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All Journal Journal of Science and Applicative Technology Vygotsky: Jurnal Pendidikan Matematika dan Matematika AMMA : Jurnal Pengabdian Masyarakat Indonesian Journal of Applied Mathematics
Muliawati, Triyana
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Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics

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Teknologi Smart Conservation Untuk Identifikasi Spesies Mangrove Di Kawasan Ekowisata Cuku Nyinyi, Lampung Muliawati, Triyana; Lestari, Fuji; Alvionita Sitinjak, Mika; Arfi, Eristia; Gahana Cindi Alfian, Devia
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 : Februari (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Mangroves are a type of forest ecosystem that grows in coastal areas along tropical and subtropical shores throughout Indonesia, particularly in Lampung Province. This ecosystem is found in regions influenced by tidal seawater. Mangroves consist of various species of trees, shrubs, and other plants that can survive in highly saline and muddy environments. Additionally, mangroves provide numerous benefits for environmental sustainability and human well-being. A healthy mangrove ecosystem contains diverse plant species with specific characteristics that help maintain ecological balance and ensure optimal ecological functions.Lampung Province has a mangrove ecotourism site located in Sidodadi Village, Teluk Pandan District, Pesawaran Regency, known as Cuku Nyinyi. Uniquely, Cuku Nyinyi is often used as a research and learning site for students, researchers, and the general public to study mangroves.One of the challenges faced by the ecotourism management team, the Bina Jaya Lestari Forest Farmers Group (KTH), is the difficulty in identifying and classifying mangrove species planted in the Cuku Nyinyi ecotourism area. Additionally, there is a lack of adequate information about mangroves, such as their age, anatomy, habitat, environmental adaptations, benefits, and other relevant details. To address this issue, researchers are developing an automatic identification system for mangrove species based on leaf morphology using a Convolutional Neural Network (CNN) approach. CNN is one of the most effective methods for pattern recognition in images and mangrove image processing. This technology is expected to be implemented in the form of a camera application that can automatically identify information from an image of mangrove leaf morphology captured in the Cuku Nyinyi ecotourism area.The Mangrove Camera Application provides new insights to KTH and the general public regarding the potential and importance of conserving natural resources.
Statistical Pattern Recognition of Lithosphere Anomalous Activity Along the Indonesian Ring of Fire S, Mika Alvionita; Satria, Ardika; Muliawati, Triyana; Lestari, Fuji; Harbowo, Danni Gathot
Journal of Science and Applicative Technology Vol. 9 No. 1 (2025): Journal of Science and Applicative Technology June Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v9i1.1850

Abstract

The introduction of statistical pattern recognition becomes highly important for assessing disaster threats such as earthquakes. This approach is significantly more comprehensive and suitable for long-term event forecasting. Therefore, in the future, efforts can be promptly made to reduce the risk of disasters resulting from anomalies in lithospheric activity, especially frequent earthquakes in the Sumatra Island region, Indonesia. Statistical pattern analysis of lithospheric activity anomalies can be categorized through classification. Earthquake classification is performed based on magnitude scale and mathematical calculations of earthquake parameter unit conversion. The classification method employed in this research includes machine learning methods like k-nearest neighbor and support vector machine. The evaluation metrics used for machine learning models are model accuracy and confusion matrix tables.
Raw Material Inventory Control Using The Period Order Quantity (POQ) Method to Reduce Stockout and Overstock Risks Nasution, Achmad Suryadi; Simbolon, Okto Bryan; Muliawati, Triyana; Edriani, Tiara Shofi; Noor, Dear Michiko Mutiara; Fauzi, Rifky
Vygotsky: Jurnal Pendidikan Matematika dan Matematika Vol. 7 No. 2 (2025): Vygotsky: Jurnal Pendidikan Matematika dan Matematika
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/voj.v7i2.1163

Abstract

The rapid growth of coffee shops in Lampung has increased demand for Robusta Lampung, Arabica Kerinci, and Arabica Aceh Gayo, causing stockouts and overstocking at a coffee roastery. This study uses the Period Order Quantity (POQ) method to optimize inventory by ordering based on predictable demand periods, reducing order frequency and costs. Using demand data from the last six months of the year, POQ outperforms the manual inventory policy. Assuming a 5% holding cost and 90%–99% service levels (ensuring product availability), POQ reduces costs by 0.119%–0.163%, boosting profitability. Adopting POQ with real-time demand tracking can balance inventory and meet rising demand.
Analisis Rantai Markov dalam Memprediksi Status Pasien COVID-19 di Indonesia Putri, Nabila Nurita; Muliawati, Triyana
Indonesian Journal of Applied Mathematics Vol. 1 No. 2 (2021): Indonesian Journal of Applied Mathematics Vol. 1 No. 2 April Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v1i2.352

Abstract

COVID-19 is an infectious disease caused by a new type of corona virus, beta coronavirus. The spread of COVID-19 can occur through human interactions. On March 9, 2020 the WHO (World Health Organization) officially declared COVID-19 a pandemic. This means that COVID-19 has spread widely in the world. Until now, there has not been found a drug to treat COVID-19. So, it is necessary to predict when the COVID-19 pandemic will end. This study discusses the Markov chain method in predicting the status of COVID-19 patients in Indonesia. The prediction of the number of people who are positive for COVID-19, recovered, and die can be one of the government's bases for determining when the large-scale social restrictions (PSBB) will end. The results of the study stated that the COVID-19 pandemic in Indonesia would end at the end of 2020. On December 5, 2020 there were no more people infected with COVID-19 with a cure rate of 29.815% of patients with COVID-19 and a death rate of 3, 5933%.
Co-Authors Achmad Suryadi Nasution Alvionita Sitinjak, Mika Danni Gathot Harbowo Edriani, Tiara Shofi Eristia Arfi Fuji Lestari Fuji Lestari Gahana Cindi Alfian, Devia Mika Alvionita Sitinjak Noor, Dear Michiko Mutiara Putri, Nabila Nurita Rifky Fauzi Satria, Ardika Simbolon, Okto Bryan