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All Journal Jurnal Geocelebes Jambura Journal of Mathematics eProceedings of Engineering Journal of Statistics and Data Science
Lestari, Wina Ayu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Forecasting Export Value of Bengkulu Province Through Pulau Baai Harbour with ARIMA, ANN, and Hybrid ARIMA-ANN Approach Lestari, Wina Ayu; Nugroho, Sigit; Widodo, Fanani Haryo Widodo
Journal of Statistics and Data Science Vol. 3 No. 1 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i1.41289

Abstract

Forecasting is a process of predicting future events based on past event data. One of the time series models that can be used for forecasting is the Autoregressive Integrated Moving Average (ARIMA). The advantages of ARIMA are in the accuracy and flexibility of its forecasting in representing several different types of time series, but the main limitation is the linear form of the model which causes ARIMA to be unable to capture non-linear patterns in the data. An alternative model for time series modeling is Artificial Neuron Network (ANN). ANN can overcome the weaknesses of ARIMA, but cannot handle linear and nonlinear patterns of the data simultaneously. As an effort to improve forecasting accuracy, Hybrid ARIMA-ANN is carried out by taking advantage of the supremacy of ARIMA and ANN. This study aims to obtain the best model for forecasting the export value of Bengkulu Province, a model generated by the time series data of export values issued by Pulau Baai Harbour from January 2014 to June 2022. The result shows that the best model for predicting the export value of Bengkulu Province is the ARIMA-ANN hybrid model with MAAPE of 0.5289 and MASE of 0.7664.
Pengembangan Sistem Pertanian Terpadu Skala Mikro Berbasis IoT pada Front-End Website Lestari, Wina Ayu; Muayyadi, Achmad Ali; Armi, Nasrullah
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

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Abstract

Website pada Sistem Pertanian Terpadu Skala Mikro Berbasis IoT mengintegrasikan Sektor Pertanian, Peternakan, dan Perikanan dalam satu sistem infoemasi. Pengguna dapat memantau dan mengontrol budidaya secara real-time melalui website tersebut. Penelitian ini menguji kemudahan penggunaan, responsivitas, dan kecepatan pemuatan website. Angket Google Form diisi oleh 79 responden untuk mengevaluasi kemudahan penggunaan. Mayoritas responden memberikan penilaian baik terhadap tampilan, kemudahan menemukan informasi, kejelasan konten, tata letak, gambar, fitur-fitur, instruksi, dan panduan penggunaan. Namun, perbaikan diperlukan untuk meningkatkan pengalaman pengguna. Pengujian responsivitas menggunakan Inspect pada browser menunjukkan bahwa website responsif pada berbagai ukuran layar. Semua elemen dan konten tetap terlihat baik, navigasi mudah, dan teks serta gambar jelas. Pengujian kecepatan dengan Lighthouse dan Hostinger menunjukkan performa yang baik dalam kategori Performance. Meskipun demikian, aksesibilitas beberapa halaman perlu diperbaiki. Website pada Sistem Pertanian Terpadu Skala Mikro Berbasis IoT telah memenuhi spesifikasi kemudahan penggunaan, responsivitas, dan kecepatan pemuatan yang baik. Perbaikan masih diperlukan untuk meningkatkan pengalaman pengguna secara keseluruhan. Kata kunci: IoT, Website, Kemudahan Penggunaan, Responsivitas, Kecepatan Pemuatan.
Microclimatic Temperature Variability and Trends in Bengkulu Province: ANOVA and Regression-Based Analysis Norfahmi, Siti Hairunnisa; Samdara, Rida; Supiyati, Supiyati; Lestari, Wina Ayu
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.33376

Abstract

This study investigates the microclimatic variability and trends of air temperature across three meteorological stations—Fatmawati, Bengkulu, and Kepahiang—in Bengkulu Province, Indonesia. Using five years of daily data (June 2020 to May 2025), minimum (Tmin), maximum (Tmax), and average (Tavg) temperatures were analyzed to understand both spatial patterns and temporal changes in surface air temperature. One-way ANOVA was conducted to assess whether mean temperatures differed significantly across stations, followed by Tukey  post hoc test for pairwise comparisons. The analysis revealed a consistent and statistically significant difference in all temperature variables (p 0.05), particularly between the inland highland station (Kepahiang) and the two coastal stations. In addition, monthly averages of Tavg were analyzed using simple linear regression, with significance tested via regression-based ANOVA. All three stations exhibited statistically significant warming trends (p 0.005), with slopes ranging from +0.0152 to +0.0213 °C/month (~0.18–0.26 °C/year), despite relatively modest coefficients of determination (R² = 0.14–0.24). These results highlight a dual climatic dynamic in the region: strong seasonal and spatial variability, overlaid with emerging baseline warming. The study underscores the importance of localized climate analysis for adaptation planning, particularly in topographically diverse tropical regions facing increased exposure to climate variability and change.
Comparative Analysis of SARIMA, FFNN, and Hybrid Models for Sea Surface Temperature Prediction at Enggano Island (2018–2024) Natisharevi, Raditya Janaloka; Rizal, Jose; Firdaus, Firdaus; Novianti, Pepi; Lestari, Wina Ayu
JURNAL GEOCELEBES Vol. 9 No. 2: October 2025
Publisher : Departemen Geofisika, FMIPA - Universitas Hasanuddin, Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70561/geocelebes.v9i2.46445

Abstract

Sea Surface Temperature (SST) is a key oceanographic variable that influences fish distribution and the livelihoods of coastal communities. On Enggano Island, where most residents rely on fishing, SST is critical for identifying optimal fishing grounds due to limited accessibility and high operational costs. Accurate modeling and forecasting of SST are therefore essential for effective fisheries management and sustainable resource use. This study analyzes and predicts monthly SST patterns in Enggano Island using Seasonal Autoregressive Integrated Moving Average (SARIMA), Feed Forward Neural Network (FFNN), and Hybrid SARIMA-FFNN models. SARIMA effectively captures linear trends and seasonal variations but struggles with nonlinear dynamics and requires statistical assumptions. Conversely, FFNN models nonlinear relationships without such assumptions but is less efficient in representing linear and seasonal structures. The hybrid SARIMA-FFNN combines the strengths of both approaches, integrating linear-seasonal accuracy with nonlinear adaptability. Monthly SST data from January 2018 to December 2024, covering northern, eastern, southern, and western regions of Enggano Island, were analyzed. Results show that all models achieved high predictive accuracy, with MAPE values below 10%. Based on RMSE, FFNN outperformed the other models across all regions (north: 1.173, east: 0.999, south: 1.245, west: 1.049), confirming FFNN as the most accurate model for SST prediction. Predicted SST values across the four regions exhibited only minor differences, offering fishermen flexibility in selecting fishing grounds. Sustainable fishing strategies should also consider species-specific temperature preferences and other ecological factors influencing fish distribution.
Co-Authors Achmad Ali Muayyadi Firdaus Firdaus Jose Rizal Nasrullah Armi Natisharevi, Raditya Janaloka Norfahmi, Siti Hairunnisa Pepi Novianti Rida Samdara Sigit Nugroho Supiyati . Widodo, Fanani Haryo Widodo