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PENGARUH PREMI DAN KUALITAS PELAYANAN TERHADAP KEPUASAN NASABAH DI PT. BRI LIFE CABANG JAMBI Emi Eriyani; Sucipto Sucipto; Agustina Mutia
Jurnal Kajian dan Penalaran Ilmu Manajemen Vol. 2 No. 1 (2024): Januari : Jurnal Kajian dan Penalaran Ilmu Manajemen
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jkpim.v2i1.261

Abstract

The purpose of this study was to determine the influence and how much the premium and service quality have on customer satisfaction at pt. Bri life Jambi branch. This research uses a quantitative approach with primary data collection techniques in the form of a questionnaire of 48 respondents. It can be concluded that the variable premium and service quality have a significant effect on customer satisfaction. The results showed that the calculated t value for the premium variable (Xi) was 2.589 with a significance level of 0.013 because t calculated was greater than t table (2.589 <0.05), so it could be concluded that Ho was rejected and Ha was accepted which stated that there was premium significantly positive effect on customer satisfaction Bank BRI Life
Prediksi Jumlah Kasus Penyakit Demam Berdarah Dengue Menggunakan Metode Long Short-Term Memory (LSTM) Nurmelidia Larasati; Sucipto Sucipto; Syarifah Putri Agustini Alkadri
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 6 No. 1 (2026): Maret : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v6i1.2100

Abstract

Dengue Hemorrhagic Fever (DHF) an infectious disease with fluctuating case numbers that can suddenly increase, posing significant public health challenges. In Pontianak City, 106 cases with 1 death were recorded in 2019, decreasing from 195 cases with 3 deaths in 2018. In 2020, the number dropped further to 27 cases with no fatalities. This condition indicates the need for a prediction system capable of accurately estimating the number of cases to support decision-making processes. This study aims to develop a model for predicting daily DHF cases in Pontianak City using the Long Short-Term Memory (LSTM) method. The data used includes daily DHF cases, average temperature, average humidity, and rainfall from 2020 to 2025. The research stages included data cleaning, normalization using Min-Max Scaling, historical data formation, model training, and evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The best model employed a single LSTM layer with 64 neurons, 50 epochs, and a batch size of 32, yielding an RMSE of 0.87 and MAE of 0.63. These results indicate that the LSTM method is capable of generating predictions close to actual values and is reliable for estimating daily DHF cases in Pontianak City. The developed Streamlit-based application provides interactive visualization and accurate predictions, making it a valuable tool for health authorities in DHF prevention and control efforts.