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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) FINANSIA : Jurnal Akuntansi dan Perbankan Syariah Jurnal Keperawatan Widya Gantari Indonesia Jurnal Ilmu Perbankan dan Keuangan Syariah REMITTANCE: JURNAL AKUNTANSI KEUANGAN DAN PERBANKAN SENADA : Semangat Nasional Dalam MengabdI IJISTECH Simpatik: Jurnal sistem Informasi dan Informatika Jurnal Keuangan dan Perbankan(KEBAN) Al Hisab: Jurnal Ekonomi Syariah Riwayat: Educational Journal of History and Humanities Al-Muhasib: Journal of Islamic Accounting and Finance Ziswaf Asfa Journal
Sari, Anggi Puspita
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Religion Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Neuroscience Public Health Social Sciences Transportation

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Journal : IJISTECH

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Improved Naive Bayes Algorithm with Particle Swarm Optimization to Predict Student Graduation Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 6 (2024): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i6.338

Abstract

Timely graduation is very important for educational institutions such as universities, especially for students. Because it can prove that the University and students are able to undergo the learning process theoretically and practically. But many students do not pay attention to graduation, especially those who are already working or married. Therefore, analysis is needed to predict student graduation so that solutions can be found by the University. Data mining was chosen as a method to process data to get new information. The algorithm used in data mining is Naïve Bayes. The research stages include loading data into excel, cleaning empty data, selecting databases related to graduation and taking data from 300 students majoring in Informatics Engineering. The next stage is data transformation by categorizing student data, namely personal data attributes (gender, age, marital status, job status) and academic data (grade). Data testing, application of Naïve Bayes algorithm and accuracy testing were carried out with Rapis Miner software version 10.3.001. The results of data processing with Rapid Miner using the Naïve Bayes algorithm are shown with the Confusion Matrix and ROC Curve. The results of confusion matrix from data processing with Naïve Bayes in the form of accuracy, precision, and recall have the same result of 100%. The percentage of the Confusion Matrix indicates that the model created can classify correctly and accurately. The ROC curve depicted with AUC yields a value of 1, which means that the test showed excellent results
Prediction of Student Graduation using the K-Nearest Neighbors Method Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 3 (2023): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i3.318

Abstract

Predictions on the accuracy of student graduation are designed to support study programs in guiding students so that they can graduate on time. The number of student graduations will influence the university's accreditation score. Graduation predictions can provide very useful information in decision-making; therefore, research was conducted on student graduation data. This data will be processed using the K-Nearest Neighbor method. The dataset used consisted of 150 students majoring in informatics engineering. The variables included gender, age, marital status, grade, and job status. The research methodology used in this study consists of 6 stages: Data Collection, Data Selection, Preprocessing, Transformation, Testing, and Evaluation. In the preprocessing or cleaning stage, the data can be fully utilized because all fields have been filled in correctly. Meanwhile, in the transformation stage, the data is categorized as follows: age (young: 19-24, old: 25-50) and grade (large: 3-4, small: 1-2.9). The K-Nearest Neighbor (KNN) method can predict student graduation rates. The KNN method, processed with the RapidMiner 9.9 tool, obtained an average accuracy of 100%. Based on the results of 100% accuracy and an AUC value of 1, it can be concluded that the KNN method is highly accurate in classifying graduation data for the 150 students.
Customer Loyalty Classification With Random Forest Algorithm Sari, Anggi Puspita; Noviriandini, Astrid; Fauziah, Sifa
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.393

Abstract

Customer loyalty is very important for the survival of the company. Because with customers who have customer loyalty, they will make purchases regularly. Customer loyalty needs to be maintained to increase profits. The method is to classify loyal customers with non-loyal ones, in order to retain loyal customers and set strategies for non-loyal customers. The method used is classification with random forest with cleaning stages that can clean data from noise or empty data or data that does not match, selection that can select some data to be processed for classification, transformation that can change data into two or three formats, classification with random forest with split validation using testing data and training data and with rapidminer software. Evaluation by checking the results of the classification with random forest in the form of accuracy, precision, recall, and AUC. The results of the classification show from the accuracy table that the prediction of loyal and true loyal customers is 129 more than the prediction of not loyal and true not loyal customers which is 32. The accuracy result is 96.41% which shows that the data is really accurate with very high results. The recall result is 98.47%, while the precision result is 96.99%.
Co-Authors Abadi, Muhammad Kurnia Rahman Andita, Ai Selpia Astrid Noviriandini Badie Uddin Budi Sukardi Deasy Siregar, Santy Devianto , Yudo Dzihni, Muhamad Faaqih Eka Wulansari Fridayanthie, Eka Wulansari Embun Fajar Wati Gunawan, Wawan Hartono Hartono Herlina, Santi Indradewa, Rhian Johannes Bastira Ginting Muhammad Alan Nur Oktavia, Melyn Perangin-Angin, Elvi Sunita Rasidi, Muhammad Fathur Sandy Pratama Situmorang, Lusiana Jesika Suryani Suryani Varonitti, Aldian Wawan Gunawan Yudo Devianto