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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Elkom: Jurnal Elektronika dan Komputer Seminar Nasional Informatika (SEMNASIF) WARTA Emitor: Jurnal Teknik Elektro Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Computatio : Journal of Computer Science and Information Systems INTECOMS: Journal of Information Technology and Computer Science Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat Building of Informatics, Technology and Science Indonesian Journal of Business Intelligence (IJUBI) International Journal of Informatics and Computation Journal of Applied Engineering and Technological Science (JAETS) INFORMASI (Jurnal Informatika dan Sistem Informasi) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pendidikan dan Teknologi Indonesia Emerging Information Science and Technology Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal Bakti Nusa Prosiding University Research Colloquium Mestro Shahih : journal of islamicate multidisciplinary The Indonesian Journal of Computer Science ABDIMASTEK Computing and Information System Journal Dedikasi Nusantara Indonesian Journal on Learning and Advanced Education (IJOLAE)
Aris Rakhmadi
Universitas Muhammadiyah Surakarta
Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Empowering Teachers in Muhammadiyah Boarding School Yogyakarta toward Safer Digital Behavior through Smartphone Security Education Rakhmadi, Aris; Wintolo, Hero; Putri Silmina, Esi; Soyusiawaty, Dewi; Sunardi; Fadlil, Abdul
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol. 6 No. 4 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i4.2843

Abstract

Abstract: This community-service program was implemented through the Program Pemberdayaan Umat (PRODAMAT) of Universitas Ahmad Dahlan with the aim of enhancing digital literacy and cybersecurity awareness among teachers at Muhammadiyah Boarding School (MBS) Yogyakarta. The activity focused on smartphone account security education through practical steps such as password management, two-factor authentication (2FA), and phishing awareness. A participatory approach was applied through training involving 15 teachers and staff, combining interactive discussions, demonstrations, and pretest–posttest evaluation. The results showed an increase in the average knowledge score from 4.63 to 4.90, digital awareness from 4.05 to 4.45, and intention and safe digital behavior from 4.35 to 4.73. These improvements reflect positive changes in participants’ understanding, awareness, and behavior toward digital security. The program highlights the importance of integrating technological skills with ethical and religious values to promote sustainable digital empowerment in Islamic educational environments.
Empowering pilgrims through digital literacy: Evaluating ritual understanding via mobile hajj applications at KBIHU Ahmad Dahlan Rakhmadi, Aris; Hartono, Susilo; Muchlas, Muchlas; Yudhana, Anton
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 9, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v9i1.3059

Abstract

This community service initiative evaluates the impact of mobile applications, namely Haji Pintar–Satu Haji and Kawal Haji, in enhancing digital literacy and ritual understanding among pilgrims at KBIHU Ahmad Dahlan, Pringsewu. The program was implemented over a one-month period and involved participants engaging in mobile-based tutorials and mentoring activities designed to support comprehensive Hajj preparation. The learning materials focused on improving participants’ understanding of Hajj rituals while simultaneously strengthening their ability to use digital applications effectively. Survey results indicated that nearly all participants had access to Android smartphones, with 41.3% of respondents assessing their digital skills as “very good.” Among the available features, video tutorials were the most frequently accessed, accounting for 34.8% of usage, as they were perceived as clear and easy to understand. The majority of pilgrims reported improved comprehension of Hajj rituals after participating in the program. Despite these positive outcomes, several challenges were identified, including infrequent application usage, accessibility limitations for some users, and participant requests for offline learning content. Overall, the findings demonstrate the significant potential of mobile applications to support Hajj preparation through religious education and digital engagement. The study recommends further development through more inclusive application design, offline accessibility, and sustained digital engagement strategies to enhance the effectiveness of community outreach programs in religious education.
AUTOMATED ACNE TYPE IDENTIFICATION THROUGH FORWARD CHAINING APPROACH Rakhmadi, Aris; Fikamelyalla, Naura; Winiarti, Sri; Silmina, Esi Putri; Fadlillah, Umi; Nugroho, Yusuf Sulistyo
Indonesian Journal of Business Intelligence (IJUBI) Vol 8 No 1 (2025): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v8i1.5377

Abstract

Acne, a prevalent dermatological condition, poses significant physical and psychological challenges. Despite its widespread impact, timely and accessible diagnosis remained a barrier for many, emphasizing the need for innovative solutions. This study introduced an online consultation system for acne-type identification, leveraging a forward chaining approach within an AI-powered expert system. The system analyzed user-reported symptoms—such as severity, location, and appearance—using a rule-based inference mechanism to provide accurate diagnoses and tailored treatment recommendations. Developed using a prototype model, the system’s knowledge base was enriched through observations, literature reviews, and expert interviews, ensuring reliability and clinical relevance. Iterative testing, including black-box evaluations and a System Usability Scale (SUS) assessment, confirmed the system's functionality and user satisfaction, with a SUS score of 86.5, indicating high acceptance. The system bridged critical gaps in dermatological care, particularly for underserved communities, by enabling rapid, user-centric diagnostics and personalized recommendations. The research underscored the transformative potential of artificial intelligence and expert systems in healthcare. By integrating accessibility, scalability, and precision, the proposed system addressed the challenges of acne management and set a foundation for future advancements in dermatological diagnostics.
Pengenalan Pola Huruf Hijaiyyah dengan Metode CNN untuk Bahasa Isyarat Arab Khoirunnisa, Siska; Rakhmadi, Aris
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 12 (2025): JPTI - Desember 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1172

Abstract

Bahasa Isyarat Arab (Arabic Sign Language/ArSL) merupakan sarana komunikasi utama bagi penyandang tunarungu, termasuk dalam pembelajaran Al-Qur’an. Namun, keterbatasan teknologi dalam mengenali bahasa isyarat secara otomatis menjadi hambatan serius terhadap akses pendidikan agama yang inklusif. Penelitian ini bertujuan untuk mengenali pola huruf hijaiyyah dalam ArSL dengan memanfaatkan metode Convolutional Neural Network (CNN) melalui pendekatan transfer learning dan fine-tuning pada empat arsitektur pralatih, yaitu MobileNetV2, EfficientNetB0, VGG16, dan ResNet50. Dataset yang digunakan terdiri dari 7.856 citra RGB tangan yang mewakili 31 huruf hijaiyyah, yang dibagi menjadi data pelatihan, validasi, serta pengujian. Evaluasi dilakukan menggunakan metrik accuracy, precision, recall, F1-score, serta efisiensi komputasi berdasarkan ukuran model dan waktu inferensi. Hasil penelitian memperlihatkan bahwa ResNet50 memperoleh akurasi tertinggi sebesar 98,35%, diikuti MobileNetV2 (97,84%), EfficientNetB0 (97,71%), dan VGG16 (97,07%). Meskipun demikian, MobileNetV2 memiliki ukuran model terkecil dan kecepatan inferensi tercepat, sehingga paling sesuai untuk implementasi pada perangkat dengan keterbatasan sumber daya. Analisis confusion matrix juga menunjukkan kesalahan klasifikasi terutama pada huruf yang memiliki kemiripan visual, seperti dal–dzal dan ta–tha. Penelitian ini menegaskan efektivitas CNN berbasis transfer learning dalam pengenalan huruf hijaiyyah bahasa isyarat Arab serta memberikan kontribusi nyata terhadap pengembangan sistem pembelajaran agama yang lebih inklusif bagi penyandang tunarungu.
Implementation of an Integrated E-Learning Module for Academic Summarization in English for Academic Purposes Rakhmadi, Aris; Haryanti, Yanti
ABDIMASTEK Vol. 4 No. 2 (2025): Desember
Publisher : Universitas Muhammadiyah Jember

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

Abstract

This study aims to develop and implement an integrated e-learning module to enhance academic summarization skills in English for students at Universitas Muhammadiyah Surakarta (UMS). The module was designed to support English for Academic Purposes (EAP) learning and was integrated into regular courses. Through a combination of online and face-to-face learning, the module covered four key areas: summary organization, grammatical accuracy, academic vocabulary, and summarization techniques. The implementation involved a series of workshops and guided practice sessions, supported by self-directed learning through an e-learning platform. Quantitative evaluation was conducted using the English Proficiency Exam (EPE) and the Computerized Assessment System (CAS) across three student groups, with assessments administered on three platforms: traditional, Schoology, and OpenLearning. The results showed that students using the Schoology platform achieved the highest average EPE score (422.99). In contrast, CAS results were comparable across platforms (3.20 for the traditional group, 3.16 for Schoology, and 3.15 for OpenLearning). These findings indicate that the e-learning module is efficacious in improving academic summarization skills and can be sustainably implemented to support academic literacy at UMS.
CNN-Based SIBI Sign Language Recognition Alphabet: Exploring the Impact of Hardware on Model Training Rakhmadi, Aris; Yudhana, Anton; Sunardi, Sunardi
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.7071

Abstract

The recognition of Sign Language Alphabets (SLA) plays a vital role in human-computer interaction, especially for individuals with auditory disabilities. This study aims to evaluate the impact of different hardware configurations—specifically CPU, GPU, and memory setups—on the training efficiency and recognition performance of a Convolutional Neural Network (CNN)-based model for SLA using the SIBI dataset. The novelty of this research lies in its focus on hardware-aware deep learning optimization for Indonesian sign language (SIBI), an underexplored area. The model was trained on 3,468 labeled hand gesture images representing 24 SIBI alphabet signs. Experiments were conducted on CPU (Intel Xeon 2.00 GHz) and GPU (Nvidia Tesla T4) platforms using a consistent CNN architecture. The training time was significantly reduced by 45.5%, from 1 hour 39 minutes to just 54 minutes, while the accuracy remained consistent at 96.7%, showing no significant change between the two setups. These results demonstrate the significance of parallel processing and memory bandwidth in enhancing model convergence and generalization. The findings are relevant for real-time SLA deployment with hardware constraints on embedded or mobile platforms. Overall, the study underscores the importance of hardware optimization in accelerating CNN training and improving performance in sign language recognition systems.
Analisis Ketahanan Model ResNet-50 pada Klasifikasi Bahasa Isyarat Arab terhadap Degradasi Citra Bawah Air Ilham, Muhammad; Rakhmadi, Aris
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9479

Abstract

Automatic sign language recognition using deep learning, particularly Convolutional Neural Networks (CNNs), has shown significant potential. The ResNet architecture, through transfer learning, is frequently reported to achieve high accuracy for Arabic Sign Language Alphabet classification under ideal conditions. However, the robustness of these models against real-world visual distortions remains a significant, yet under-explored challenge. This research aims to develop a ResNet-50-based classification model while comprehensively analyzing its robustness. The primary contribution of this research is mapping the tolerance limits and the extent of performance degradation of the ResNet architecture when facing image degradation. Evaluation was conducted on both ideal test data and test data digitally modified to simulate underwater visual effects. This underwater simulation was selected as an extreme stress test scenario because it technically represents an accumulation of simultaneous real-world optical distortions, such as contrast reduction, turbidity (haziness), and light refraction. Quantitative evaluation results show that the model performs excellently with an accuracy of 96.95% under ideal conditions. However, exposure to underwater distortion resulted in an accuracy drop of 4.24%, reducing it to 92.71%. Despite this noticeable performance reduction, the model maintained an F1-Score of 92.79%. These findings provide empirical evidence regarding the capability limits of the ResNet architecture when facing visual degradation, while also emphasizing the importance of robustness testing before deep learning models can be reliably deployed in non-ideal environments full of visual uncertainties.
Deep Learning-Enhanced Kitabah Application for Inclusive and Adaptive Quranic Sign Language Education Rakhmadi, Aris; Yudhana, Anton; Sunardi, Sunardi; Rahmawati, Yuli
Indonesian Journal on Learning and Advanced Education (IJOLAE) Vol. 8, No. 2, May 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijolae.v8i2.15390

Abstract

People with hearing and speech disabilities (PHSD) continue to face barriers in accessing Quranic literacy education due to the dominance of auditory–verbal instructional approaches and the limited availability of adaptive digital learning environments. Although sign language recognition (SLR) technologies have advanced significantly, most existing systems are not aligned with pedagogically and theologically grounded Quranic learning frameworks. This study aims to develop and evaluate a deep learning-enhanced Kitabah application to support inclusive, adaptive, and technology-enhanced Quranic sign language education for PHSD learners. The study employed two approaches: (1) the development of a Kitabah-based mobile learning application integrating interactive visual–motor learning features, and (2) the implementation of a deep learning-based SLR model using ResNet-18 with transfer learning for static Hijaiyyah gesture recognition. The mobile application was evaluated through black-box testing and the System Usability Scale (SUS), while the SLR model was assessed using accuracy, precision, recall, and F1-score metrics. Results showed that all application functionalities operated successfully, with the application achieving a SUS score of 78.06, indicating good usability and accessibility. The SLR model achieved 98% classification accuracy across 31 Hijaiyyah sign classes, demonstrating strong recognition performance. These findings indicate that integrating the Kitabah method with deep learning and mobile learning technology can support progressive, inclusive, and adaptive Quranic literacy education through AI-assisted and learner-centered educational experiences for PHSD learners.
Sistem Inventori Berbasis Web Menggunakan Metode FIFO Pada CV. Tirtaria Perusahaan Penyedia Ikan Konsumsi Air Tawar Wahyu Akbar; Aris Rakhmadi
INFORMASI (Jurnal Informatika dan Sistem Informasi) Vol 18 No 1 (2026): INFORMASI (Jurnal Informatika dan Sistem Informasi)
Publisher : LPPM STMIK Indonesia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37424/informasi.v18i1.557

Abstract

Pengelolaan barang pada perusahaan penyedia ikan konsumsi yaitu CV. Tirtaria hingga kini masih dijalankan secara konvensional sehingga memunculkan ketidaktepatan data, ketidaktepatan waktu pelaporan, dan ketidaksesuaian stok yang berdampak pada efek- tivitas operasional serta kualitas pelayanan. Penelitian bertujuan membangun sistem in- formasi inventori berbasis web untuk meningkatkan akurasi manajemen stok dan efisiensi kerja. Pengembangan sistem menggunakan metode Waterfall dimana setiap fase pengerjaan wajib diselesaikan sebelum ke fase berikutnya. Pengembangan sistem dilaksanakan melalui serangkaian tahapan yang mencakup analisis kebutuhan, perancangan sistem, implementasi sistem, dan pengujian sistem agar sesuai dengan kebutuhan operasional perusahaan. Metode First In First Out (FIFO) digunakan untuk memastikan produk yang lebih awal diterima akan menjadi produk yang lebih awal didistribusikan sehingga kualitas produk tetap terjaga. Pengujian sistem menggunakan metode Black Box membuktikan bahwa keseluruhan fungsionalitas sistem telah berjalan sebagaimana mestinya. Selain itu, pengujian System Usability Scale (SUS) terhadap 23 responden menghasilkan skor 78,59 dengan kategori Good. Hasil penelitian memper- lihatkan bahwa sistem inventori yang dikembangkan berhasil mengoptimalkan efisiensi pengelolaan data dan meningkatkan kualitas layanan pada CV Tirtaria
PENGEMBANGAN ALAT MONITORING DAN PENDETEKSI KUALITAS UDARA BERDASARKAN PARAMETER CO DAN CO2 BERBASIS IOT Atha Rahmad Zulfikhar; Aris Rakhmadi
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 2 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/6xw9k946

Abstract

Polusi udara menjadi permasalahan serius di kota-kota besar di Indonesia seperti Jakarta, Bandung, Semarang, dan Surabaya akibat meningkatnya jumlah kendaraan bermotor dan aktivitas industri. Kondisi ini menyebabkan tingginya kadar gas berbahaya seperti karbon monoksida (CO) dan karbon dioksida (CO2) yang berdampak negatif terhadap kesehatan manusia. Penelitian ini mengembangkan sistem pemantauan kualitas udara berbasis Internet of Things (IoT) untuk memantau kadar polutan secara real-time menggunakan sensor MQ-135 dan mikrokontroler NodeMCU ESP8266. Hasil penelitian menunjukkan bahwa alat berhasil dikalibrasi dengan alat acuan, dengan rata-rata error sebesar 9,23% untuk CO dan 1,05% untuk CO2, sehingga pembacaan sensor dinyatakan akurat dan stabil. Data hasil pengukuran dikirim secara real-time ke platform ThingSpeak untuk visualisasi serta ke Telegram sebagai notifikasi otomatis. Sistem juga dilengkapi LCD 16x2 dan LED RGB sebagai indikator kondisi kualitas udara. Pengujian dilakukan menggunakan berbagai sumber polutan seperti asap pembakaran kertas, rokok, gas korek api, dan tisu, yang menunjukkan bahwa sensor mampu merespons perubahan kualitas udara dengan cepat. Selain itu, pengujian software membuktikan bahwa sistem dapat mengirimkan data secara konsisten setiap 10 menit dengan hasil yang sesuai antara Telegram dan ThingSpeak. Secara keseluruhan, sistem yang dikembangkan mampu bekerja dengan baik dan efektif sebagai alat monitoring kualitas udara, serta memberikan informasi yang cepat, akurat, dan mudah dipahami oleh pengguna.  
Co-Authors Abdul Azis Abdul Fadlil Ady Purna Kurniawan Agus Triyono Aji, Doni Kurnia Aldino Kemal Adi Gumawang Anshori Anshori, Anshori Anton Yudhana Anton Yudhana Ariyanto, Ragil Astika AyuningTyas, Astika Atha Rahmad Zulfikhar Dedi Ary Prasetya Dewi Soyusiawaty Dienulloh Ulil Abshar, Muhammad Fadlillah, Umi Fadlillah, Umi Faqih Dzulqarnain, Muhammad Fatah Yasin Fikamelyalla, Naura Fikamelyalla, Naura Gumawang, Aldino Kemal Adi Hartono, Susilo Ihsan Adi, Mahfud Ihsan Cahyo Utomo Iqbal Firdaus, Malvin Khoirunnisa, Siska Lukito Edi Nugroho Mega Fatimah Rosana Muchlas Muchlas Muchlas Muhammad Muhammad Ilham Muhammad Nashif, Haidar Novilia Purwita Putri Nugroho, N Nurgiyatna Puspitasari, Yuliana Putri Silmina, Esi Putri, Novilia Purwita Qanza, Aiza Fravy Ragil Ariyanto Rahmawati, Nila Dwi Ratnasari Nur Rohmah Rochmadi, Tri Rochmijatun, Siti Safiq Rosad Sarmini Silmina, Esi Putri Silmina, Esi Putri Sri Winiarti Sunardi Sunardi Sunardi Sunardi Sunardi Ulva Erida Nur Rochmah Wahyu Akbar Wahyusari, Retno Winiarti, Sri Wintolo, Hero Wulandari, Novita Desi Yanti Haryanti Yuli Rahmawati Yusrifa, Khumaila Masfarina Yusuf Sulistyo Nugroho