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All Journal JAAKFE UNTAN ( (Jurnal Audit Dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura) Jurnal Teknisi Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research JITCoS : Journal of Information Technology and Computer System
Faiza, Nayla
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Social Sciences Other

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Implementasi Multilayer Perceptron untuk Klasifikasi Berita Hoax dalam Media Sosial Amanda, Hervilla; Faiza, Nayla; Sofinah Harahap, Lailan
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 2 No. 1 (2025): NOVEMBER 2024 - JANUARI 2025
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v2i1.2336

Abstract

The very fast dissemination of information via social media in the current digital era has facilitated the spread of fake news or hoaxes. Hoax news is false information, often created deliberately to spread or manipulate public opinion. The spread of hoaxes on social media can have serious impacts, such as public unrest. Therefore, automatic detection of hoax news is very important to maintain the integrity of information circulating in society. This research aims to implement the Multilayer Perceptron (MLP) algorithm in classifying news as "hoax" or "not hoax". The MLP algorithm works by learning from training data containing labeled news text. Based on certain patterns and features, this model is expected to be able to detect whether a piece of news is a hoax or not. The implementation of Perceptron for hoax news classification aims to provide a system that can help social media users filter information, so that it can support a healthier and more trustworthy social media ecosystem. This research uses data collection methods from various social media and news sites, data preprocessing, MLP model formation, system implementation, and model evaluation. The implementation results show that the MLP model is able to classify hoax news with an accuracy of 63.1%. It is hoped that these findings can contribute to the development of accurate and efficient hoax detection technology.
Rancang Bangun Aplikasi Berbasis Web Perpustakaan Fakultas Dakwah dan Komunikasi UINSU Boby Amari, Ahmad; Faiza, Nayla
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 2 No. 1 (2025): NOVEMBER 2024 - JANUARI 2025
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v2i1.2401

Abstract

This research aims to design and build a web-based library application at the Faculty of Da'wah and Communication, State Islamic University of North Sumatra. (UINSU). In the digital era, conventional library management faces various challenges, including difficulties in book search and member data management. Therefore, an integrated and efficient system is needed to enhance accessibility and simplify the process of borrowing and returning books. The designed application is expected to provide features such as online book search, borrowing, and real-time member data management. The research method used is Research and Development (R&D) with a Waterfall approach, which includes needs analysis, system design, implementation, and testing. The technologies used in the development of this application include the PHP programming language, MySQL database, and Laravel framework. The research results show that this application not only improves the operational efficiency of the library but also provides a better user experience. With the presence of automatic notification features for book returns and an analytical dashboard for usage monitoring, this application is expected to reduce late returns and assist management in decision-making related to the library collection. In addition, this system is designed to be integrated with the academic information system at UINSU, enabling more efficient data exchange. The development of this application is a strategic step in modernizing library services in the higher education environment, in line with the ongoing digital transformation vision. Thus, this web-based library application is expected to serve as a model for other faculties in developing similar systems, providing benefits to the academic community, and improving the overall quality of library services.
APPLICATION OF WEIGHTED AVERAGE ALGORITHM IN RECREATIONAL PARK TOURIST DESTINATION RECOMMENDATION SYSTEM BASED ON GOOGLE MAPS USER RATINGS Faiza, Nayla; Siregar, Hervilla Amanda R.; Sitorus, Nur Shafwa Aulia; Nugroho, Agung; Aulia, Muhammad Fathir; Furqan, Mhd
JURNAL TEKNISI Vol 5, No 2 (2025): Agustus 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v5i2.3790

Abstract

Abstract: The development of digital technology has changed the behavior patterns of tourists in choosing travel destinations. Google Maps is now not only used to find restaurant locations but has also become the main source for searching nearby tourist destinations based on user ratings and reviews. This research aims to build a recommendation system for recreational park tourist destinations in Medan City by applying the Weighted Average algorithm using Google Maps user rating data. The data used comes from reviews by five users of five popular recreational parks in Medan City during the period from January 1, 2025, to April 30, 2025. The Weighted Average algorithm was chosen because it can provide a more objective and fair assessment by taking into account the weight of each rating given by users. As a result, this system can recommend the best recreational parks based on user experiences related to cleanliness, parking facilities, toilets, security, running paths, and accessibility. It is hoped that this system can help tourists choose destinations that meet their needs and preferences, as well as provide a more enjoyable and satisfying travel experience.Keywords : digital technology; google maps; recommendation system; weighted average algorithmAbstrak: Perkembangan teknologi digital telah mengubah pola perilaku wisatawan dalam memilih destinasi wisata. Google Maps kini tidak hanya digunakan untuk mencari lokasi restoran, tetapi juga menjadi sumber utama dalam mencari destinasi wisata terdekat berdasarkan rating dan ulasan pengguna. Penelitian ini bertujuan untuk membangun sistem rekomendasi destinasi wisata taman rekreasi di Kota Medan dengan menerapkan algoritma Weighted Average menggunakan data rating pengguna Google Maps. Data yang digunakan berasal dari lima ulasan pengguna terhadap lima taman rekreasi populer di Kota Medan selama periode 1 Januari 2025 hingga 30 April 2025. Algoritma Weighted Average dipilih karena mampu memberikan penilaian yang lebih objektif dan adil dengan memperhatikan bobot setiap rating yang diberikan pengguna. Hasilnya, sistem ini dapat merekomendasikan taman rekreasi terbaik berdasarkan pengalaman pengguna terkait aspek kebersihan, fasilitas parkir, toilet, keamanan, lintasan lari, dan aksesibilitas. Diharapkan sistem ini dapat membantu wisatawan dalam memilih destinasi yang sesuai dengan kebutuhan, preferensi, dan memberikan pengalaman wisata yang lebih menyenangkan dan memuaskan. Kata Kunci: google maps; sistem rekomendasi; teknologi digital; weighted average algorithm
Penerapan Algoritma Naive Bayes untuk Prediksi Financial Distress pada Perusahaan Publik sebagai Upaya Digitalisasi Analisis Akuntansi Sitorus, Nur Shafwa Aulia; Aulia, Muhammad Fathir; Faiza, Nayla; Siregar, Hervilla Amanda R.
JAAKFE UNTAN (Jurnal Audit dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura) Vol 14, No 2 (2025): Jurnal Audit dan Akuntansi Fakultas Ekonomi Universitas Tanjungpura
Publisher : Jurusan Akuntansi, Fakultas Ekonomi dan Bisnis, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jaakfe.v14i2.101099

Abstract

Perkembangan teknologi digital mendorong penerapan kecerdasan buatan dalam bidang akuntansi untuk meningkatkan akurasi dan efisiensi analisis keuangan. Penelitian ini bertujuan menerapkan algoritma Naive Bayes guna memprediksi kondisi financial distress pada perusahaan publik sektor manufaktur di Indonesia sebagai bagian dari digitalisasi analisis akuntansi. Data yang digunakan berupa 150 entri rasio keuangan, meliputi Current Ratio (CR), Return on Assets (ROA), Net Profit Margin (NPM), dan Total Asset Turnover (TATO). Model dievaluasi menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan akurasi sebesar 86,67%, precision 75%, recall 25%, dan F1-score 37,5%. Temuan ini menunjukkan bahwa Naive Bayes cukup efektif dalam mengklasifikasi perusahaan Non-Distress, namun masih perlu pengembangan untuk meningkatkan deteksi kelas Distress. Model ini berpotensi menjadi dasar pengembangan sistem deteksi dini berbasis digital dalam analisis keuangan perusahaan.
Modeling and Simulation of Indoor Temperature Dynamics Using Random Forest and Multi-Layer Perceptron Methods Risky, T. Tanzil Azhari; Faiza, Nayla; Hasibuan, Mhd Fikry Hasrul; Nasution, Mhd Syahru Ramadhan
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): Volume 1 Number 2, December 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.39

Abstract

Modeling and simulating indoor temperature changes is crucial for improving the energy efficiency of HVAC systems in smart buildings. This study created and compared two models, Random Forest and Multi-Layer Perceptron (MLP), to study indoor temperature changes and make 24-hour temperature predictions. The dataset used contained 97,606 readings from IoT sensors on Kaggle, which were then processed into 38,334 observations with a 5-minute interval. The feature engineering process included creating lag features, moving statistics, and temperature differences in order to capture the time patterns and thermal properties of the building. The Random Forest model showed better results with MAE of 0.146°C, RMSE of 0.285°C, and R² of 0.986, far better than the MLP which had MAE of 0.470°C, RMSE of 0.731°C, and R² of 0.907. A 24-hour simulation proved the Random Forest's ability to make step-by-step predictions, achieving an MAE of 0.057°C and an R² of 0.993 without any cumulative errors. Random Forest was able to capture dynamic temperature changes (29.5-35°C), while MLP provided more stable results (32.5-35°C). The results of the study show that Random Forest is more efficient in modeling temperature changes, with the potential for HVAC energy savings of 15-25% through more precise settings based on predictions.
Co-Authors Amanda, Hervilla Aulia, Muhammad Fathir Boby Amari, Ahmad Hasibuan, Mhd Fikry Hasrul Mhd Furqan Nasution, Mhd Syahru Ramadhan Nugroho, Agung Risky, T. Tanzil Azhari Siregar, Hervilla Amanda R. Sitorus, Nur Shafwa Aulia Sofinah Harahap, Lailan