Article Per Year (5 Year)
p-Index From 2021 - 2026
1.978
P-Index
This Author published in this journals
All Journal Panrita Abdi - Jurnal Pengabdian pada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA IJISTECH (International Journal Of Information System & Technology) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Jurnal Informatika dan Rekayasa Elektronik Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) IJISTECH KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT)
Nelly Khairani Daulay
Prodi Rekayasa Sistem Komputer, Universitas Bina Insan, Lubuklinggau, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Penerapan Metode Waspas Untuk Efektifitas Pengambilan Keputusan Pemutusan Hubungan Kerja Nelly Khairani Daulay
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 2 No. 2 (2021): Januari 2021
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v2i2.2773

Abstract

Layoffs are the most feared thing for every employee.  There are many reasons behind a termination.  For example, the company is experiencing a decline in terms of revenue so it feels necessary for a reduction in employees.  Then the mistakes made by the employee itself such as criminal acts or mistakes in the work, or perhaps the personal desire of the employee himself due to personal reasons. But in the event of termination of employment, the leadership should be wise so as not to happen things that can interfere with the sustainability of the company. Because often the assessment given to employees is not subjective so it harms the employee itself. To overcome this, a decision support system is needed that can help the leadership to be able to take good policies. This decision-making system (SPK) requires a method in the process of completion. There are many methods that can be used one of them is the WASPAS method. The purpose of this research is to give an overview to the leadership in order to make good decisions so as not to harm employees. The results obtained by Karywan with the smallest value will be laid off. The smallest value is obtained by employee no. 4 with a value of 0.75
Klasifikasi Kematangan Buah Pinang (Areca catechu L.) Menggunakan Hybrid Deep Feature Fusion dan XGBoost Anggi; Nelly Khairani Daulay; Ahmad Sobri
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2864

Abstract

Areca nut (Areca catechu L.) maturity is one of the factors affecting harvest quality. Visual maturity identification still has limitations because it can be influenced by observer subjectivity and environmental conditions. This study aims to classify areca nut maturity levels using a Hybrid Deep Feature Fusion approach by combining ResNet50 and EfficientNetB0 as feature extractors with XGBoost as the classification algorithm. The dataset used in this study was a primary dataset consisting of 1,200 areca nut images categorized into three maturity classes: unripe, semi-ripe, and ripe. The research stages included image preprocessing, feature extraction using CNN models, feature combination through feature concatenation, classification using XGBoost, and performance evaluation using accuracy, precision, recall, F1-score, confusion matrix, and 5-Fold Cross Validation. The experimental results showed that ResNet50 + XGBoost and Hybrid Deep Feature Fusion + XGBoost achieved accuracy, precision, recall, and F1-score values of 100%, while EfficientNetB0 + XGBoost achieved an accuracy of 99.16%. These results indicate that CNN-based features are able to represent the visual characteristics of areca nut images in the dataset used. The Hybrid Deep Feature Fusion approach provides an analysis of feature combination from two different CNN architectures, although increasing the feature dimensions does not always improve evaluation performance when a single feature extractor is already capable of representing dataset characteristics effectively. Future research can be conducted by increasing dataset variations to evaluate the generalization capability of the method under more diverse environmental conditions.
Penentuan Kelayakan Penerima Bantuan Program Keluarga Harapan Menggunakan Algoritma Support Vector Machine Nys. Sinta Audina; Tri Hasanah Bimastari Aviani; Nelly Khairani Daulay
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i4.8064

Abstract

Technological developments, particularly in the field of machine learning, have had a significant impact on supporting data-driven decision making. One of the challenges faced in implementing PKH in Tiang Pumpung Kepungut Subdistrict, Musi Rawas Regency, is [A1] the process of determining aid recipients, which is still done manually. Data is still manually recorded into Excel based on data obtained during the population census. This often causes errors and mistakes during the aid distribution process. To overcome this problem, this study proposes the use of the Support Vector Machine algorithm in the PKH beneficiary classification process. Support Vector Machine is an effective classification method for handling complex and non-linear data with a high degree of accuracy. This study aims to develop a Support Vector Machine-based system to improve efficiency, accuracy, and transparency in the selection process for determining the eligibility of aid recipients. A total of 250 PKH data sets were successfully obtained. The data obtained or collected included several variables, namely name, age, number of family members, occupation, income, number of dependent children, and status. The data was then divided into two sets: 80% for training and 20% for testing from a total of 250 data points. After cleaning the data, the number of data points became 244, with 195 for training and 49 for testing. The results of this study showed that 39 families were eligible to receive assistance and 10 families were not eligible. It is hoped that the resulting system can serve as an innovative solution to support more targeted social assistance distribution in the region.
Co-Authors Ahmad Sobri Anggi Armanto Armanto Asep Toyib Hidayat Bunga Intan Eka Pratiwi Sumantri Eno Febra Wardani Handika Sanjaya Juni Trianto M. Agus Syamsul Arifin M. Giatman Meli Pitriyanti Mesran, Mesran Muhammad Irvai Novi Lestari Nur Alamsyah, Muhammad Nys. Sinta Audina Rahmi Syafriyeti Refdi Andri Ronal Watrianthos Satrianansyah Simatupang, Wakhinuddin Supriyanto Syafrida Hafni Sahir Tri Hasanah Bimastari Aviani Yohanes Susanto