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All Journal Journal of Science and Applicative Technology Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Pengabdian Masyarakat Asia Kalbiscientia Jurnal Sains dan Teknologi Journal of Science and Technology Enthusiastic : International Journal of Applied Statistics and Data Science Journal of Fundamental Mathematics and Applications (JFMA) Jurnal Kesehatan dan Pengelolaan Lingkungan Jurnal Matematika Integratif RENATA Jurnal Pengabdian Masyarakat Kita Semua AMMA : Jurnal Pengabdian Masyarakat Indonesian Journal of Applied Mathematics TeknoKreatif: Jurnal Pengabdian kepada Masyarakat
Fuji Lestari
Institut Teknologi Sumatera
Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Social Sciences Transportation Other

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Pelatihan Olah Data dan Visualisasi Data Statistik dalam Peningkatan Kompetensi Perangkat Desa Badran Sari, Lampung Selatan Sofia, Ayu; Lestari, Fuji; Rivai, Muklas; andirasdini, indah Gumala; Julianty, Dila Tirta
TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Vol 5 No 1 (2025): TEKNOKREATIF : Jurnal Pengabdian kepada Masyarakat Volume 5 No 1
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M), Institut Teknologi Sumatera, Lampung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/teknokreatif.v5i1.1873

Abstract

The use of data processing skills is something that is very important in various fields. The data processing process can use various applications, one of which is the number/data processing application which is commonly known as the Ms. Excel application. Ms. Excel is a software program that allows users to process and calculate numerical data so that calculations and reading data are no longer done manually. The problem with partners is the lack of competence of village officials regarding the use of technology in processing village data. Based on the problems faced by partners, the PkM team offers a solution, namely providing training in processing and analyzing statistical data using Ms. Excel which aims to help village officials to be able to process data and be able to visualize the data into images/graphs that are more attractive to the community so that able to improve the quality of data processing contained in village officials.
Perhitungan Premi Asuransi Pertanian Pada Tanaman Padi Di Provinsi Jawa Barat Menggunakan Metode Copula Lestari, Fuji
Indonesian Journal of Applied Mathematics Vol. 5 No. 2 (2025): Indonesian Journal of Applied Mathematics Vol. 5 No. 2 October 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.v5i2.2435

Abstract

Agricultural insurance is an important instrument to protect farmers from risks that can threaten the sustainability of farming, such as crop failure due to natural disasters, pest attacks, and plant diseases. Rice, as a strategic commodity for national food security, is a priority in insurance protection programs. This study aims to calculate rice insurance premiums in West Java Province using the copula method, based on secondary monthly data of rice production and dry grain prices (GKG) from January 2020 to December 2023, as well as daily rainfall data. The methodology includes data exploration, distribution fitting, copula parameter estimation, and premium calculation based on rainfall threshold values. The analysis results indicate that the Gaussian copula is the best model for capturing the dependence between variables, with the lowest AIC and BIC values. Premium calculations based on rainfall indices show variations according to threshold values, with lower premiums for smaller thresholds, confirming the effectiveness of the Gaussian copula in modeling risk for agricultural insurance premiums.
Pelatihan Pembuatan Laporan Keuangan Pelaku Usaha Mikro, Kecil, Dan Menengah (UMKM) Di Desa Badran Sari Julianty, Dila Tirta; Listiani, Amalia; Lestari, Fuji; Sofia, Ayu; Rivai, Muklas; Mahrani, Dwi; Fitriawati, Andi
RENATA: Jurnal Pengabdian Masyarakat Kita Semua Vol. 3 No. 3 (2025): Renata - Desember 2025
Publisher : PT Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/1.renata.140

Abstract

Hingga saat ini terdapat sekitar 15 Usaha Mikro, Kecil, dan Menengah yang ada di Desa Badran Sari. Kegiatan usaha yang dilakukan oleh UMKM tidak terlepas dari transaksi keuangan baik transaksi masuk maupun keluar. Apabila transaksi tersebut diolah dengan baik maka dapat menghasilkan laporan keuangan yang tepat untuk berbagai periode waktu. Dengan adanya laporan keuangan yang tepat, maka bisa didapatkan informasi yang tepat mengenai kondisi keuangan dari suatu usaha sehingga bisa membuat keputusan yang tepat untuk masa yang akan datang. Dengan rendahnya kemampuan masyarakat desa dalam mengolah informasi sehingga menjadikan para pelaku UMKM tidak dapat memanfaat informasi keuangan yang mereka miliki sebagai dasar dalam pengambilan keputusan dan pengembangan usahanya. Oleh karena itu perlu diadakannnya suatu pelatihan terkait pembuatan laporan keuangan untuk meningkatkan kemampuan masyarakat Desa Badran Sari yang memiliki UMKM sehingga tercapainya masyarakat informasi dan berbasi pengetahuan. Hal ini diharapkan dapat meningkatkan ukuran usaha UMKM dan meningkatkan kegiatan perekonomian di Desa Badran Sari, Kecamatan Punggur, Kabupaten Lampung Tengah. Setelah dilaksanakannya pelatihan mengenai penyusunan laporan keuangan, jumlah peserta yang memiliki tingkat pemahaman rendah mengalami penurunan dari semula 68,18% menjadi 36,36% dan jumlah responden dengan tingkat pemahaman sedang mengalami peningkatan lebih dari dua kali lipat dari semula 27,27% menjadi 59,09%
Teknologi Smart Conservation Untuk Identifikasi Spesies Mangrove Di Kawasan Ekowisata Cuku Nyinyi, Lampung Triyana Muliawati; Fuji Lestari; Mika Alvionita Sitinjak; Eristia Arfi; Devia Gahana Cindi Alfian
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 1 : Februari (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Implementation of Amortization and Sinking Fund in Financial Literacy Education to Prevent Online Loan Traps Among Students of MAN 1 Bandarlampung Indah Gumala Andirasdini; Fuji Lestari; Dila Tirta Julianty; Muklas Rivai
Jurnal Pengabdian Masyarakat Vol. 7 No. 1 (2026): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v7i1.2837

Abstract

Purpose: This community service initiative aims to strengthen students’ financial literacy by introducing key concepts such as amortization tables and sinking fund strategies. A significant proportion of students exhibit limited understanding of high-interest debt structures and lack essential personal financial management skills. The concept of amortization was introduced to demystify loan repayment mechanisms, while the sinking fund approach was presented as a disciplined strategy for long-term saving and financial goal achievement. Method: The program was implemented through a series of interactive socialization sessions, hands-on educational activities, and simulation workshops. Students actively developed amortization schedules and sinking fund models, enabling them to apply theoretical knowledge in practical contexts. To measure the impact, pre-test and post-test questionnaires were administered to assess improvements in students’ comprehension of financial principles. Practical Applications: The program successfully produced a range of accessible educational resources, including learning modules, student-friendly booklets, and digital Excel templates. These tools empower students to independently simulate loan repayment scenarios and savings plans, promoting self-reliance and informed financial decision-making. Conclusion: The results demonstrated a marked improvement in students’ financial literacy, with scores increasing from an average of 5.67 to 8.00. The integrated, experiential approach not only enhanced understanding but also fostered greater financial resilience among youth, equipping them with the knowledge to avoid predatory online lending practices that are increasingly common among students today.
Black-Scholes Method for Rainfall Index-Based Agricultural Insurance Premiums Lestari, Fuji; Julianty, Dila Tirta; Vikarti, Maulida Magdalena
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art7

Abstract

Agriculture plays an important role in national economic development. However, it has the highest risk of loss due to its dependence on climate conditions. One of the efforts to reduce the risk of crop failure is through an agricultural insurance program. This study aimed to analyze the value of the rainfall climate index used and the calculation of agricultural insurance premiums based on it. The method used to determine the rainfall climate index was the historical burn analysis method, while the method used to calculate agricultural insurance premiums was the Black-Scholes method. The study showed significant spatial variation in rainfall index-based agricultural insurance premiums across Sumatra. Premiums rose with higher percentiles, with North Sumatra the highest (IDR 3.28–3.55 million) and Aceh the lowest (IDR 100–137 thousand). The inclusion of all rainfall stations revealed a more detailed spatial pattern. Overall, premiums strongly reflect local climatic conditions and can aid risk assessment and insurance planning.
Pemodelan Hybrid Radial Basis Function Neural Network Berbasis Ensemble Learning dalam Prediksi Parameter Nitrat (NO₃⁻) pada Sungai di Wilayah Tropis Rifka Noor Azizah; Azahra Sukesih; Isti Dina Febriyanti; Nur Faizaturrohmah; Fuji Lestari
Jurnal Kesehatan dan Pengelolaan Lingkungan Vol. 7 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jkpl.v7i2.16292

Abstract

Nitrate (NO₃⁻) concentration is one of the key indicators for evaluating river water quality, particularly in tropical regions with relatively high temperatures, which accelerate the nitrification process in the nitrogen cycle. Increasing human activities, such as residential development, agricultural practices, livestock farming, and aquaculture near rivers, can increase the nitrogen nutrient load, posing a risk of degrading water quality. Traditional methods such as the Pollutant Index (PI) and Water Quality Index (WQI) only reflect current conditions and cannot accurately predict changes in water quality. Therefore, this study aims to analyze the effectiveness of the Radial Basis Function Neural Network (RBFNN) model in predicting nitrate concentrations based on water quality parameters in rivers in tropical regions. This study was conducted by analyzing 164 publications providing data on water physico-chemical parameters, such as temperature, pH, TDS, TSS, DO, BOD, COD, nitrite, ammonia, and electrical conductivity. The obtained data were then processed through data cleaning, logarithmic transformation, feature engineering, and model training using an RBFNN combined with Gradient Boosting and Random Forest methods via an ensemble approach. Model evaluation was performed using the coefficient of determination (R²). The results of the study indicate that this model exhibits excellent predictive capability, with an R² value of 0.9907 for residential riverbanks and 0.9953 for agro-aquatic riverbanks, outperforming conventional ANN models in similar studies in Indonesia, which generally yield R² values in the range of 0.90–0.97, and comparable to the best RBFNN models ever reported in the international literature. Sensitivity analysis indicates that the parameter most influential on nitrate concentration is nitrite (40.25%), followed by electrical conductivity (19.80%) and TDS (11.10%). These results indicate that the RBFNN is effective in modeling nonlinear relationships among water quality parameters and has the potential to be developed as a core component in an early warning system for nitrate pollution, a decision support system for water resource managers, and a more efficient and adaptive data-driven water quality monitoring instrument to support the sustainable management of tropical rivers. Keywords: radial basis function, nitrate, tropical rivers, water quality, neural network
Co-Authors Andi Fitriawati Ayu Sofia Azahra Sukesih Azizah Dzakiya Danni Gathot Harbowo Devia Gahana Cindi Alfian Dila Tirta Julianty Dila Tirta Julianty Dwi Mahrani Eristia Arfi Haninda Bharata Indah Gumala Andirasdini Isti Dina Febriyanti Listiani, Amalia Mika Alvionita Sitinjak Muklas Rivai Muliawati Nur Faizaturrohmah Rafli Ramadhan Rifka Noor Azizah Rini Asnawati Rivai, Muklas Satria, Ardika Sitorus, Kristiani Sumarna, Nur Alvyanti Vikarti, Maulida Magdalena Yulita, Tiara