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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Building of Informatics, Technology and Science Informatika International Journal Of Science, Technology & Management (IJSTM) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Journal of Student Development Information System (JoSDIS) Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Pendidikan Indonesia (Japendi) Dike
Nasution, Marnis
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Journal : Building of Informatics, Technology and Science

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Implementation of the Naïve Bayes Algorithm to Predict New Student Admissions Salsabila, Aulia; Nasution, Marnis; Irmayanti, Irmayanti
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5363

Abstract

New student admissions are critical to the success of an educational institution because they determine the existence and financial sustainability of that institution. The number of prospective students who register changes every year. The school cannot anticipate the number of students who will come. Additionally, data on prospective students who enroll is collected annually without being analyzed to extract valuable information. The school must make predictions to estimate the number of new students in the next school year. Predictions are essential for effective planning, both in the long and short term. This research aims to apply the Naïve Bayes algorithm with Gaussian type to predict new student admissions. To find out whether the Naïve Bayes algorithm works well, an evaluation matrix is used. The methods applied include the dataset collection process, data preprocessing, split data training and testing, feature engineering, the implementation of Naïve Bayes, and results evaluation. The dataset is divided into 70% training data and 30% testing data. The research results show an accuracy score of 86.11% during training and an accuracy score of 90.62% during model testing, with an increase of 4.51%. These results show that there is no indication of overfitting in the machine learning algorithm used. The evaluation matrix produces an accuracy score of 90.62%, precision of 100%, recall of 90.62%, and f1-score of 95.08%. From the results of the evaluation matrix scores, it can be concluded that the naive Bayes algorithm with Gaussian type succeeded in predicting new student admissions well.
Decision Support System for Determining the Quantity of Brick Production Using the Fuzzy Tsukamoto Method Nurpa, Murni Zaliah; Masrizal, Masrizal; Nasution, Marnis
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5497

Abstract

Maximum profits are obtained from maximum sales. Maximum sales are those that can meet existing demands. There is a determination of the planned production amount to meet production levels to meet planned sales levels or market demand levels. Factors that need to be considered in determining production quantities include: the amount of inventory and the amount of demand. The amount of demand and supply is an uncertainty. Fuzzy logic is a science that can analyze uncertainty. One of the fuzzy rule methods is Tsukamoto, which is a method that is often used to build a system whose reasoning resembles human intuition or feelings. The calculation process is quite complex so it takes a relatively long time, but this method provides results with quite high accuracy. Ratu Batubata Refinery is a factory that produces large quantities every day. Therefore, planning the amount of brick production is very important. In order to meet market demand appropriately and in appropriate quantities. By using this application, it is hoped that the company can make it easy for the company to predict production quantities based on the amount of demand and existing inventory data, in order to achieve maximum profits.
Co-Authors Ah, Rahma Muti Ahmad Habibullah, Imam Akbar Ritonga, Ali Ambarita, Mira Nanda Andini, Novira Dwi Bangun, Budianto Cahya, Susilo Tiadi Cahyani, Nilam Christoval, Peter Haris Munandar Hasibuan, Ardiansyah Hasibuan, Mila Nirmala Sari Hasibuan, Muhammad Adlin Irmayani, Deci Irmayanti Irmayanti, Irmayanti Juledi, Angga Putra Lestari, Putri Anggraini Muhammad Halmi Dar Munthe, Ibnu Rasyid Muti'ah, Rahma Muti’ah, Rahma Nasution, Fahri Emil Afandi Nilam Sari Nurjannah, Eka Nurpa, Murni Zaliah Pohan, Sentosa Purba, Cinrewina Putri Lestari, Putri Rambe, Diana Indriani Rambe, Inny Rahayu Rasyid Munthe, Ibnu Ray Silaen, Belinda Ritonga, Ali Akbar Ritonga, Irmayanti Rohani Rohani Salsabila, Aulia Santika, Linda Saputri, Yuni Siahaan, Rahma Aulia Sinurat, Shondy Raja Ferdinand Siregar, Peni Ungkasara Sofiani, Anggi Surasih, Winda Syah, Rahmayuni Syaiful Zuhri Harahap Trisno Trisno Wijaya, Alief Achmad ZURAIDAH ZURAIDAH