The Combination of Naive Bayes and Particle Swarm Optimization Methods of Student’s Graduation Prediction

Purnamasari, Evi; Rini, Dian Palupi; Sukemi, Sukemi

doi:10.26555/jiteki.v5i2.15272

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…In future studies, the tuning parameters in the Random Forest can be used to improve the results of even better accuracy. This can be done using optimization algorithms such as Simulated Annealing [18]- [20], Particle Swarm Optimization [21], or Genetic Algorithm [22]- [24]. In addition, the use of deep learning can also be considered to determine the effect it has on the accuracy, MSE, and RMSE [25][26].…”

Section: Discussionmentioning

confidence: 99%

Jatropha Curcas Disease Identification using Random Forest

Saragih

Wijayaningrum

Haekal

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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As one of the most versatile plants, Jatropha curcas is spread in various regions around the world because of the great benefits it provides. However, Jatropha curcas is easily attacked by viruses which then cause damage to the plant, such as yellowing leaves and secreting sap, making it necessary to identify Jatropha curcas disease to deal with the problem as early as possible so that the losses incurred are not too large. An expert system was built to be able to identify Jatropha curcas disease by adopting expert knowledge. The use of the Random Forest algorithm as one of the classification algorithms was applied in this study. By using a random forest, several disease prediction classes are generated by each decision tree that has been formed. The disease class with the most votes was used as the final result. In this study, the data used were 166 data with 9 diseases and 30 symptoms. The results showed that Random Forest outperformed other algorithms such as Fuzzy Neural Network and Extreme Learning Machine with an accuracy of 98.002% using the composition of training data and test data of 70:30.

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Section: Discussionmentioning

confidence: 99%

Jatropha Curcas Disease Identification using Random Forest

Saragih

Wijayaningrum

Haekal

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…Dalam hal ini sampel data yang diambil dari beberapa sumber Perguruan Tinggi terkemuka di kota Palembang baik itu Universitas, Institut, ataupun Sekolah Tinggi sebagai penentu dalam hal prediski tingkat kelulusan mahasiswa tepat waktu, normalnya mahasiswa yang dapat lulus tepat waktu adalah mahasiswa yang masa pendidikannya mampu ditempu selama empat (4) tahun masa studi, jikalewat dari itu maka mahasiswa tersebut digolongkan mahasiswa tidak tepat waktu. Nilai akurasi peneltian ini mencapai 100% dari 10 sampel yang digunakan berdasarakan sampel acak pada mahasiswa UIGM Palembang (Purnamasari et al, 2019).…”

Section: Pendahuluanunclassified

Penerapan Model Klasifikasi Naïve Bayes Untuk Prediksi Mahasiswa Tepat Waktu

Qisthiano

2023

semnasristek

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Banyak teknik pengukuran dalam prediksi mahasiswa tepat waktu, kriteria yang digunakan mulai dari jurusan, jenis kelas dan nilai semester mahasiswa yang merupakan salah satu penentu bagaimana mahasiswa bisa lulus tepat waktu atau tidak di akhir pendidikannya. Maka dibutuhkan suatu model untuk melakukan prediksi tingkat kelulusan mahasiswa tepat waktu, dengan menggunakan data alumni yang datanya didapat dari beberapa Perguruan Tinggi di Kota Palembang. Model yang digunakan ini menggunakan algoritma Naïve Bayes yang berfungsi sebagai salah satu model untuk melakukan klasifikasi. Dataset yang digunakan adalah data alumni yang sudah dikumpulkan dari beberapa Perguruan Tinggi, sedangkan kriteria dan attribut yang digunakan adalah jurusan, Perguruan Tinggi setiap mahasiswa, jenis kelas pilihan dan nilai setiap semester mahasiswa yang diambil dari semester 1 sampai dengan 4, tahun lulus setiap mahasiswa, dan angkatan pada saat mahasiswa masuk. Lalu dari attribut dan model yang digunakan, peneliti menggunakan alat bantu utnuk mengelola data yaitu Rapidminer untuk melakukan pemrosesan dataset yang sudah disiapkan. Selanjutnya penguji menggunakan 5 kali uji validasi dengan membagi dataset kedalam training dan testing. Hasil penelitian ini merupakan akurasi dari model prediksi yang dilakukan, yang dimana hasil akurasi yang didapat dari alat bantu Rapidminer dan algoritma Naïve Bayes adalah 0.8264.

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“…Pohon keputusan bisa juga digunakan dalam menyelesaikan berbagai tugas dalam proses belajar termasuk jugs klasifikasi [16], regresi serta untuk analisis survival. Atas fungsi dan manfaatnya yang bagus serta unik, pohon keputusan sudah menjadi sebuah pendekatan yang cukup paling kuat serta paling populer pada bidang ilmu data [17].…”

Section: Decision Treeunclassified

Prediksi Perkembangan Nilai Impor Komoditas Utama di Provinsi Sumatera Selatan Menggunakan C4.5

Purnamasari

2023

jidt

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Currently, our world is still being hit by the Covid-19 virus which has impacted society, experiencing a rapid decline in the economy. At present we are entering a market where the economy is very minimal, especially in the province of South Sumatra. This affects the import value of main commodities in South Sumatra Province. So in this study, researchers conducted trials in classifying matters related to cases of predicting the level of development of the value of imports of main commodities in South Sumatra Province. In this study, predictions will be made on the level of development of the value of imports of key commodities by applying the C4.5 method, so that they can be used as material for decision-making in building a more advanced government. Based on the results of the trial, the conclusion obtained is that as an importer, you should be wiser in determining the value of imports, especially if the wealth in our province is of greater interest to other countries. So far, the South Sumatra Province has a higher import value of fertilizer compared to other types of imports. This can be used in making a policy that is significant enough to be able to make the import value prices in the South Sumatra Province all high. Based on the trial results using the orange application, an accuracy of 96.15% was obtained, meaning it had an error rate of 13.85%. This shows from the results of testing using the Decision Tree that it is very good enough to be used in predicting the level of import value.

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The Combination of Naive Bayes and Particle Swarm Optimization Methods of Student’s Graduation Prediction

Cited by 7 publications

References 5 publications

Jatropha Curcas Disease Identification using Random Forest

Jatropha Curcas Disease Identification using Random Forest

Penerapan Model Klasifikasi Naïve Bayes Untuk Prediksi Mahasiswa Tepat Waktu

Prediksi Perkembangan Nilai Impor Komoditas Utama di Provinsi Sumatera Selatan Menggunakan C4.5

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