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All Journal Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal Teknik Informatika (JUTIF) TIERS Information Technology Journal Jurnal Pemberdayaan Masyarakat Indonesia (JPMI) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Andi Sudiarso, Andi
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Kajian Inovasi Pewarnaan Kain Batik dengan Metode Penyemprotan Mesin CNC Zainal Arifin, Muhamad; Sudiarso, Andi
Journal Pemberdayaan Masyarakat Indonesia Vol 4 No 1 (2022): Jurnal Pemberdayaan Masyarakat Indonesia (JPMI)
Publisher : Pusat Pengabdian kepada Masyarakat (PPKM) Universitas Prasetiya Mulya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21632/jpmi.4.1.95-100

Abstract

Batik merupakan warisan budaya Indonesia yang telah diakui dunia melalui UNESCO. Pada proses pembuatan kain batik terdapat tiga proses utama yaitu, pemberian motif dengan malam (lilin), pewarnaan dan pelorotan. Pada perkembangan teknologi berpengaruh pada proses perlakuan dalam pembuatan batik, pada revolusi industry 4.0 dapat menjadi langkah dalam melestarikan produk batik dan meningkatkan jumlah produksi. Munculnya mesin pembuatan motif batik tulis pada kain menggunakan CNC (Computer Numerical Control) menjadi ide terhadap inovasi pewarnaan batik dengan cara penyemprotan warna pada kain batik menggunakan mesin CNC 3 Axis (X, Y, Z) agar dapat meningkatkan jumlah produksi kain batik, mempercepat proses pewarnaan dan melestarikan batik pada tingkat UMKM. Dalam hal ini peluang penelitian yang dapat direkomendasikan adalah inovasi baru pada proses pewarnaaan kain batik menggunakan metode penyemprotan dengan mesin dan menerapkan DOE (Design of Experiment) Metode Taguchi dan ANOVA sehingga dapat menentukan parameter optimal, selain itu penambahan uji beda warna untuk mengetahui kerataan warna dan konsistensi warna yang dihasilkan pewarnaan pada kain batik menggunakan metode penyemprotan.
New Product Development Method Trends and Future Research : A Systematic Literature Review Khannan, Muhammad Shodiq Abdul; Tontowi, Alva Edi; Herliansyah, Muhammad Kusumawan; Sudiarso, Andi
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 23 No. 1 (2021): June 2021
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.23.1.11-24

Abstract

Research on new product development (NPD) has led to tools, methods, models, and frameworks that enable researchers to develop better products. However, a comprehensive review of the methods, models and frameworks related to NPD is lacking. This literature study aims to identify research trends, methods, and frameworks used in NPD between 2010 and 2019. A systematic literature review is conducted by developing a structured research protocol. An analysis of 50 selected papers shows that research on NPD can be categorized into 15 conceptual papers, six review papers, 28 case studies, and one survey paper. This paper provides an overview of each tool and presents future research opportunities. This paper concludes that future research can be directed toward combining several methods to design products that satisfy consumer desires with shorter design times, aspects of NPD collaboration, and aspects of changing consumer preferences.
Comparative Analysis of YOLOv5n and YOLOv8n Deep Learning Models for Precision Detection of Klowong Defects in Batik Fabric Hamidi, Rifqi Restu; Herliansyah, Muhammad Kusumawan; Atmaja, Denny Sukma Eka; Sudiarso, Andi
TIERS Information Technology Journal Vol. 6 No. 1 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i1.6499

Abstract

This study presents a comparative analysis of two deep learning object detection models, YOLOv5n and YOLOv8n, for the precies identification of Klowong defects in batik fabric. The evaluation was carried out using a custom dataset consisting of 3,138 annotated images, with 921 allocated for testing and containing 1,295 defect instances across nine defect classes. The main findings show that YOLOv8n outperforms YOLOv5n in both speed and accuracy. YOLOv8n achieved a higher F1-score of 0.87 at a lower confidence threshold (0.297), compared to YOLOv5n’s F1-score of 0.86 at a higher threshold (0.46). In addition, YOLOv8n reduced training time significantly (0.320 hours vs. 0.868 hours) and delivered much faster inference speed (2.9 ms/image), nearly three times quicker than YOLOv5n. Although both models performed well in detecting common defects, YOLOv8n showed more stable results on complex defect types. These improvements make YOLOv8n more suitable for real-time applications in batik production environments. Its efficiency and accuracy support the development of fast and reliable automated quality control systems in traditional textile industries. This research emphasizes the importance of using modern lightweight architectures like YOLOv8n to enhance defect detection performance in practical manufacturing settings.
Multi-architectural Transfer Learning CNN for Klowong Batik Fabric Defect Classification Pratama, Dhika Wahyu; Sudiarso, Andi; Atmaja, Denny Sukma Eka; Herliansyah, Muhammad Kusumawan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4806

Abstract

Klowong is a base cloth that has been given a hot wax pattern as the initial stage in the batik making process but has not yet become a finished batik. Nowdays, written batik machine are available but still limited and production defects still occur, reducing the value of batik. Manual QC makes subjective assessments, so an accurate and efficient automated inspection system is needed for SMEs.This study proposes a defect classification approach on batik klowong fabric based on transfer learning using deep convolutional neural networks (CNN) architecture that has been verified to be reliable in image classification schemes. The basic models used include VGG16, ResNet50V2, InceptionV3, and MobileNetV2, with modifications to the fully connected layers to reduce parameter complexity. The dataset consists of 1000 klowong fabric images with a resolution of 224×224 pixels, with a ratio of 80:10:10 for training, validation, and testing. Data augmentation was applied to improve the generalization of the model. Evaluation is performed based on accuracy, precision, recall, F1-score, and inference time. The experimental results show that VGG16 has the best performance in the testing stage with 92% accuracy. The combination of VGG16 with conventional classifiers (SVM and Random Forest) significantly speeds up the inference time (up to 0.0001 seconds per image) but with a decrease in accuracy to 81-83%. Therefore, the VGG16 model with the modified final layer is recommended as the optimal solution with the best trade-off between classification performance and computational efficiency, especially for application scenarios on low-resource devices such as batik SMEs.
Pengujian Ketahanan Luntur Warna Cokelat Pada Kain Batik Katun Dengan Pewarna Alami Kharisma, Yulia; Sudiarso, Andi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 4, No 1 (2020): SEMNAS RISTEK 2020
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v4i1.2517

Abstract

Penelitian ini bertujuan untuk melakukan analisis pengujian ketahanan luntur warna terhadap hasil proses pemungutan warna dengan menggunakan bahan alami. Penggunaan bahan alami dilakukan sebagai upaya untuk memberi inovasi spektrum warna pada kain katun batik dan juga upaya mengurangi pencemaran lingkungan. Metode yang digunakan untuk pemungutan warna yaitu metode eksperimen. Larutan pewarna yang dihasilkan dari perebusan kulit kayu tingi, kulit kayu jambal dan kulit kayu tegeran dengan penambahan basa jenis soda abu dalam 1x jumlah pencelupan dan penggunaan fiksasi tunjung, memberikan hasil arah warna cokelat. Hasil pengujian ketahanan luntur warna terhadap kain memberikan hasil yang baik. Dengan nilai pengujian ketahanan luntur warna terhadap pencucian sabun yaitu 4-5 (baik) untuk nilai kelunturan dan 5 (baik sekali) untuk nilai penodaan. Nilai nilai 4-5 (baik) didapatkan dari pengujian terhadap sinar matahari, sedangkan untuk pengujian terhadap gosokan basah bernilai 3 (cukup).
Ketahanan Luntur Kain Batik Dengan Pewarna Alami Daun Suji Ilmi, Azizah Nur; Sudiarso, Andi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 4, No 1 (2020): SEMNAS RISTEK 2020
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v4i1.2451

Abstract

Pemerintah mulai gencar mensosialisasikan penggunaan pewarna alami dengan mengembangkan sumber daya manusia dan teknologi mengenai serat dan pewarna alami. Beberapa bahan alami telah diteliti untuk dapat digunakan sebagai pewarna alami, salah satunya adalah daun suji (Dracaena angustifolia). Pada perkembangan penelitian mengenai pewarna alami, masih terdapat beberapa kekurangan seperti warna yang masih belum stabil dan sifatnya yang mudah luntur. Untuk mengetahui kelayakan dari bahan alami daun suji (Dracaena angustifolia) dilakukanlah penelitian dengan metode eksperimen dan uji tahan luntur warna kain. Hasilnya, untuk uji gosokan kain (basah) dan uji pencucian sabun (kelunturan) mendapatkan nilai 4–5 atau baik, sedangkan untuk uji pencucian sabun (penodaan) bernilai 5 atau baik sekali. Namun, untuk hasil uji sinar matahari bernilai 1–2 (jelek).
Co-Authors Denny Sukma Eka Atmaja Hamidi, Rifqi Restu Ilmi, Azizah Nur Kharisma, Yulia Muhammad Kusumawan Herliansyah Muhammad Shodiq Abdul Khannan Pratama, Dhika Wahyu Tontowi, Alva Edi Zainal Arifin, Muhamad