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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Jurnal Script Jurnal Teknoinfo Jurnal Ilmiah Media Sisfo Bantenese Jurnal Pengabdian Masyarakat Building of Informatics, Technology and Science Jurnal Mantik Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Engineering Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Scientific Journal of Informatics Jnanadharma
Utami, Annisaa
Institut Teknologi Telkom Purwokerto
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences

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Website Based Academic Information System Design Using Extreme Programming Method Prasetyo, Deni; Utami, Annisaa; Laksana, Tri Ginanjar
Journal of INISTA Vol 6 No 2 (2024): May 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v6i2.1214

Abstract

SMP Negeri 3 Watukumpul is a junior high school located in Bongas Village, Watukumpul District, Pemalang Regency, Central Java. This school has implemented website-based technology as a medium for conveying information, but the website is no longer usable. The data obtained from the interview results indicate the need for an academic information system website as a medium for disseminating information and managing academic data digitally. Based on the existing problems, researchers will re-create a website-based Academic Information System for SMP Negeri 3 Watukumpul as a medium for delivering information and managing academic data. This research uses the Extreme Programming method, which is one of the development methodologies of Agile Software Development Methodologies. The Extreme Programming method has several stages: Planning, Design, Coding, and Testing. The system uses black box testing on student, admin, and teacher accounts. Blackbox testing on student accounts includes login, register, profile, news, wall magazine, course material, grades, contacts, and logout menus. Blackbox testing on teacher accounts consists of login menus, course materials, grades, and grade page details. Black box testing on admin accounts consists of login menus, school profiles, teachers and employees, news, wall magazines, extracurriculars, and lesson schedules. In testing this system, researchers use black box testing to test its functionality. The black box test from the student's account got a result of 100%, the black box test result from the teacher's account got 100%, and the black box test from the admin account was 98.57%. Thus, the average black box test result from the three users was 99.52%. With the existence of this school website, it is hoped that information dissemination activities and academic data management activities will become more effective and efficient.
Performance Comparison of Random Forest (RF) and Classification and Regression Trees (CART) for Hotel Star Rating Prediction Utami, Annisaa; Permadi, Dimas Fanny Hebrasianto; Rosita, Yesy Diah; Unjung, Jumanto
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.11068

Abstract

Purpose: This study proposes to evaluate the effectiveness of Random Forest (RF) compared to Classification and Regression Trees (CART) in prediction of hotel star ratings. The objective is to identify the algorithm that provides the most reliable and accurate classification outcomes based on diverse hotel attributes in accordance with the standard categorization of star hotel categories. This is necessary due to the important role of accurate star ratings in guiding consumer choices and enhancing competitive positioning in the hospitality industry. Method: This study conducted a comprehensive dataset about Hotel in Banyumas Regency, including location, facilities, the size of rooms, type of rooms, price of rooms, and customer reviews, subjected to training through both RF and CART algorithms. Both algorithms are evaluated using accuracy, precision, recall, and F1 score. Additionally, both algorithms due to in the same preprocessing while performing hyperparameter tuning improve the efficacy of each model. Result: The results showed that RF achieved the best overall accuracy and robustness than CART across all tests conducted. Furthermore, RF also outperformed CART in classification effectiveness among classes, including enhanced precision and recall scores across multiple stars rating categories, signifying increased generalization and consistency in classification tasks. RF classifier consistently surpassed the CART classifier in terms of both accuracy and F1-score throughout all random states and test sizes, with a highest score of 0.9932 at a random state of 100 and a test size of 0.4. The most reliable results were obtained using RF with 42 random states and a test size of 0.2, resulting in an accuracy of 0.9909, precision of 1.0, recall of 1.0, and F1 score of 1.0. Simultaneously, CART shows values of 0.9818, 1.0, 1.0, and 1.0, respectively, while maintaining the same variation. This consistent performance, regardless of fluctuations, illustrates the robustness and suitability of RF for classification tasks compared to CART. Novelty: This study offered new insights about the implementation of machine learning about hotel star rating predictions using RF and CART algorithms. Also, the novelty of the collected hotel dataset used in this study. A detailed comparative analysis was also provided, contributing to the existing literature by showing the effectiveness of RF over CART for this specific application. Future studies could explore the integration of additional machine learning methods to further enhance prediction accuracy and operational efficiency in the hospitality industry.
Co-Authors Abednego Dwi Septiadi Affiyanti, Rakhma Yuli Agus Sihabuddin Alon Jala Tirta Segara Ananda Aulia Rizky Auliya Burhanuddin Bita Parga Zen Dasril Aldo Dedy Agung Prabowo Deni Prasetyo, Deni Dimas Fanny Hebrasianto Permadi Erfanti Fatkhiyah Faisal Dharma Adhinata Fauzan, Fikrian Akmal Firmansyah, Muhammad Raafi'u Ikadhanny Yudyan Pratama Indriyati, Rini Jesica Emarapenta Br Sinulingga Jumanto Unjung Lutfi Hakim, Ahmad Mike, Amran Muhamad Azrino Gustalika Narantyo Maulana Adhi Nugraha Nicolaus Euclides Wahyu Nugroho Novian Adi Prasetyo Ramadhani, Ike Fitria Ratih Windu Arini Reva Rahardian RR. Yuliana Rachmawati Sapta Eka Putra Siti Khomsah, Siti Syam, Siti Nur Fadilah Teguh Rijanandi Tri Dimas Cipto Satrio Wibowo Tri Ginanjar Laksana Trihastuti Yuniati Usman, Muhammad Lulu Latif Wanda Ilham Yaqutina Marjani Santosa Yohanes Suyanto Yohani Setiya Rafika Nur