Article Per Year (5 Year)
p-Index From 2021 - 2026
2.147
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Ekonomi (JUPE) Interest : Jurnal Ilmu Kesehatan Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal Pendidikan Nonformal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jiko (Jurnal Informatika dan komputer) International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Aplikasi Statistika & Komputasi Statistik Jurnal Kajian Pariwisata Jurnal Informatika: Jurnal Pengembangan IT PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Ekonomi Kependudukan dan Keluarga
Nooraeni, Rani
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Job Competency Extraction in Information and Technology Sector Using K-Means and Non-Negative Matrix Factorization (NMF) Algorithms Rifa Geandra, Alfitra; Mumtaz Siregar, Amir; Nooraeni, Rani
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.684

Abstract

The advancement of information technology has led to a surge in online job vacancy data, which contains valuable information about the skill demands in the digital labor market. This study aims to extract job competency in the information and technology sector using a combination of KMeans clustering and Non-Negative Matrix Factorization (NMF). A total of 350 job postings were collected from the Kalibrr platform and processed through web scraping, text preprocessing, and feature representation using TF-IDF. The clustering results indicate that the optimal configuration consists of 10 clusters, as evaluated using the Silhouette Score and Davies-Bouldin Index. Each cluster represents a specific job topic, such as backend development, data science, QA automation, cybersecurity, and digital marketing. The results offer a structured overview of digital skill demands and can be utilized by educational institutions, training providers, and labor policy makers. However, the dataset’s limited size, reliance on a single job platform, and the use of traditional machine learning techniques may not capture all semantic variations and complexities present in the broader job market. Consequently, future work should involve larger and more diverse datasets as well as advanced deep learning text representation approaches to enhance the robustness and generalizability of the results. 
Pemodelan Topik Komentar Terhadap Aplikasi Allstat BPS Tahun 2017-2025 Simanungkalit, Gabriella Elisabeth; Shafira, Hervira Nur; Nooraeni, Rani
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9350

Abstract

Penelitian ini dilatarbelakangi oleh meningkatnya kebutuhan akan data statistik yang mudah diakses melalui aplikasi mobile, salah satunya adalah aplikasi AllStat BPS. Tujuan dari penelitian ini adalah untuk menganalisis sentimen dan mengidentifikasi topik utama dalam ulasan pengguna aplikasi AllStat BPS pada periode 2017–2025. Metode yang digunakan mencakup analisis sentimen berbasis lexicon dengan kamus InSet dan klasifikasi menggunakan algoritma Naive Bayes, Random Forest, dan Support Vector Machine (SVM). Pemodelan topik dilakukan dengan pendekatan Latent Dirichlet Allocation (LDA). Hasil penelitian menunjukkan bahwa model Random Forest memberikan performa klasifikasi terbaik dengan akurasi pada data latih sebesar 88,16% dan nilai kappa 0,8046. Selain itu, LDA berhasil mengidentifikasi delapan topik utama dari ulasan pengguna, dengan Topik 1 memiliki nilai koherensi tertinggi (0,1784) yang mengindikasikan kekuatan semantik antar kata dalam topik tersebut. Topik-topik ini kemudian dipetakan ke dalam kerangka kualitas perangkat lunak berdasarkan standar ISO/IEC 25010, dengan aspek Functional Suitability dan Performance Efficiency sebagai topik dominan. Kesimpulan dari penelitian ini adalah bahwa kombinasi metode Random Forest dan LDA efektif dalam mengklasifikasikan sentimen serta menggambarkan fokus isu dalam ulasan pengguna aplikasi AllStat BPS.
Co-Authors A, Yolanda Rizkie Adi Lumaksono Agung Budi Santoso Al Baihaqi, Rakai Anasrul, Anasrul Astriani, Vina Briliyanto, Muhamad Bagus Adji Candra, Arya Candra Dewi, Aprilia Lutviana Firmansyah, Adrian Fitri, Muti Nurjannah Fitri, Nurhidayatul Hakim, Syifa Rahmawati Hamdani, Febri Helmiah, Najia Hirma, Emalia Septiani Khoirun Nisa', Intan Maulida Lestariningsih, Eni Mahendra, Fiqih Tri MS, Muhammad Muawwad Mumtaz Siregar, Amir Munia, Nufaisa Muqiit WS, Andriansyah Nulkarim, Aldi Rochman Prasetya, Ananda Galuh Intan Pratama, Firza Refo Adi Pulungan, Ridson Alfarizal Putra, Amran Pratama Rifa Geandra, Alfitra Rizki, M. Alfa Salsabila, Atikah Saputri, Aprillia Anis Saragih, Meilani Thereza Br. Sari, Esa Anindika Satria, Manca Shafira, Hervira Nur Shariati, Ilhamsyah Ali Simanungkalit, Gabriella Elisabeth Siregar, Aisyah Anjani Putri Sofyan, Sabiq Sudianto, Fauzan Bayu Hera Susanti, Amalia Taufiqurrahman, Febi Yusnissa, Hertina Yustiani, Dwi Zekha F, Noval Irgi