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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Knowledge Engineering and Data Science JMM (Jurnal Masyarakat Mandiri) 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 Control Systems and Optimization Letters
Dimas Chaerul Ekty Saputra
Telkom University
Aerospace Engineering Automotive 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 Other

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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.
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.
Implementation of Machine Learning and Deep Learning Models Based on Structural MRI for Identification Autism Spectrum Disorder Dimas Chaerul Ekty Saputra; Yusuf Maulana; Thinzar Aung Win; Raksmey Phann; Wahyu Caesarendra
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26094

Abstract

Autism spectrum disorder (ASD) is a developmental disability resulting from neurological disparities. People with ASD frequently struggle with communication and social interaction, as well as limited or repetitive interests or behaviors. People with ASD may also have unique learning, movement, and attention styles. ASD sufferers can be interpreted as 1 in every 100 individuals in the globe having ASD. Abilities and requirements of autistic individuals vary and may change over time. Some autistic individuals are able to live independently, while others have severe disabilities and require lifelong care and support. Autism frequently interferes with educational and employment opportunities. Additionally, the demands placed on families providing care and assistance can be substantial. Important determinants of the quality of life for persons with autism are the attitudes of the community and the level of support provided by local and national authorities. Autism is frequently not diagnosed until adolescence, despite the fact that autistic traits are detectable in early infancy. This study will discuss the identification of Autism Spectrum Disorders using Magnetic Resonance Imaging (MRI). MRI images of ASD patients and MRI images of patients without ASD were compared. By employing multiple machine learning and deep learning techniques, such as random forests, support vector machines, and convolutional neural networks, the random forest method achieves the utmost accuracy with 100% using confusion matrix. Therefore, this technique is able to optimally identify ASD through MRI.
An Innovative Artificial Intelligence-Based Extreme Learning Machine Based on Random Forest Classifier for Diagnosed Diabetes Mellitus Dimas Chaerul Ekty Saputra; Elvaro Islami Muryadi; Raksmey Phann; Irianna Futri; Lismawati Lismawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28690

Abstract

Since 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In recent years, the fatality rate attributable to diabetes has increased by 13% in low- and middle-income countries. Statistics collected by the World Health Organization indicate that the number of persons diagnosed with diabetes has increased from 108 million in 1980 to 422 million in 2014. The objective of this study is to construct a model capable of diagnosing persons with diabetes reliably, correctly, and consistently. This research used secondary data offered by Kaggle. The original data came from the National Institute of Diabetes and Digestive and Kidney Diseases. Each of the up to 768 data points consists of nine characteristics and two outputs, such as diabetes and non-diabetes in the provided example. In this study, a single algorithm is constructed by integrating two separate algorithms. Random forest algorithms, which are based on machine learning, and extreme learning machines, which are based on deep learning, have generated extraordinarily accurate results. When the confusion matrix is used, 98.05% accuracy is attained. Therefore, it is feasible to conclude that the suggested method was successful in completing an adequate analysis and classifying the data.
Resource-Efficient Optimization for Multi-Class Hematological Diagnosis: A Hybrid BPSO-Extra Trees Approach with Data Imbalance Handling Dimas Chaerul Ekty Saputra; Zahid Abdullah Nur Mukhlishin; Affifah Mutiara Pertiwi; Mochammad Zulfikar Alfany; Irianna Futri; Raksmey Phann
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.15703

Abstract

Complete Blood Count (CBC) remains the cornerstone for initial screening of hematological disorders, yet manual interpretation is often challenged by overlapping biological patterns and substantial inter-patient variability. Although machine learning approaches have demonstrated promise for automated diagnosis, many existing studies prioritize classification accuracy while neglecting computational efficiency and the persistent class imbalance inherent in medical datasets. This study develops a lightweight yet effective diagnostic framework for classifying nine hematological conditions using routine CBC parameters. Evaluated on a public dataset of 1,281 records from Kaggle, the proposed model is benchmarked against standard Random Forest, XGBoost, and Support Vector Machine (SVM) classifiers. The approach integrates the Synthetic Minority Oversampling Technique (SMOTE) to mitigate class imbalance, and Binary Particle Swarm Optimization (BPSO) to identify a compact and clinically informative feature subset of exactly 6 parameters, referred to as a clinical fingerprint, optimized for the Extra Trees classifier. Evaluated using ten-fold cross-validation, the BPSO-Extra Trees model achieved an average accuracy of 87.43 percent and an F1 score of 82.75 percent, while demonstrating superior resource efficiency with peak memory consumption of only 0.249 MB, corresponding to a 46.6 percent reduction compared with the standard Random Forest baseline. These findings confirm that swarm intelligence optimized models can effectively balance diagnostic performance with extreme computational frugality, enabling the potential deployment of accurate hematology-based decision support systems on portable devices and in resource-limited laboratory environments.
An Extreme Gradient Boosting for Blood Disease Classification Using Hematological Parameters: A Comparative Evaluation with Ensemble and Non-Ensemble Models Dimas Chaerul Ekty Saputra; Vessa Rizky Oktavia; Irianna Futri; Affifah Mutiara Pertiwi
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.
Cognitive EEG Differentiation with Hypnosis-Based Noise Reduction and K-Harmonic Means for Personalized Brainwave Modeling Azhari, Ahmad; Saputra, Dimas Chaerul Ekty
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score of 0.9515, which demonstrates strong cluster separation and robustness against noise. Compared with baseline approaches such as K-Means and Fuzzy C-Means, KHM achieved higher stability and consistency in differentiating cognitive tasks. This performance highlights the advantage of harmonic averaging in mitigating the influence of outliers during clustering. The findings suggest that hypnosis can meaningfully enhance EEG signal quality, thereby improving downstream cognitive state differentiation. Overall, this research contributes to advancing EEG-based cognitive analysis and personalized brainwave modeling, with potential applications in brain–computer interfaces, cognitive diagnostics, and neurofeedback systems. The integration of behavioral noise control (hypnosis) with advanced clustering methods presents a novel hybrid framework for improving the reliability of EEG-based cognitive state identification.
PELATIHAN CANVA PADA SMP BAITUSSALAM SEBAGAI MEDIA PEMBELAJARAN Muhammad Dzulfikar Fauzi; Vessa Rizky Oktavia; Dimas Chaerul Ekty Saputra
JMM (Jurnal Masyarakat Mandiri) Vol 10, No 4 (2026): Agustus
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v10i4.40168

Abstract

Abstrak: Pemanfaatan media digital dalam pembelajaran di tingkat SMP masih belum optimal akibat keterbatasan keterampilan siswa dalam menggunakan aplikasi digital secara produktif untuk mendukung kegiatan belajar. Padahal, penguasaan literasi digital merupakan salah satu kompetensi penting yang perlu dimiliki peserta didik di era digital. Kegiatan pengabdian ini bertujuan untuk meningkatkan pengetahuan dan keterampilan siswa dalam memanfaatkan aplikasi Canva sebagai media pendukung pembelajaran. Metode yang digunakan meliputi tahap persiapan melalui wawancara dan diskusi dengan pihak sekolah, pelaksanaan pelatihan dan workshop Canva, serta evaluasi menggunakan pre-test dan post-test. Kegiatan diikuti oleh 52 siswa kelas IX SMP Baitussalam Surabaya. Materi yang diberikan meliputi pengenalan Canva, pemanfaatan fitur-fitur dasar, serta praktik pembuatan poster dan presentasi sederhana. Hasil evaluasi menunjukkan adanya peningkatan pemahaman peserta, yang ditunjukkan oleh kenaikan nilai rata-rata dari 92,1 pada pre-test menjadi 96,2 pada post-test peningkatan dari kegiatanaadalah 4,45 % yang terlihat sangat sedikit, karena siswa yang belum mengetahui canva tidak mengisi pre-test, jika siswa yang tidak mengisi dijadikan pembagi maka peningkatan sebanyak 32,42 kali lipat. Selain itu, peserta menunjukkan antusiasme dan keterlibatan yang tinggi selama kegiatan berlangsung. Pelatihan Canva terbukti dapat meningkatkan literasi digital dan kreativitas siswa dalam menghasilkan media visual yang mendukung proses pembelajaran.Abstract: The use of digital media in learning at the junior high school level is still not optimal due to limited student skills in using digital applications productively to support learning activities. In fact, mastery of digital literacy is one of the important competencies that students need to have in the digital era. This community service activity aims to improve students' knowledge and skills in utilizing the Canva application as a learning support medium. The methods used include a preparation stage through interviews and discussions with the school, the implementation of Canva training and workshops, and evaluation using pre-tests and post-tests. The activity was attended by 52 ninth-grade students of Baitussalam Surabaya Junior High School. The material provided included an introduction to Canva, the use of basic features, and the practice of making simple posters and presentations. The evaluation results showed an increase in participant understanding, as indicated by an increase in the average score from 92.1 in the pre-test to 96.2 in the post-test. The increase from the activity was 4.45% which looks very small, because students who are not familiar with Canva did not fill out the pre-test, if students who did not fill out were used as a divisor, the increase was 32.42 times. In addition, participants showed high enthusiasm and involvement during the activity. Canva training has been proven to improve students' digital literacy and creativity in producing visual media that supports the learning process.
Broad Learning System: A Derivation-Based Mathematical Formulation Dimas Chaerul Ekty Saputra; Dyah Putri Rahmawati; Affifah Mutiara Pertiwi; Muhammad Ijaz Shafarin; Kharisma Monika Dian Pertiwi; Thinzar Aung Win; Irianna Futri; Pima Hani Safitri
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.294

Abstract

Broad Learning System is a wide learning framework that constructs nonlinear feature representations while enabling efficient model training through analytical solutions. This paper presents a derivation-based formulation of Broad Learning System that explains the mathematical structure underlying the learning process. The model constructs an expanded feature representation through feature mapping nodes followed by enhancement nodes that further enrich the learned representation. The learning problem is then expressed as a linear model in the constructed feature space, and the output weights are obtained using ridge regularized least squares optimization. This formulation allows the training process to be solved directly using matrix operations without iterative gradient based procedures. In addition, an incremental learning mechanism is introduced to enable efficient parameter updates when new samples or additional nodes are incorporated into the model. The presented formulation highlights how Broad Learning System combines nonlinear feature construction with computationally efficient closed form learning, providing a clear theoretical interpretation of the learning process.
Co-Authors Affifah Mutiara Pertiwi Afiahayati Afiahayati, Afiahayati Ahmad Azhari Ahmad Watsiq Maula Alfian Ma’arif Apriliansyah, Feby Arfiani Nur Khusna Caibigan, Ritchie Natuan Dahlawy, Arriz Dyah Putri Rahmawati Elvaro Islami Muryadi Futri, Irianna Hizir Sofyan Irianna Futri Irianna Futri Khairul, Moh Kharisma Monika Dian Pertiwi Krisvan Patra Delasano Kruba, Rumaisa Lismawati Lismawati Mardalena, Selvi Mochammad Zulfikar Alfany Muhammad Dzulfikar Fauzi Muhammad Ijaz Shafarin Muliadi Muryadi, Elvaro Islami Nurhayati, Fitria Phann, Raksmey Pima Hani Safitri Putri, Okhy Kharisma Raksmey Phann Raksmey Phann Raksmey Phann Sasmita, Novi Reandy Sunat, Khamron Teuku Rizky Noviandy Thinzar Aung Win Thinzar Aung Win Tri Ratnaningsih Tun, Su Sandi Hla Vessa Rizky Oktavia Wahyu Caesarendra Win, Thinzar Aung Yusuf Maulana Zahid Abdullah Nur Mukhlishin