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All Journal PENA SAINS Indonesia Chimica Acta Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Towolioe, Sherly
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Identification of Anticancer and Antioxidant Potentials of Katang-Katang Flower Extract (Ipomea pes-caprae Linn) with Water Solvent Tamuddin, Yusriadi; Utami, Hermin Hardyanti; Husna, Saadatul; Lestari, Mega Fia; Salawali, Rismul Trianto; Towolioe, Sherly; Aras, Neny Rasnyanti M; Trifany, Andi Wahyu; Papriani, Nada Pertiwi
Jurnal Akta Kimia Indonesia (Indonesia Chimica Acta) Volume 16, No 2: December 2023
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ica.v16i2.25353

Abstract

Cancer is a group of deadly diseases that attack human organs with an increasing number of cases and an increasing number of deaths from year to year. Free radicals as unstable molecules are one of the triggers for the emergence of cancer. Katang-katang or I. pes-caprae L. is a plant that could be found on almost all tropical coasts. So it has the potential to be developed as herbal medicine. This study used Flowers of I. pes-caprae L. as the research sample. This study aimed to identify the anticancer and antioxidant potential of the flower extract of I. pes-caprae L extracted with water as a solvent. This research included the phytochemical test of secondary metabolites, identification of anticancer potential based on the toxicity test using the Brine Shrimp Lethality Test (BSLT), and the antioxidant activity test using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method. The results showed that the flower extract of I. pes-caprae L. from water solvent contained active compounds including alkaloids, flavonoids, tannins, saponins, and terpenoids. The extract also has the potential as an anticancer with an LC50 value of 170.8441 ppm and strong antioxidant activity with an IC50 value of 71.7895 ppm.
MACHINE LEARNING MODELS FOR PREDICTING STRESS VALUE IN THE TENSILE STRENGTH OF BIOFILMS FROM STARCH AND HAIR WASTE Utami, Hermin Hardyanti; Fitrah, Muhammad Aqdar; Yusriadi, Yusriadi; Ardiansah, Ardiansah; Arminas, Arminas; Lestari, Mega Fia; Towolioe, Sherly
JURNAL PENA SAINS Vol 11, No 2 (2024): Jurnal Pena Sains
Publisher : Program Studi Pendidikan IPA, Fakultas Ilmu Pendidikan, Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/jps.v11i2.26227

Abstract

Biofilms, structured communities of microorganisms, have emerged as a subject of significant interest across various industries due to their unique biodegradable and sustainable characteristics. Hair waste is an incredibly rich source of keratin, and this abundance makes it a promising candidate as a fundamental building block for the development of biodegradable plastics. This study focuses on sustainable biofilms derived from biodegradable materials, specifically a unique combination of starch and hair waste. Machine Learning models, implemented in RapidMiner, were utilized to predict the tensile strength of these biofilms, with the goal of enhancing quality control in their production. Neural Networks and Deep Learning methods were employed to compare their predictive capabilities, assessing both their strengths and limitations. Through rigorous data collection, feature identification, and detailed data analysis, critical factors influencing the quality of the biofilms were identified. The results revealed the remarkable predictive accuracy of the Neural Net model, particularly for Ratio 40, while the performance of the Deep Learning model varied across different ratios. The lower RMSE of the Neural Net model indicated a more precise alignment between the predicted and actual values, distinguishing it as the superior model. This research contributes to the advancement of sustainable biofilm development, offering eco-friendly solutions through the use of unconventional materials. Both models offer valuable predictive capabilities, and the choice between them may depend on the specific requirements and contexts of the application. In conclusion, the performance of the Neural Net and Deep Learning models in predicting stress in tensile strength varies across different ratios.
Diversifikasi Produk Poteng (Tape) dan Peningkatan Produktivitas UMKM Rezki Onto Kabupaten Bantaeng Ratuhaji, Faruq; Arief, Maipha Deapati; Nurhajawarsi, Nurhajawarsi; Towolioe, Sherly; M. Ratlalan, Roberth
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 5 (2025): September 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v5i5.830

Abstract

UMKM Rezki Onto di Kabupaten Bantaeng menghadapi kendala dalam meningkatkan nilai ekonomi produk poteng akibat keterbatasan variasi produk, penggunaan kemasan yang kurang menarik, serta strategi pemasaran yang terbatas. Untuk menjawab permasalahan tersebut, kegiatan pengabdian kepada masyarakat dilaksanakan melalui tiga tahap utama, yaitu pelatihan diversifikasi produk, pengadaan alat pres dan cool box, serta pendampingan branding. Kegiatan ini diikuti oleh 10 pelaku UMKM dengan metode evaluasi menggunakan angket skala Likert 1–5 untuk menilai pemahaman, keterampilan, dan kepuasan peserta. Hasil pelaksanaan menunjukkan peningkatan pemahaman peserta sebesar 80% dengan mayoritas berada pada kategori baik, peningkatan produksi dari 50 bungkus menjadi 90 bungkus per minggu, kenaikan omset bulanan dari Rp2.000.000 menjadi Rp3.600.000, serta lahirnya dua varian produk baru yang lebih kompetitif. Dengan capaian tersebut, kegiatan ini terbukti mampu meningkatkan produktivitas, kualitas, dan daya saing UMKM Rezki Onto.
Co-Authors Aqdar Fitrah, Muhammad Aras, Neny Rasnyanti M Ardiansah, Ardiansah Arief, Maipha Deapati Arminas, Arminas Faruq Ratuhaji Husna, Saadatul Lestari, Mega Fia M. Ratlalan, Roberth Nurhajawarsi, Nurhajawarsi Papriani, Nada Pertiwi Salawali, Rismul Trianto Tamuddin, Yusriadi Trifany, Andi Wahyu Utami, Hermin Hardyanti Yusriadi Yusriadi