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All Journal Building of Informatics, Technology and Science Scientica: Jurnal Ilmiah Sains dan Teknologi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science Jurnal Informatics Nivedita
Tshamaroh, Muthia
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences Other

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AUDIT TATA KELOLA WEBSITE KAMPUNG KB PADA BKKBN PROVINSI RIAU DENGAN MENGGUNAKAN COBIT 2019 Mawaddah, Zuriatul; Ananta , Nur Via; Tshamaroh, Muthia; Megawati, Megawati
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 2 No. 8 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

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Abstract

Penelitian ini bertujuan untuk mengevaluasi tata kelola situs web Kampung KB pada BKKBN Provinsi Riau menggunakan kerangka kerja COBIT 2019. COBIT 2019 menyediakan panduan yang komprehensif untuk tata kelola dan manajemen Teknologi Informasi (TI), membantu organisasi dalam mengatasi tantangan yang ada. Penelitian ini fokus pada domain DSS04 (Managed Continuity), MEA01 (Managed Performance and Conformance Monitoring), dan BAI01 (Managed Program). Data dikumpulkan melalui observasi, wawancara, dan kuesioner yang diolah untuk mengukur tingkat kapabilitas tata kelola TI pada BKKBN Riau. Hasil penelitian menunjukkan bahwa proses DSS04, MEA01, dan BAI01 telah mencapai tingkat kapabilitas level 5 dengan kategori Fully Achieved (F). Hal ini menunjukkan bahwa situs web Kampung KB di BKKBN Riau sudah terdefinisi dengan baik, namun tetap perlu pemantauan dan peningkatan kinerja untuk mempertahankan kualitas tata kelola TI yang ada.
Amazon Stock Price Prediction Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Tshamaroh, Muthia; Nasution, Nur Shabrina; Nadhirah, Nurin; Alfira, Rizka Ayu; Xintong, Zeng
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.1656

Abstract

Stocks have become one of the largest and most intricate financial markets globally due to their high popularity, making them very challenging to predict as they can process millions of transactions rapidly. The objective of this study is to enhance the field by creating a dependable and accurate model for predicting the stock price of Amazon. This will be achieved via the use of advanced algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This research utilised historical data on Amazon's stock price from the past five years, which was acquired from Yahoo Finance. The data was partitioned using a hold-out validation technique, allocating 80% for training and 20% for testing. The model underwent training using different optimizers (Adam, SGD, RMSprop), batch sizes (8, 16, 32), and learning rates (0.001, 0.0001). The evaluation criteria comprised of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results suggest that the GRU model, when trained with the RMSprop optimizer using a batch size of 16 and a learning rate of 0.0001, as well as with the SGD optimizer using a batch size of either 16 or 32 and a learning rate of either 0.001 or 0.0001, produced the lowest error metrics, indicating superior performance. This study enables more precise forecasts of stock prices and more efficient investment techniques.
Analisis Dampak Risiko IT Pada Website Sistem Informasi Pelayanan Administrasi Surat Menyurat (SIASY) Menggunakan Metode FMEA Zarry, Cindy Kirana; Tshamaroh, Muthia; Agesti, Suci; Megawati
Journal Informatics Nivedita Vol 1 No 1 (2024): Journal Informatics Nivedita
Publisher : Universitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25078/nivedita.v1i1.4390

Abstract

Sistem Informasi Pelayanan Administrasi Surat Menyurat (SIASY) di perguruan tinggi menghadapi berbagai tantangan dan risiko IT yang dapat mengganggu efisiensi layanan administrasi. Isu-isu seperti akses terbatas bagi pimpinan, ketidakakuratan data, dan masalah sinkronisasi dengan sistem keuangan menjadi fokus utama yang perlu dianalisis untuk meningkatkan keandalan sistem. Penelitian ini bertujuan untuk mengeksplorasi penerapan metode Failure Mode and Effects Analysis (FMEA) dalam mengidentifikasi dan mengelola risiko yang ada pada SIASY. Dengan demikian, diharapkan dapat memberikan rekomendasi bagi pengelola sistem untuk meningkatkan kualitas dan keandalan layanan administrasi. Metode FMEA juga berguna untuk mengidentifikasi titik-titik lemah dalam sistem, mengevaluasi risiko yang terkait, dan memberikan solusi untuk memitigasi masalah yang ada. Data yang digunakan dalam riset ini meliput informasi tentang proses administrasi yang dilakukan melalui SIASY, serta hasil wawancara dan survei dengan pengguna sistem (mahasiswa, dosen, dan pegawai) untuk mendapatkan wawasan mengenai tantangan dan kebutuhan mereka. Hasil penelitian menunjukkan bahwa penerapan FMEA dapat membantu dalam mengidentifikasi dan mengelola risiko TI yang ada pada SIASY. Dengan mengatasi masalah seperti akses terbatas, ketidakakuratan data, dan sinkronisasi sistem, diharapkan efisiensi dan kepuasan pengguna dalam proses administrasi di perguruan tinggi dapat meningkat. Penelitian ini memberikan kontribusi penting bagi pengelola sistem dalam upaya meningkatkan keandalan dan kualitas layanan administrasi
Perbandingan Algoritma LSTM, Bi-LSTM, GRU, dan Bi-GRU untuk Prediksi Harga Saham Berbasis Deep Learning Tshamaroh, Muthia; Permana, Inggih; Salisah, Febi Nur; Muttakin, Fitriani; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7252

Abstract

Stock price prediction is an important component in making investment decisions. This study aims to compare the performance of four deep learning models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU, in predicting stock prices, in order to find the most optimal model for the implementation of an accurate stock price prediction system. Five years of historical data undergoes normalization, windowing, and is separated into training data, validation data, and test data. Model training is conducted with different settings of batch size, timestep, and three kinds of optimizers (Adam, SGD, RMSprop). Performance assessment employs MSE, RMSE, MAE, and R² measurements. The findings indicate that the Bi-GRU model utilizing Adam optimizer settings, a batch size of 8, and a timestep of 21 yields the highest performance, achieving an MSE of 0.0003, an RMSE of 0.0169, an MAE of 0.0129, and an R² of 0.9438. This model demonstrates a strong capability to identify intricate patterns and long-term temporal relationships, outperforming other models in accuracy. The results advocate for the establishment of a predictive system that aids investors and firms in making strategic decisions based on data.
Co-Authors Agesti, Suci Alfira, Rizka Ayu Ananta , Nur Via Febi Nur Salisah, Febi Nur Inggih Permana M. Afdal Mawaddah, Zuriatul Megawati Megawati - Muttakin, Fitriani Nadhirah, Nurin Nasution, Nur Shabrina Xintong, Zeng Zarry, Cindy Kirana