Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JEMST: Journal of Education in Mathematics, Science, and Technology Indonesian Council of Premier Statistical Science
Lira Maylia Purwaningsih
Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT Economics, Econometrics & Finance Education Mathematics Public Health Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PENERAPAN METODE K-MEANS CLUSTERING DALAM PENGELOMPOKAN HASIL PRODUKSI PABRIK KELAPA SAWIT DI PERAWANG Lira Maylia Purwaningsih; Rahmawati Rahmawati; Rahmadeni Rahmadeni; Aprijon Aprijon
JEMST (Journal of Education in Mathematics, Science, and Technology) Vol. 9 No. 1 (2026): JEMST VOL 9 NO 1 2026
Publisher : Faculty of Education and Teacher Training, State Islamic University of Sulthan Thaha Saifuddin Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30631/jemst.v9i1.188

Abstract

  Pabrik Kelapa Sawit (PKS) menghasilkan data produksi yang terus bertambah dan memiliki karakteristik yang beragam, sehingga diperlukan suatu metode untuk mengelompokkan data berdasarkan tingkat kemiripannya. Penelitian ini bertujuan menerapkan metode K-Means Clustering untuk mengelompokkan data hasil produksi PT Perawang Agro Sejahtera periode 01 November 2025 sampai 24 Januari 2026 berdasarkan variabel Tandan Buah Segar (TBS), Crude Palm Oil (CPO), Kernel, dan Cangkang. Tahapan penelitian meliputi normalisasi data menggunakan metode Min-Max, penentuan jumlah cluster, penentuan centroid awal, perhitungan jarak menggunakan Euclidean Distance, serta proses iterasi hingga mencapai kondisi konvergen. Hasil penelitian menunjukkan bahwa proses clustering mencapai konvergen pada iterasi ke-7 dan menghasilkan tiga cluster, yaitu produksi tinggi sebanyak 46 data, produksi sedang sebanyak 1 data, dan produksi rendah sebanyak 27 data. Hasil pengelompokan ini dapat digunakan sebagai informasi pendukung dalam evaluasi, monitoring, dan pengambilan keputusan untuk meningkatkan efisiensi serta menjaga kestabilan produksi perusahaan.
Comparison of Weibull, Exponential, and Log-Normal Distribution Performance on Survival Data Using a Parametric Approach Delva Sari; Lira Maylia Purwaningsih; Suci Oktavia Ramadani; Tiara Wandini Zahputri
Indonesian Council of Premier Statistical Science Vol. 5 No. 1 (2026): February 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v5i1.40438

Abstract

Survival analysis is a statistical method used to analyze the time until a certain event occurs, such as death, equipment failure, or a patient's recovery. The parametric approach in survival analysis assumes that the survival time data follows a given probability distribution, so the selection of the right distribution greatly determines the model's accuracy. This study aims to compare the performance of three commonly used parametric distributions, namely Weibull, Exponential, and Log-Normal distributions, on survival data using goodness-of-fit criteria. Parameter estimation was carried out through the Maximum Likelihood Estimation (MLE) method, while the model evaluation used Log-Likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) values. The data used were simulation data generated from the Weibull distribution with the form parameter k = 1.5 and the scale parameter λ = 0.05, with n = 100 observations and 20% censored data. The results show that the Weibull distribution produces the smallest AIC = 214.73 and BIC = 219.54 values compared to the Exponential (AIC = 238.61, BIC = 240.81) and Log-Normal (AIC = 218.45, BIC = 223.26) distributions, so the Weibull distribution is chosen as the best model. These findings confirm that the flexibility of the shape parameters on the Weibull distribution provides an advantage in modeling survival data with non-constant hazards
Co-Authors Aprijon Aprijon Delva Sari Rahmadeni Rahmadeni Suci Oktavia Ramadani Tiara Wandini Zahputri