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All Journal Agents: Journal of Artificial Intelligence and Data Science
Faisal
Universitas Islam Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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APLIKASI EDUKASI MITIGASI DAN PELAPORAN KEJADIAN BENCANA BERBASIS ANDROID Luthfiya Anggraini; Faisal
AGENTS: Journal of Artificial Intelligence and Data Science Vol 1 No 2 (2021): Maret - Agustus
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (927.448 KB) | DOI: 10.24252/jagti.v1i2.19

Abstract

Makassar City is one of the cities in Indonesia with a high potential for disaster threats, including earthquakes, tsunamis, landslides, floods, droughts, extreme weather, epidemics, and social conflicts. Due to the geographical condition of the city of Makassar which is vulnerable to disaster threats, it requires fast and effective coordination and communication in the government sector, in this case the Regional Disaster Management Agency (BPBD) in order to take emergency response actions against disasters. This study aims to create an application that can help the community to report surrounding conditions when a disaster occurs to the Makassar City BPBD, as well as to educate the public about disaster mitigation. The type of research is an experimental method with a qualitative research approach and the design method used is the Waterfall. System testing using Blackbox and Beta is done by conducting interviews with pre-determined sources. The results of this study reveal that this application helps the community in reporting disaster events that occur around them to the Makassar City BPBD, and also educate the public in terms of disaster mitigation guidelines, and is expected to help Makassar City BPBD in handling disaster emergency response and socialization of disaster mitigation to society.
Implementasi Data Mining untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Random Forest Zaskila Nurfadilla; Faisal
AGENTS: Journal of Artificial Intelligence and Data Science Vol 2 No 2 (2022): Maret - Agustus
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (422 KB) | DOI: 10.24252/jagti.v2i2.45

Abstract

The level of accuracy of student graduation in tertiary institutions is one of the criteria for assessing campus accreditation. The more students who graduate on time, the better the college's performance will be. Students' graduation rates are difficult to predict early, resulting in delays in graduation. To reduce the rate of delay in graduating college for students, it is necessary to be educated seriously in order to graduate on time. One method of solving this problem is by predicting the accuracy of student graduation by using data mining or data mining methods. The purpose of this system is to make it easier for lecturers on campus to classify students who are classified as graduating on time using the Random Forest method. The results of the classification using the Random Forest Algorithm using 1,351 data, then the evaluation results with an accuracy value of 90.74% by dividing the training and testing data as much as 80:20 The system successfully displays data visualization to predict graduation on time by implementing data mining.
Co-Authors Luthfiya Anggraini Zaskila Nurfadilla