Article Per Year (5 Year)
p-Index From 2020 - 2025
0.778
P-Index
This Author published in this journals
All Journal Bulletin of Computer Science Research Journal of Informatics Management and Information Technology
Nelly Khairani Daulay
Unknown Affiliation
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Penentuan Program Indonesia Pintar (PIP) Pada Siswa Kurang Mampu dengan Metode Preference Selection Index (PSI) Berbasis Web Nelly Khairani Daulay; Asep Toyib Hidayat; Shelfia Shepty
Bulletin of Computer Science Research Vol. 4 No. 1 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i1.296

Abstract

The Indonesia Smart Program (PIP) is a scholarship assistance provided to individuals with the aim of continuing their education as tuition assistance. This program is the government's idea to reduce the high number of children who drop out of school due to lack of funds. This high dropout rate will also later lead to a high crime rate because children who drop out of school cannot work properly. To determine whether or not students are eligible to receive the Indonesia Pintar Program, a decision support system (SPK) is needed that can provide input for schools to assess properly, which students really deserve the scholarship. By using the Preference Selection Index (PSI) method, it is hoped that this method will be able to select the best alternative from a number of alternatives based on criteria from predetermined aspects.  In the Preference Selection Index (PSI) method which is used as a measure of assessment is the value of alternatives, matrix normalization, average performance value, preference variation value, preference value deviation, criteria weight, calculate the final value, and determine the ranking that will determine the optimal alternative, namely students who are entitled to a scholarship.  The purpose of this research is to determine which students are eligible and not eligible to receive assistance from the government in the form of PIP. The results of this study are in the form of rankings with the highest value of 0.7686 and ranked first or rank 1 and the lowest value of 0.7091 is ranked 5th.
Penerapan Metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) dalam Seleksi Penerimaan Peserta Kegiatan Program Pendidikan Kecakapan Wirausaha Fazlur Rahman; Abdi Harfani; Mesran; Kelik Sussolaikah; Nelly Khairani Daulay; Ronal Watrianthos
Journal of Informatics Management and Information Technology Vol. 3 No. 1 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v3i1.238

Abstract

The Entrepreneurial Skills Education Program (PKW), is one of the programs from the Ministry of Education and Culture in 2019. PKW is an educational service through courses and training to provide knowledge, skills, and foster an entrepreneurial mental attitude in managing self-potential and the environment as a basis for entrepreneurship. This study aims to select the acceptance of participants in the Entrepreneurial Skills Education (PKW) program. The method used in the selection is by applying the MOORA method. The MOORA method is a method with very simple steps. The results showed that A1 is the best compared to several other alternatives with a value of 0.304
Metode Hybrid Dalam Pengelompokkan Kemampuan Calistung Siswa Berbasis Machine Learning Salsabila, Amanda; Andri Anto Tri Susilo; Nelly Khairani Daulay
Journal of Informatics Management and Information Technology Vol. 5 No. 2 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i2.500

Abstract

Students reading, writing, and arithmetic abilities (reading, writing, and arithmetic) are an important foundation in the academic development of elementary school students. This study aims to group students' reading, writing, and arithmetic abilities using a hybrid method based on machine learning, with grade data from two Elementary Schools in Lubuklinggau City. The method applied combines the K-Means Clustering algorithm for initial grouping and K-Nearest Neighbors (KNN) for classification. The analysis process includes data preprocessing, application of K-Means, cluster validation using Silhouette Score, and classification with KNN to ensure accuracy. As a result, K-Means successfully grouped students into three clusters: Middle (0), Low (1), and High (2). The KNN model with k = 3 which has the highest accuracy of 95% provides very good accuracy in testing the K-Nearest Neighbors (KNN) classification model with an accuracy of 97%, with very good precision, recall, and F1-score values for all clusters. These findings indicate that this hybrid approach is effective in classifying students' reading, writing and arithmetic abilities, which has implications for the development of more targeted learning strategies based on the characteristics of each group of students.
Sistem Klasifikasi Kelayakan Penerima Bantuan Langsung Tunai Menggunakan Metode K-Nearest Neighbor (KNN) Berbasis Website Dewi Sriwani; Lukman Hakim; Nelly Khairani Daulay; Asep Toyib Hidayat
Journal of Informatics Management and Information Technology Vol. 5 No. 3 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i3.702

Abstract

Poverty is the condition of a person's inability to fulfill the basic needs of life, which is often measured by income that is lower than the average in a region. In Indonesia, the poverty rate, including in Kabupaten Jombang, continued to increase from 2012 to 2017, with various government efforts to overcome this through social programs such as BLT (Direct Cash Assistance), Community Health Insurance, and the Family Hope Program (PKH). However, despite these programs, the data collection process for beneficiaries in some areas, such as Tanah Periuk Village, is still done manually, causing inaccurate targeting in the provision of assistance. For this reason, a more efficient solution is needed in determining the eligibility of beneficiaries. One of the technologies that can be used is data mining, especially the classification method, to analyze beneficiary data based on certain criteria. This research uses the K-Nearest Neighbor (K-NN) algorithm to build a classification system for the eligibility of direct cash transfer recipients in Tanah Periuk Village, with the aim of improving accuracy and efficiency in the beneficiary selection process. This system is web-based, which allows ease of processing and updating data centrally. The results of this study provide eligibility scores for BLT (Direct Cash Assistance) recipients based on the criteria provided. The criteria that are assessed are: House Condition, Income, Occupation and Number of Dependents. One of the families who “DESERVE” to receive assistance is Martina with semi-permanent house conditions, an income of IDR 1,000,000/month, a housewife's job, and 3 dependents.
Co-Authors Abdi Harfani Andri Anto Tri Susilo, Andri Anto Asep Toyib Hidayat Dewi Sriwani Fazlur Rahman Kelik Sussolaikah Lukman Hakim Mesran, Mesran Ronal Watrianthos Salsabila, Amanda Shelfia Shepty