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All Journal Journal of Applied Informatics Science
Ananda Sathria Maulana Amri
Universitas Islam Negeri Siber Syekh Nurjati Cirebon
Computer Science & IT Control & Systems Engineering

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Journal : journal of applied informatics science

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Sistem Pendukung Keputusan Penentuan Siswa Berprestasi Menggunakan Metode Weighted Product Berbasis GUI Python pada SMK Plus Al Hilal Tegalgubug Sokid; Ananda Sathria Maulana Amri; Afifah Zayyin Amatillah
Journal of Applied Informatics Science Volume 2 Issue 1 (2026)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v2.i1.92

Abstract

Penentuan siswa berprestasi di SMK Plus Al Hilal Tegalgubug selama ini dilakukan secara manual dengan penilaian yang bersifat subjektif dan membutuhkan waktu lama. Penelitian ini bertujuan untuk membangun Sistem Pendukung Keputusan (SPK) penentuan siswa berprestasi menggunakan metode Weighted Product (WP) berbasis aplikasi GUI Python dengan database MySQL. Penelitian menggunakan metode pengembangan sistem Waterfall dan melibatkan 43 siswa kelas XII sebagai alternatif dengan lima kriteria penilaian, yaitu nilai akademik, kehadiran, sikap, prestasi non-akademik, dan pelanggaran. Hasil penelitian menunjukkan sistem yang dibangun mampu menghasilkan peringkat siswa berprestasi secara objektif dan konsisten untuk tiga program keahlian: TJKT, TO, dan AKL. Pengujian sistem menggunakan Black Box Testing menunjukkan seluruh fitur berjalan sesuai kebutuhan. Sistem ini diharapkan dapat membantu pihak sekolah dalam pengambilan keputusan yang lebih efisien dan transparan
Integration of Particle Swarm Optimization in Bidirectional Memory Networks for Improved Daily Climate Forecasting Accuracy Ardi Susanto; Heru Purnomo Kurniawan; Lia Farhatuaini; Muhammad Iszul Wilsa; Gina Khayatun Nufus; Ananda Sathria Maulana Amri
Journal of Applied Informatics Science Volume 1 Issue 2 (2025)
Publisher : GWS Tech Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65897/jais.v1.i2.85

Abstract

Global climate change has caused rainfall patterns to become increasingly fluctuating and difficult to predict using conventional weather forecasting methods. Accurate daily rainfall prediction is crucial for hydrometeorological disaster mitigation and agricultural sector planning. Although Deep Learning models such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) are capable of handling complex time-series data, the manual determination of hyperparameters often results in suboptimal models and entrapment in local optima. This study proposes the integration of the Particle Swarm Optimization (PSO) algorithm to automatically optimize hyperparameters (number of hidden neurons, dropout rate, and learning rate) in LSTM and BiLSTM architectures. The models were evaluated using a multivariate daily climate observation dataset encompassing temperature, humidity, wind speed, and actual rainfall. Experimental results indicate that PSO-based optimization significantly enhances prediction performance compared to baseline models. The PSO-LSTM approach successfully reduced the Root Mean Square Error (RMSE) to 17.59 mm and Mean Absolute Error (MAE) to 9.07 mm, comparable to the performance of PSO-BiLSTM, which achieved an RMSE of 17.59 mm and an MAE of 9.20 mm. These findings prove that automatic parameter tuning using swarm intelligence algorithms can highly optimize sequential neural network architectures in capturing rainfall pattern volatility, making it highly recommended as a foundation for a more accurate early warning system.
Co-Authors Afifah Zayyin Amatillah Ardi Susanto Gina Khayatun Nufus Heru Purnomo Kurniawan Lia Farhatuaini Muhammad Iszul Wilsa Sokid