Article Per Year (5 Year)
p-Index From 2021 - 2026
6.127
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL INSTEK (Informatika Sains dan Teknologi) JURNAL TEKNOLOGI DAN OPEN SOURCE Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics bit-Tech Journal of Economics, Business, and Government Challenges Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Advances in Data and Information Systems Jurnal Sistem Informasi dan Sains Teknologi Nusantara Science and Technology Proceedings Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Engineering, Science and Information Technology Jurnal Ilmiah Wahana Pendidikan KLIK: Kajian Ilmiah Informatika dan Komputer JITSI : Jurnal Ilmiah Teknologi Sistem Informasi COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Teknik Mesin, Industri, Elektro dan Informatika Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Teknik Informatika dan Komunikasi Scientica: Jurnal Ilmiah Sains dan Teknologi Coreid Journal Journal of Elektronik Sistem InformasI Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Journal of Emerging Information Systems and Business Intelligence (JEISBI) Journal of Information Technology and its Utilization Jurnal Teknik Informatika dan Teknologi Informasi
Permatasari, Reisa
Department Of Information System Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
Backend Design and Development of Thesis Management and Scheduling Application Using Rule-Based Algorithm in Physics Department UPN “Veteran” Jatim Andhika Rizky Aulia; Eka Dyar Wahyuni; Reisa Permatasari
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4386

Abstract

Thesis administration in higher education can be complex, especially in programs like Physics that previously lacked a structured system for managing thesis activities and seminar schedules. To address this, a backend system was developed to streamline these processes using a rule-based algorithm. The system supports multiple user roles—including students, lecturers, thesis coordinators, program coordinators, and administrators—by providing web-accessible API services. Key features include user authentication, pre-proposal submission, advisor assignment, seminar scheduling (proposal and final), oral examination coordination, and graduation document submission. The development followed the Scrum methodology over six sprint cycles, with each cycle aimed at improving functionality and ensuring system stability. To ensure the system met all functional requirements, black-box testing was conducted. The final version was deployed on a cloud hosting platform using Cloud Run, enabling public access to its API services. This solution is intended to enhance efficiency, reduce administrative workload, and provide a centralized, accessible platform for all stakeholders involved in the thesis process.
BCA Stock Price Prediction Using Time Series Method With GRU (Gated Recurrent Unit) Nugraha, Rizky; Abdul Rezha Efrat Najaf; Reisa Permatasari
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4500

Abstract

Stock price prediction is a crucial component in investment decision-making, enabling investors to plan strategies more accurately and minimize risks. This study applies the Gated Recurrent Unit (GRU) model to predict the stock prices of blue-chip banking companies in Indonesia using data from the period 2019 to 2024. The model utilizes historical stock data to forecast future trends. The results from the first testing scheme, with a data split ratio of 70% / 30%, using GRU units (128,256) with the Adam optimizer, show that the GRU model is the most optimal in terms of prediction, measured by metrics such as MSE, RMSE, and MAPE. This study also proposes a web-based dashboard that visualizes the predicted stock prices and provides decision-support tools for investors. The findings highlight the effectiveness of deep learning in financial forecasting and underscore its potential to enhance investment strategies.
House Price Prediction in Surabaya Using Backpropagation Neural Network Pamungkas, Dimas Fajri; Najaf, Abdul Rezha Efrat; Permatasari, Reisa
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4829

Abstract

This research develops a house price prediction system in Surabaya using the Backpropagation Neural Network (BPNN) method. The dataset was obtained through web scraping of property listings, resulting in 3,435 records with 52 attributes. To improve stability, the target variable (house price) was transformed using natural logarithms. Several neural network architectures were tested, and the best configuration [32, 64, 32] achieved Mean Absolute Error (MAE) of 0.3125, Root Mean Squared Error (RMSE) of 0.4201, R² of 0.7138, and Mean Absolute Percentage Error (MAPE) of 1.46%. A multi-run evaluation of 20 iterations confirmed consistency of results. The model was implemented as a web-based application using Flask, allowing users to predict house prices in real-time. This research shows that BPNN is reliable for property price forecasting and can support decision-making in the housing market.
Rancang Bangun Sistem Reservasi dan Manajemen Lapangan Futsal Berbasis Website (Studi Kasus Alena Soccer) RIZKY ALAMSYAH BIMANTARA; NUR CAHYO WIBOWO; REISA PERMATASARI
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 12 No. 3 (2025): Prosisko Vol. 12 No. 3 November 2025
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v12i3.10909

Abstract

Alena Soccer merupakan penyedia layanan lapangan futsal yang masih mengandalkan sistem pencatatan manual dalam operasionalnya. Hal ini menyebabkan sejumlah permasalahan, seperti terjadinya booking fiktif, kesulitan verifikasi pembayaran transfer, pencatatan data yang tersebar di berbagai tempat, serta kurangnya integrasi data antar layanan yang ditawarkan. Untuk mengatasi permasalahan tersebut, penelitian ini merancang dan membangun sistem informasi reservasi dan manajemen lapangan futsal berbasis website menggunakan framework Laravel dan database MySQL. Metode Waterfall digunakan untuk memastikan pengembangan sistem dilakukan secara bertahap dan terstruktur. Sistem yang dikembangkan memiliki fitur utama seperti reservasi lapangan online dengan pembayaran QRIS melalui Midtrans, manajemen membership dengan sistem tagihan berkala, penyewaan peralatan olahraga, layanan fotografer, fitur Open Mabar untuk berbagi biaya, sistem poin reward, Point of Sale untuk admin, serta dashboard laporan keuangan untuk owner. Berdasarkan hasil pengujian menggunakan metode black-box testing pada 125 skenario, seluruh fitur sistem berjalan sesuai dengan spesifikasi yang ditetapkan dan diterima dengan baik oleh pengelola Alena Soccer.
PENENTUAN PRIORITAS PEMBANGUNAN DESA RENGEL BERBASIS SISTEM PENDUKUNG KEPUTUSAN DENGAN METODE FAHP-WP Izra Noor Zahara Aliya; Rizka Hadiwiyanti; Reisa Permatasari
Journal of Information Technology and Its Utilization Vol 7 No 2 (2024): December 2024
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.7.2.5887

Abstract

Villages play a strategic role in national development, particularly in improving the quality of life and community welfare. Rengel Village, located in Tuban Regency, East Java, holds significant potential for development. However, decision-making in development through the “Musrenbangdes” (village development planning forum) is often hindered by conflicts of interest between neighborhood units (RT and RW) and the lack of an objective system to assess priorities. Therefore, a Decision Support System (DSS) utilizing the Fuzzy Analytic Hierarchy Process (F-AHP) and Weighted Product (WP) methods is needed to prioritize development. F-AHP determines criterion weights based on relative importance and addresses uncertainty in assessments, while WP ranks alternatives based on these weights. This study involves data collection, calculation of criterion weights, alternative development ranking, system development, and deriving conclusions. The results show that the CR value of 0.033 indicates a good level of consistency in comparisons, yielding the following criterion weights: RPJM (0.331), Urgency Level (0.278), Impact and Benefits (0.237), Regulatory Compliance (0.14), and Budget (0.014). Meanwhile, the alternative development ranking results indicate the following priority order: AP41 (1), AP06 (2), AP33 (3), AP49 (4), AP12 (5), AP40 (6), AP13 (7), AP11 (8), AP43 (9), and AP28 (10).
Evaluasi dan Redesign Website Qema Farm Menggunakan Metode User Experience Lifecycle Dwi Shahita; Reisa Permatasari; Nambi Sembilu
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5535

Abstract

This study aims to evaluate and redesign the Qema Farm website, a digital platform operating in the livestock sector and offering investment services through digital media. As user demands have increased, several usability issues have been identified, adversely affecting the overall user experience. An initial evaluation was conducted using the Heuristic Evaluation method, which revealed five usability problems, four categorized as cosmetic issues (severity rating 1) and one as a minor issue (severity rating 2). These findings indicate the need for improvements to enhance user interaction quality. Based on the evaluation results, a redesign process was carried out using the User Experience Lifecycle (UXL) method, encompassing the stages of analysis, design, prototyping, and evaluation. The redesigned interface was then tested through Usability Testing and the System Usability Scale (SUS) involving 6 respondents. Post-redesign evaluation demonstrated a significant improvement in the average usability score, increasing from 2.71 to 4.54 on a 5-point scale, with the highest score observed in the efficiency aspect (4.67). Additionally, the SUS score rose from an average of 57.50, classified as a “D” grade, to an average of 91.25, corresponding to an “A+” grade. These results demonstrate that the application of the UXL method was effective in enhancing both the interface quality and user experience of the Qema Farm website.
Bitcoin Price Prediction Using a Deep Learning Approach with an LSTM Algorithm Muhammad, Fathur Rahmansyah Maulana; Permatasari, Reisa; Najaf, Efrat Abdul Rezha
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3613

Abstract

The rapid advancement of digital financial technologies has accelerated the adoption of cryptocurrencies, with Bitcoin emerging as the dominant asset characterized by extreme price volatility and investment risk. Despite extensive studies on Bitcoin forecasting, existing predictive models remain limited in capturing long-term volatility dynamics and complex temporal dependencies, leading to unstable performance under fluctuating market conditions. This study addresses this gap by developing a deep learning-based forecasting framework using the Long Short-Term Memory (LSTM) algorithm integrated with a real-time web-based application. Historical Bitcoin price data were preprocessed through Min–Max normalization and transformed into time-series sequences using sliding window techniques. The proposed model consists of two stacked LSTM layers with 100 hidden units each, followed by a dense output layer, and was trained using the Adam optimizer with early stopping to prevent overfitting. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The experimental results demonstrate that the proposed LSTM model achieved a Test MAE of 2.27%, indicating substantially higher accuracy compared to conventional statistical forecasting approaches reported in prior studies. The model effectively tracks long-term price trends, although extreme short-term spikes remain challenging due to inherent market volatility. Furthermore, the integration of the trained model into a Flask-based web application enables interactive real-time price prediction, representing a practical innovation beyond offline forecasting models. Overall, this research demonstrates the effectiveness of deep learning for supporting cryptocurrency investment decisions in real-world practice.
Co-Authors Abdul Rezha Efrat Najaf Agung Brastama Putra Al-Ghiffari, Syafiq Amalia Anjani Arifiyanti Amalia Anjani Arifiyanti Andhika Rizky Aulia Anindo Saka Fitri Anindo Saka Fitri, Anindo Saka Fitri Aqsa Arumdapta, Gemintang Arrasyid, Nizar Maulana Aryo Sulistiono, Wisnu Aulia Putri Fajar Aviolla Terza Damaliana Ayu Pangestuti, Roro Bahri, Elsa Maya Bonda Sisephaputra Bonda Sisephaputra Daniar, Ivan Faiz Dhian Satria Kartika Yudha Dhian Satria Yudha Kartika Dwi Shahita Fatzali, Abrila Febriany, Asri Kinanti Fitri Ana Wati, Seftin Ghea Sekar Palupi Gosal, Andika Hakim, Alif Nur Rahman Hanifa, Fatya Hilman Habib Habibi, Muhammad Ivan Faiz Daniar Izra Noor Zahara Aliya Jannah Arum Kemangi, Anisya Khanza Afiatul Lavenia, Nur Lickha Manti, Rival Septian Jeflin Margono, Ferdi Puguh Mochamad Suhri Ainur Rifky Muhammad Daffa Muhammad Muharrom Al Haromainy Muhammad, Fathur Rahmansyah Maulana Najaf, Efrat Abdul Rezha Nugraha, Rizky Nur Aini Rakhmawati Pamungkas, Dimas Fajri Perdana, Firman PUSPITASARI, DIANITA Putra, Muhammad Ardiansyah Eka Putrawanto, Daris Irfan Ratih Aisyah Rizka Hadiwiyanti RIZKY ALAMSYAH BIMANTARA Ronggo Alit Safitri, Eristya Maya Sakti, Ciptagusti Sila Sembilu, Nambi Shafira, Putri Dian Sila Sakti, Ciptagusti Trinanda, Fiqi Akbar Wahyuni, Eka Dyar Wati, Seftin Fitri Ana Wibisono, Mahendra Priyo Wibowo, Nur Cahyo Yovan Febriawan Nurpratama Yuniar, Sella