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All Journal International Journal of Advances in Applied Sciences TEKNIK INFORMATIKA Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik JURNAL ILMIAH INFORMATIKA Generation Journal Jurnal Mnemonic Jurnal Teknik Informatika (JUTIF) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Abdimas Terapan Dedication : Jurnal Pengabdian Masyarakat
M. Rosidi Zamroni
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Islam Lamongan
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Implementation of the SAW Method for Mobile Phone Selection Recommendations at Holida Seluler Store Mu'afi, Achmad Faiq; Rohman, M Ghofar; Zamroni, Moh Rosidi
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.26505

Abstract

Public demand for mobile phones continues to increase as mobile phones evolve as tools for communication, work, entertainment, and access to digital information. With so many products with varying specifications to choose from, consumers often find it difficult to determine which mobile phone suits their needs. Holida Seluler, a store that sells various types of mobile phones, still uses a manual approach in providing recommendations to customers, which can potentially result in inaccurate decisions. This study aims to develop a website using the Simple Additive Weighting (SAW) method to assist customers in determining the best mobile phone, as well as to design a system capable of presenting objective calculation results based on predetermined criteria weights that can be directly applied in the recommendation process. The data used consists of 50 mobile phone products available in stores, with seven main criteria, namely: price, RAM, internal memory, camera, battery capacity, screen, and refresh rate. This system was built using the PHP programming language and MySQL database. The implementation results show that the system can objectively rank mobile phones based on user preferences, with the A45 alternative as the best choice, obtaining the highest score of 0.9100. This system is capable of providing fast, accurate, and data-driven recommendations, thereby increasing service effectiveness and enhancing the customer experience in choosing the right product
Decision Support System For Determining The Major Of New Students At SMKS Sunan Drajat Sugio Using The Method SAW (Simple Additive Weighting) Rohma, Riska Dwi Elida Yahyatul; Rohman, M. Ghofar; Zamroni, M. Rosidi
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.26553

Abstract

Determining majors for new students at vocational schools is an important process that can affect student achievement and future prospects. However, this process is often still carried out manually and is not very objective, which can potentially lead to a mismatch between the chosen major and the potential and interests of the students. Therefore, a system is needed that can help schools determine majors more accurately, efficiently, and based on data. This study aims to design a Decision Support System (DSS) for determining the majors of new students at SMKS Sunan Drajat Sugio using the Simple Additive Weighting (SAW) method. The SAW method was chosen because of the ease of calculation and its ability to process multi-criteria data to produce systematic decisions. The criteria used in this system include report card averages, basic competency test results, and student interests. This system is web-based with a user-friendly interface. Testing was conducted using 65 new student data for the 2024/2025 academic year by comparing the system's calculation results with manual calculations using the SAW method that had been validated by the school. The test results showed a 100.0% match between the system results and manual calculations, indicating that the system is capable of implementing the SAW method accurately and consistently. Thus, the developed system can be used as a tool to assist
Implementation of the Content-Based Filtering Method in Menu Recommendations at Pandawa Pondok Kopi Saputra, Muhammad Hanes Eka; Rohman, M.Ghofar; Zamroni, M.Rosidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1838

Abstract

The rapid growth of the coffee shop industry and the wide variety of menu offerings at Pandawa Pondok Kopi demand a system capable of delivering accurate and personalized menu recommendations. This study aimed to develop a web based menu recommendation application using Content Based Filtering (CBF), leveraging TF-IDF for document vectorization and Cosine Similarity to measure product description similarity.The system was implemented with PHP and MySQL, featuring a responsive interface across three main modules: the homepage (displaying the menu list), the menu detail page (providing full information and similar recommendations), and the admin dashboard (for menu data management). Menu descriptions were preprocessed (tokenization, stop word removal, and stemming) before computing TF-IDF weights. Given a user’s selected menu item, the system calculated Cosine Similarity between its description vector and those of all other menu items, then presents the top three matches. Functionality was verified via Black Box Testing to ensure that admin login, menu addition/editing, recommendation displays, and interface navigation conform to specifications. Test results showed an average Cosine Similarity score ranging from 0.62 to 0.78, indicating satisfactory accuracy in matching user preferences. The system also achieved an average response time of under one second under standard load, meeting efficiency criteria.In conclusion, the Content Based Filtering implementation successfully enhances the relevance of menu recommendations and user experience, thereby supporting increased customer satisfaction and operational effectiveness at Pandawa Pondok Kopi.
Hybrid deep learning approach for Indonesian hoax detection: a comparative evaluation with IndoBERT Mujilahwati, Siti; Zamroni, Moh. Rosidi; Sholihin, Miftahus
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp322-332

Abstract

The spread of hoaxes in Indonesia has escalated significantly, with over 12,547 cases recorded between 2018 and 2023. Low public literacy and uncontrolled information flow contribute to the rapid dissemination of false content that fuels disinformation and social unrest. Previous studies have utilized artificial intelligence (AI) approaches such as Indonesia bidirectional encoder representations from Transformers (IndoBERT) and deep learning models like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), convolutional neural network (CNN), and Transformer-based methods. However, most relied on a single modeling paradigm and did not address the trade-offs between classification performance and computational efficiency. This study proposes a hybrid architecture that integrates IndoBERT with bidirectional gated recurrent unit (BiGRU) and BiLSTM to enhance Indonesian hoax detection. Using 4,312 news articles and 10-fold cross-validation, we compare the performance of IndoBERT–BiGRU, IndoBERT–BiLSTM, and the proposed hybrid IndoBERT–BiGRU BiLSTM model. Evaluation metrics include accuracy, precision, recall, F1 score, and training time. The hybrid model achieved the best performance with 98.73% accuracy, 99.01% recall, 98.04% precision, and 98.98% F1 score, while also reducing training time compared to single models. These findings demonstrate that combining BiGRU and BiLSTM within the IndoBERT framework effectively balances performance and efficiency, making it a robust solution for Indonesian text classification.
Co-Authors Abdul Kholiq Agung Wahyudi Ahmad Fauzi Hendratmoko Alfarisi, Muhammad Nur Fikri AlMuhibbi, Muhammad Rayendra Anam, M. Khairul Azizah, Luluk Nur Azza Abidatin Bettaliyah AZZA ABIDATIN BETTALIYAH Dinar Mahdalena Leksana 1 Erna Hayati, Erna Erry Anggraini ERRY ANGGRAINI Farizki, Achmad Nurasel FATHARANI, ATIKA Fatkhul U, M. Miftah Firdaus, Muhammad Alvin Fudzee, Mohd Farhan Md Hamid, Rahayu A Ismail, Mohd Norasri KIKI SEPTARIA Lilik Anifah M. Ghofar Rohman M. ZAKI QOMARUDDIN Miftahus Sholihin Mu'afi, Achmad Faiq Mufrody, Moh Adam Nurroziqin, M Chabib Nurul Aswa Omar Prastowo, Diko Prsatama, Febrian Abie Qabilah Cita K. N. S Rahayu A Hamid Retno Wardhani Rohma, Riska Dwi Elida Yahyatul Saputra, Muhammad Hanes Eka Siti Mujilahwati Sulaiman, Akhmad Nurali Umam, Moch. Zuhrul Zirby, Qonit