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All Journal Scientific Journal of Informatics NUMERICAL (Jurnal Matematika dan Pendidikan Matematika)
Nurul Rohmawati, Nurul
Universitas Singaperbangsa Karawang
Computer Science & IT Mathematics

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K-Medoid Algorithm in Clustering Student Scholarship Applicants Defiyanti, Sofi; Jajuli, Mohamad; Rohmawati, Nurul
Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i1.8212

Abstract

Data Grouping scholarship applicants Bantuan Belajar Mahasiswa (BBM) grouped into 3 categories entitled of students who are eligible to receive, be considered, and not eligible to receive scholarship. Grouping into 3 groups is useful to make it easier to determine the scholarship recipients fuel. K-Medoids algorithm is an algorithm of clustering techniques based partitions. This technique can group data is student scholarship applicants. The purpose of this study was to measure the performance of the algorithm, this measurement in view of the results of the cluster by calculating the value of purity (purity measure) of each cluster is generated. The data used in this research is data of students who apply for scholarships as many as 36 students. Data will be converted into three datasets with different formats, namely the partial codification attribute data, attributes and attribute the overall codification of the original data. Value purity on the whole dataset of data codification greatest value is 91.67%, it can be concluded that the K-Medoids algorithm is more suitable for use in a dataset with attributes encoded format overall. 
K-Medoid Algorithm in Clustering Student Scholarship Applicants Defiyanti, Sofi; Jajuli, Mohamad; Rohmawati, Nurul
Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i1.8212

Abstract

Data Grouping scholarship applicants Bantuan Belajar Mahasiswa (BBM) grouped into 3 categories entitled of students who are eligible to receive, be considered, and not eligible to receive scholarship. Grouping into 3 groups is useful to make it easier to determine the scholarship recipients fuel. K-Medoids algorithm is an algorithm of clustering techniques based partitions. This technique can group data is student scholarship applicants. The purpose of this study was to measure the performance of the algorithm, this measurement in view of the results of the cluster by calculating the value of purity (purity measure) of each cluster is generated. The data used in this research is data of students who apply for scholarships as many as 36 students. Data will be converted into three datasets with different formats, namely the partial codification attribute data, attributes and attribute the overall codification of the original data. Value purity on the whole dataset of data codification greatest value is 91.67%, it can be concluded that the K-Medoids algorithm is more suitable for use in a dataset with attributes encoded format overall.
K-Medoid Algorithm in Clustering Student Scholarship Applicants Defiyanti, Sofi; Jajuli, Mohamad; Rohmawati, Nurul
Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i1.8212

Abstract

Data Grouping scholarship applicants Bantuan Belajar Mahasiswa (BBM) grouped into 3 categories entitled of students who are eligible to receive, be considered, and not eligible to receive scholarship. Grouping into 3 groups is useful to make it easier to determine the scholarship recipients fuel. K-Medoids algorithm is an algorithm of clustering techniques based partitions. This technique can group data is student scholarship applicants. The purpose of this study was to measure the performance of the algorithm, this measurement in view of the results of the cluster by calculating the value of purity (purity measure) of each cluster is generated. The data used in this research is data of students who apply for scholarships as many as 36 students. Data will be converted into three datasets with different formats, namely the partial codification attribute data, attributes and attribute the overall codification of the original data. Value purity on the whole dataset of data codification greatest value is 91.67%, it can be concluded that the K-Medoids algorithm is more suitable for use in a dataset with attributes encoded format overall. 
Perbandingan Penggunaan Phyton dan Excel dalam Menyelesaikan Persamaan Tak Linier Metode Newton Raphson Rohmawati, Nurul; Inayah, Istiqomah Sarah Nur; Wibowo, Ari
Numerical: Jurnal Matematika dan Pendidikan Matematika VOL. 9 NO. 1 (2025)
Publisher : Universitas Ma'arif Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25217/numerical.v9i1.5798

Abstract

Persamaan tak linier sering dijumpai dalam berbagai bidang, seperti fisika, teknik, dan ekonomi. Salah satu metode numerik yang sering digunakan untuk mencari akar dari persamaan tak linier adalah metode Newton-Raphson. Dengan kemajuan teknologi, berbagai perangkat lunak kini digunakan untuk mempermudah perhitungan numerik. Dalam konteks alat yang digunakan untuk analisis data, perbandingan antara Python dan Microsoft Excel telah menjadi bahan diskusi. Penelitian ini menggunakan pendekatan kualitatif deskriptif dengan metode komparatif untuk menganalisis perbedaan hasil antara objek yang dibandingkan serta mengetahui mana yang lebih efektif dan efisien. Hasil penelitian menunjukkan bahwa Python lebih unggul dalam hal akurasi hasil, automasi iterasi, kecepatan perhitungan, fleksibilitas perubahan fungsi, reproduksibilitas, dan skalabilitas. Namun, Python memerlukan pengetahuan pemrograman dan kurang user-friendly bagi pemula. Sedangkan alat bantu Microsoft Excel lebih unggul dalam hal kemudahan penggunaan dan kompatibilitas dengan software Microsoft, tetapi kurang cocok untuk masalah yang kompleks atau memerlukan banyak iterasi. Microsoft Excel juga rentan terhadap kesalahan manual dan kurang skalabel.
Co-Authors Ari Wibowo INAYAH, ISTIQOMAH SARAH NUR Sofi Defiyanti TATI NURHAYATI