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All Journal Elkom: Jurnal Elektronika dan Komputer Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Jurnal Khatulistiwa Informatika Jurnal Informatika Universitas Pamulang Antivirus : Jurnal Ilmiah Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Computer Science (CO-SCIENCE) Jurnal Teknik Informatika Journal of Artificial Intelligence and Digital Business Akademika
Diantika, Sri
Universitas Bina Sarana Informatika
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Journal : Antivirus : Jurnal Ilmiah Teknik Informatika

 1 
ANALISIS PENGARUH KUALITAS WEBSITE PPDB TERHADAP KEPUASAN PENGGUNA MENGGUNAKAN REGRESI LINIER BERGANDA Silvia, Hana; Aprilia Kartini, Kasih; Dafa, Muhammad; Nuris, Nuzuliarini; Diantika, Sri
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 18 No 2 (2024): November 2024
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v18i2.3936

Abstract

In today’s digital era, many individuals rely on websites to quickly and efficiently obtain information. Vocational High Schools (SMK) are not left behind in leveraging modern technology through the website-based New Student Admission System (PPDB). While this system offers convenience, crucial questions arise regarding the quality of the PPDB Bersama website and its impact on user satisfaction. This study aims to analyze the effect of website quality on user satisfaction at SMKS 28 Oktober 1928 II. Utilizing the Webqual 4.0 method, the research identifies three independent variables: usability, information quality, and interaction quality, with user satisfaction as the dependent variable. Data processing was conducted using Excel and SPSS. The results indicate that all independent variables significantly contribute to user satisfaction. The coefficient of determination (R²) of 0.813 suggests that 81.3% of user satisfaction can be explained by these variables, while the remaining percentage is influenced by other factors. These findings affirm that the quality of the PPDB Bersama website has a significant positive impact on user satisfaction at SMKS 28 Oktober 1928 II, highlighting the importance of enhancing and maintaining platform quality to support better educational processes.
IMPLEMENTATION OF MULTI-CLASS GRADIENT BOOSTING TO CLASSIFY ANIMAL SPECIES IN ZOOS: IMPLEMENTASI MULTI-CLASS GRADIENT BOOSTING UNTUK MENGKLASIFIKASIKAN JENIS HEWAN PADA KEBUN BINATANG Sri Diantika; Hiya Nalatissifa; Riki Supriyadi; Nurlaelatul Maulidah; Ahmad Fauzi
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 17 No 1 (2023): Mei 2023
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v17i1.2812

Abstract

Animals are one of the living things that have various types. Grouping types of animals based on similarities and differences in characteristics owned is one of the important activities carried out To make it easier to compare, recognize, study certain types of animals and be able to find out kinship relationships between animals, So if a new type of animal is found that does not yet have a name, it will be easier for us to give a name to the animal based on the type and based on the group. In research on the classification of animal species in zoos that have multi-class, the best classification is obtained by applying gradient boosting parameters with n_estimators of 50, max_depth 3, sub-sample of 1.0, learning rate of 0.1, and using criterion friedman Mse. And by implementing Split validation or division between training data by 80% for training data and 20% for testing data. The results stated that the proposed model was better than some other models that had also been tested with an accuracy value of 93.75%, recal of 94%, precision of 96% and MSE to measure the average magnitude of error in a series of classifications of 12.5%, the smaller the MSE value, the better it would be in classifying.
Co-Authors AA Sudharmawan, AA Agus Subekti Ahmad Fauzi Ahmad Fauzi Akrom, Akrom Aprilia Kartini, Kasih Dafa, Muhammad Fauzi Ahmad Muda Gata, Windu Hiya Nalatissifa Hiya Nalatissifa Khoirun Nisa Mareanus Lase Mawadatul Maulidah Nurlaelatul Maulidah Nuzuliarini Nuris Rifandi, Julian Riki Supriyadi Salsabila, Nina Nur Silvia, Hana Windu Gata