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All Journal Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Jurnal Khatulistiwa Informatika Paradigma EVOLUSI : Jurnal Sains dan Manajemen Jurnal Teknologi Informasi dan Terapan (J-TIT) Indonesian Journal of Business Intelligence (IJUBI) Wasana Nyata JUSTIAN - Jurnal Sistem Informasi Akuntansi Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Informatics and Computer Engineering Journal Jurnal Pengabdian Masyarakat Bangsa WASANA NYATA Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat
Candra Agustina
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Indonesian Journal of Business Intelligence (IJUBI)

 1 
IMPLEMENTASI ALGORITMA J48 DENGAN TEKNIK BAGGING UNTUK PREDIKSI KIPI PESERTA VAKSINASI COVID-19 Eka Rahmawati; Candra Agustina
Indonesian Journal of Business Intelligence (IJUBI) Vol 5, No 1 (2022): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Program Studi S1 Sistem Informasi Fakultas Komputer dan Teknik Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v5i1.2072

Abstract

The Covid 19 vaccination is considered to be the most effective way to prevent the spread of the Corona Virus, in addition to a clean lifestyle such as washing hands, wearing masks, and keeping a distance from other people. Several large vaccine manufacturing companies in the world have issued a product in the form of a Covid-19 vaccine with various levels of effectiveness. The vaccine is still being distributed throughout the world, including Indonesia. The vaccine obtained an emergency distribution permit from the authorized institution and was administered to community groups that meet the requirements. However, during the implementation of the vaccine, many AEFIs (Post Immunization Adverse Events) were found, such as dizziness, fever, headaches, and some even fainted. Although not dangerous but quite disturbing for people with solid activities. Therefore, it is necessary to predict whether participants will get AEFI or not. The data consists of 8 Attributes, after being processed using the J48 Algorithm, the results show that the attributes that have a strong influence are 7 Attributes, while the rest have no major effect. The accuracy level of the prediction model obtained is 91,22% with this level of accuracy, it means that the model can be utilized by the parties concerned to then be able to anticipate.
IMPLEMENTASI SMOTE DAN ALGORITMA MACHINE LEARNING UNTUK MENINGKATKAN AKURASI REKOMENDASI HOTEL Agustina, Candra; Rahmawati, Eka; Irawan, Denny; Tristanti, Vriska wahyu
Indonesian Journal of Business Intelligence (IJUBI) Vol 7, No 2 (2024): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v7i2.5141

Abstract

Pariwisata memiliki peran penting dalam perekonomian global, dengan destinasi seperti Candi Borobudur menarik berbagai jenis pengunjung. Untuk meningkatkan pengalaman wisatawan, rekomendasi hotel yang akurat menjadi sangat penting. Namun, data yang tidak seimbang, seperti ulasan positif yang terlalu dominan, sering kali mengurangi kinerja model machine learning yang digunakan untuk rekomendasi. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan Synthetic Minority Over-sampling Technique (SMOTE) guna menyeimbangkan dataset dan meningkatkan akurasi rekomendasi hotel. Beragam algoritma machine learning, termasuk Random Forest, Support Vector Machines, dan Neural Networks, diterapkan dan dievaluasi. Hasil penelitian menunjukkan bahwa penerapan SMOTE secara signifikan meningkatkan kinerja semua model, dengan Random Forest memberikan hasil terbaik. Studi ini menunjukkan bahwa SMOTE, dalam kombinasi dengan teknik machine learning, memberikan solusi yang kuat terhadap ketidakseimbangan kelas pada sistem rekomendasi hotel, sehingga menghasilkan rekomendasi yang lebih andal dan relevan bagi wisatawan. Temuan ini memiliki implikasi penting bagi manajemen hotel dan sektor pariwisata secara keseluruhan.
Co-Authors - Amik BSI Yogyakarta, Gunawan Budi S Akhmad Syukron Akrom, Akrom Angga Ardiansyah Budi Al Amin Budi Al Amin eka rahmawati F Febiyanti Galih Setiawan Nurohim Gunawan Budi Sulistyo Haryanto, Wawan Hendri Hermawan Adinugraha Heribertus Ary Setyadi Irawan, Denny Maryani, Ina Nani Purwati Pribadi, Denny Rahmawati-Universitas Bina Sarana Informatika, Eka Rousyati, Rousyati Sardiarinto Sardiarinto Sri Mulyani Tri Wahyudi - AMIK BSI Yogyakarta Tri Wahyudi - AMIK BSI Yogyakarta Tristanti, Vriska wahyu Wahyu Setyo Prabowo Wawan Haryanto