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All Journal Journal of Intelligent Systems Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika Budimas : Jurnal Pengabdian Masyarakat Jurnal Infortech International Journal Of Science, Technology & Management (IJSTM) Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Algoritme Jurnal Mahasiswa Teknik Informatika INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Pengabdian Kreatif Cemerlang Indonesia AMMA : Jurnal Pengabdian Masyarakat Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Prawara Jurnal Pengabdian Masyarakat KOMPUTASI
Tyas Setiyorini
Universitas Nusa Mandiri
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Journal : Jurnal Pilar Nusa Mandiri

 1 
PENERAPAN GINI INDEX DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI TINGKAT KOGNITIF SOAL PADA TAKSONOMI BLOOM Setiyorini, Tyas; Asmono, Rizky Tri
Jurnal Pilar Nusa Mandiri Vol 13 No 2 (2017): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (893.144 KB)

Abstract

Sebagai pedoman dalam merancang ujian yang layak, yang terdiri dari soal-soal yang memiliki berbagai tingkatan secara kognitif, Taksonomi Bloom telah diterapkan secara luas. Saat ini, kalangan pendidik mengidentifikasi tingkat kognitif soal pada Taksonomi Bloom masih menggunakan cara manual. Hanya sedikit pendidik yang dapat mengidentifikasi tingkat kognitif dengan benar, sebagian besar melakukan kesalahan dalam mengklasifikasikan soal-soal. K-Nearest Neighbor (KNN) adalah metode yang efektif untuk klasifikasi tingkat kognitif soal pada Taksonomi Bloom, tetapi KNN memiliki kelemahan yaitu kompleksitas komputasi kemiripan datanya besar apabila dimensi fitur datanya tinggi. Untuk menyelesaikan kelemahan tersebut diperlukan metode Gini Index untuk mengurangi dimensi fitur yang tinggi. Beberapa percobaan dilakukan untuk memperoleh arsitektur yang terbaik dan menghasilkan klasifikasi yang akurat. Hasil dari 10 percobaan pada dataset Question Bank dengan KNN diperoleh akurasi tertinggi yaitu 59,97% dan kappa tertinggi yaitu 0,496. Kemudian pada KNN+Gini Index diperoleh akurasi tertinggi yaitu 66,18% dan kappa tertinggi yaitu 0,574. Berdasarkan hasil tersebut maka dapat disimpulkan bahwa Gini Index mampu mengurangi dimensi fitur yang tinggi, sehingga meningkatkan kinerja KNN dan meningkatkan tingkat akurasi klasifikasi tingkat kognitif soal pada Taksonomi Bloom.
IMPLEMENTATION OF GAIN RATIO AND K-NEAREST NEIGHBOR FOR CLASSIFICATION OF STUDENT PERFORMANCE Setiyorini, Tyas; Asmono, Rizky Tri
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (895.904 KB) | DOI: 10.33480/pilar.v16i1.813

Abstract

Predicting student performance is very useful in analyzing weak students and providing support to students who face difficulties. However, the work done by educators has not been effective enough in identifying factors that affect student performance. The main predictor factor is an informative student academic score, but that alone is not good enough in predicting student performance. Educators utilize Educational Data Mining (EDM) to predict student performance. KK-Nearest Neighbor is often used in classifying student performance because of its simplicity, but the K-Nearest Neighbor has a weakness in terms of the high dimensional features. To overcome these weaknesses, a Gain Ratio is used to reduce the high dimension of features. The experiment has been carried out 10 times with the value of k is 1 to 10 using the student performance dataset. The results of these experiments are obtained an average accuracy of 74.068 with the K-Nearest Neighbor and obtained an average accuracy of 75.105 with the Gain Ratio and K-Nearest Neighbor. The experimental results show that Gain Ratio is able to reduce the high dimensions of features that are a weakness of K-Nearest Neighbor, so the implementation of Gain Ratio and K-Nearest Neighbor can increase the accuracy of the classification of student performance compared to using the K-Nearest Neighbor alone.
COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING ELECTRICITY CONSUMPTION Setiyorini, Tyas; Frieyadie, Frieyadie
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1459

Abstract

Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data-related problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset, A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption, dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afforded to improve performance better than linear regressions. This study to prove that there is a nonlinear relationship in the electricity consumption dataset used in this study, and compare which performance is better between using linear regression and neural networks.
Co-Authors Achmad Rifai ACHMAD RIFAI Aditiya Yoga Pratama Albert Riyandi Alengka, Son Gohan Angga Subrata Anggie Andriansyah Dedi Saputra Fariq Mulia, Akbar Fitra Septia Nugraha Frieyadie Frieyadie Frieyadie Harsih Rianto Ibnu Alfarobi Ibnu Rusdi Istiyanto, Ilham Jordy Lasmana Putra Kudiantoro Widianto Kudiantoro Widianto, Kudiantoro Maryanah Safitri Maryanah Safitri Muhamad Ryansyah Nofarenzi, Yosari Nur Mutia Eka Pusparani Nurajijah Rismaniah Rismaniah Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Romi Satria Wahono Sita Anggraeni Sita Anggraeni Sita Anggraeni, Sita Siti Nurdiani Solehudin, Rizki Sri Syaifur Rahmatullah Syaifur Rahmatullah, Syaifur