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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 2 No 2 (2022): Maret - Agustus" : 5 Documents clear
Sentiment Analysis Terhadap Review Aplikasi Maxim di Google Play Store Menggunakan Support Vector Machine (SVM) Muhammad Nur Akbar; Nur Hasanahlmar'iyah Rusydi; M. Hasrul H.; Nurul Shaumi Ramadhanti; Erfiana
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 (1507.007 KB) | DOI: 10.24252/jagti.v2i2.39

Abstract

Before selecting and installing applications on the Google Play Store, users often read reviews of other users. This makes user review analysis very attractive for app owners to make future decisions. One of them is the Maxim application, a new online transportation application that provides different services from similar applications. This study aims to analyze user reviews of the maxim application on the Google Play Store using sentiment analysis. The research data is taken from the Google Play Store website, while the data taken is in the form of a review text. This user review analysis uses the Support Vector Machine (SVM) method producing an accuracy of 79%.
Deteksi Penyakit pada Tanaman Padi Menggunakan MobileNet Transfer Learning Berbasis Android Herwina; Ashabul Kahfi Ash Shiddiq; Theddy Dzikrullah Syahputra; Darmatasia
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 (1556.62 KB) | DOI: 10.24252/jagti.v2i2.41

Abstract

Rice is a staple food in several countries, including Indonesia. To produce quality rice, maintenance of rice plants is required from planting to harvest. One of the problems often experienced by farmers is the presence of diseases that attack rice plants. The limited knowledge of some farmers means that farmers do not understand the condition of their plants, resulting in delays in handling when the plants are attacked by disease. This research aims to build an application that can detect diseases in rice plants that attack rice leaves. The types of diseases that will be detected are Leaf Smut, Brown Spot, and Bacterial Leaf Blight. This research uses a transfer learning approach with the Convolutional Neural Network algorithm to detect diseases in rice leaves. The architecture used is MobileNetV1 with an accuracy of 94% and MobileNetV2 with an accuracy of 95%. The input image used is 224x224 pixels in size. The trained model is then integrated into an Android-based application. Test results on the Android application show that the model can detect diseases on rice leaves.
Implementasi Metode Perbandingan Eksponensial dalam Sistem Pendukung Keputusan Seleksi Mahasiswa Penerima Beasiswa Muammilul Khair; A. Muhammad Syafar; Darmatasia
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 (1988.125 KB) | DOI: 10.24252/jagti.v2i2.42

Abstract

Scholarship is one of the programs initiated by the institution that is awarded to a student based on academic achievement or other criteria that may include financial need. Scholarships are awarded selectively in accordance with the scholarships held. The most problem in scholarship program is the selection process to decide scholarship awardees. The conventional selection model in scholarship selection is commonly not transparent and misdirected. This research was conducted in UIN Alauddin Makassar which aims to develop a scholarship selection Decision Support System to facilitate the decision makers in selecting scholarship recipients. The method used in the decision-making process is the Exponential Comparison method. This method is a method with exponential calculation, the difference in value between criteria can be distinguished depending on the ability of the person who judges. The system that has been built is then tested by BlackBox testing. The results of the study show that all features of the system can function properly. This research is expected to facilitate decision makers in selecting scholarship recipients transparently and effectively.
Sistem Pengawasan Pelanggaran P2TL pada PT. PLN (Persero) Berbasis Website Erfina; Abdul Haq
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 (454.411 KB) | DOI: 10.24252/jagti.v2i2.43

Abstract

P2TL monitoring system at PT. PLN (Persero) North Makassar Area against electricity theft there are several obstacles faced by the P2TL team, where many people commit fraud to gain profits so that technology for monitoring violations is needed to minimize the occurrence of electricity theft at PLN. The type of research used is descriptive qualitative by using data collection methods namely observation, interviews, and questionnaires. The programming language used is Hypertext Preprocessor (PHP) and MySQL as the database. The system design in this study uses Agile methods and black box testing. This research produces a Website-Based Electric Theft Violation Monitoring Application. Based on the results of the feasibility test of the system, the final results obtained an average of 87.19% of respondents strongly agree with the existence of the system.
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.

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