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All Journal Jurnal Pseudocode IT JOURNAL RESEARCH AND DEVELOPMENT JITU : Journal Informatic Technology And Communication TIN: TERAPAN INFORMATIKA NUSANTARA Industrika : Jurnal Ilmiah Teknik Industri Jurnal Sains dan Teknologi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Dinamika Kerajinan dan Batik: Majalah Ilmiah Industrial Innovation
Eka Legya Frannita
Department Of Leather Product Processing Technology, Politeknik ATK Yogyakarta
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Toward Better Analysis of Breast Cancer Diagnosis: Interpretable AI for Breast Cancer Classification Alifia Revan Prananda; Eka Legya Frannita
IT Journal Research and Development Vol. 7 No. 2 (2023)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.11563

Abstract

Recently, some countries have been distressing with the increasing number of breast cancer cases. Those cases were extremely increased in every year. Practicaly, the increasing number of patients was caused by the manual examination. Recently, some researchers have been done in the development of AI method for solving this problem. However, AI itself still has limitation since it worked in the black-box approach which was difficult to be trusted. Thus, to overcome those problems, we proposed a method that was able to classify breast ultrasound images into two classes (benign and malignant) and able to explain how the prediction was made. Our proposed method consisted of four processes i.e., pre-processing step, development of CNN model, interpretable step and evaluation. In this research work, our proposed method performed into 780 breast ultrasound images divided into three classes (133 normal, 210 malignant, and 437 benign). In the training process, our proposed method obtained training accuracy of 0.9795, training loss of 0.0675. The validation process obtained validation accuracy of 0.8000 and validation loss of 0.5096. While, in the testing process, our proposed method achieved accuracy of 0.7923. In the interpretable process using LIME, the LIME result is covered by doctor visualization. It was indicated that LIME was suitable enough in visualizing the important features of breast cancer severity. Regarding to the results, our proposed method has a potensial to be implemented as an early detection method for classifying malignancy of breast cancer in order to help the doctor in the screening process
Toward Adaptive Manufacturing Development: Implementation of Artificial Intelligence for Identifying Leather Defects Alifia Revan Prananda; Eka Legya Frannita
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 10 No 2 (2023): List of the Accepted Article for Future Issues
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v10i2.4329

Abstract

Artificial intelligence was the powerful approach that was proven to be impactful for solving several problems. In the leather inspection cases, artificial intelligence also contributed some research works that effected for leather inspection process. In this research, we employed NasNet architecture conducted by using fine-tunning transfer learning method to distinguish the types of leather defects. We used 3600 images that was distributed into six classes which are folding marks, grain off, growth marks, loose grains, pinhole and non-defective. Our proposed solution successfully achieved accuracy for training data is 0.9788 with loss of 0.0198. While the maximum accuracy in validation data is 0.8059 with loss of 0.2126. In the testing data, our experiment obtained accuracy of 0.8603 with loss of 0.1603. These results indicated that our proposed solution was suitable to recognize the characteristics of leather defects and suitable to distinguish them.
Klasifikasi Jenis Cacat pada Kulit Menggunakan Arsitektur GoogLeNet Prananda, Alifia Revan; Frannita, Eka Legya
Jurnal Pseudocode Vol 11 No 1 (2024): Volume 11 Nomor 1 Februari 2024
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.11.1.15-20

Abstract

Deep learning has been proven to be able to provide significant contributions to several fields, including industry. It has also been proven that it has resulted in an outstanding performance for classification, detection, and even segmentation processes. In the leather industry, it also successfully gave valuable results, especially for the leather defect inspection process. This study aims to develop deep learning architecture for classifying leather defect. We used 3600 leather digital images distributed in six types of leather defects. In this study we employed GoogLeNet for classifying the data. Our experiment successfully achieved accuracy of 0.904 in training process and 0.885 in testing process. This result indicated that GoogLeNet provided powerful performance for classifying the type of leather defects.
Klasifikasikan Jenis Cacat Kulit Menggunakan SMOTE-GoogLeNet Prananda, Alifia Revan; Frannita, Eka Legya; Pramitaningrum, Erlita; Hidayat, Anwar; Setiawan , Wawan Budi; Purwaningsih , Nunik
JITU Vol 8 No 1 (2024)
Publisher : Universitas Boyolali

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

Abstract

Deep learning has been proven to be able to provide significant contributions to several fields, including industry. It has also been proven that it has resulted in an outstanding performance for classification, detection, and even segmentation processes. In the leather industry, it also successfully gave valuable results, especially for the leather defect inspection process. However, despite its outstanding performance, it remained a drawback because it produced insignificant results if employed in a small or imbalanced dataset. This research work focuses on the analysis of the implementation of the data balancing method for improving the performance of the deep learning method for classifying the types of leather defects. This research work was done by employing three processes. In the first step, we utilized the data balancing method to balance the data proportion. In the next step, we employed GoogLeNet as a deep learning architecture for training and testing processes. Our experiment was conducted in two scenarios. The first scenario was done by using the original dataset. Whereas the second scenario was accomplished by utilizing the data balancing method before training and testing. According to the experiment results, implementing the data balancing method successfully increased the performance of the deep learning method by more than 15%. It can be inferred that the proportion or the number of data strongly affected the performance of deep learning models.
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.
Analisis Kekuatan Material Hasil Teknologi Fused Deposition Modelling Sebagai Material Alternatif Shoelast Oktavian, Dicky; Prihadianto, Braam Delfian; Setyawan, Wawan Budi; Frannita, Eka Legya
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3411

Abstract

Pada industri alas kaki penggunaan shoelast merupakan aspek penting dalam proses manufaktur alas kaki. Dalam produksiMass, shoelast materials are generally made of wood, metal, or plastic which requires complex and high-costmanufacturing processes. For limited production needs, prototypes, or custom made materials require quite high costs andlong manufacturing process time. For limited production needs, alternative materials are needed that can be used asshoelast materials. This study aims to analyze the mechanical strength of the material produced by Fused DepositionModelling (FDM) technology as an alternative to shoelast making materials that are more flexible and efficient. The typesof materials used in this study are Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), and PolyethyleneTerephthalate Glycol (PETG). The three types of thermoplastic materials were printed with fill density variationparameters using a 3d printer and pressed testing was carried out. The data were analyzed by calculating the average valueand standard deviation of the compressive strength. The results showed that all materials have a compressive strengthhigher than 5 MPa, thus meeting the basic mechanical requirements as a shoelast material. Furthermore, PLA material hasthe characteristics that are suitable for making precise shoelast prototypes, but it is not suitable for repetitive lastingprocesses. While ABS material is more relevant for repetitive lasting processes and PETG material is relevant for limitedproduction but still requires durability
AR-FootIN 4.0 : Aplikasi Pengenalan Teknologi Industri 4.0 Pada Bidang Alas Kaki Berbasis Mobile Augmented Reality Prananda, Alifia Revan; Marwanto, Marwanto; Frannita, Eka Legya; Hidayat, Anwar
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 Frannita, Eka Legya; Prananda, Alifia Revan
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%.
REVITALISASI PRODUK KERAJINAN KULIT MANDING DENGAN PENGAPLIKASIAN ORNAMEN TAMANSARI YOGYAKARTA Hidayahtullah, Mochammad Charis; Frannita, Eka Legya; Utanto, Taufik Rudhi
Dinamika Kerajinan dan Batik: Majalah Ilmiah Vol. 42 No. 1 (2025): DINAMIKA KERAJINAN DAN BATIK : MAJALAH ILMIAH
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Industri Kerajinan dan Batik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22322/dkb.v42i1.8452

Abstract

UMKM lokal merupakan aspek yang penting untuk meningkatkan perekonomian di Yogyakarta, salah satunya adalah UMKM kerajinan kulit di Desa Manding. Problematika yang dihadapi sekarang ini oleh UMKM Manding adalah kurangnya inovasi pengembangan produk. Tak hanya itu, ornamen Kala Makara Tamansari merupakan ornamen tradisional ikonik dari Yogyakarta yang patut dilestarikan dan diaplikasikan pada produk kulit. Tujuan penelitian ini adalah revitalisasi produk kulit UMKM Manding dan pelestarian tradisi Yogyakarta. Metode penelitian ini menggunakan metode ATUMICS untuk mengembangkan produk UMKM Manding yang lebih ikonik. Hasil dari penelitian didapatkan data bahwa Kala Makara memiliki makna sebagai simbol penolak bala/bencana dan transformasi. Pada revitalisasi produk kulit yang dimodernkan adalah elemen artefak dengan teknik (Technique) produksi menggunakan mesin autocutting, bentuk tas kulit (Shape) dibuat lebih modern dengan ikonisasi (Icon) ornamen Kala Makara. Sedangkan elemen yang tetap sama adalah kegunaan produk tas (Utility), material kulit (Material), serta konsepnya (Concept). Produk revitalisasi ini diharapkan memberikan dampak cultural dan economy motivation untuk UMKM Manding Yogyakarta.
Co-Authors Abdillah, Mokhammad Nurkholis Alifia Revan Prananda Anwar Hidayat Anwar Hidayat Bagus Fatkhurrozi Burhan, Gilang Fatikhul Dwi Wulandari Fauzi Ashari Gilang Fatikhul Burhan Hidayat, Anwar Jamila Jamila Latifah Listyalina Majidah, Zharifa Nur Marwanto Marwanto, Marwanto Mochammad Charis Hidayahtullah Mochammad Charis Hidayatullah Naimah Putri Oktavian, Dicky Pramitaningrum, Erlita Pramitaningrum, Erlita Pramitaningrum Prihadianto, Braam Delfian Purwaningsih , Nunik Putri, Naimah Rahmawati, Atiqa Setiawan , Wawan Budi Setyawan, Wawan Budi Suamanda Ika Novichasari Sugiyanto Totok Yulaidin Utanto, Taufik Rudhi Wahyu Ratnaningsih Wawan Budi Setyawan Wibowo, Fadhila Syahida Windra Bangun Nuswantoro Yuafni