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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Telcomatics Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan
Yefta Christian
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Analisis Perbandingan Algoritma Untuk Prediksi Performa Akademik Mahasiswa Pada Pembelajaran Daring Herman Herman; Yefta Christian
Journal of Applied Informatics and Computing Vol 6 No 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4854

Abstract

Student academic performance is one of the important factors for student graduation. Therefore, many studies have been conducted in the field of education to identify factors that affect student performance. This research focuses on academic performance in online learning conditions by studying cases at XYZ university. Data were collected using Machine Learning techniques with the application of the Distributed Random Forest model, Naïve Bayes, Generalized Linear Model, and Gradient Boosting Machine algorithms. The results of this study indicate that the Distributed Random Forest and Gradient Boosting Machine models have an average accuracy of 99.83%. Researchers found variables that affect student learning performance, especially online learning, are final exam scores, midterm scores, attendance, assignment scores, amount of material given, number of assignments given, and number of clicks on material. From these findings, the researcher recommends that to improve the performance of the next learning, the implementation of learning should focus on improving the implementation of the Final Exams and the material on the learning platform
Rental Price Prediction of Boarding Houses in Batam City Using Linear Regression and Random Forest Algorithms Jerry Jerry; Yefta Christian; Herman Herman
Journal of Applied Informatics and Computing Vol 7 No 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6732

Abstract

Boarding houses, commonly known as "kost," are residential places typically rented by individuals, serving a function similar to hotels, but with more affordable pricing. With the proliferation of boarding house businesses, residents and newcomers in Batam city face challenges in selecting suitable accommodation based on both price and amenities. Leveraging machine learning, a branch of artificial intelligence (AI), and incorporating various algorithms, a system can be developed to predict the rental prices of boarding houses. This helps individuals make informed decisions regarding the suitability of a boarding house based on their preferences and budget. The algorithms utilized in this study are Linear Regression and Random Forest. The modeling process resulted in R2 Scores, with Linear Regression achieving a score of 64%, while Random Forest outperformed with an impressive 99% R2 Score. Due to the higher R2 Score of Random Forest, this model was selected for the development of a website using the Scrum framework. The outcome of this research is a predictive pricing website for boarding houses, offering a valuable tool for residents and visitors in Batam when seeking to rent or lease a boarding house.
Co-Authors Chris Tan Herman Herman Jerry Jerry Kelvin Kelvin Moh Fariq Aziz Rara Tri Kencana Ricky Rickena Exendy Robin Robin Ronny Triputra. AM Santo Tjiam Tina Rani Utari Afnesia