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All Journal Tematik : Jurnal Teknologi Informasi Komunikasi Adpebi Science Series INSTALL: Information System and Technology Journal
Septyan Eka Prastya
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science

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Implementasi Metode K-Means Clustering untuk Meningkatkan Penjaringan Mahasiswa Muhammad Zulfadhilah; Mambang; Septyan Eka Prastya
TEMATIK Vol 9 No 2 (2022): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2022
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v9i2.1053

Abstract

Rekrutmen mahasiswa di perguruan tinggi swasta memiliki tantangan tersendiri bagi pihak kampus. Pengelompokan mahasiswa berdasarkan asal sekolah, kota dan provinsi merupakan salah satu metode rekrutmen mahasiswa baru; penggunaan data mining dalam pengelompokan dapat dilakukan dengan menggunakan algoritma K-Means Clustering. Implementasi Algoritma K-Means Clustering relatif mudah dan memiliki komputasi yang cepat, sehingga dapat digunakan untuk menganalisis data profil mahasiswa perguruan tinggi untuk membantu meningkatkan seleksi mahasiswa baru pada tahun berikutnya. Pada data yang telah dikelompokkan dengan K-Means Clustering terdapat 3 cluster, cluster 1 (cluster_0) berdomisili kota-kota di Provinsi Kalimantan Tengah, dan cluster 2 (cluster_1) didominasi oleh mahasiswa dari berbagai kota di Provinsi Kalimantan Selatan, dan kluster 3 (cluster_2) didominasi oleh mahasiswa dari kota Banjarmasin, provinsi Kalimantan Selatan. Strategi pemilihan mahasiswa bisa lebih baik lagi berdasarkan analisis menggunakan K-Means Clustering.
Artificial Intelligence and Digital Economy: Comparative Adoption of Regions and Populations in ASEAN Countries Using EDA Samita, Mambang; Mambang; Muhammad Zulfadhilah; Septyan Eka Prastya; Finki Dona Marleny
Adpebi Science Series 2022: 1st AICMEST 2022
Publisher : ADPEBI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this paper is to make a comparative analysis of artificial intelligence adoption and the potential of the digital economy in ASEAN countries. The regions of countries and populations of the ASEAN Region correlate with the adoption of artificial intelligence and the potential of the digital economy. This paper uses qualitative methods and experiments with secondary data sources from online websites. The data used has been validated with other online sources that are credible and follow global information provisions. This proposed paper has four variables used as indicators in data visualization related to AI Adoption, Area, Population, and the digital economy. The four countries analyzed are members of ASEAN. The results of exploratory data analysis using the Seaborn library using the Python programming language obtained correlation results consisting of the variables Adoption of AI, Area, Population, and Digital Economy. The correlation of the Adoption of AI variables with the Digital Economy correlates 0.94. Adoption of AI with Population correlates 0.93. Adoption of AI with an Area of 0.86. Furthermore, the Area or region variable has a correlation value of 0.97 with the digital economy. Areas with a population have a correlation value of 0.98. The Population variable has a very strong correlation with the digital economy of 1. Further research can add several variables such as the potential for future jobs and the number of countries so that it is not limited to ASEAN countries alone.
The Image Segmentation Of Ornamental Plants Typical Of South Kalimantan Using The Convolutional Neural Network Method: Segmentasi Citra Tanaman Hias Khusus Kalimantan Selatan Menggunakan Metode Convolutional Neural Network Lufila, Lufila; Septyan Eka Prastya; Finki Dona Marleny
INSTALL: Information System and Technology Journal Vol 1 No 1 (2024): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v1i1.544

Abstract

One of the very important processes in the recognition of visually presented objects. Image segmentation is one of the important topics in computer science, especially in the field of digital image processing. The research method used is image segmentation using the Convolutional Neural Network (CNN) method; the results obtained in this study are accurate to the image of plants selected as the sample of this study. The dataset in this study used pictures or objects of ornamental plants, namely Black Orchids, Betel Lurih, and Aglonema Tri-Color. As for the samples used in this study, namely for these three types of objects, 50 pictures were taken for each object used. By using epochs of 15, researchers have determined to reduce system performance time and by epoch times of 17s, 18s, and 24s. The number of epochs that will be used also affects the time that will be taken by modeling training. Due to the increasing number of epochs, the time that will be required for training will be longer. Then, the accuracy value of the data trained is 0.7667 with a loss value of 0.4039, and the val_loss value is 0.4611 with a val_accuracy of 0.7333. The segmentation results obtained using the convolutional neural network model have a fairly good accuracy level of 0.7667 and a validation accuracy of 0.7333.
Co-Authors Finki Dona Marleny Lufila, Lufila Mambang Muhammad Zulfadhilah Samita, Mambang