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All Journal Prosiding SNATIKA Vol 01 (2011) JOURNAL SCIENTIFIC OF MANDALIKA (JSM)
Eka Putra, I Gede Juliana
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Humanities Languange, Linguistic, Communication & Media Other

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Rancang Bangun Sistem Informasi Akademik Berbasis Website pada Yayasan Perguruan Raj Yamuna Ananda Surya, Ni Putu Risna Diana; Eka Putra, I Gede Juliana; Krisna Juliharta, I Gede Putu
Prosiding SNATIKA Vol 4 (2017): Prosiding Snatika (Seminar Nasional Teknologi, Informasi, Komunikasi dan Aplikasinya)
Publisher : LPPM STIKI Malang

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Abstract

Permasalahan yang menjadi kendala selama ini saat menggunakan sistem manual di Yayasan Perguruan Raj Yamuna adalah pada bagian akademik seperti data siswa yang selama ini masih manual berupa Microsoft Excel, data guru dan data akademik hingga data nilai siswa yang masih tersimpan dalam satu file yang menyebabkan kehilangan data dan memperlambat kinerja pihak yayasan. Penerapan sistem informasi akademik bertujuan untuk meningkatkan kinerja pihak yayasan agar menjadi lebih efektif dan mempermudah proses pelaporan dan pemberian informasi untuk siswa terkait nilai. Penelitian ini menggunakan metode Waterfall yang dimulai dari observasi dan wawancara ke tempat penelitian, merancang sistem, implementasi, testing, hingga penyebaran kuesioner yang bertujuan guna untuk mengetahui keefektifitasan sistem. Penelitian ini menghasilkan sebuah website sistem informasi akademik yang membantu pengolahan data akademik, user dapat melakukan pengolahan data akademik, pengolahan nilai siswa, memberikan informasi nilai, informasi dashboard dan grafik akademik. Hasil kuesioner menyimpulkan bahwa sistem informasi akademik yang diterapkan efektif dikembangkan untuk menunjang kinerja pihak yayasan dalam pengelolaan data akademik hingga pelaporan akademik.
Klasifikasi Kategori Feedback EDOM Primakara University dengan Algoritma RNN LSTM Ichwanto, Rizky Aditya; Fredlina, Ketut Queena; Eka Putra, I Gede Juliana
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 6 No. 6 (2025)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol6iss6pp1522-1532

Abstract

This research aims to apply the Long Short-Term Memory (LSTM) algorithm in sentiment analysis of the Student Evaluation of Teaching (EDOM) at Primakara University, with the goal of improving higher education quality through the evaluation of faculty performance. Primary data was collected through interviews with the Quality Assurance Agency, while secondary data was obtained from EDOM for the academic years 2020/2021 to 2022/2023. A sentiment classification model was constructed using LSTM, initially dividing the data into 20 categories. To balance the data distribution, these categories were then merged into 6 main categories. The model was trained and tested using cross-validation, achieving an accuracy of 97%. However, when the model was tested with 100 new EDOM data points, the accuracy decreased to 74%, which is suspected to be caused by the emergence of new vocabulary that was not recognized or stored in the trained machine learning model. This decline in accuracy indicates the limitations of the model in handling new EDOM data that differs from the training data, and highlights the importance of periodic model updates or the use of out-of-vocabulary techniques to improve model performance in the future.
Co-Authors Ananda Surya, Ni Putu Risna Diana I Gede Putu Krisna Juliharta Ichwanto, Rizky Aditya Ketut Queena Fredlina