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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab IKRA-ITH ABDIMAS
Muhamad Mustamiin
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Computer Science & IT Engineering

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Penerapan Mikro Kontrol Untuk Peningkatan Budidaya Lobster Air Tawar Raswa Raswa; Muhamad Mustamiin; Willy Permana Putra
IKRA-ITH ABDIMAS Vol 5 No 2 (2022): IKRAITH-ABDIMAS No 2 Vol 5 Juli 2022
Publisher : Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1022.848 KB)

Abstract

Budidaya Lobster Air Tawar (LAT) merupakan pemanfaatan potensi aquacultureyang memiliki peluang untuk dikembangkan dan menjadi salah satu alternatif seseorangberwirausaha pada bidang perikanan. Pada prakteknya pembudidaya LAT tersebut masihmenemui kedala yang berkaitan dengan metode pengendalian lingkungan budidaya.Kegiatan ini bertujuan mengembangkan keterampilan berwirausaha dan mentransferteknologi mikro kontrol kepada dua kelompok masyarakat pembudidaya lobster air tawardi Kecamatan Suranenggala Kabupaten Cirebon. Metode kegiatan berupa penyuluhan yangmencakup pelatihan kewirausahaan, penerapan mikro kontrol dalam proses budidaya.Evalusi kegiatan dilakukan untuk mengukur tingkat pengetahuan, keterampilan,keefektifan teknologi mikro kontrol dalam implementasinya terhadap usaha mitra
OPTIMASI ARSITEKTUR BI-LSTM DENGAN ATTENTION MECHANISM UNTUK KLASIFIKASI PERUBAHAN PERANGKAT LUNAK PADA ULASAN PERANGKAT BERGERAK Alifia Puspaningrum; Muhamad Mustamiin; Meyer Mega Eklesia Silaban; Esti Mulyani
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8338

Abstract

Analyzing mobile application user reviews plays a crucial role in software evolution. However, manual processes are often constrained by the large volume of data and language ambiguity. This research develops an automated classification model to categorize reviews into bug reports, feature requests, and non-informative using a Bi-Long Short-Term Memory (LSTM) architecture reinforced with an Attention Mechanism. Experimental results show that the model achieves 94.33% training accuracy and 71.95% testing accuracy, outperforming the standard Bi-LSTM which only reached 87.58% in training accuracy and 70.85% in testing. In terms of efficiency, this model converges faster, reaching peak performance in only 100 epochs, compared to 500 epochs for the standard Bi-LSTM. Furthermore, experiments show that increasing architectural complexity, such as combining Bi-LSTM with Attention, triggers overfitting and weight fluctuations. Thus, the integration of the Attention Mechanism in BiLSTM is proven to provide an optimal balance between computational efficiency and prediction accuracy, effectively supporting decision-making systems for developers.
Co-Authors Alifia Puspaningrum Esti Mulyani Meyer Mega Eklesia Silaban Raswa Raswa Willy Permana Putra