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All Journal Jurnal Pseudocode IT JOURNAL RESEARCH AND DEVELOPMENT Jurnal Ilmiah Manajemen Kesatuan JITU : Journal Informatic Technology And Communication TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Sains dan Teknologi Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Dinamika Kerajinan dan Batik: Majalah Ilmiah
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
EKSPLORASI KULIT KAYU LANTUNG DENGAN METODE DESIGN THINKING UNTUK PENGEMBANGAN PRODUK UMKM LANTUNG BENGKULU Mochammad Charis Hidayahtullah; Eka Legya Frannita; Yuafni; Sugiyanto; Naimah Putri; Fauzi Ashari; Latifah Listyalina; Wahyu Ratnaningsih
Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Vol 21 No 2 (2022): Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit
Publisher : Politeknik ATK Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1354.292 KB) | DOI: 10.58533/bptkspk.v21i2.170

Abstract

Indonesia is one of the countries that have contributed to the apparel, leather, and textile industry in the international market. Not only that, but Indonesian MSME handicrafts are also one of the commodities that can increase the country's foreign exchange, one of which is the lantung bark craft from Bengkulu. However, the creative industry is constantly facing competition with the entry of cheap products from China that get easy access with the increasing e-commerce technology. Providing value for the Lantung Bengkulu MSME products to compete locally and globally, one of the steps is to apply the Design Thinking Method and take the distinctive character of the stylized Serawai Tribe Weaving in the exploration process. The results of this study have obtained data that 47 Bengkulu women consumers as much as 89.4% like the results of the re-design and exploration of lantung shoes that have been designed. In determining the selling price of MSME Lantung Bengkulu should also set a price range of Rp. 110.000 to Rp. 500.000 because it corresponds to the purchasing power of female consumers in Bengkulu. The results of this study directly contribute to providing product value to the typical Bengkulu Lantung MSMEs. The Design Thinking method has been successfully increasing the value of the Lantung Bengkulu MSME products, this method can be applied to similar research to develop local MSMEs in Indonesia.
IMPLEMENTASI ARTIFICIAL INTELLIGENCE PADA PENGEMBANGAN SISTEM DETEKSI LIMBAH INDUSTRI PRODUK KULIT OTOMATIS Eka Legya Frannita; Naimah Putri; Mochammad Charis Hidayahtullah
Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Vol 21 No 2 (2022): Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit
Publisher : Politeknik ATK Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (951.953 KB) | DOI: 10.58533/bptkspk.v21i2.171

Abstract

Rapid globalization has led to the growth of a significant amount of waste, including in the industry. Due to the considerable amount of growth of waste every year, effective and efficient waste management is needed to protect our environment. In the leather product industry, waste management is strongly important since it may have a significant impact to the employee and production process. Regarding those issues, waste management technology is considered proposed in order to solve those problems. Current research reported the outstanding work of implementing artificial intelligence for detecting and recognizing industrial waste. Artificial intelligence was proven to be a highly recommended approach that is able to classify several waste types with outstanding performance. Regardless of those facts, artificial intelligence still remains several hurdles, such as the high computational demands, especially for deep learning networks. Regarding the mentioned issue, we proposed a more proper deep learning network for recognizing industrial waste. In this research work, we use Single Shot Detector (SSD) to recognize and classify industrial waste. Our proposed solution was performed in the TrashNet dataset and Waste Picture dataset. Our proposed solution successfully achieve mAP of 0.8813, accuracy of 0,9795, precision of 0,9985 and recall of 0,9693 in the training process. Whereas, in the testing process we achieve average accuracy of 0,8254. According to those results, we can conclude that our proposed solution is suitable for industrial waste detection and has potential to be implemented as an embedded system for recognizing industrial waste automatically.
ISOLASI DAN IDENTIFIKASI STAPHYLOCOCCUS AUREUS DARI LUKA KULIT SAPI PERAH SECARA IN VITRO Naimah Putri; Eka Legya Frannita; Mochammad Charis Hidayatullah
Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Vol 21 No 2 (2022): Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit
Publisher : Politeknik ATK Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (628.832 KB) | DOI: 10.58533/bptkspk.v21i2.176

Abstract

The skin is the largest organ of the body that can be infection. One of the causes of infections is microorganisms. The presence of Staphylococcus aureus on the skin of dairy cattle can affect the quality of leather. This research was conducted to isolate and identify S. aureus from the skin of injured dairy cattle. The sample was taken from a dairy farm in Surabaya. Isolation of S. aureus was carried out using NA and MSA selective media, while characterization was carried out using gram staining, catalase test, and coagulase test. The research results showed that samples isolated from wounds on the skin of dairy contained S. aureus. Gram staining test showed gram positive bacteria with coccus morphology and clustered arrangement. The catalase test was positive with the formation of H2O and O2. Catalase test was indicated by the formation of coagulation or clots in the blood plasma. These results indicated that the skin with contains S. aureus bacteria which can cause infection so that it can affect the quality of the skin to be tanned.
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.
LITERATURE REVIEW IN IMPLEMENTATION OF INDUSTRY 4.0 FOR FOOTWEAR INDUSTRY Eka Legya Frannita; Mochammad Charis Hidayahtullah
Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Vol 22 No 2 (2023): Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit
Publisher : Politeknik ATK Yogyakarta

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

Abstract

Industry 4.0 is an era of digitalization that offers convenience, efficiency and productivity in every field, including of footwear industry. This study aims to investigate the application of technology 4.0 in the footwear industry. This study is mainly discussing about the product cycle of the footwear industry, the distribution map of the application of technology 4.0 in the product cycle of footwear industry, a review of each technology related to footwear industry, as well as the challenges of implementing technology 4.0 in the footwear industry in the future. To reach the significant data, we collect some relevant publications from some reputable publishers in term of the employment of technology for footwear industry. According to the literature results, some technologies such as 3D printing, adaptive manufacturing, automation systems, IoT, augmented reality and artificial intelligence have been employed in the footwear industry. These technologies have been proven to provide benefits such as efficiency and effectiveness of the production process. However, despite technology 4.0 has been proven to provide significant benefits, implementing technology 4.0 still has challenges. The main challenges faced in implementing technology 4.0 are digital data problems, cost issues, connectivity issues and technology literacy issues for the community. Thus, a reasonably in-depth analysis is needed regarding these issues to support the implementation of technology 4.0.
THE APPLICATION OF ARTIFICIAL INTELLIGENCE FOR DESIGNING BATIK MOTIF Eka Legya Frannita; Anwar Hidayat; Wawan Budi Setyawan
Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Vol 23 No 1 (2024): Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit
Publisher : Politeknik ATK Yogyakarta

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

Abstract

Artificial intelligence was a popular technology that was successfully utilized in several fields including of fashion industry. Recently, several research communities have been done in employing artificial intelligence for increasing the productivity. This research work aims to create batik pattern using artificial intelligence which was conducted in three major processes. The three processes were brainstorming process, designing process, and validation. Brainstorming process aimed to determine the restriction of this research work. Designing process focused on the utilization of artificial intelligence for creating batik pattern. In this process, we used online illustration maker based artificial intelligence. Validation process aimed to evaluate the originality of result. According to evaluation process using Google Lens, it can be concluded that the resulted design was original in which there is another batik patterns that similar to our result.
THE DEVELOPMENT OF SENTIMENT ANALYSIS APPROACH FOR IDENTIFYING CUSTOMER INTEREST IN LEATHER BAG Eka Legya Frannita; Anwar Hidayat; Wawan Budi Setyawan
Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Vol 23 No 1 (2024): Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit
Publisher : Politeknik ATK Yogyakarta

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

Abstract

Analyzing user interest has become one of the most popular research topics since identifying the user satisfaction played the essential role to prevent the company profit. This research work aimed to apply sentiment analysis approach for identifying the user interest in leather bag. This research was extremely beneficial to determine the satisfaction of leather bag customers. This research work was done by employing four stages. In the first process, we conducted brainstorming process for determining the exclusion and inclusion of research. In the next process, we collected the data by conducting crawling process from social media review. After collecting the data, we conducted data analysis process by labelling each review into three labels, which were positive sentiment, neutral sentiment and negative sentiment. In the last process, we visualized the result and we summarized the result. The visualization process illustrated that positive sentiment achieved percentage of 28%, neutral sentiment achieved percentage of 69% and negative sentiment achieved percentage of 3%. This result indicates that markets are neutral or not interest enough in leather bag. The analysis process also concluded that News obtained the highest total mentions indicating that Batik is not regularly mention in popular social media. According to those results, innovation or creativity about Batik is should be regularly generated in order to increase the market interest.
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.
Co-Authors Abdillah, Mokhammad Nurkholis Alifia Revan Prananda Anwar Hidayat Bagus Fatkhurrozi Dewi Susita Dwi Wulandari Fauzi Ashari Hidayat, Anwar Jayanti, Aulia Rosalinda Baladika Latifah Listyalina Majidah, Zharifa Nur Marwanto Marwanto, Marwanto Mochammad Charis Hidayahtullah Mochammad Charis Hidayatullah Naimah Putri Oktavian, Dicky Pramitaningrum, Erlita Prihadianto, Braam Delfian Purwaningsih , Nunik Putri, Naimah Rahmawati, Atiqa Setiawan , Wawan Budi Setyawan, Wawan Budi Sonny Taufan Suamanda Ika Novichasari Sugiyanto Utanto, Taufik Rudhi Wahyu Ratnaningsih Wawan Budi Setyawan Wibowo, Fadhila Syahida Yuafni