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PENENTUAN SETTING PARAMETER PEMBUATAN PRODUK JERIGEN 5 L PADA PROSES BLOW MOULDING DENGAN MENGGUNAKAN RESPONSE SURFACE METHODOLOGY mario sariski dwi ellianto; Erlita Pramitaningrum; Muhammad Ikhwan
ELEMEN : JURNAL TEKNIK MESIN Vol 9 No 1 (2022)
Publisher : POLITALA PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/je.v9i1.191

Dalam proses pembuatan produk jerigen 5 liter masih belum memiliki standar operasional prosedur dalam pengoperasian mesin dan memperoleh output produk yang sesuai standar. Tahap pengolahan data menggunakan metode response surface dengan tujuan mempermudah mendapatkan nilai optimal dari parameter yang berpengaruh. Penelitian dilakukan dengan menvariasikan blowing pressure, blowing time, dan idle time yang bertujuan untuk mendapatkan setting parameter dan waktu siklus yang optimal pada proses pembuatan produk jerigen 5 liter. Rancangan penelitian mengkombinasikan tiga variabel faktor yaitu blowing pressure 5, 6, dan 7 bar, blowing time 21, 22, dan 23 detik, dan idle time 7, 8, dan 9 detik dengan variabel respon yaitu Cycle Time dan Netto. Dengan metode tersebut didapatkan kondisi Cycle Time optimum sebesar 37,7623 detik dan Netto sebesar 0,2617 gram dengan kondisi optimum dicapai saat blowing pressure sebesar 5 bar, blowing time sebesar 22,2727 detik dan idle time sebesar 9 detik.

PENENTUAN SETTING PARAMETER PEMBUATAN PRODUK JERIGEN 5 L PADA PROSES BLOW MOULDING DENGAN MENGGUNAKAN RESPONSE SURFACE METHODOLOGY mario sariski dwi ellianto; Erlita Pramitaningrum; Muhammad Ikhwan
ELEMEN : JURNAL TEKNIK MESIN Vol. 9 No. 1 (2022)
Publisher : POLITALA PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/je.v9i1.191

Dalam proses pembuatan produk jerigen 5 liter masih belum memiliki standar operasional prosedur dalam pengoperasian mesin dan memperoleh output produk yang sesuai standar. Tahap pengolahan data menggunakan metode response surface dengan tujuan mempermudah mendapatkan nilai optimal dari parameter yang berpengaruh. Penelitian dilakukan dengan menvariasikan blowing pressure, blowing time, dan idle time yang bertujuan untuk mendapatkan setting parameter dan waktu siklus yang optimal pada proses pembuatan produk jerigen 5 liter. Rancangan penelitian mengkombinasikan tiga variabel faktor yaitu blowing pressure 5, 6, dan 7 bar, blowing time 21, 22, dan 23 detik, dan idle time 7, 8, dan 9 detik dengan variabel respon yaitu Cycle Time dan Netto. Dengan metode tersebut didapatkan kondisi Cycle Time optimum sebesar 37,7623 detik dan Netto sebesar 0,2617 gram dengan kondisi optimum dicapai saat blowing pressure sebesar 5 bar, blowing time sebesar 22,2727 detik dan idle time sebesar 9 detik.

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

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.

Supply Chain Analysis for Water and Sanitation Technology Research Project (Case Study in Morotai Island, North Maluku Province) Dimas Hastama Nugraha; Rezeki Peranginangin Peranginangin; Erlita Pramitaningrum Pramitaningrum
Spektrum Industri Vol. 19 No. 1: April 2021
Publisher : Universitas Ahmad Dahlan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/si.v19i1.17906

Morotaiâ€™s sanitation and clean water technology is covering sanitation technology (bio filter, sanitary pond) and clean water (SPC, Reserve Osmose). This technology is ready to be applied in Daruba Pantai, Morotai, North Maluku and all of its technology originated from Java. Research problemâ€™s focus on supply chain. Research objectives eager to see to what extent the supply chain of sanitation and clean water technology, from the technology producers which are mostly located in Java and the application location is in Morotai. The study of Supply Chain in Clean Water and Sanitation Technology Implementation using an applied based research approach. This study refers to scientific research and attempt to solve practical problems. The result of this study, any supply chain for four technologies in Morotai. The results consist of identifying supply chain components, determining network dimension structure that consists of three types dimension, determining type of business chain, and mapping supply chain. For the sanitation technology (bio filter), there are six certified bio filter suppliers and one of them will be selected as the supplier of the construction project in Morotai area. At the sanitary ponds technology, the constituent component/material can be retrieved from local suppliers in Morotai. For Simple Water Management Installation-Quick Sand Filter/SPC, the technology constituent component itself comprises of ground water tank, pump, roof tank, and Quick Sand Filter (made of fibre). This material is brought from the supplier named CV. Lusika in Bandung. Then, the RO technology constituent component is quite many comprising of water inlet valve, pre-filter, RO membrane, post-filter, automatic inspection valves, flow blockers, reservoir, faucet, and exhaust pipe. RO system unit is brought from the supplier called PT. Zeofilt Water Treatment that located in Bandung.

Market and Industrialization Opportunities of Rumah Unggul Sistem Panel Instan (RUSPIN) Technology Using Business Model Canvas Pramitaningrum, Erlita; Nugraha, Dimas Hastama
Spektrum Industri Vol. 21 No. 1 (2023): Spektrum Industri - April 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/si.v21i1.58

Rumah Unggul Sistem Panel Instan (RUSPIN) technology is the development of Rumah Instan Sederhana Sehat (RISHA) with several improvements. Besides resistance to earthquakes, this technology has advantages such as ease and speed of installation compared to conventional houses. As a new technological innovation that has been proven, to develop RUSPIN technology to be an industrialization model in the future, therefore a study of technology business plan is required. Research objectives eager to see the market and industrialization opportunities of RUSPIN technology. The study using Business Model Canvas (BMC) concept which strived to combine 9 business aspects such as customer segments, value proposition, channels, customer relationships, revenue streams, key resources, key activities, key partners, and cost structure into one complete concept map. As the result of this study, the market opportunity for RUSPIN technology is very large with the market segmentation is families that do not own a home and the target market for Low-Income Families (MBR). RUSPIN technology also has an opportunity to be industrialized by looking at the demand side (market opportunities), while from the supply side, efforts are needed to increase the number of certified RUSPIN applicators and developers.

Modeling of women shoes sizing system based on 3D foot scanner result using machine learning approach Jamila Jamila; Eka Legya Frannita; Gilang Fatikhul Burhan; Windra Bangun Nuswantoro; Anwar Hidayat; Erlita Pramitaningrum; Totok Yulaidin
Industrial Innovation Vol. 2 No. 2 (2025): Industrial Innovation
Publisher : Politeknik ATK Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58533/aqn1f925

Accurate shoe sizing plays a crucial role in ensuring comfort, performance, and consumer satisfaction, particularly for women whose foot shapes exhibit considerable anatomical variability. To address this challenge, this research proposes a data-driven modeling framework for developing a women’s shoe sizing system based on three-dimensional foot scanner data. The study was carried out through a systematic process consisting of data preprocessing, clustering using the K-Means algorithm, and evaluation of the clustering performance. The clustering analysis identified four optimal clusters within the dataset, representing distinct patterns in foot dimension measurements. The evaluation result, with a Silhouette Score of 0.25, indicates a moderate yet acceptable level of cohesion and separation among the clusters. These findings demonstrate that the proposed model can effectively capture the underlying structure of women’s foot morphology, providing a scientific foundation for establishing more accurate, customized, and ergonomically appropriate shoe sizing standards.

STUDY OF ORGANOLEPTIC TESTING ON SPORT SHOE Eka Legya Frannita; Erlita Pramitaningrum; Muhammad Charis Hidayatullah
Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Vol. 23 No. 2 (2024): Berkala Penelitian Kulit, Sepatu, dan Produk Kulit
Publisher : Politek ATK Yogyakarta Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58533/wnj3rm47

Currently sports shoes successfully attract a lot of consumers. Sports shoes were originally designed to support sports activities and reduce the risk of injury when doing sports. Nowadays, sports shoes are starting to be designed by adding an aesthetic value that increases fashion value. However, even though sports shoes have started to appear aesthetics side, the quality of the shoes is also an important factor when the shoes are used. To maintain the quality of shoes, it is necessary to test the quality of shoes. One of the important tests is organoleptic testing. The organoleptic testing can be carried out by following the requirements in accordance with SNI 12- 7072-2005 concerning fitness sports shoes with synthetic soles with a glue system, SNI 12-7075- 2005 about sports shoes with printed soles with a glue system, SNI 12-7195- 2006 about glue system jogging sports shoes, and SNI 12-7196-2006 concerning cross-country sports shoes. Several things that need to be considered are design aesthetics and workmanship inspection. This research focuses on conducting organoleptic test on eleven samples of sports shoes. Based on the results of tests that have been carried out based on SNI for sports shoes, there are several defects in five pairs of shoes.

Nano-fatliquoring agent berbasis minyak sawit untuk industri penyamakan kulit: Review Study Atiqa Rahmawati; Heru Budi Susanto; Fadzkurisma Robbika; Baskoro Ajie; Erlita Pramitaningrum
Berkala Penelitian Teknologi Kulit, Sepatu, dan Produk Kulit Vol. 25 No. 1 (2026): Berkala Penelitian Kulit, Sepatu, dan Produk Kulit
Publisher : Politek ATK Yogyakarta Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58533/g0gr5w98

The leather tanning industry is currently transforming toward sustainability through the application of nanotechnology. The fatliquoring process is a crucial stage for providing softness and mechanical strength to leather. Palm oil has great potential as a raw material for fatliquoring agents due to its abundant availability and favorable fatty acid composition. This review discusses the development of palm oil-based nano-fatliquoring agents, which covers fabrication techniques via chemical modifications (such as the use of organosilicon), sonochemical (ultrasonic) physical methods, and the stability of Pickering emulsions. Characterization is emphasized on the analysis of droplet size, zeta potential, and leather fiber morphology using SEM/TEM. The advantage of nanotechnology lies in the deeper penetration of droplets into collagen fibers, which not only enhances mechanical properties but also provides additional functions such as UV resistance and antimicrobial activity. Overall, the utilization of palm oil by-products like deodorizer distillate (DD) in a nano-emulsion offers strong future viability for cleaner production in the global leather industry.

Deteksi Defect Sepatu Berbasis Fine-Tuned CNN Menggunakan Arsitektur DenseNet untuk Mendukung Proses Quality Control Jamila Jamila; Eka Legya Frannita; Gilang Fatikhul Burhan; Nunik Purwaningsih; Windra Bangun Nuswantoro; Anwar Hidayat; Erlita Pramitaningrum; Totok Yulaidin; Alifia Revan Prananda
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.9712

Indonesia is a leading global exporter of footwear. Despite successfully exports million pair of shoes, shoes industry in Indonesia faces significant challenges due to conventional quality control (QC) limitations. This study proposes a deep learning-based defect detection model to enhance the efficiency and accuracy of the QC process. This study uses primary datasets collected from Laboratory of PIDI 4.0 Politeknik ATK Yogyakarta and employed in four experimental scenarios. The dataset consists of 2,828 images comprising of two classes (good class and reject class). This study utilizes fine-tuned CNN using DenseNet architecture for distinguishing two classes. Before employing the fine-tuned CNN method, we separate the data into three components, which are training, validation and testing data, with proportion of 80:10:10. The results demonstrate that the integration of data augmentation and the Adam optimizer yielded the highest performance, achieving accuracy, precision, recall and F1-score of 0.9546, 0.9551, 0.9546, 0.9545, respectively. These findings suggest that implementing automated deep learning models has potential to reduce rejection rates by modernizing traditional inspection methods, which can strengthen the competitiveness of Indonesia’s footwear industry in the global market.

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