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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 Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat Indonesian Journal of Business Intelligence (IJUBI) Journal of Applied Engineering and Technological Science (JAETS) 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
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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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.
Co-Authors Abdul Azis Abdul Fadlil Ady Purna Kurniawan Agus Triyono Aji, Doni Kurnia Aldino Kemal Adi Gumawang Anshori Anshori, Anshori Anton Yudhana Ariyanto, Ragil Ary Prasetya, Dedi Astika AyuningTyas, Astika Dewi Soyusiawaty Dienulloh Ulil Abshar, Muhammad Fadlillah, Umi Faqih Dzulqarnain, Muhammad Fatah Yasin Fikamelyalla, Naura Gumawang, Aldino Kemal Adi Hartono, Susilo Haryanti, Yanti Ihsan Adi, Mahfud Ihsan Cahyo Utomo Iqbal Firdaus, Malvin Khoirunnisa, Siska Lukito Edi Nugroho Muchlas Muchlas Muchlas Muhammad Muhammad Nashif, Haidar Muhammad Syukri 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 Sri Winiarti Sunardi Sunardi Sunardi Ulva Erida Nur Rochmah Wahyusari, Retno Wintolo, Hero Wulandari, Novita Desi Yusrifa, Khumaila Masfarina Yusuf Sulistyo Nugroho