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All Journal Jurnal Mantik IT (INFORMATIC TECHNIQUE) JOURNAL Jurnal Sains dan Teknologi
Silvia Lestari
Universitas Potensi Utama
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Implementation of Artifical Neural Networks with Multilayer Perceptron for Analysis of Acceptance of Permanent Lecturers Hartono Hartono; Muhammad Sadikin; Dian Maya Sari; Nur Anzelina; Silvia Lestari; Wulan Dari
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.954.pp1389-1396

Abstract

Lecturer acceptance selection is the first step in building an education. The Multilayer Perceptron method can be applied in the case of permanent lecturer admissions. The problem faced in the admission of permanent lecturers is that reception is still subjective. This research will prove the ability of the Multilayer Perceptron algorithm to classify eligibility as a lecturer or not. Inputs from this study were prospective applicants' data, namely age, grade point average (GPA), written test score, interview value, and home base status. Sample data amounted to 100 data. 75% of the data is used as training data, and 25% as test data. The test results of the accuracy of the data are known that the multilayer perceptron neural network method has an accuracy rate of 98.7% and with a ROC Area value of 0.989. This proves that the application of the model used belongs to the classification category very well because it has a ROC value between 0.90-1.00.
Analisis Tingkat Kepuasan Pelanggan Dalam Membeli Peralatan Kue Dengan Menggunakan Metode Rough Set Pada PT. XYZ Silvia Lestari
INSOLOGI: Jurnal Sains dan Teknologi Vol. 1 No. 3 (2022): Juni 2022
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.039 KB) | DOI: 10.55123/insologi.v1i3.563

Abstract

The level of customer satisfaction is very important for every company that carries out its business activities by selling the goods it produces. A company usually does a lot of activities to attract market prey to attract customers to buy its products, therefore paying attention to customer satisfaction is very important to know how the company's image among customers and among people who use the products of a company. making companies work harder in providing services, especially in controlling the feasibility of their products to satisfy customers. Cake equipment or cake ingredients are growing rapidly at this time, therefore researchers are trying to help the company in knowing the level of customer satisfaction using one of the existing methods. in the field of data mining, namely the Rough Set method. This method is expected to be effective in measuring the level of customer satisfaction and where later from the results to be obtained the company will know more about the level of customer satisfaction and the provision of menstruation for customers. for customers with different satisfaction criteria.
Implementasi Algoritma K-Means Untuk Penerimaan Siswa Baru Di SMANPAS Berdasarkan Nilai Rapot dan Hasil Tes Efani Desi; Siti Aliyah; Silvia Lestari; Wulan Dari
IT (INFORMATIC TECHNIQUE) JOURNAL Vol 10, No 1 (2022): IT JOURNAL APRIL 2022
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/it.10.1.2022.01-10

Abstract

SMANPAS atau dikenal dengan SMA Negri Pasirian merupakan lembaga pendidikan yang cukup populer dikalangan lembaga pendidikan lainnya, selain prestasi yang telah didapatkan di SMANPAS, rata rata lulusan dari SMANPAS akan mendapatkan kemudahan untuk masuk Universitas. Sehingga tidak sedikit para pelajaran lulusan smp berlomba agar bisa diterima di SMANPAS. Melihat banyaknya pelajar yang ingin diterima di SMANPAS membuat SMA ini kesulitan dalam menyeleksi para pelajar yang mendaftar sehingga SMANPAS mulai menerapkan Algoritma K-Means guna menyeleksi para pelajar.  Metode K-Means merupakan salah satu alternatif yang cukup efektif, dimana  Metode K-Means ini merupakan sebuah metode yang berfungsi untuk mengelompokan data yang ada ke dalam Cluster berdasarkan data yang telah ditetapkan atau data yang sama[1,2,3]. Dipenelitian ini diambil 8 Pelajar baru yang memiliki 2 nilai , yaitu nilai raport dan nilai tes ujian  dimana dari hasil penelitian ini akan  menghasilkanan 4 orang pelajar baru akan diterima berdasarkan perhitungan K-Means dengan titik acuan atau Cluster dengan nilai 91 untuk nilai raport dan 88 untuk hasil tes ujian, 2 Orang pelajar baru  sebagai cadangan dengan Cluster nilai 70 untuk raport dan 70 untuk hasil tes ujian, dan 2 Orang pelajar yang tidak diteriman di SMANPAS dengan Cluster nilai 60 untuk raport dan 70 untuk hasil tes ujian..
ANALISIS SENTIMENT DATA TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES TERHADAP PEMBERITAAN PERKEMBANGAN PANDEMIK CORONA Dahri Yani Hakim Tanjung; Silvia Lestari
IT (INFORMATIC TECHNIQUE) JOURNAL Vol 9, No 2 (2021): IT JOURNAL OKTOBER 2021
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/it.9.2.2021.111-120

Abstract

Twitter merupakan salah satu media sosial yang sangat banyak digunakan untuk memberikan kritik akan sebuah pemberitaan atau masalah pribadi mereka.pada saat ini sedang banyak pembicaraan mengenai perkembangan virus corona di indonesia maupun di dunia, banyak media dunia yang memberikan pemberitaan melalui twitter dan ada pula para penggunaan yang memeberikan tanggapan terhadap pemberitaan tersebut. Maka dari itu pada penelitian ini dilakukannya analisis terhadap tweet atau tanggapan para pengguna twitter terhadap perkembangan atau isu pandemik korea. Penelitian ini dilkukan dengan cara memisahkan sentiment atau tanggapan yang negatif dan positf menggunakan algoritma naive bayes, dan Support Vector Machine ( SVM ). Hasil pengujian yang dilakukan dapat menunjukkan bahwa tingkat akurasi dengan term frequency memberikan hasil hasil akurasi yang lebih baik daripada akurasi dengan fitur TF-IDF. Metode Support Vector Machine menghasilkan akurasi yang lebih baik daripada metode Naive Bayes baik dalam klasifikasi sentimen maupun dalam klasifikasi kategori. Namun demikian, secara keseluruhan penggunaan metode Support Vector Machine dan Naive Bayes sama-sama memiliki performansi yang cukup baik untuk melakukan klasifikasi tweet.
Analisis Algoritma Regresi Linear Sederhana dalam Memprediksi Tingkat Penjualan Album KPOP Silvia Lestari
INSOLOGI: Jurnal Sains dan Teknologi Vol. 2 No. 1 (2023): Februari 2023
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v2i1.1692

Abstract

Music albums in the current era of globalization are quite sought after by their fans, not only by young people, but fans are starting to reach all walks of life, from children, young people and even adults who also buy albums for their own pleasure and hobby needs. In this study, researchers tried to dig up information about music album sales, especially Korean album sales, which are currently on the rise and are enjoyed by many people, namely NCT127. Which is where NCT127 itself is one of the music groups from the country of Gineng, namely South Korea, which recently held a concert in Jakarta which had an extraordinary effect. So from that the researchers tried to analyze the level of sales of nct127 music albums by using the estimation algorithm in data mining. namely Linear Regression to find out and predict the level of Korean album sales by looking for the values ​​of X, Y, X2, Y2, XY which is done by looking for the coefficient values ​​and the dependent variable of each attribute or label.
Co-Authors Dahri Yani Hakim Tanjung Dian Maya Sari Efani Desi Hartono Hartono Muhammad Sadikin Nur Anzelina Siti Aliyah Wulan Dari