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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

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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.
Integration Of Garch Models And External Factors In Gold Price Volatility Prediction: Analysis And Comparison Of Garch-M Approach Arisandi Langgeng Tardiana; Habibullah Akbar; Gerry Firmansyah; Agung Mulyo Widodo
Eduvest - Journal of Universal Studies Vol. 4 No. 5 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i5.1195

Abstract

This study investigates the volatility of gold prices by applying the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and extending it with the GARCH-M model, incorporating the Federal Reserve's interest rate as an external variable. The GARCH(1,1) model revealed a positive average daily return for gold, with high sensitivity to recent price changes, indicated by the significant estimation of mu and a high alpha1 value. The persistence of past volatility on current volatility is reflected by a beta1 value close to one. In the GARCH-M model development, a significant negative relationship was found between the Federal Reserve's interest rates and gold returns, suggesting that an increase in the Federal Reserve's interest rates could potentially decrease gold returns. An increase in the Log Likelihood value and improvements in information criteria such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) indicate that the GARCH-M model provides a better fit than the GARCH(1,1) model that uses only gold price data. The study concludes that macroeconomic factors like the Federal Reserve's interest rates play a crucial role in influencing gold price volatility, and these findings can aid investors and portfolio managers in devising more effective risk management strategies. Additionally, the findings contribute to financial theory by highlighting the importance of multivariate models in the analysis of asset price volatility.
Product Recommendations Using Adjusted User-Based Collaborative Filtering on E-Commerce Platforms Gilang Romadhanu Tartila; Habibullah Akbar; Gerry Firmansyah; Agung Mulyo Widodo
Eduvest - Journal of Universal Studies Vol. 5 No. 1 (2025): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i1.50224

Abstract

Product recommendations on e-commerce platforms play a crucial role in supporting customers' purchasing decisions by leveraging user data to provide relevant product suggestions. With the increasing volume of e-commerce data, recommendation methods are needed that are not only accurate but also capable of being applied to diverse datasets. This research focuses on evaluating three product recommendation methods, namely User-Based Collaborative Filtering, Item-Based Collaborative Filtering, and Content-Based Filtering, using various datasets from the Kaggle platform, including transaction data and user reviews. The main problem identified is how to ensure that these three recommendation methods remain optimal despite using different datasets. Through an experimental approach, this research aims to implement and evaluate the performance of these recommendation methods. The results of this study are expected to demonstrate that one of the recommendation methods can work generally on various datasets, thereby making a significant contribution to the selection of the appropriate product recommendation method on e-commerce platforms.
Implementation of YOLOv5 Algorithm for Exam Cheating Movement detection Made Aka Suardana; Habibullah Akbar; Martin Saputra; Agung Mulyo Widodo; Budi Tjahjono
Eduvest - Journal of Universal Studies Vol. 5 No. 6 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i6.51480

Abstract

The decline in academic integrity due to cheating during exams has become increasingly relevant, particularly following the shift to online learning systems. The absence of direct supervision in online exams creates opportunities for cheating practices that evade detection by the naked eye. This study addresses this challenge by developing an object detection model for cheating behavior using a deep learning approach based on the YOLOv5 algorithm. The dataset comprised 60 ten-second videos, extracted into 1,200 images representing four suspicious head movement patterns. Each image was manually annotated before training five YOLOv5 variants. Models were evaluated using object detection metrics (precision, recall, and mAP at IoU thresholds 0.5–0.95) and analyzed via confusion matrices. Results indicate that the YOLOv5x variant achieved peak performance, with mAP@0.5:0.95 of 83.06% and perfect classification accuracy across all classes. This demonstrates that an object detection–based approach provides a reliable preliminary solution for monitoring cheating during online exams.
Evaluation Of It System Operational Services Using The Itil Framework In The Service Desk Domain (A Case Study Of PT Erafone Dotcom) Restamauli br Nainggolan; Budi Tjahjono; Agung Mulyo Widodo; Habibullah Akbar
Eduvest - Journal of Universal Studies Vol. 5 No. 8 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i8.51908

Abstract

PT Erafone dotcom is one of the mobile phone and tablet retailer companies in Indonesia from various well-known brands. PT Erafone dotcom uses the service desk as an after-sales support system service for customers or users in the smooth transaction process. The current problem with service desk services is the slow response to handling and resolving obstacles. Evaluation is needed to be able to improve operational services. The Information Technology Infrastructure Library (ITIL) V4 will be used to evaluate service desk services in IT Operational at PT Erafone Dotcom. The purpose of this study is to evaluate IT Support in operational services using the ITIL V4 framework with 2 practices in the domain of General Management Practice and 5 practices in the domain of Service Management Practice. The results of this study are that the level of service in IT Operational and the level of capability are at level 3 (Defined), which means that IT Operational support to users has run optimally referring to management practice procedures and response to incidents. To increase the value of IT Operational support from the maturity level to match expectations and can improve management. The recommendation for improvement is that even though it is at level 3, there is still a gap in the practices used so that it is necessary to improve the recording of incidents and problems that occur, so that they can be analyzed and identified to help handle and prevent the recurrence of incidents and problems.
Design and Development of the RPG Game Novus: A Sole Remnant as a Stem Learning Media Focused on Basic Mathematics Using Godot Engine Kevin Valeri; Gerry Firmansyah; Agung Mulyo Widodo; Habibullah Akbar
Eduvest - Journal of Universal Studies Vol. 6 No. 6 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i6.53022

Abstract

The integration of Science, Technology, Engineering, and Mathematics (STEM) education has become increasingly important in developing students’ critical thinking, problem-solving, and innovation skills. However, elementary school students often experience difficulties in mathematics learning due to conventional teaching methods, low engagement, and mathematics anxiety. This study aims to design and develop Novus: Sole Remnant, a narrative Role-Playing Game (RPG) built with the Godot Engine, as a STEM-oriented learning medium focused on basic mathematics for elementary school students. The research employed a hybrid methodology combining Research and Development (R&D) and Research through Design (RtD) approaches. Data were collected through observations, in-depth interviews, documentation, and qualitative evaluations involving elementary school teachers, students, and casual gamers using purposive sampling techniques. The findings indicate that the game successfully integrates mathematical concepts into gameplay through diegetic learning, allowing students to engage with arithmetic and number pattern challenges naturally within the narrative context. The implementation of visual scaffolding features effectively supported independent learning and enhanced conceptual understanding, while the checkpoint and respawn mechanisms reduced mathematics anxiety and encouraged a growth mindset. Teachers also confirmed the game’s alignment with the Independent Curriculum and its potential as an alternative homework medium. In conclusion, Novus: Sole Remnant demonstrates strong potential as an engaging, pedagogically effective, and technologically feasible digital learning tool for improving elementary mathematics learning and supporting STEM education.
Comparative Analysis of Automated Testing Tools (Automation Testing) Using Cypress and Selenium with Testng Framework on Web Portal Testing Performance in A Banking Company Agam Aprianto; Gerry Firmansyah; Agung Mulyo Widodo; Habibullah Akbar
Eduvest - Journal of Universal Studies Vol. 6 No. 6 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i6.53023

Abstract

The increasing complexity of web-based banking applications has intensified the need for efficient and reliable software testing methods to ensure system quality, security, and operational stability. Traditional manual testing often faces limitations in terms of execution speed, consistency, and scalability, making automated testing an essential approach in modern software development. This study aims to compare the performance of two widely used automated testing tools, Cypress and Selenium WebDriver integrated with the TestNG framework, in testing a banking web portal application. A quantitative experimental approach was employed by executing 104 automated test scenarios, consisting of 41 Login scenarios and 63 Customer Care scenarios, under identical testing conditions. The evaluation focused on execution time, test success rate, stability through repeated testing, and reporting capabilities. The findings reveal that both tools achieved a 100% test success rate across all scenarios. However, Cypress demonstrated significantly better performance in terms of execution efficiency, requiring only 511 seconds to complete all test cases, compared to 2,184 seconds required by Selenium WebDriver with TestNG. Repeated testing also confirmed the stability and consistency of Cypress results. In terms of reporting, Cypress provided a simpler and more user-friendly reporting mechanism, whereas Selenium TestNG with Allure offered more detailed analytical reporting features. In conclusion, Cypress is a more efficient automated testing solution for banking web portal applications, particularly when execution speed, ease of implementation, and testing productivity are prioritized.
Comparative Performance Analysis of Xception and ResNet50 Architectures for Facial Expression Recognition Using the FER-2013 Dataset Aryani, Diah; Akbar, Habibullah; Delio, Ferdinand Defin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.117591

Abstract

This study aims to evaluate and compare the performance and computational efficiency of the Xception and ResNet50 architectures in facial expression classification tasks. Facial Expression Recognition (FER) plays an important role in the development of intelligent systems capable of interpreting human emotions. This technology has various applications, including adaptive learning systems, emotion-aware customer service, and mental health support systems. This research compares two widely used Convolutional Neural Network (CNN) architectures, Xception and ResNet50, for facial expression classification using the FER-2013 dataset. The dataset contains 35,887 grayscale facial images with a resolution of 48×48 pixels categorized into seven basic emotions. All images were resized to 224×224 pixels and converted into RGB format to match the input requirements of pretrained ImageNet models.Both architectures were trained using a transfer learning strategy with selective fine-tuning on specific layers. Data augmentation techniques were applied to increase dataset variability and reduce overfitting. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that Xception architecture outperforms ResNet50, achieving a validation accuracy of 70.69% and a weighted F1-score of 0.71. These findings demonstrate that appropriate architecture selection and structured training strategies can significantly improve FER performance in practical intelligent systems.
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