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All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Rekayasa Mesin SMARTICS Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Pendidikan Fisika dan Teknologi INTI Nusa Mandiri Quantum Teknika Journal of Mathematics Education and Science Inovasi Fisika Indonesia (IFI) Journal of Mechanical Engineering, Science, and Innovation Abdimas Indonesian Journal Scientific Journal of Informatics Almantiq
Pelangi Eka Yuwita
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Religion Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Effect of Layering and Fiber Orientation on The Tensile, Impact, and Flexural Properties of Polyester/Ramie Fiber Composites Maghzari Zaka Ahmad; Taufik Azhary; Pelangi Eka Yuwita
Quantum Teknika : Jurnal Teknik Mesin Terapan Vol. 7 No. 1 (2025): October
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jqt.v7i1.28402

Abstract

Natural fiber-based composite materials have recently emerged as a promising alternative to synthetic materials due to their eco-friendly characteristics and ability to provide good mechanical strength. One of the natural fibers with strong potential as reinforcement is ramie fiber, which, when combined with a polyester matrix, can produce strong yet lightweight composite materials. This study was conducted to evaluate the effect of fiber orientation and the number of layers on the mechanical properties of polyester (ramie fiber) composites. The specimens were fabricated using the hand lay-up method with three variations: without fiber, two layers of unidirectional fibers, and two layers of woven fibers. The test results revealed that the woven fiber specimens achieved the highest performance in the tensile test, with a tensile strength of 33.31 MPa and an elastic modulus of 1.8 GPa. In the flexural test, this specimen also recorded the highest stress of 16.39 MPa and a flexural modulus of 58.3 MPa. The impact test showed that the woven fiber specimen had the highest energy absorption capacity of 4.97 Joules and an impact strength of 130.46. Overall, the use of woven ramie fibers significantly enhanced the tensile strength and impact toughness of the composite. Nevertheless, the effectiveness of reinforcement strongly depends on the proper arrangement and distribution of the fibers.
ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP SKINCARE DENGAN METODE SUPPORT VECTOR MACHINE (SVM) Dwi Tiyas Novitasari; Mula Agung Barata; Pelangi Eka Yuwita
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6297

Abstract

The Originote Hyalucera Moisturizer skincare product has attracted public attention because it offers superior quality at an affordable price. Social media, especially Twitter, is used by consumers to express opinions regarding this product, whether positive, negative, or neutral. However, the large number of reviews with various sentiments can confuse potential consumers in assessing product quality. Therefore, this study aims to understand user perception through sentiment analysis and evaluate the effectiveness of the Support Vector Machine (SVM) algorithm in sentiment classification. A total of 1,820 tweets were collected using the crawling technique with Python. The data undergoes preprocessing, including text cleaning, tokenization, stopword removal, and stemming, reducing it to 902 tweets. Key text features are extracted using Term Frequency-Inverse Document Frequency (TF-IDF). For sentiment classification, this study used the SVM algorithm, which is known as an effective method in text processing. Model evaluation showed good results with an accuracy of 87%, precision of 89%, and recall of 87%. This study provides insight into public perception of The Originote Hyalucera Moisturizer and measures the effectiveness of SVM in social media-based sentiment analysis. The results of the study can be utilized by manufacturers for more targeted marketing strategies, product quality improvement, and more effective communication in responding to opinions on social media. In addition, this study contributes to the development of machine learning-based sentiment analysis methods in the context of skincare products.
Co-Authors Aditya, Ziedan Agus Sulistiawan Amalia, Salsabila Dani Azzahra, Rahma Dina, Intan Rachma Dwi Purbo Yuwono Dwi Tiyas Novitasari Dwi Tiyas Novitasari Erlangga Dwi Aprila Vibianto Faila, Rizka Nur Farikhah, Ainun Nurul Habib, Ahsanul Hilmi, Allif Rosyidy Ifa Khoiria Ningrum Ilmiyah, Miftakhul Indra Kusuma Maghzari Zaka Ahmad Maulani, Vicka Rizqi Midya Yuli Amreta Moh. Mu'id Efendi Mula Agung Barata Nisa, Siti Khoirun Nur Mahmudah Nur Mahmudah Nurma*, Nur Mahmudah Pangestu, Prayoga Dewa Prayoga Dewa Pangestu Roihatur Rohmah Roihatur Rohmah Sa’diyah, Zumrotus Taufik Azhary Togik Hidayat Vibianto, Erlangga Dwi Aprila