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Agents: Journal of Artificial Intelligence and Data Science
Published by Universitas Islam Negeri Alauddin Makassar
ISSN : 27469204     EISSN : 27469190     DOI : https://doi.org/10.24252/jagti.v4i1.74
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
The AGENTS published the original manuscripts from researchers, practitioners, and students in the various topics of Artificial Intelligence and Data Science including but not limited to fuzzy logic, genetic algorithm, evolutionary computation, neural network, hybrid systems, adaptation and learning systems, biologically inspired evolutionary system, system life science, distributed intelligence systems, network systems, human interface, machine learning, and knowledge discovery.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 3 No 1 (2023): September - Februari" : 5 Documents clear
PERBANDINGAN ANALISIS SENTIMEN ALGORITMA SUPPORT VECTOR MACHINE DAN NAÏVE BAYES TERHADAP TANGGAPAN PUBLIK TENTANG PEMBELAJARAN ONLINE DI MASA PANDEMI COVID-19 Yulia Ardana; Ridwan A. Kambau; Mustikasari
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 1 (2023): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (572.173 KB) | DOI: 10.24252/jagti.v3i1.46

Abstract

At the beginning of 2020, COVID-19 began to spread throughout the world, including Indonesia. The government continues to look for ways to prevent the chain from spreading, one of which is by implementing online learning. The background of this research is to use twitter to find out the response and public sentiment about online learning during the covid-19 pandemic. The purpose of this research is to find out public opinion about the application of online learning and also to compare the performance level of support vector machine and naïve Bayes algorithms. In conducting this research, the type of research used is qualitative research in order to be able to understand well what kind of phenomena experienced by the research subjects. The best sentiment analysis results are obtained by comparing two classification algorithms, support vector machine and naïve Bayes. Testing based on k-fold cross validation aims to obtain accuracy, precision, and recall values. The best algorithm will produce the right output with a higher test score.
ANALISIS CLUSTERING UNTUK SEGMENTASI PENGGUNA KARTU KREDIT DENGAN MENGGUNAKAN ALGORITMA K-MEANS DAN PRINCIPAL COMPONENT ANALYSIS Muhammad Nur Akbar; Azizah Salsabila; Aldi Perdana Asri; Muhammad Syawir
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 1 (2023): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (832.897 KB) | DOI: 10.24252/jagti.v3i1.56

Abstract

Customer segmentation is a process used by companies to group customers based on common characteristics. The goal is to understand customer needs and preferences better so that companies can provide products and services that match customer needs. One way to segment customers is to use clustering algorithms, such as k-means. This algorithm groups data into adjacent clusters with randomly selected centroids. In the case of credit card customer segmentation, the k-means algorithm can be used to group customers based on characteristics such as number of transactions, amount of payments, and credit history. Thus, companies can better understand the needs and preferences of credit card customers and determine more effective marketing strategies. The advantages of the k-means algorithm and the clustering method are that the developed models can help companies determine more effective marketing strategies, easy-to-use algorithms with fast computation time and accurate results, and the PCA algorithm is also used to reduce dimensions and makes data visualization easier. Based on the test results and analysis of credit card customer data, the performance of the k-means algorithm is considered relatively good for segmentation with the number of clusters = 3 and the Davies Bouldin value = -0.778.
IMPLEMENTASI JARINGAN SARAF TIRUAN UNTUK MENENTUKAN KELAYAKAN MAHASISWA DALAM MENDAPATKAN PEKERJAAN Ayu Azhari Zainal; Azizah Salsabila Azizah Salsabila; Hastuti Hastuti; Darmatasia
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 1 (2023): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1233.462 KB) | DOI: 10.24252/jagti.v3i1.58

Abstract

Job recruitment selection is one of the processes carried out by agencies or companies to determine whether a person is eligible for a certain job position or not. The selection process is often carried out in a subjective manner so that it can be detrimental to companies or job applicants. In the process of determining a person's eligibility to be accepted or get a particular job, a company usually has set certain criteria. In addition, a company also often holds regular employee recruitment. This study aims to implement one of the machine learning algorithms, namely an Artificial Neural Network to build a model that can assist companies in predicting a person's eligibility for employment. The model is built with reference to certain criteria data that has been set by the company such as educational history, work experience, and capabilities. The best accuracy result of 91.18% is obtained from a model built using a learning rate parameter of 0.1 and the number of hidden layers is 10.
SISTEM MANEJEMEN DAN MONITORING BIMBINGAN TUGAS AKHIR BERBASIS WEB Salmi; Darmatasia
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 1 (2023): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (832.897 KB) | DOI: 10.24252/jagti.v3i1.59

Abstract

The process of supervising student final assignments experiences many obstacles that can hinder the process of completing the final assignment itself. In addition, lecturers also carry out other tri-dharma activities, namely research and community service which require lecturers to carry out activities outside the campus. Differences in activity and liveliness between lecturers and students can be one of the obstacles in the final assignment guidance process. The purpose of this research is to design a system that can manage and monitor processes as well as provide advice related to web-based graduation projects, to facilitate processes, suggestions and assurance of information at any time and the accuracy of work processes in improving student graduation performance, especially in the final project process. The results of the accuracy test conducted on respondents and the level of satisfaction of respondents on the feasibility of the system is 80%. Keywords: Management, Guidance, Monitoring, Final Project..   Keywords: Management, Guidance, Monitoring, thesis
SISTEM UJI KELAYAKAN PENERIMA BLT BPJS MENGGUNAKAN METODE KLASIFIKASI ALGORITMA NAIVE BAYES Anita Rusmana Lefya; Mustikasari Mustikasari
AGENTS: Journal of Artificial Intelligence and Data Science Vol 3 No 1 (2023): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1409.593 KB) | DOI: 10.24252/jagti.v3i1.63

Abstract

The large number of registrants who receive Cash Direct Assistance for the Social Security Administering Body (BLT BPJS) in the village of Tamatto, Ujung Loe sub-district, Bulukumba district, makes village staff as managers take a long time to get a decision in the form of whether or not someone is eligible to get assistance. A system of due diligence for beneficiaries of BLT BPJS assistance is needed that can assist village staff. The purpose of this study is to design a system to determine the results of the decisions of recipients who are eligible for BLT BPJS assistance. The type of research carried out is quantitative research with an experimental research approach, the method of data collection is observation. The method used is the nave Bayes algorithm classification method. The results of this study indicate that the eligibility test system for BLT BPJS assistance recipients using the nave Bayes algorithm classification method can help to convey information in the form of the eligibility of a BPJS BLT aid recipient with an accuracy of 83.689% results using as many as 1036 data samples.

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