Article Per Year (5 Year)
p-Index From 2021 - 2026
10.426
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah KOMPUTASI Format : Jurnal Imiah Teknik Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika Jurnal Komputasi Jurnal Penelitian Pendidikan IPA (JPPIPA) JITK (Jurnal Ilmu Pengetahuan dan Komputer) IKRA-ITH Informatika : Jurnal Komputer dan Informatika Sebatik Jiko (Jurnal Informatika dan komputer) Astonjadro Simtek : Jurnal Sistem Informasi dan Teknik Komputer CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research Abdimas Universal Informatika IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Applied Science, Engineering, Technology, and Education JUKI : Jurnal Komputer dan Informatika Jurnal Abdidas International Journal of Industrial Optimization (IJIO) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) International Journal Of Science, Technology & Management (IJSTM) Journal of Technology and Informatics (JoTI) Indonesian Journal of Multidisciplinary Science Journal Of World Science Buletin Sistem Informasi dan Teknologi Islam Jurnal Locus Penelitian dan Pengabdian Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Ilmu Multidisplin Jurnal Indonesia Sosial Teknologi Jurnal Indonesia Sosial Sains Journal Research of Social Science, Economics, and Management Eduvest - Journal of Universal Studies Kohesi: Jurnal Sains dan Teknologi SmartComp Jurnal Informatika Polinema (JIP) Asian Journal of Social and Humanities Paradigma: Jurnal Filsafat, Sains, Teknologi, dan Sosial Budaya Jurnal Komputasi
Akbar, Habibullah
Universitas Esa Unggul
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

Published : 86 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 86 Documents
Search

 5   6   7   8   9 
Penguatan Kompetensi Digital Guru PKBM Maleo melalui Pelatihan Pemanfaatan Aplikasi Artificial Intelligence untuk Persiapan Pembelajaran Aryani, Diah; Asri, Jefry Sunupurwa; Asianto, Andriyanti; Akbar, Habibullah; Sutejo, Bayu Sulistiyanto Ipung; Suharti, Dwi Sloria
Abdimas Universal Vol. 8 No. 1 (2026): April
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Balikpapan (LPPM UNIBA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36277/abdimasuniversal.v8i1.2832

Abstract

The development of Artificial Intelligence (AI) technology has opened up new opportunities to improve the efficiency of the learning process, particularly in terms of how teachers can prepare teaching materials more innovatively and efficiently. However, the use of AI technology in education still faces several challenges, primarily due to teachers' limited digital literacy. The purpose of this Community Service (PKM) activity is to help PKBM Maleo teachers improve their digital competency by teaching them how to use AI applications, specifically ChatGPT, when preparing lessons. In this activity, Participatory Action Research (PAR) is the approach method used in this activity. PAR supports active partner participation in every step of the process, such as problem identification, program planning, training implementation, evaluation, and reflection. The training activity was held in January 2026 with 14 teachers participating, with the results of the activity evaluation carried out using pre-test and post-test methods to measure the level of participants' understanding of the material provided. The pre-test results showed a significant increase in participant understanding, with an average score of 97.14, while the post-test results showed an increase to 85.7% to 100%. The results of this PKM activity demonstrate that AI training with ChatGPT can help teachers improve their digital literacy skills and better prepare learning materials. It is hoped that this activity will encourage the wise and productive use of artificial intelligence technology in learning in non-formal schools.
Semantic brain tumor segmentation from 3D MRI using u2-net with custom dilated and residual u-block Elvaret Elvaret; Habibullah Akbar; Nanna Suryana Herman; Marwan Kadhim Mohammed Al-shammari
International Journal of Industrial Optimization Vol. 7 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v7i1.11576

Abstract

Segmentation of brain tumors in volumetric medical images is challenging due to the complexities of the tumor structure, the types, and the heavy-weight 3D data processing. In contrast, 2D-based segmentation methods on the slice data reduce the amount of information due to the anisotropic shape of the tumors and lead to poor segmentation results. This study proposes a 3D network structure combining ReSidual U-Block (RSU), custom dilated block, and U2-Net for automatic segmentation of brain tumors from MRI images, namely 3D RSU U2-Net+. The RSU and custom dilated block are embedded and joined in the nested U-Net structure to obtain multi-resolution features and global information, enhancing segmentation accuracy while reducing computational overhead. The proposed method outperformed the segmentation results of the standard U-Net, on brain tumor data in the medical segmentation Decathlon (MSD) dataset. The proposed model achieves an average validation soft dice loss of 0.1320 and dice score coefficient of 78% and intersection over union of 64% for testing. Although having 3 times parameters, the model requires less GPU time (397.7 minutes) than U-Net (433.6 minutes), demonstrating improved computational efficiency resulting from the effective use of residual and dilated blocks. Moreover, the model achieves 75.4% average sensitivity and 99% specificity for edema, enhancing, and non-enhancing tumors. These experimental results show that the 3D RSU U2-Net+ has been able to outperform the U-Net. However, the model’s performance on non-enhancing tumors remains relatively lower compared to other tumor types, indicating on opportunity for further optimization.
Performance Analysis of Provider and Riverpod State Management Library on Flutter Applications Jonathan Aditya Puryanto; Habibullah Akbar
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1164

Abstract

State management libraries are essential components in Flutter app development. This research aims to compare the performance of the state management library Provider and its successor, Riverpod, to assist Flutter developers in choosing the right solution. Two versions of the MovieDB app were built, each utilizing Provider and Riverpod. Performance testing was conducted using three metrics: CPU Utilization, Memory Usage, and Execution Time, across three data volumes (1,000, 5,000, and 10,000). The results showed that CPU Utilization varied by only 0.1–0.2% with Riverpod being slightly more efficient at 1,000 and 10,000 data volumes. Execution Times also showed minimal differences, with Riverpod being marginally faster by approximately 0.01 seconds at 5,000 and 10,000 data volumes. Riverpod excelled in Memory Usage, demonstrating an average reduction of about 3–6% across all data volumes, particularly at higher data volumes. In conclusion, the performance of both libraries is fundamentally similar, but Riverpod is offers better memory efficiency and architectural flexibility. Therefore, Riverpod is recommended for new projects, while Provider remains a viable option for stable existing applications that already use it.
Sistem Pemantauan EKG 1-Lead Berbasis ESP32 dan Wireless Fidelity dengan Visualisasi Real Time Pada Smartphone Nugroho Budhisantosa; Noval Rizky Ramadhan; Habibullah Akbar; Gerry Firmansyah
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.5457

Abstract

Penelitian ini membahas pengembangan sistem pemantauan detak jantung berbasis Internet ofThings (IoT) menggunakan mikrokontroler ESP32, sensor ECG AD8232, protokol MQTT, danplatform Node-RED sebagai antarmuka visual. Sistem ini dirancang untuk melakukan akuisisisinyal jantung, mengolah data menjadi nilai Beats Per Minute (BPM), serta menampilkaninformasi secara real-time melalui dashboard berbasis web yang dapat diakses menggunakansmartphone maupun perangkat lain. Metodologi yang digunakan adalah Waterfall, dengan tahapananalisis kebutuhan, perancangan, implementasi, dan pengujian sistem. Hasil penelitianmenunjukkan bahwa sistem mampu menampilkan sinyal EKG dengan latensi rata-rata ±180 ms,sesuai dengan target real-time (<200 ms). Selain itu, sistem berhasil mengklasifikasikan kondisijantung ke dalam kategori normal, tachycardia, bradycardia, atrial flutter, dan arrhythmia, dengantingkat akurasi mencapai 92% berdasarkan pengujian menggunakan data simulasi (dummy)maupun data sensor aktual. Fitur tambahan seperti notifikasi visual dan audio, serta fungsi resetgrafik (Clear Graph) berjalan sesuai rancangan dan meningkatkan pengalaman pengguna. Validasidengan alat referensi (pulse oximeter) menunjukkan selisih nilai BPM rata-rata ±5 bpm, yangmasih dapat diterima untuk aplikasi non-klinis. Dengan hasil tersebut, sistem ini dapat digunakansebagai solusi monitoring mandiri untuk kesehatan jantung, serta memiliki potensi pengembanganlebih lanjut melalui integrasi penyimpanan data historis, enkripsi komunikasi, maupunimplementasi aplikasi mobile
Perbandingan Performa Xception dan InceptionV1 untuk Pengenalan Ekspresi Wajah Ferdinand Defin Delio; Diah Aryani; Habibullah Akbar; Mohamad Yusuf; Yaya Sudarya Triana
FORMAT Vol 15 No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2026.v15.i1.003

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan performa dua arsitektur Convolutional Neural Network (CNN) populer, yaitu Xception dan InceptionV1, dalam tugas pengenalan ekspresi wajah (Facial Expression Recognition/FER). Penelitian ini dilakukan dengan pendekatan transfer learning dan fine-tuning menggunakan dataset FER-2013 yang berisi 35.887 citra wajah grayscale berukuran 48×48 piksel yang diklasifikasikan ke dalam tujuh emosi dasar. Setiap citra diubah ukurannya menjadi 224×224 piksel, dinormalisasi, dan diproses dengan teknik augmentasi untuk meningkatkan generalisasi model terhadap variasi ekspresi wajah, pencahayaan, dan pose. Proses pelatihan dilakukan selama 30 epoch menggunakan optimizer Adam dengan learning rate 0.0001 dan batch size 64. Strategi fine-tuning dilakukan dengan membuka 30% lapisan atas model untuk mengoptimalkan bobot fitur yang telah dipelajari sebelumnya dari dataset ImageNet. Evaluasi kinerja dilakukan berdasarkan metrik akurasi, presisi, recall, F1-score, serta efisiensi komputasi yang diukur dari waktu pelatihan dan inferensi. Hasil eksperimen menunjukkan bahwa Xception mencapai akurasi validasi 70,69% dengan waktu inferensi rata-rata 20–25 ms, sedangkan InceptionV1 mencapai 65,80% dengan waktu inferensi 43–126 ms. Arsitektur Xception terbukti lebih efisien secara komputasi karena memanfaatkan depthwise separable convolution yang mengurangi jumlah parameter tanpa menurunkan akurasi. Temuan ini menunjukkan bahwa Xception lebih sesuai untuk aplikasi FER real-time dan perangkat dengan sumber daya terbatas, serta memberikan dasar yang kuat bagi penelitian lanjutan dalam pengembangan sistem pengenalan ekspresi wajah berbasis video dan lingkungan dunia nyata.
Comparative Analysis of Convolutional Neural Network (ResNet-50) and Vision Transformer (ViT-B/16) for Histopathological Image Classification of Colorectal Cancer Muhammad Fazly Qusyairy; Habibullah Akbar; Wahyu Purnama Magribi; Khusnul Fajri Rhomadon
Jurnal Penelitian Pendidikan IPA Vol 12 No 7 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i7.14880

Abstract

The diagnosis of colorectal cancer (CRC) through histopathological images requires high accuracy to support appropriate clinical decisions. Although Convolutional Neural Networks (CNN) have become the gold standard in medical image analysis, the emergence of Vision Transformer (ViT) architecture offers a new paradigm based on global attention mechanisms (self-attention) that is claimed to be superior on large-scale datasets. However, the effectiveness of ViT-B/16 on medical datasets with limited sample sizes and high texture variation remains debatable. This study aims to comprehensively evaluate the performance of the ViT-B/16 architecture compared to ResNet-50 on the NCT-CRC-HE-100K histopathology dataset, which consists of 9 network classes. The performance of both models was tested using equivalent training scenarios. The evaluation was conducted multidimensionally, covering classification metrics (Accuracy, F1-Score), training stability, feature space separability (t-SNE), visual interpretability (Grad-CAM), and computational efficiency. The experimental results show that ResNet-50 significantly outperforms ViT-B/16 with a test accuracy of 93.24%, compared to ViT-B/16 which only achieves 57.11%. The t-SNE analysis revealed that ViT-B/16 failed to form well-separated feature clusters due to a lack of inductive bias to recognize local features such as cell membrane edges. Failure analysis shows that ViT-B/16 often misclassifies adipose cells as mucus and smooth muscle as tumors. In terms of efficiency, ResNet-50 is 5.8 times lighter in storage size and has lower inference latency. This study concludes that CNN-based architecture (ResNet-50) is still far superior, more stable, and more feasible for clinical implementation than ViT-B/16 in the context of medium-scale histopathological image classification.
Co-Authors Adi Widiantono Agam Aprianto Agus Satriawan Aisyah, Zhavira Alexander Alexander, Alexander Alvin Barata Amelia Sholikhaq Andini, Ketrin Vani Andriana, Dian Andriyanti Asianto Anwar Nasihin Ardiansyah, Miri Ari Pambudi Arif Pami Setiaji Arisandi Langgeng Tardiana Ary Prabowo Astamar Putra, Ichlasul Fikri Azizah, Anik Hanifatul Bayu Sulistiyanto Ipung Sutejo Bob Tjahjono Budi Tjahjono Calvin Ramadhani Alfahrezi Chiuman, Felix Decky Ryansyah Delio, Ferdinand Defin Deni Pamungkas Gelantoro Putra Diah Aryani, Diah Dodo, La Dudy Fathan Ali Dwi Pamungkas, Eric Dwiputra, Dedy Elvaret Elvaret Eric Dwi Pamungkas Eric Julianto Fathan Ali, Dudy Fatonah, Nenden Siti Ferdinand Defin Delio Franky Leonard Gerry Firmansyah Gerry Firmansyah Gilang Banuaji Gilang Romadhanu Tartila Hadi, Muhammad Abdullah Hafizah Safira Kaurani Hani Dewi Ariessanti Haryoto, Iin Sahuri Hendy Hendy Herwanto, Agus Husni Sastra Mihardja Husni Satra Mihardja Husni Satra Mihardja Indri Handayani, Indri Intan Setya Palupi Jefry Sunupurwa Asri Jonathan Aditya Puryanto Kevin Valeri Khusnul Fajri Rhomadon La Dodo Latumapayahu, Febrian Firmansyah Made Aka Suardana Mahmudin, Hajon Mahdy Martin Saputra Marwan Kadhim Mohammed Al-shammari Marwan, Rudi Heri Marzuki Pilliang Mochamad Wahyudi Mochamad Welly Rosadi Mohamad Yusuf Mohamad Yusuf Mohammed Al-shammari, Marwan Kadhim Muhamad Septian Nugraha Muhammad Fajrul Aslim Muhammad Fazly Qusyairy Muhammad Yusuf Morais Mukhamad Abduh Munawar Nanna Suryana Herman Narul Sakron Nasihin, Anwar Nila Rusiardi Jayanti Nizirwan Anwar Noval Rizky Ramadhan Noviandi Noviandi Nugroho Budhisantosa Nugroho, Irfan Hari Pilliang, Marzuki Pramesty, Feranti Destina Putra, Sipky Jaya Putra, Syahrizal Dwi Rachman, Riyandi Patu Randy Swandy Restamauli br Nainggolan Reyhan, Athallah Rifqi Adi Prasetya Rizky Yananda Rosnanto, Imam Rudy Setiawan Sabri Alim Sakron, Narul Sandfreni, Sandfreni Saputra, Rahdian Sea, Rona Aulia Wangsa Sejati, Puteri Setiawati, Popong Sfenrianto Sfenrianto Sinaga, Matius Eliezer Suhandi Junaedi Suharti, Dwi Sloria Supriyade Supriyade Supriyade, Supriyade Sutanto, Imam Tantrisna, Ellen Tino Saputra Trenggana Natadirja Ulum, M. Bahrul Ulum, Muhamad Bahrul Wahyu Purnama Magribi Widodo, Agung Mulyo Wijaya, Jacob S Yaya Sudarya Triana Yaya Sudarya Triana