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All Journal TEKNIK INFORMATIKA TELKOMNIKA (Telecommunication Computing Electronics and Control) ELTIN Journal: Journal of English Language Teaching in Indonesia Jurnal Ilmiah Universitas Batanghari Jambi Jurnal Komputasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) IKRA-ITH Informatika : Jurnal Komputer dan Informatika JURNAL ILMIAH INFORMATIKA Sebatik Jurnal Pengabdian Masyarakat AbdiMas Jurnal ULTIMATICS IKRA-ITH ABDIMAS Jurnal Teknologi Aptisi Transactions on Technopreneurship (ATT) Journal of Information System, Applied, Management, Accounting and Research JIIP (Jurnal Ilmiah Ilmu Pendidikan) Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Buletin Poltanesa International Journal of Engineering, Science and Information Technology Ilmu Komputer untuk Masyarakat TECHSI - Jurnal Teknik Informatika Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Jurnal Akuntansi, Manajemen dan Perbankan Syariah Indonesian Journal of Education And Computer Science Blantika : Multidisciplinary Journal
Ulum, Muhamad Bahrul
Universita Esa Unggul
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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Comparative Analysis of NIJ and NIST Methods for MicroSD Investigations: A Technopreneur Approach Anwar, Nizirwan; Widodo, Agung Mulyo; Sekti, Binastya Anggara; Ulum, Muhamad Bahrul; Rahaman, Mosiur; Ariessanti, Hani Dewi
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 2 (2024): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i2.407

Abstract

This research aims to compare the performance of two forensic investigation methods, the National Institute of Justice (NIJ) and the National Institute of Standards and Technology (NIST), specifically for evidence analysis of MicroSD cards. MicroSD cards are frequently used as external storage in various digital devices, making them critical in digital forensic investigations. The study evaluates the effectiveness of these methods using tools such as Access Data FTK Imager and autopsy. The NIJ method enthis comparative passes detailed stages of preparation, collection, examination, analysis, and reporting, while the NIST method includes stages of collection, examination, analysis, and reporting. Results indicate that the NIJ method provides more comprehensive and detailed results, while the NIST method offers a faster investigation process. Additionally, tables and graphs illustrating performance metrics are included to substantiate the findings. This comparative analysis provides valuable insights for technopreneurs in optimizing digital forensic methods for better data integrity and efficiency, ultimately enhancing decision-making processes in technological entrepreneurship. Furthermore, this study aligns with the United Nations' Sustainable Development Goals (SDGs), particularly Goal 9: Industry, Innovation, and Infrastructure, by promoting innovative forensic methods that support the development of resilient infrastructure and foster innovation in the digital age. This study highlights the importance of effective forensic methods in supporting technopreneurial ventures.
Development of an Archival Management Application for Integration and Monitoring of Documents in Coordination Meetings Syarof, Siti Minatus; Ulum, Muhamad Bahrul
Sebatik Vol. 28 No. 2 (2024): December 2024
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v28i2.2210

Abstract

Archival management is of significant importance in institutions, requiring proper maintenance and organization of activity records. The Coordinating Ministry for Economic Affairs (Kemenko Perekonomian), as a government body, is responsible for coordination, synchronization, and control in the economic sector. Its coordination activities include inter-ministerial meetings aimed at formulating strategic policies and actions in the economic field. The Secretariat Division is tasked with facilitating these coordination meetings. The coordination meeting process involves preparing several key components, such as meeting invitations, materials, and documentation, including participant lists, recordings, minutes, and transcripts. Currently, the archiving of meeting documents is fragmented across various sub-sections within the Secretariat Division, leading to inefficiencies in document management. Furthermore, the absence of a centralized system to accommodate all coordination meeting records makes document monitoring challenging. To address these issues, an application has been designed to store meeting documents in a unified database, enabling more efficient document management, integration, and monitoring. The development of this application utilizes the waterfall model with UML diagram modeling. This application is expected to enhance document management processes within the Secretariat Division and provide greater convenience for users.
Implementation of Deep Learning Model for Identification of Skin Diseases by Utilizing Convolutional Neural Network Apriani, Lysa; Ulum, Muhamad Bahrul
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3753

Abstract

Skin diseases are health problems that affect many individuals worldwide. Rapid and accurate diagnosis of skin diseases is essential for effective treatment. In an effort to improve diagnosis, information technology and artificial intelligence have taken on increasingly significant roles. This study focuses on the implementation of deep learning models for skin disease identification using CNN architectures EfficientNetB0, Xception and VGG16. The models were trained and tested on a dataset of 1800 images with 5 dermatitis classes and 1 normal class. Confusion matrices were used to assess the performance of the three deep learning models on the components of accuracy, recall, precision, and F1-score. The results of the deep learning model that can classify dermatitis skin diseases with a performance of more than 90% for each evaluation matrix are deep learning models utilizing EfficientNetB0 transfer learning with an accuracy of 93%. In contrast, the Xception model indicates overfitting with a training accuracy of 99.96% and a validation accuracy of 86.38%. The VGG16 model indicates underfitting with a training accuracy of 69.71% and a validation accuracy of 46.79%.
Implementation of Design Thinking Method in UI/UX Redesign of Public Complaint Application (Case Study: Go Siaga App) Rafi Kurnia Pangestu; Muhamad Bahrul Ulum
JURNAL TEKNIK INFORMATIKA Vol 16, No 2 (2023): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v16i2.27416

Abstract

Go Siaga App is a mobile-based application by Tangerang Sub-district Police Office that provides special community services for the Tangerang sub-district community which provides features in the form of reports of disturbances in public security and reports of loss or damage. Since it is a new application released in March 2021 on Google Playstore, there are several things that need to be considered to maintain the usability of the application. This research aims to redesign the user interface and user experience (UI/UX) of the Go Siaga application using Design Thinking Method in the design process. Some of the supporting aspects for testing the user satisfaction such as effectiveness, efficiency, usefulness, satisfaction, and learnability are met in the usability testing. The results showed that the percentage of all the aspects in usability from the redesigned version were all higher than the current one with 80% of effectiveness, 80% of efficiency, 80% of usefulness, 86.67% of satisfaction, and 73.33% of learnability. Therefore, based on the research results, the redesign of Go Siaga is more effective, more efficient, more useful, more satisfying, and also easy to learn.
APPLICATION OF MACHINE LEARNING MODELS FOR FRAUD DETECTION IN SYNTHETIC MOBILE FINANCIAL TRANSACTIONS Imam Mulyana; Muhamad Bahrul Ulum
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6420

Abstract

The financial industry faces challenges in detecting fraud. The 2023 Basel Anti-Money Laundering (AML) Index report shows a worsening money laundering risk trend over the last five years in 107 countries. And according to the Financial Action Task Force (FATF) in 2023, this is exacerbated by financial institutions which have problems with low reporting of suspicious financial transactions (Suspicious Transaction Report). Limited access to confidential financial transaction data is an obstacle in developing machine learning-based fraud detection models. To overcome this challenge, the research uses PaySim synthetic datasets that mimic real financial transaction patterns. The CRISP-DM approach is used, including the Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment stages. The algorithms used are Decision Tree, Random Forest, and XGBoost. Model evaluation is carried out using accuracy, precision, recall, F1-score, specificity, cross-validation and ROC-AUC metrics. The results show that the Random Forest algorithm has the best performance with 99% accuracy, followed by XGBoost (98.9%) and Decision Tree (97%). Data analysis shows that cash-out and transfer transactions have the highest risk of fraud. This model has proven effective in detecting suspicious financial transactions with a high level of accuracy. This research makes a significant contribution to mitigating financial risks, supporting anti-fraud policies, and encouraging innovation in fraud detection using synthetic data.
PELATIHAN PEMBUATAN BAHAN AJAR ADAPTIF BERBASIS ARTIFICIAL INTELLIGENCE (AI) UNTUK MENINGKATKAN KOMPETENSI DIGITAL GURU DI SD PENGGILINGAN 01 JAKARTA Sadikin, Irma Savitri; Fatonah, Khusnul; Santosa, Imam; Fadli, Muhammad Rijal; Ulum, Muhamad Bahrul; Sari, Yumelda
Jurnal Abdimas Ilmiah Citra Bakti Vol. 6 No. 2 (2025)
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jailcb.v6i2.5056

Abstract

Guru-guru di SD Penggilingan 01 Jakarta menghadapi tantangan dalam mengembangkan bahan ajar yang adaptif dan interaktif akibat keterbatasan pemahaman dan keterampilan dalam memanfaatkan teknologi berbasis Artificial Intelligence (AI). Kegiatan pengabdian ini bertujuan untuk meningkatkan kompetensi digital guru melalui pelatihan pembuatan bahan ajar adaptif berbasis AI. Mitra dalam kegiatan ini adalah 20 guru dari SD Penggilingan 01 Jakarta yang terlibat secara aktif dalam seluruh tahapan kegiatan. Metode pelaksanaan mencakup identifikasi kebutuhan, pelatihan, workshop praktik, pendampingan langsung, dan evaluasi. Kegiatan berlangsung selama empat bulan (Oktober 2024–Januari 2025), dengan memanfaatkan berbagai platform digital seperti Canva, Flipping Book, Socrative, Baamboozle, Padlet, dan Kahoot. Hasil pelatihan menunjukkan bahwa 90% guru merasa lebih percaya diri dalam menggunakan teknologi berbasis AI dalam pembelajaran, dan 85% menyatakan bahwa pelatihan sangat relevan dan aplikatif. Selain itu, guru berhasil mengembangkan bahan ajar digital yang sesuai dengan tingkat kemampuan siswa serta mendukung pembelajaran yang lebih personal dan inovatif. Kegiatan ini memberikan dampak positif dalam peningkatan keterampilan digital guru dan mendorong terciptanya ekosistem pembelajaran berbasis teknologi yang adaptif di sekolah dasar.
EXPLORING PRE-SERVICE TEACHERS' EXPERIENCES WITH DIGITAL MULTIMODAL COMPOSING IN NARRATIVE STORYTELLING: MENGEKSPLORASI PENGALAMAN CALON GURU DENGAN PENYUSUNAN MULTIMODAL DIGITAL DALAM PENCERITAAN NARATIF Sadikin, Irma Savitri; Santosa, Imam; Fadli, Muhammad Rijal; Fatonah, Khusnul; Ulum, Muhamad Bahrul
ELTIN Journal Vol 13 No 1 (2025): VOLUME 13, ISSUE 1, APRIL 2025
Publisher : STKIP Siliwangi

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

Abstract

In the 21st century, digital competency is a fundamental requirement for language instruction, necessitating the integration of technology into teacher training programs. This study investigates four pre-service teachers’ experiences in designing digital narrative videos using the Digital Multimodal Composing (DMC) framework. Adopting a narrative inquiry approach, data were gathered through reflective diaries and semi-structured interviews to explore how participants engaged with the critical, creative, and technical domains of multimodal composition. Findings indicate that pre-service teachers initially faced challenges in conceptualizing multimodal elements but gradually developed confidence in integrating text, visuals, and audio to enhance their instructional materials. They critically analyzed multimodal texts, structured their narrative-based lessons for greater engagement, and refined their technical proficiency with digital tools. Peer discussions and online tutorials played a significant role in helping them navigate the complexities of multimodal composing, fostering collaborative learning and iterative refinement. This study emphasizes the role of DMC in preparing pre-service teachers for technology-enhanced instruction through structured support, hands-on practice, and digital literacy training. By merging creativity with pedagogy, it highlights the importance of integrating multimodal composing into teacher education programs in EFL instruction.
Augmented Reality Animasi Surat Pendek Al-Quran Berbasis Android riyadi, Virgy andrean; ulum, Muhamad Bahrul
Blantika: Multidisciplinary Journal Vol. 3 No. 9 (2025): Special Issue
Publisher : PT. Publikasiku Academic Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57096/blantika.v3i9.415

Abstract

The development of technology has significantly impacted various fields, including education. One of the emerging technologies, Augmented Reality (AR), provides interactive and engaging learning experiences. This research focuses on the design and development of an Android-based application that utilizes AR technology to animate short Quranic surahs (Juz Amma) for children at Taman Pendidikan Al-Qur'an (TPQ). The application aims to make Quranic learning more engaging, interactive, and effective for young learners by integrating AR with traditional Quranic teaching methods. The research methodology includes the identification of problems, literature review, data collection through observation, interviews, and library study, as well as the design and development of the AR-based learning application. The findings demonstrate that AR can enhance children's engagement and retention in learning short surahs, making the Quran more accessible and enjoyable for younger audiences. This innovative approach is expected to offer a solution to the challenges faced in Quranic education today.
Pengaruh Marketing Mix Terhadap Keputusan Pembelian Melalui Minat Beli Sebagai Variabel Intervening Wijayanti, Ratna; Ulum, Muhamad Bahrul
Jurnal Akuntansi, Manajemen dan Perbankan Syariah Vol 4 No 2 (2024): April 2024
Publisher : UP2MF Fakultas Ekonomi dan Bisnis, Universitas Sains Al-Qur'an (UNSIQ) Jawa Tengah di Wonosobo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/jamasy.v4i2.6981

Abstract

Tujuan - tujuan penelitian ini adalah untuk menentukan pengaruh produk, harga, promosi dan lokasi terhadap minat beli; pengaruh produk, harga, promosi dan lokasi terhadap keputusan pembelian; dan pengaruh produk, harga, promosi dan lokasi terhadap keputusan melalui minat beli. Metode - Konsumen UD. Manfaat Snack di Kabupaten Wonosobo adalah sampel penelitian ini. Penelitian ini dilakukan dengan metode kuantitatif. Untuk mengumpulkan data, digunakan kuesioner. Data primer dan sekunder digunakan dalam penelitian ini. Studi ini menggunakan Model Equation Structural (SEM) dengan Analysis of Moment Structure (AMOS) v.23. Hasil - hasil penelitian menunjukan (1) Tidak terdapat pengaruh positif dan signifikan produk terhadap minat beli karena nilai CR ≤ 1,96 yaitu 1,464 dan nilai probabilitas ≥ 0,05 yaitu 0,143. (2) Tidak terdapat pengaruh positif dan signifikan harga terhadap minat beli karena nilai CR ≤ 1,96 yaitu -0,439 dan nilai probabilitas ≥ 0,05 yaitu 0,661. (3) Tidak terdapat pengaruh positif dan signifikan promosi terhadap minat beli karena nilai CR ≤ 1,96 yaitu 0,565 dan nilai probabilitas ≥ 0,05 yaitu 0,572. (4) Tidak terdapat pengaruh positif dan signifikan tempat terhadap minat beli karena nilai CR ≤ 1,96 yaitu 0,947 dan nilai probabilitas ≥ 0,05 yaitu 0,343. (5) Tidak terdapat pengaruh positif dan signifikan produk terhadap keputusan pembelian karena nilai CR ≤ 1,96 yaitu -1,615 dan nilai probabilitas ≥ 0,05 yaitu 0,106. (6) Tidak terdapat pengaruh positif dan signifikan harga terhadap keputusan pembelian karena niai CR ≤ 1,96 yaitu -0,234 dan nilai probabilitas ≥ 0,05 yaitu 0,815. (7) Tidak terdapat pengaruh positif dan signifikan promosi terhadap keputusan pembelian karena nilai CR ≤ 1,96 yaitu 0,165 dan nilai probabilitas ≥ 0,05 yaitu 0,869. (8) Tidak terdapat pengaruh positif dan signifikan tempat terhadap keputusan pembelian karena nilai CR ≤ 1,96 yaitu -0,622 dan nilai probabilitas ≥ 0,05 yaitu 0,534. (9) Terdapat pengaruh positif dan signifikan minat beli terhadap keputusan pembelian karena nilai CR ≥ 1,96 yaitu 2,865 dan nilai probabilitas ≤ 0,05 yaitu 0,004. (10) Produk tidak berpengaruh positif terhadap keputusan pembelian melalui minat beli karena nilai standardized direct effect yaitu -0,276 ≤ standardized indirect effect yaitu 0,122 . (11) Harga tidak berpengaruh positif terhadap keputusan pembelian melalui minat beli karena standardized direct effect yaitu -0,029 = standardized indirect effect yaitu -0.029. (12) Promosi tidak berpengaruh positif terhadap keputusan pembelian karena nilai standardized direct effect yaitu 0,023 ≤ standardized indirect effect yaitu 0,042. (13) Tempat berpengaruh positif terhadap keputusan pembelian melalui minat beli karena nilai standardized direct effect yaitu -0,085 ≥ sect effect yaitu 0,072. Implikasi - dapat membantu dalam menentukan strategi pemasaran yang tepat untuk membantu konsumen membuat pilihan pembelian yang lebih baik.
Implementation of Deep Learning Model for Identification of Skin Diseases by Utilizing Convolutional Neural Network Apriani, Lysa; Ulum, Muhamad Bahrul
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3753

Abstract

Skin diseases are health problems that affect many individuals worldwide. Rapid and accurate diagnosis of skin diseases is essential for effective treatment. In an effort to improve diagnosis, information technology and artificial intelligence have taken on increasingly significant roles. This study focuses on the implementation of deep learning models for skin disease identification using CNN architectures EfficientNetB0, Xception and VGG16. The models were trained and tested on a dataset of 1800 images with 5 dermatitis classes and 1 normal class. Confusion matrices were used to assess the performance of the three deep learning models on the components of accuracy, recall, precision, and F1-score. The results of the deep learning model that can classify dermatitis skin diseases with a performance of more than 90% for each evaluation matrix are deep learning models utilizing EfficientNetB0 transfer learning with an accuracy of 93%. In contrast, the Xception model indicates overfitting with a training accuracy of 99.96% and a validation accuracy of 86.38%. The VGG16 model indicates underfitting with a training accuracy of 69.71% and a validation accuracy of 46.79%.
Co-Authors Achmad Dwitama Karisma Achmad Fansuri Achmad Junaedi Sitika Akbar, Habibullah Alivia Yufitri Apriani, Lysa Azizah, Anik Hanifatul Binastya Anggara Sekti Budi Tjahjono Calvin Ramadhani Alfahrezi Chrystian Dwi Putra Yunus Devira Asha Diah Aryani, Diah Doni Antoro Erry Yudhya Mulyani Fadli, Muhammad Rijal Fahreza Bahran Fatonah, Khusnul Fikri Saefullah Hani Dewi Ariessanti Hapiz Nuddin Setiadi Hendra Bratanata Hendry Gunawawan Ichwani, Arief Imam Mulyana Imam Santosa Irawan, Bambang Irma Savitri Sadikin Kundang Karsono Juman Meria, Lista Mukhamad Abduh Nina Nurhasanah, Nina Nixon Erzed Nizirwan Anwar Oktian Fajar Nugroho Pangestu, Rafi Kurnia Rafi Kurnia Pangestu Rahaman, Mosiur RATNA WIJAYANTI Rian Adi Pamungkas Riska Zulfiqoh Riya Widayanti riyadi, Virgy andrean Roesfiansjah Rasjidin Rozali, Yuli Asmi Ryan Putra Laksana Sadikin, Irma Savitri Sahrudin Sahrudin Samuel Agave Sandfreni, Sandfreni Sari, Yumelda Susilo, Muhammad Rizky Aulia Syahiradania, Azahra Syahrir Akil Syahrizal Dwi Putra Syarof, Siti Minatus Tarigan, Masmur Ummanah Ummanah, Ummanah Vitri Tundjungsari WAHYU DEWANTORO Widodo, Agung Mulyo Yulfitri, Alivia Yulhendri Yulhendri Yunita Fauzia Achmad