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All Journal TEKNIK Jurnal Pseudocode Journal of Information Systems Engineering and Business Intelligence Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini IT JOURNAL RESEARCH AND DEVELOPMENT JITU : Journal Informatic Technology And Communication TIN: TERAPAN INFORMATIKA NUSANTARA Journal Evaluation in Education (JEE) Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Harmoni Sosial : Jurnal Pengabdian Dan Solidaritas Masyarakat Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Manfaat: Jurnal Pengabdian Pada Masyarakat Indonesia Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat
Alifia Revan Prananda
Department Of Information Technology, Faculty Of Engineering, Universitas Tidar
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Journal : TIN: TERAPAN INFORMATIKA NUSANTARA

 1 
AR-FootIN 4.0 : Aplikasi Pengenalan Teknologi Industri 4.0 Pada Bidang Alas Kaki Berbasis Mobile Augmented Reality Alifia Revan Prananda; Marwanto Marwanto; Eka Legya Frannita; Anwar Hidayat
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.4956

Abstract

Rapid development of technology gave a positive impact on the footwear industry. The emergence of various types of technology as part of the industrial revolution 4.0 has greatly helped various types of work in industry. However, technology also need to be supported by good quality resource. Knowledge regarding how to use and maintain these technologies is needed so that the benefits of these technologies can be utilized. An alternative way is by developing good quality of human resource to being proficient in using technology. Furthermore, cultivating technological literacy is also one of the essential factors. Regarding to this situation, we proposed research that aims to develop the AR-FootIN 4.0 application as a learning media for introducing industry 4.0 in the footwear sector. This learning media is developed by employing mobile augmented reality. The proposed learning media is developed by using the SDLC method. The resulted learning media is then evaluated by conducting two types of evaluation, which are expert evaluation and user evaluation. The results of expert evaluation and user evaluation obtain a percentage of 93.33% and 86% respectively, which means that the feasibility of the application to support the technological literacy process in the footwear industry is very good.
Penerapan Metode CNN (Convolutional Neural Network) untuk Mengklasifikasikan Jenis Cacat pada Kulit Hewan Eka Legya Frannita; Alifia Revan Prananda
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5390

Abstract

Recently, leather industry was rapidly growth in several countries. In Indonesia, leather industry became one of the government's priority industries since there were quite a lot of leather industries developing in various regions in Indonesia. On the other hand, there were large number of consumer demand for leather products. Regarding to this fact, maintaining the quality of leather was strongly important. An alternative solution for maintaining leather quality is to conduct leather quality inspection process. However, currently the leather inspection process was still carried out manually by identifying directly the types of defects found on the surface of the leather. This manual inspection process certainly has several hurdles such as time consuming, requiring high accuracy, and requiring experienced operators. This research aimed to develop convolutional neural network architecture that can classify types of leather defects. This research was done by conducting four main processes which were literature study and data collection processes, develop CNN architecture, training process, and testing process. This research work used public dataset consisting of 3600 digital leather images distributed into six classes (folding mask, grain off, growth marks, loose grains, pinhole, non-defective). Based on the training and testing process, the model obtained training accuracy of 90.43% and testing accuracy of 88.47%.
Penerapan Faster RCNN + ResNet 50 untuk Mengidentifikasi Spesies dan Stadium Parasit Plasmodium Malaria Prananda, Alifia Revan; Novichasari, Suamanda Ika; Fatkhurrozi, Bagus; Abdillah, Muhammad Nurkholis; Frannita, Eka Legya; Majidah, Zharifa Nur; Wibowo, Fadhila Syahida
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7187

Abstract

Malaria is one of the epidemic health diseases and is well-known as a serious infectious disease. The malaria examination process had occurred by analyzing the digital microscopic images using a microscope. Those examination procedures were conducted manually, which lead to some hurdles such as misinterpretation, misdiagnosis and may produce subjective results. This research aims to develop a method for detecting the Plasmodium parasite and identifying the species and stage of Plasmodium parasite. The proposed method was performed into 488 raw data comprising of 538 parasites. The proposed method was started by conducting a data augmentation process for balancing the number of data, training model, testing model, evaluation. In this study, both the training and testing processes were performed by applying Faster RCNN + ResNet-50. The result of the testing process shows that Faster RCNN + ResNet-50 successfully achieved mAP of 0,603. It also achieved accuracy of 93.91%, sensitivity of 66.20%, specificity of 96.10%, PPV of 60.14% and NPV of 97.30%. This result indicates that the proposed method is powerful for detecting Plasmodium parasites and identifying all species and stadiums.
Pengembangan Intelligent Leather Inspection Method Berbasis Interpretable Artificial Intelligence Frannita, Eka Legya; Wulandari, Dwi; Putri, Naimah; Rahmawati, Atiqa; Prananda, Alifia Revan
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7425

Abstract

The Industry 4.0 revolution, characterized by the widespread adoption of artificial intelligence and automation, has fundamentally transformed quality inspection processes in manufacturing sectors. Nevertheless, the leather tanning industry continues to rely on conventional visual inspection methods conducted by human operators, which are inherently susceptible to subjectivity, inter-operator variability, and inconsistent outcomes. This study proposes an integrated deep learning framework utilizing the NasNet-Large architecture combined with Local Interpretable Model-Agnostic Explanations (LIME) to automate objective defect detection and quality classification of pickled leather. The research employs a digital image dataset comprising four distinct leather grade categories, each annotated with expert-validated ground truth labels and professional interpretations. Experimental results demonstrate consistent model performance with 75% accuracy in both training and validation phases while achieving improved testing accuracy of 79%. LIME-based interpretability analysis reveals significant spatial convergence between model-identified defect regions and expert-annotated ground truth references. These findings indicate that the developed model exhibits remarkable competence in replicating professional leather quality inspection capabilities. The proposed approach not only enhances inspection efficiency by reducing human-dependent errors but also provides transparent decision-making interpretability - a critical requirement for reliable AI implementation in industrial applications. This research contributes to the advancement of explainable AI systems in material quality assessment, offering methodological innovation and practical implementation value for the leather manufacturing sector.
Co-Authors Abdillah, Muhammad Nurkholis Adam, Otmar Shah Aminuyati Anisa Lestari Annisa Istiqomah, Annisa Anwar Hidayat Bagus Fatkhurrozi Cornelius Rangga Surya Kusuma D Jayus Nor Salim Damar Wicaksono Daniel Gunawan Delfiyan Widiyanto Dhanang Suwidagdho Dinda Kusumaningrum Dwi Wulandari Eka Legya Frannita Galih Slamet Hidayat, Anwar Ibrahim Nawawi Imam Adi Nata Irfandy Thalib Lidia Faridah Majidah, Zharifa Nur Mashud Syahroni Mohammad Norman Aulia Mufasir Mujtahidah, Tholibah Nabila Fairuzzahra Niken Orisa Putri Novitasari Novitasari Pramitaningrum, Erlita Purwaningsih , Nunik Putri, Naimah Rahmawati, Atiqa Restu Rakhmawati Rhema Chandrawati Rheza Ari Wibowo Sapto Nisworo Setiawan , Wawan Budi Setya Wijaya, Vania Amelia Suamanda Ika Novichasari Syifa Fauziah Terbit Bagaskara Ahmad Terra Rhebekka Wibowo, Fadhila Syahida