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All Journal Jurnal Gaussian Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI EMBRIO : Jurnal Kebidanan Jurnal Inventa Jurnal Ilmiah Media Sisfo Jurnal Ilmiah Tatengkorang Indonesian Journal of Intellectual Publication (IJI Publication) Jurnal Mandiri IT Jurnal Sistem Informasi dan Teknik Informasi Jurnal Teknik Informatika JoEELE Journal of Artificial Intelligence and Digital Business Jurnal Pengabdian Masyarakat Bangsa Lokawati: Jurnal Penelitian Manajemen dan Inovasi Riset EcoProfit : Sustainable and Environtment Business Kurva: Jurnal Ekonomi Manajemen Keuangan dan Bisnis Indonesian Journal of Intellectual Publication (IJI Publication)
Ira Puspita Sari
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Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Jurnal Mandiri IT

 1 
Data-driven approach for batik pattern classification using convolutional neural network (CNN) Sari, Ira Puspita; Elvitaria, Luluk; Rudiansyah, Rudiansyah
Jurnal Mandiri IT Vol. 13 No. 3 (2025): January: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i3.361

Abstract

Batik is one of Indonesia's cultural heritages with complex and diverse patterns, possessing high artistic value and deep philosophy. Manual classification of batik patterns requires time and depends on expert knowledge, making the process inefficient. This study aims to develop a batik pattern classification model using Convolutional Neural Network (CNN) with a data-driven approach, enabling automatic and accurate pattern recognition. The dataset used consists of 4,284 batik images divided into five pattern classes: Kawung, Lereng, Ceplok, Parang, and Nitik. In this research, the CNN model was developed by using transfer learning techniques with MobileNetV3 pre-trained on a large dataset. The training process involved data augmentation to enhance the model's robustness against variations in batik patterns. The evaluation was conducted by measuring the model's accuracy and loss. The results show that the CNN model achieved an average accuracy of 93.42% on the training data and 93.88% on the testing data. This research demonstrates that the data-driven approach using CNN is effective for batik pattern classification, providing more accurate results compared to manual methods and offering an efficient solution for the digitalization of the batik industry. The developed model can serve as a foundation for broader applications in cultural preservation and the advancement of artificial intelligence-based technology.
Classification of mushroom types based on digital image processing using convolutional neural network Sari, Ira Puspita; Elvitaria, Luluk
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.387

Abstract

In this research, a classification of mushroom types based on digital image processing using a Convolutional Neural Network (CNN) is conducted. The method employs the EfficientNet-B4 architecture as the base model utilizing transfer learning and fine-tuning processes. The dataset consists of 3000 types of mushrooms, each categorized into 10 classes with 300 images per class. The CNN model is implemented using the Python programming language on Google Colab editor. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score metrics to measure the model's performance. A comparison is made between all models with various training parameters, including identical and different settings. Additionally, the ratio of data splits, whether identical or different, is considered. Model 1, which utilizes a custom freeze layer and a data split ratio of 80% for training, 10% validation, and 10% testing, achieved the highest accuracy (90.00%), precision (90.09%), recall (89.63%), and F1-Score (89.59%) compared to other models. Therefore the implementation of a custom freeze layer to reduce the$ number of trainable parameters significantly impacts the accuracy level of the trained and tested model. Moreover, the determination of the data split ratio also slightly influences the accuracy level of the trained and tested model.
Co-Authors Adrian Adi Putra Anindita, Arzeti ANIP FEBTRIKO Aqil Farras Ariif, Fatihah Mohd Arisandi, Diki Astuti Setiyani As’ad Isma Azaria, Damarati Bambang Hadi Sugito Berliana Putri Darjati Debi Setiawan, Debi Dedi Kurniawan Demes Nurmayanti Diana Dwi Astuti Endarini, Lully Hani Evi Yunita N Evi Yunita Nugrahini Evy Diah Woelansari Fadhilah, Muhammad Naufal Farras, Aqil Ginarsih, Yuni Hadi Suryono Hakim, Ridho Abdul Hasbi Yasin Hermiyanti, Pratiwi Hustinawati Ilmi, Mainatul Indah Lestari, Indah Ira Rahayu Tiyar Isfentiani, Dina Izah Yoelanda Jacky Junaidi Juliana Christyaningsih Kasiati Khambali, Khambali Kiaonarni O.W Kiaonarni OW Lembunai Tat Alberta Lembunai Tat Alberta, Lembunai Tat Liadesvita, Rinne Liza Trisnawati Lully Hani Endarini Luluk Elvitaria Luluk Elvitaria, Luluk Muhamasri, Crisna Mujayanto Museyaroh, Museyaroh Mutiarawati, Diah Titik N. S. Widodo Nia Silviana Noni, Nurdin NUR AENI Nur Hatijah Nur Hatijah Nurjanah, Dinda Rajma Nurwening TW P, Teresia Retna Pengge, Nuning Marina Pratiwi, Yuharika Puspitadewi, Teresia Retna Putri, Zhena Younantha Verronika Rahayu Sumaningsih Rahayuningsih, Christ Kartika Rahmadani, Ade Rahmantya, Yanneri Elfa Kiswara Ramalia Noratama Putri Retno Sasongkowati Rudiansyah Sahputri, Sella Inda Salamun Salamun Sambas, Febriana Siagian, Hotmaida Siti Alfiah Sukri Sukri Suliati Sumasto, Hery Syaid Alarbi Teta Puji Rahayu Tri Rahayuningsih Triastuti Wuryandari Victor Diwantara Wahyudi, Mochammad Erlangga Wahyuni, Maulida Yohanes Kambaru W Yusianti Silviani