Rosyid Ridlo Al Hakim
Universitas Global Jakarta, Depok

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Pengembangan Sistem Klasifikasi Tipe Kepribadian Siswa Secara Psikologis dengan Algoritma Decision Tree C.45 Rini Nuraini; Rosyid Ridlo Al Hakim; Tuti Lisnawati; Wieke Tsanya Fariati
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): Desember 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.474 KB) | DOI: 10.47065/bits.v3i3.1045


In the world of education knowing the personality type of students is very important. This is because a person's personality is influential in his learning activities and how he digests and captures the material presented by the teacher. For this reason, knowing the classification of students' personalities needs to be identified so that teachers or students themselves can optimize self-change in a better and positive direction. This study aims to develop a psychological classification system for student personality types using the C.45 decision tree algorithm. The personality type used as a class in the classification is based on psychology, including: Sanguine, Phlegmatic, Choleric and Melancholic. In this study, a web-based system was developed, so that it is easy to use for teachers and students to recognize the personality of these students. To determine the personality of students psychologically, students answer questions in the system, then the system will classify based on the answers from these students. The C.45 decision tree algorithm serves to find knowledge or patterns of characteristic similarity in a particular group or class. From the test results, the pecision value is 90%, the recall is 85% and the accuracy is 88%. This shows that the C.45 decision tree algorithm can perform personality type classification well
Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk Arief Herdiansah; Rohmat Indra Borman; Desi Nurnaningsih; Alfry Aristo J Sinlae; Rosyid Ridlo Al Hakim
JURIKOM (Jurnal Riset Komputer) Vol 9, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i2.4066


Since ancient times until now herbal plants have been used for treatment and have been applied in the world of health to this day. All parts of the plant can be used as medicine, one of which is the leaves. However, there are still many people who are not familiar with the medicinal leaves. This is because the leaves at first glance look almost the same, making it difficult to tell them apart. Actually, if you look closely, the leaves have characteristics that can be distinguished from one leaf to another. The purpose of this study is to classify images of herbal leaf species using the Backpropagation Neural Network (BNN) algorithm with shape feature extraction utilizing metric and eccentricity parameters. BNN is a type of supervised learning algorithm that consists of several layers and uses an error output as a modifier of the weight value backwards. In this study, the extraction of shape features that become input for the BNN algorithm will go through morphological operations to improve the segmentation results so that the classification results are more optimal. The test results show an accuracy of 88.75%, this shows the developed model can classify herbal leaves well