Tun, Su Sandi Hla Vessa Rizky Oktavia Wahyu Caesarendra Win, Thinzar Aung Yusuf Maulana