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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 International Journal Of Computer, Network Security and Information System (IJCONSIST) 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 Biner : Jurnal Ilmiah Informatika dan Komputer Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Teknik Informatika dan Komunikasi Scientica: Jurnal Ilmiah Sains dan Teknologi Coreid Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer 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 Ilmu Teknologi Informasi Indonesia (JITIFNA) 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
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OPTIMASI SVM DAN RANDOM FOREST DENGAN PSO PADA ASPECT-BASED SENTIMENT ANALYSIS ULASAN APLIKASI QPON Dea Ananda Refiza Rahma; Abdul Rezha Efrat Najaf; Reisa Permatasari
Biner : Jurnal Ilmiah Informatika dan Komputer Vol. 5 No. 2 (2026): Juli
Publisher : Program Studi Teknik Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Sains Al-Qur'an

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/biner.v5i2.11589

Abstract

Ulasan pengguna aplikasi QPon memuat berbagai opini terhadap aspek layanan, seperti fungsionalitas, transaksi dan pembayaran, serta customer service. Analisis sentimen umum belum mampu menangkap opini secara spesifik pada setiap aspek, sehingga penelitian ini bertujuan mengimplementasikan algoritma machine learning yang dioptimasi menggunakan Particle Swarm Optimization (PSO) untuk Aspect-Based Sentiment Analysis (ABSA) ulasan aplikasi QPon. Data diperoleh melalui scraping ulasan Google Play Store, kemudian melalui tahap filtering, pelabelan manual, preprocessing, dan pembobotan fitur menggunakan TF-IDF dengan maksimum 5.000 fitur. Model yang digunakan adalah SVM dan Random Forest dengan enam skenario, yaitu baseline, PSO, dan SMOTE-PSO. Evaluasi dilakukan menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa pengaruh PSO bersifat kondisional. Pada aspek fungsionalitas, RF + SMOTE + PSO memperoleh F1-score terbaik sebesar 72,46%, meningkat 11,66 poin persentase dibanding RF baseline. Pada aspek transaksi dan pembayaran, SVM + PSO memperoleh F1-score 62,98%, meningkat 0,36 poin persentase dibanding SVM baseline. Sementara itu, pada aspek customer service, SVM + PSO menurunkan F1-score sebesar 4,95 poin persentase dibanding SVM baseline. Kontribusi ilmiah penelitian ini adalah memberikan bukti empiris bahwa efektivitas PSO dalam ABSA tidak bersifat universal, melainkan bergantung pada karakteristik data setiap aspek. Dengan demikian, pemilihan model terbaik perlu dilakukan secara spesifik pada setiap aspek.
Bitcoin Price Prediction Using a Deep Learning Approach with an LSTM Algorithm Fathur Rahmansyah Maulana Muhammad; Reisa Permatasari; Efrat Abdul Rezha Najaf
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.
Analisis Performa Frontend Website Menggunakan Web Vitals Pada Halaman Community Yayasanmangroveindonesia.com najoan rizki; Reisa Permatasari; Abdul Rezha Efrat Najaf
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/q8mw3v50

Abstract

Frontend performance is a critical factor influencing website user experience, particularly in terms of page loading speed, visual stability, and rendering efficiency. This study aims to analyze the frontend performance of the community page on yayasanmangroveindonesia.com using the Core Web Vitals approach supported by two widely used automated testing tools, namely Google Lighthouse and GTmetrix. A descriptive quantitative method was employed by conducting performance testing three times on each tool, and the results were averaged to obtain more representative measurements. The evaluation focused on the metrics available in both tools, including Largest Contentful Paint (LCP) and Cumulative Layout Shift (CLS) as the primary metrics, supported by First Contentful Paint (FCP), Total Blocking Time (TBT), Speed Index, and Time to Interactive (TTI). The results show that Google Lighthouse achieved a Performance Score of  95, with an average LCP of 2.77 seconds, CLS of 0.03, FCP of 0.97 seconds, TBT of 56.67 ms, and Speed Index of 1.23 seconds. Meanwhile, GTmetrix obtained a Performance Score of 99 and a Structure Score of 100, with an average LCP of 0.48 seconds, CLS of 0.06, FCP of 0.48 seconds, TBT of 5.33 ms, and TTI of 0.66 seconds. Overall, the website demonstrates good frontend performance, although the LCP value measured by Google Lighthouse slightly exceeds the recommended Core Web Vitals threshold, indicating opportunities for further optimization. The findings suggest that combining Google Lighthouse and GTmetrix provides a more comprehensive performance evaluation and can serve as a reference for implementing frontend optimization strategies to improve user experience.
Co-Authors Abdul Rezha Efrat Najaf Abdul Rezha Efrat Najaf 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 Lintang Pratiwi Ayu Pangestuti, Roro Azis Suroni Bahri, Elsa Maya Bonda Sisephaputra Bonda Sisephaputra Bonda Sisephaputra Daniar, Ivan Faiz Dea Ananda Refiza Rahma Dhava Gilang Ramadhan Dhian Satria Kartika Yudha Dhian Satria Yudha Kartika Dimas Fajri Pamungkas Dwi Shahita Efrat Abdul Rezha Najaf Fathan Orvala Fathur Rahmansyah Maulana Muhammad Fatzali, Abrila Febriany, Asri Kinanti Fitri Ana Wati, Seftin Ghea Sekar Palupi Glenn Aurora Arapenta Surbakti 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 Kesya Sakha Nesya Arimawan Lavenia, Nur Lickha Manti, Rival Septian Jeflin Margono, Ferdi Puguh Marsyanda Firlyandita Mochamad Suhri Ainur Rifky Muhammad Daffa Muhammad Muharrom Al Haromainy najoan rizki Nur Aini Rakhmawati Perdana, Firman PUSPITASARI, DIANITA Putra, Muhammad Ardiansyah Eka Putrawanto, Daris Irfan Rahmat Nugroho Saputra Ratih Aisyah Rizka Hadiwiyanti RIZKY ALAMSYAH BIMANTARA Rizky Nugraha Ronggo Alit Ronggo Alit Safitri, Eristya Maya Sakti, Ciptagusti Sila Seftin Fitri Ana Wati 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