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All Journal 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 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
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Clustering-Based Identification of Student Support Needs in Higher Education Transition Mochamad Welly Rosadi; Nenden Siti Fatonah; Gerry Firmansyah; Habibullah Akbar
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31031

Abstract

The transition from secondary to higher education represents a critical phase influenced by both academic readiness and socio-economic conditions. This study proposes a clustering-based approach to identify student support needs during this transition by analyzing multidimensional student profiles. Using secondary data from 1,226 senior high school students, three unsupervised clustering algorithms—K-Means, DBSCAN, and BIRCH—were applied to academic performance and socio-economic variables. Cluster quality was assessed using internal validation metrics, including the Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index. The results indicate that clustering-based methods provide richer insights than traditional rule-based approaches by capturing heterogeneous student profiles and revealing atypical cases. Among the evaluated algorithms, BIRCH demonstrated the most balanced performance in terms of cluster compactness and separation, while K-Means offered stable and interpretable results, and DBSCAN was effective in identifying outliers. Interpreted within the college readiness framework, the identified clusters highlight differentiated student support needs, enabling more targeted and equitable intervention strategies. These findings underscore the potential of educational data mining to support data-driven decision-making in facilitating students’ transition to higher education.
Perbandingan Performa Xception dan InceptionV1 untuk Pengenalan Ekspresi Wajah Delio, Ferdinand Defin; Aryani, Diah; Akbar, Habibullah; Yusuf, Mohamad; Triana, Yaya Sudarya
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.
Pengembangan Prototipe Aplikasi Augmented Reality (AR) untuk Pemantauan dan Analisis Kondisi Lingkungan Secara Real-Time Berbasis IoT Aryani, Diah; Akbar, Habibullah; Handayani, Indri
JUKI : Jurnal Komputer dan Informatika Vol. 7 No. 2 (2025): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2025
Publisher : Yayasan Kita Menulis

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

Abstract

Penelitian ini bertujuan untuk mengembangkan prototipe aplikasi Augmented Reality Monitoring for Environmental Analysis (ARMAN) sebagai solusi inovatif untuk pemantauan dan analisis kondisi lingkungan secara real-time. Aplikasi ini dirancang untuk membantu masyarakat dan lembaga pemerintah dalam meningkatkan efisiensi pengelolaan lingkungan serta kesadaran akan kualitas udara, suhu, kelembaban, dan pengelolaan limbah. Sistem ARMAN dibangun melalui integrasi teknologi Internet of Things (IoT), komputasi awan, dan Augmented Reality (AR) untuk menghasilkan sistem yang interaktif, informatif, dan adaptif. Metodologi penelitian mencakup lima tahap utama, yaitu konsep, desain, pengembangan, pengujian, dan evaluasi. Sensor DHT11 dan MQ-135 digunakan untuk mendeteksi parameter suhu, kelembapan, dan kualitas udara, dan data tersebut kemudian dikirim ke Firebase Cloud Database melalui mikrokontroler ESP32 secara berkala. Data yang disimpan diproses dan ditampilkan secara visual melalui aplikasi AR berbasis Unity 3D dan SDK Vuforia, yang memungkinkan pengguna melihat informasi lingkungan dalam bentuk overlay interaktif. Selain itu, panel admin backend berbasis web disediakan untuk lembaga pemerintah atau pengelola lingkungan untuk memantau data agregat, tren, dan laporan historis dari berbagai titik sensor. Hasil uji coba menunjukkan bahwa sistem ARMAN mampu menampilkan informasi lingkungan secara real-time, akurat, dan menarik, serta mendukung kolaborasi antara masyarakat umum dan otoritas dalam pemantauan lingkungan. Penelitian ini membuktikan bahwa kombinasi teknologi IoT, cloud, dan AR dapat diimplementasikan secara efektif dalam sistem pemantauan lingkungan dan berpotensi menjadi dasar pengembangan lingkungan cerdas menuju implementasi konsep kota cerdas yang berkelanjutan.
Analisis Kesiapan PT. XYZ Sebagai Penyedia Layanan TI Dalam Mengadopsi Standar Nist CSF dan ISO 27001 Sea, Rona Aulia Wangsa; Fatonah, Nenden Siti; Firmansyah, Gerry; Akbar , Habibullah
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 11 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i11.5084

Abstract

This study aims to analyze the readiness level of PT. XYZ, an information technology service provider, in adopting technology security frameworks, specifically NIST Cybersecurity Framework (CSF) and ISO 27001. A qualitative approach was applied through interviews with five informants from the executive level and technical teams. The data were analyzed using thematic analysis. Findings indicate that the company is still at an early stage of readiness and lacks a systematic approach to managing information security. The main inhibiting factors include the absence of formal policies, limited resources, and a low level of understanding of international standards. However, the management’s awareness and desire to enhance client trust serve as important driving factors. Recommended improvement strategies include conducting training, establishing formal security policies, forming dedicated security teams, and integrating security into business processes. This study provides a preliminary overview for the company in designing a standardized security strategy and serves as a reference for similar studies in the IT services sector.
Analisis Perbaikan Kurikulum SMK Berbasis Pada Framework Cobit 2019 Rosnanto, Imam; Fatonah, Nenden Siti; Firmansyah, Gerry; Tjahjono, Budi; Akbar , Habibullah
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 11 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i11.5146

Abstract

This study aims to analyze improvements to the Vocational High School (SMK) curriculum based on the COBIT 2019 framework as a standard for IT governance. Amid the rapid development of Industry 4.0 and digital transformation, the SMK curriculum must be adaptive and relevant to equip students with competencies aligned with workforce demands. The research uses a qualitative approach involving literature review, interviews, and an IT governance audit in vocational education. The analysis focuses on the Governance and Management domains of COBIT 2019 and their relation to the current SMK curriculum structure. The audit and analysis reveal gaps in risk management, information security, and curriculum implementation compared to ideal IT governance principles. Recommendations include strengthening curriculum planning, integrating digital competencies, and applying outcome-based performance measurement. This study is expected to contribute to improving vocational education quality through the effective and sustainable application of IT governance principles.
Semantic brain tumor segmentation from 3D MRI using u2-net with custom dilated and residual u-block 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.
E-Commerce Product Image-Based Recommendation System Kalcare.Com Using Deep Learning Trenggana Natadirja; Habibullah Akbar; Gerry Firmansyah; Budi Tjahjono
Jurnal Indonesia Sosial Teknologi Vol. 4 No. 8 (2023): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v4i8.669

Abstract

Recommendation systems have now become an important part of a digital service, one example is e-commerce. The facts show that the COVID-19 pandemic has had a significant impact on customers by making them spend more time surfing online to get daily necessities products by shopping on e-commerce sites. With the rapid development of deep learning technology today, of course, it can be used to help in terms of the process of producing a product image-based recommendation system that has a fairly high level of similarity. This study will discuss how to produce an image-based product recommendation system architecture by comparing the results of the application of algorithms 8 pre-trained models that are available and have also been widely used in various studies and the information technology industry. The dataset used is product images sourced from the kalcare.com website. After testing the pre-trained model, then an application prototype was made to be tested then in the final stage of this research a poll was conducted to determine the response and opinion of users to the protopine recommendation system made for e-commerce kalcare.com using deep learning.
KLASIFIKASI CITRA BREAST CANCER BERBASIS ARSITEKTUR MICROSERVICES Akbar, Habibullah; Sinaga, Matius Eliezer
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 1 (2026): JISAMAR (February 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i1.2298

Abstract

Perkembangan pesat Kecerdasan Buatan (AI) dan Pembelajaran Mesin (ML) telah mendorong inovasi di bidang medis, khususnya dalam sistem diagnosis berbasis citra seperti deteksi kanker payudara. Penelitian ini bertujuan untuk merancang dan mengimplementasikan arsitektur berbasis microservices untuk klasifikasi citra kanker payudara guna mencapai skalabilitas, modularitas, dan fleksibilitas yang lebih baik. Sistem yang diusulkan membagi fungsi menjadi tiga layanan pra-pemrosesan, klasifikasi, dan penyimpanan yang terhubung melalui API RESTful. Layanan model menggunakan FastAPI (Python) yang terintegrasi dengan model pembelajaran mendalam (breast_cancer_model.h5), sedangkan Java Spring Boot berfungsi sebagai backend dan Angular.js sebagai frontend. Pengujian eksperimental menggunakan Postman dan JMeter menunjukkan waktu respons 8,39 ms untuk GET /, 654 ms untuk POST /predict, dan 971,99 ms untuk GET /reload-model. Hasil ini menunjukkan bahwa sistem berbasis microservices memberikan kinerja yang efisien, modularitas tinggi, dan pemeliharaan yang lebih baik untuk aplikasi klasifikasi citra medis bertenaga AI.
Aplikasi DonasiKu Berbasis Android Muhamad Bahrul Ulum; Habibullah Akbar; Anik Hanifatul Azizah
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2933

Abstract

Used goods suitable for use are old goods that have been used once or more than once and are still reasonable to be reused. Most of these used goods are no longer used because they already have other, better substitutes. The current management system for these items is usually only collected and stored in the warehouse or even many are left scattered in the corner of the house until the items become a pile. Rather than being left alone, it is better to reuse these items. For example, by donating items that are still reasonable to be reused to people who need it more. Donations in the form of used goods are still poorly managed, information on donation activities that are held is less spread out and if there is, it is not necessarily reliable. So far, the management of the existing donation system is only for donations in the form of money, so the idea of a solution emerged in the form of developing an Android-based donation system application. To analyze the problem used causal analysis based on the data to be collected. The method used for software development is extreme programming. This application can be a solution to the problem of managing the used goods donation system so that people can make donations in the form of goods that are faster, easier to collect and distribute.
Analisis Komparatif Metode Pengurangan Derau Klasik dan Pembelajaran Mendalam untuk Meningkatkan Kualitas Citra Parasit Malaria Wahyu Purnama Magribi; Habibullah Akbar; Muhammad Fazly Qusyairy; Tino Saputra; Eric Julianto; Decky Ryansyah
Jurnal Penelitian Pendidikan IPA Vol 12 No 4 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i4.14840

Abstract

Malaria diagnosis accuracy depends on microscopic image quality, often compromised by noise. This study comprehensively evaluates classical denoising (morphological, median, bilateral filters) against deep learning architectures (DnCNN, Autoencoder, U-Net) for malaria parasite images. Using the Cell Images for Detecting Malaria dataset with synthetic Gaussian, salt-and-pepper, and mixed noise, experiments measured PSNR, SSIM, and processing time. Results indicate U-Net achieved superior performance (PSNR 36.69 dB, SSIM 0.9577), significantly outperforming Autoencoder (PSNR 26.12 dB) and classical methods (PSNR 23.14 dB). The baseline DnCNN architecture did not achieve competitive performance (PSNR 8.42 dB), indicating that domain-specific parameter tuning and data normalization adjustments are necessary for effective application to microscopic imaging. Autoencoder demonstrated the highest computational efficiency (1.64 ms per image), though the 10.57 dB PSNR gap relative to U-Net suggests that the quality trade-off may limit its suitability in accuracy-critical diagnostic scenarios. U-Net best preserved morphological details crucial for diagnosis and is recommended as the primary choice for malaria diagnostic systems prioritizing accuracy, while Autoencoder represents the most computationally efficient alternative for resource-constrained deployment. These findings support developing robust computer-aided diagnosis systems and contribute a comprehensive quantitative benchmark for denoising methods in malaria microscopy.
Co-Authors Adi Widiantono 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 Ary Prabowo Astamar Putra, Ichlasul Fikri Azizah, Anik Hanifatul 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 Eric Dwi Pamungkas Eric Julianto Fathan Ali, Dudy Fatonah, Nenden Siti Franky Leonard Gerry Firmansyah Gerry Firmansyah Gilang Banuaji 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 La Dodo Latumapayahu, Febrian Firmansyah Mahmudin, Hajon Mahdy Martin Saputra, Martin Marwan Kadhim Mohammed Al-shammari Marwan, Rudi Heri Marzuki Pilliang Mochamad Wahyudi Mochamad Welly Rosadi Mohamad Yusuf Mohammed Al-shammari, Marwan Kadhim Muhamad Septian Nugraha Muhammad Fajrul Aslim Muhammad Fazly Qusyairy Muhammad Yusuf Morais Mukhamad Abduh Munawar Nainggolan, Restamauli br Nanna Suryana Herman Narul Sakron Nasihin, Anwar Nila Rusiardi Jayanti Nizirwan Anwar Noviandi Noviandi Nugroho Budhisantosa Nugroho, Irfan Hari Pilliang, Marzuki Pramesty, Feranti Destina Puryanto, Jonathan Aditya Putra, Sipky Jaya Putra, Syahrizal Dwi Rachman, Riyandi Patu Ramadhan, Noval Rizky Randy Swandy 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 Suardana, Made Aka Suhandi Junaedi Supriyade Supriyade Supriyade, Supriyade Sutanto, Imam Tantrisna, Ellen Tardiana, Arisandi Langgeng Tartila, Gilang Romadhanu Tino Saputra Trenggana Natadirja Ulum, M. Bahrul Ulum, Muhamad Bahrul Wahyu Purnama Magribi Widodo, Agung Mulyo Wijaya, Jacob S Yaya Sudarya Triana