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Elfira Umar
Universitas Stella Maris Sumba
Computer Science & IT Economics, Econometrics & Finance

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Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Naïve Bayes Dan Decision Tree Pada Universitas Stella Maris Sumba Julianti Suwartini Inda Sari; Elfira Umar; Lidia Lali Momo
Journal Of Informatics And Busisnes Vol. 2 No. 3 (2024): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v2i3.1677

Abstract

Timely graduation itself is one of the indicators of the success of students' academic performance. The study period regulations are already set in the provisions of the Minister of Education and Culture of Indonesia. To address this issue, there needs to be a technique to predict graduation. One of the techniques commonly used is data mining. In this study, the authors will compare two data mining methods, namely Naive Bayes Classifier and Decision Tree, to obtain the method with the best accuracy in predicting student graduation. The attributes used for Data Mining Classification consist of 10 attributes: Student ID, Gender, Student Status, Age, Semester 1 Grade Point Average, Semester 2 Grade Point Average, Semester 3 Grade Point Average, Semester 4 Grade Point Average, Cumulative Grade Point Average, and Result attribute. From the test results using RapidMiner tools with two methods that have been conducted, the Decision Tree (C4.5) obtained the accuracy result of 70.18%, and the Naïve Bayes method obtained the highest accuracy result of 71.24%.
Sistem Informasi Bimbingan Skripsi Berbasis Web Di Universitas Stella Maris Sumba Frederikus Deritno Malo; Elfira Umar; Paulus Mikku Ate
Journal Of Informatics And Busisnes Vol. 2 No. 3 (2024): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v2i3.1679

Abstract

Every student in the information systems study program at Stella Maris University, Sumba can start writing a thesis if they have fulfilled the terms and conditions for writing a thesis. In completing the thesis, students will be accompanied by a supervisor. The supervisor has academic responsibility for the thesis produced by the students they supervise, in terms of scientific correctness and writing techniques during the implementation of the guidance process. The thesis guidance process cannot run smoothly and on time when lecturers and students have their own busy schedules which causes there to be no suitable time to meet. This obstacle can make existing problems in writing a thesis unable to be solved immediately. Apart from that, lecturers also find it difficult to monitor the students they supervise because the number of students they supervise is not small. To avoid this, in this research a web-based thesis guidance information system was designed and built at Stella Maris University, Sumba. Results of designing and building a web-based thesis guidance information system. This web- based information system consists of three users, namely administrators or study programs, lecturers and students, where each user has different access.
Analisis Sentimen Review Hotel Menggunakan Algoritma Naive Bayes Pada Ella Hotel Tambolaka Margaretha Inya Tuku; Elfira Umar; Alexander Adis
Journal Of Informatics And Busisnes Vol. 2 No. 3 (2024): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v2i3.1680

Abstract

Wisatawan ketika sedang berlibur ke suatu kota, tentu akan memesan akomodasi di hotel. Pengunjung sering kesulitan memilih hotel ketika memesan penginapan karena banyaknya pilihan. Tripadvisior adalah sebuah platform pemesanan hotel yang memberikan fitur-fitur lengkap untuk membantu pengunjung dalam memilih hotel yang tepat. Salah satu fitur yang disediakan adalah ulasan yang menunjukkan komentar-komentar dari para pengunjung mengenai hotel tersebut. Tetapi, semakin banyak saran atau tinjauan mengenai suatu hotel akan membuat pengunjung butuh waktu lebih lama untuk memilih hotel yang mereka inginkan. Dengan permasalahan tersebut, diperlukan analisis sentimen yang dapat mengambil beberapa komentar untuk mendapatkan informasi yang berguna bagi pengunjung. The sentiment analysis system that was developed aims to create a sentiment model to evaluate comments and reviews of a hotel. Analisis sentimen diproses dengan menggunakan Naive Bayes Classifier algorithm. Pengujian menunjukkan bahwa pengklasifikasian sentiment dengan menggunakan Naïve Bayes Classifier mencapai tingkat keakuratan sebesar 90.71%.
Co-Authors Alexander Adis Frederikus Deritno Malo Julianti Suwartini Inda Sari Lidia Lali Momo Margaretha Inya Tuku Paulus Mikku Ate