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All Journal Journal of Agribusiness Science and Rural Development Journal of Data Science Theory and Application
Puspitasari, Wiji Tri
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Rancangan Simulasi Model Sistem Dinamis Pasok Kentang di Indonesia Lolanda Hamim Annisa; Puspitasari, Wiji Tri
Journal of Agribusiness Science and Rural Development Vol. 3 No. 2 (2024): JASRD
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/nn50ax39

Abstract

Potato supply chain system in this study comprises suppliers, producers, and consumers. Within the producer supply chain, potato production faces a deficit against domestic demand due to factors such as decreased productivity influenced by the type of seed used, rainfall, and harvesting processes. In this research, the Mean Absolute Percentage Error (MAPE) method is employed to assess the potato supply chain system for the next 10 years from social, economic, and environmental perspectives. This study combines explanatory and causal research, utilizing secondary data in the form of time series data. The simulation employs a suitable numerical integration method to achieve an accurate representation of processes within the system. Initialization considers the initial conditions and system parameters. Simulation results are analyzed to identify potential risks and opportunities that could impact potato supply availability. By utilizing this model, policies and strategies can be virtually tested to enhance national food security. Moreover, this simulation serves as an effective tool for designing contingency plans and mitigation measures to address various crisis scenarios that may arise in the potato supply chain, considering a productivity scenario increase from 16.56 tons/ha to 17.56 tons/ha.
ANALISIS SENTIMEN PADA CAPTION INSTAGRAM FOR BUSINESS MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE Puspitasari, Wiji Tri; Annisa, Lolanda Hamim
Journal of Data Science Theory and Application Vol. 4 No. 2 (2025): JASTA
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/wfnywf36

Abstract

Instagram merupakan media sosial yang banyak digunakan oleh masyarakat Indonesia, selain menjadi aplikasi berbagi foto dan video Instagram juga berperan dalam perkembangan bisnis UMKM karena banyak yang menggunakan fitur pada aplikasi tersebut untuk mempromosikan bisnisnya. Tujuan dari penelitian ini yaitu untuk menganalisis opini publik pengguna Instagram terhadap ulasan food vlogger pada UMKM pengguna Instagram for Business dengan menggunakan Naïve Bayes dan Support Vector Machine. Penelitian ini menggunakan 951 data dimana perbandingan dataset 90: 10 dengan 90% data latih dan 10% data uji, 80:20 dengan 80% data latih dan 20% data uji,  dan 70:30, yaitu 70% data latih dan 30% data uji. Hal ini menggambarkan bahwa opini para pengguna Instagram terhadap ulasan food vlogger pada UMKM pengguna Instagram for Business cenderung positif. Hasil evaluasi penelitian menunjukkan perbandingan 80:20 yang paling baik dengan akurasi dari Naïve Bayes sebesar 68,10% lebih tinggi dibanding Support Vector Machine yang hanya menghasilkan akurasi sebesar 62,80%. Berdasarkan hasil tersebut dapat disimpulkan bahwa algoritma Naïve Bayes lebih unggul dalam melakukan analisis sentimen terhadap ulasan food vlogger pada UMKM pengguna Instagram for Business. Hasil dari penelitian ini diharapkan dapat digunakan sebagai masukan untuk pengembangan akun UMKM tersebut.
Co-Authors Annisa, Lolanda Hamim Lolanda Hamim Annisa