scholarly journals Model Prediksi Awal Masa Studi Mahasiswa Menggunakan Algoritma Decision Tree C4.5

2019 ◽  
Vol 7 (4) ◽  
pp. 272 ◽  
Author(s):  
Elsa Paskalis Krisda Orpa ◽  
Eva Faja Ripanti ◽  
Tursina Tursina
Keyword(s):  
Decision Tree ◽  
Error Rate ◽  
Drop Out

Masa studi mahasiswa merupakan tolak ukur penilaian keberhasilan Program Studi, karena masa studi merupakan salah satu indikator keberhasilan proses belajar mahasiswa. Permasalahan mahasiswa lulus tidak tepat waktu dan mahasiswa drop out (DO) masih menjadi kendala Program Studi saat ini. Tujuan penelitian ini adalah membangun sebuah model untuk prediksi awal masa studi mahasiswa, dimana saat ini implementasinya dilakukan pada Program Studi Informatika Universitas Tanjungpura. Keterlambatan mahasiswa dalam menempuh masa studi disebabkan karena kesulitan data pengetahuan yang terbatas tentang prediksi masa studi. Prediksi adalah suatu kegiatan untuk memperkirakan kejadian yang akan terjadi dimasa depan dengan menggunakan data yang sudah ada. Penggunakan model untuk melakukan prediksi masa studi bisa digunakan untuk menangani masalah kerumitan dan ketepatan hasil prediksi, dengan menggunakan metode pendekatan yang cocok untuk prediksi salah satunya adalah algoritma Decision Tree C4.5. Pengujian sistem yang dilakukan menggunakan Cofusion Matrix, menunjukan bahwa model prediksi yang dibangun menggunakan Decision Tree C4.5 menghasilkan rule yang baik digunakan untuk prediksi masa studi mahasiswa. Karena hasil perhitungan nilai akurasi terhadap prediksi yang dihasilkan dengan kenyataan sebenarnya menunjukan nilai precision, recall dan accuracy rata-rata diatas 50% sedangkan untuk nilai error rate berada dibawah 20% .

scholarly journals Deteksi Dini Mahasiswa Drop Out Menggunakan C5.0

2021 ◽  
Vol 6 (2) ◽  
pp. 113-119
Author(s):  
Ulfi Saidata Aesyi ◽  
Alfirna Rizqi Lahitani ◽  
Taufaldisatya Wijatama Diwangkara ◽  
Riyanto Tri Kurniawan
Keyword(s):  
Information Technology ◽  
Decision Tree ◽  
Test Data ◽  
Error Rate ◽  
Research Data ◽  
Drop Out ◽  
Training Data ◽  
Dropping Out ◽  
Study Program ◽  
C5.0 Decision Tree

The decline in the number of active students also occurred at the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This greatly affects the profile of study program graduates. So it is necessary to have a system that is able to detect students who are threatened with dropping out early. In this study, the attributes chosen were the student's GPA and the percentage of attendance . This attribute is used to classify students who are predicted to drop out. The research data uses student data from the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This study uses the C5.0 algorithm to build a decision tree to assist data classification. The decision tree that was built with 304 data as training data resulted a C5.0 decision tree which had an error rate of 5%. The accuracy results obtained from the 76 test data is 93%.

scholarly journals Evaluasi Klasifikasi Penerima Bidikmisi Menggunakan Algoritma Interative Dichotomiser 3 (ID3) (Studi Kasus Institut Agama Islam Negeri Samarinda)

2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Jainuddin Jainuddin ◽  
Islamiyah Islamiyah ◽  
Gubtha Mahendra Putra ◽  
Haviluddin Haviluddin ◽  
Vina Zahrotun Kamila
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Error Rate ◽  
Confusion Matrix

Teknik klasifikasi data mining yang cukup populer adalah Decision Tree diantaranya menggunakan algoritma Interative Dichotomiser 3 (ID3).  Klasifikasi didapatkan dari pohon keputusan yang terbentuk melalui algoritma Interative Dichotomiser 3 (ID3) yang akan diukur tingkat akurasi dan error rate algortima dalam menentukan klasifikasi.  Hal ini dapat dilakukan dengan cara membentuk model pohon keputusan pada mesin learning RapidMinner menggunakan data training dan evaluasi membandingkan data nyata dengan data testing klasifikasi untuk mengukur akurasi algoritma. Tujuan dalam penelitian ini adalah untuk menghasilkan informasi klasifikasi kelayakan penerima bidikmisi menggunakan algoritma Interative Dichotomiser 3 (ID3) di Institut Agama Islam Negeri (IAIN) Samarinda dan untuk mengetahui akurasi algoritma yang digunakan. Variabel penelitian terdiri pekerjaan orang tua, jumlah penghasilan orang tua, jumlah anggota keluarga, status kepemilikan rumah, jumlah pengeluaran keluarga, dan status kepemilikan SKTM/KIP. Berdasarkan hasil analisis dengan mengukur kinerja algoritma menggunakan metode confusion matrix, dengan menghasilkan akurasi 98.3% dan error rate 1.7% dalam menentukan klasifikasi kelayakan penerima Bidikmisi di Institut Agama Islam Negeri (IAIN) Samarinda.

scholarly journals An Improved End-to-End Autoencoder Based on Reinforcement Learning by Using Decision Tree for Optical Transceivers

Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 31
Author(s):  
Qianwu Zhang ◽  
Zicong Wang ◽  
Shuaihang Duan ◽  
Bingyao Cao ◽  
Yating Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Decision Tree ◽  
Bit Error Rate ◽  
Error Rate ◽  
Forward Error Correction ◽  
Adaboost Algorithm ◽  
End To End ◽  
Learning By Using ◽  
Optical Transceivers

In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme. Moreover, we further experimentally study the Tree depth and the number of Decision Tree, which are the two main factors affecting the bit error rate performance. Experimental research afterwards showed that the effect from the number of Decision Tree as 30 on bit error rate (BER) flattens out under 48 Gb/s for the fiber range from 25 km and 75 km SSMF, and the influence of Tree depth on BER appears to be a gentle point when Tree Depth is 5, which is defined as the optimal depth point for aforementioned fiber range. Compared to the autoencoder based on a Fully-Connected Neural Network, our algorithm uses addition operations instead of multiplication operations, which can reduce computational complexity from 108 to 107 in multiplication and 106 to 108 in addition on the training phase.

scholarly journals Perbandingan Decision Tree J48, REPTREE, dan Random Tree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit Menggunakan Fuzzy Tsukamoto

2021 ◽  
Vol 8 (3) ◽  
pp. 473
Author(s):  
Tundo Tundo ◽  
Shofwatul 'Uyun
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Palm Oil ◽  
Error Rate ◽  
Oil Production ◽  
Random Tree ◽  
Actual Data ◽  
Fuzzy Method ◽  
Forecasting Error

<h2 align="center"> </h2><p class="Default">Penelitian ini menerangkan analisis <em>decision tree</em> J48, REP<em>Tree</em> dan <em>Random Tree</em> dengan menggunakan metode <em>fuzzy </em>Tsukamoto dalam penentuan jumlah produksi minyak kelapa sawit di perusahaan PT Tapiana Nadenggan dengan tujuan untuk mengetahui <em>decision tree</em> mana yang hasilnya mendekati dari data sesungguhnya. Digunakannya <em>decision tree</em> J48, REP<em>Tree</em>, dan <em>Random Tree</em> yaitu untuk mempercepat dalam pembuatan <em>rule </em>yang digunakan tanpa harus berkonsultasi dengan para pakar dalam menentukan <em>rule</em> yang digunakan. Berdasarkan data yang digunakan akurasi pembentukan <em>rule</em> dari <em>decision tree</em> J48 adalah 95,2381%, REP<em>Tree</em> adalah 90,4762%, dan <em>Random</em> <em>Tree</em> adalah 95,2381%. Hasil dari penelitian yang telah dihitung bahwa metode <em>fuzzy Tsukamoto</em> dengan menggunakan REP<em>Tree</em> mempunyai <em>error Average Forecasting Error Rate </em>(AFER) yang lebih kecil sebesar 23,17 % dibandingkan dengan menggunakan J48 sebesar 24,96 % dan <em>Random Tree</em> sebesar 36,51 % pada prediksi jumlah produksi minyak kelapa sawit. Oleh sebab itu ditemukan sebuah gagasan bahwa akurasi pohon keputusan yang terbentuk menggunakan <em>tools </em>WEKA tidak menjamin akurasi yang terbesar adalah yang terbaik, buktinya dari kasus ini REP<em>Tree</em> memiliki akurasi <em>rule</em> paling kecil, akan tetapi hasil prediksi memiliki tingkat <em>error</em> paling kecil, dibandingkan dengan J48 dan <em>Random Tree. </em></p><p class="Default"><em><br /></em></p><p class="Default"><strong><em>Abstract</em></strong></p><div><p><em>This study explains the J48, REPTree and Tree Random tree decision analysis using Tsukamoto's fuzzy method in determining the amount of palm oil production in PT Tapiana Nadenggan's company with the aim of finding out which decision tree results are close to the actual data. The decision tree J48, REPTree, and Random Tree is used to accelerate the making of rules that are used without having to consult with experts in determining the rules used. Based on the data used the accuracy of the rule formation of the J48 decision tree is 95.2381%, REPTree is 90.4762%, and the Random Tree is 95.2381%. The results of the study have calculated that the Tsukamoto fuzzy method using REPTree has a smaller Average Forecasting Error Rate </em>(AFER) <em>rate of 23.17% compared to using J48 of 24.96% and Tree Random of 36.51% in the prediction of the amount of palm oil production. Therefore an idea was found that the accuracy of decision trees formed using WEKA tools does not guarantee the greatest accuracy is the best, the proof of this case REPTree has the smallest rule accuracy, but the predicted results have the smallest error rate, compared to J48 and Tree Random.</em></p></div><p class="Default"><strong><em><br /></em></strong></p>

Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate

Data Mining ◽  
2013 ◽  
pp. 1819-1834
Author(s):  
Alan Olinsky ◽  
Phyllis A. Schumacher ◽  
John Quinn
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Logistic Regression ◽  
Decision Tree ◽  
Student Success ◽  
Predictive Models ◽  
Drop Out ◽  
Predicting Success ◽  
Best Fitting ◽  
Fitting Model

One way to enhance the likelihood that more students will graduate within the specific major that they begin with is to attract the type of students who have typically (historically) done well in that field of study. This chapter details a study that utilizes data mining techniques to analyze the characteristics of students who enroll as actuarial students and then either drop out of the major or graduate as actuarial students. Several predictive models including logistic regression, neural networks and decision trees are obtained. The models are then compared and the best fitting model is determined. The regression model turns out to be the best predictor. Since this is a very well understood method, it can easily be explained. The decision tree, although its underpinnings are somewhat difficult to explain, gives a clear and well understood output. Not only is the resulting model a good one for predicting success in the major, it also allows us the ability to better counsel students.

scholarly journals Fuzzy Decision Tree to Predict Student Success in Their Studies

2020 ◽  
Vol 1 (3) ◽  
pp. 135-144
Author(s):  
Heri Bambang Santoso
Keyword(s):  
Decision Tree ◽  
Fuzzy Inference ◽  
Drop Out ◽  
Classification Rules ◽  
University Management ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Inference System ◽  
Target Time ◽  
Id3 Algorithm

The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.

scholarly journals PREDIKSI MAHASISWA LULUS TEPAT WAKTU

2018 ◽  
Vol 5 (2) ◽  
pp. 7-13
Author(s):  
Agus Budiyantara ◽  
Irwansyah A
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Drop Out

Prediksi Mahasiswa Lulus Tepat Waktu dibutuhkan oleh Manajemen Perguruan Tinggi dalam menentukan kebijakan preventif terkait pencegahan dini kasus Drop Out (DO). Prediksi ini bertujuan untuk menentukan faktor akademis yang berpengaruh terhadap masa studi dan membangun model prediksi terbaik dengan teknik Data Mining. Atribut yang digunakan untuk Klasifikasi Data Mining ada 11 atribut yaitu NPM, Jenis Kelamin, Usia, Jurusan, Kelas, Pekerjaan, Indek Prestasi Semester 1, Indek Prestasi Semester 2, Indek Prestasi Semester 3, Indek Prestasi Semester 4 dan Keterangan sebagai atribut hasil. Dari hasil evaluasi dan validasi yang telah dilakukan menggunakan tools RapidMiner diperoleh hasil Accuracy dari Metode Decision Tree (C4.5).

scholarly journals ANALISA PREDIKSI MAHASISWA DROP OUT MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA ID3 dan C4.5

2020 ◽  
Vol 17 (2) ◽  
pp. 97
Author(s):  
Laksamana Rajendra Haidar ◽  
Eko Sediyono ◽  
Ade Iriani
Keyword(s):  
Decision Tree ◽  
Drop Out

scholarly journals OPTIMASI DECISION TREE MENGGUNAKAN PARTICLE SWARM OPTIMIZATION PADA DATA SISWA PUTUS SEKOLAH

2017 ◽  
Vol 2 (1) ◽  
pp. 7-14
Author(s):  
Mirza Yogy Kurniawan ◽  
Muhammad Edya Rosadi
Keyword(s):  
Decision Tree ◽  
Compulsory Education ◽  
Large Data ◽  
High Accuracy ◽  
Drop Out ◽  
Economic Factors ◽  
Data Set ◽  
Swarm Optimization ◽  
The Government ◽  
The Right

Education is the right of every citizen, even government makes program to promote the compulsory education of 12 years. Drop out of school has become an obstacle to the government program where the dropout is caused by many factors, including economic factors, geographical conditions, and students' own desires. ID3 is able to generate a decision tree from a very large data set. This decision tree can be used as a reference for possible drop out of students. In order to be a good reference then the resulting classification must have a high accuracy. PSO is known to increase the accuracy of various kinds of data mining classification. ID3 in this study yielded 72.5% accuracy while after optimized with PSO then ID3 will yield 85% accuracy.

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