Article Per Year (5 Year)
p-Index From 2021 - 2026
1.116
P-Index
This Author published in this journals
All Journal Jurnal Informatika Mandalika Mathematics and Educations Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Pengabdian Masyarakat Bangsa RENATA Jurnal Pengabdian Masyarakat Kita Semua
Rosni, Rosni
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENDAPATAN DOMESTIK REGIONAL BRUTO (PDRB) DI SUMATERA UTARA DENGAN PENDEKATAN REGRESI DATA PANEL Rosni, Rosni; Rivai, Muklas; Nainggolan, Lorena Ulitara
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v6i1.926

Abstract

Gross Regional Domestic Product (GRDP) is an indicator in measuring the economic growth of a region. In this research, the aim is to analyze the factors that influence GRDP in North Sumatra in 2012-2023 using the panel data regression method. Panel data regression analysis is a statistical method that combines time series and cross-section data to capture dynamics over time and differences between regions more comprehensively. The independent variables analyzed include Per Capita Expenditure, Original Regional Income (PAD), Population Density, Education Level, and Poverty Level. The selection of the best regression model was carried out through the Chow Test and Hausman Test, which showed that the Fixed Effect Model (FEM) was the most appropriate model. And it was found that the variables PAD, Education Level, and Poverty Level had a significant influence on GRDP, while Per Capita Expenditure and Population Density did not have a partially significant influence.
Literasi Pemanfaatan Software JASP Untuk Meningkatkan Keterampilan Statistik Guru di MAN 1 Bandar Lampung Andirasdini, Indah Gumala; Sofia, Ayu; Rivai, Muklas; Mahrani, Dwi; Yulita, Tiara; Irwan, Sri Efrinita; Berliana Ratam, Aldila Nur Indah; Gustina K.S., Annisa Hevita; Dewi, Karina Sylfia; Marisa, Marisa; Azzanina, Nanda; Baiti, Putri Isnaini Cahyaning; Rosni, Rosni
RENATA: Jurnal Pengabdian Masyarakat Kita Semua Vol. 3 No. 1 (2025): Renata - April 2025
Publisher : PT Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/1.renata.147

Abstract

Guru sebagai agen perubahan memiliki peran strategis dalam mengembangkan literasi digital di lingkungan kerja. Salah satu aspek penting dalam literasi digital adalah kemampuan dalam memanfaatkan teknologi dan aplikasi digital untuk mendukung proses pembelajaran dan pengolahan data. Pemanfaatan software JASP (Jeffreys's Amazing Statistics Program) menjadi salah satu cara efektif bagi guru untuk meningkatkan keterampilan statistik seperti mengolah dan menganalisis data. Dengan memanfaatkan JASP, guru dapat melakukan analisis statistik secara intuitif dan efisien, sehingga memudahkan dalam mengajarkan konsep-konsep statistik kepada siswa. Pengabdian dalam bentuk literasi pemanfaatan software JASP ini didasari oleh kebutuhan mendesak akan kemampuan memahami analisis data yang efektif di kalangan pendidik, mengingat pentingnya pengolahan data dalam proses pembelajaran dan evaluasi. Metode yang digunakan dalam pengabdian ini meliputi pelatihan intensif dan workshop yang dirancang untuk memperkenalkan fitur-fitur utama JASP, termasuk analisis statistik dasar hingga lanjutan. Peserta diberikan kesempatan untuk langsung mempraktikkan penggunaan software sehingga diharapkan dapat meningkatkan pemahaman dan keterampilan. Hasil dari kegiatan ini menunjukkan peningkatan kemampuan guru yang signifikan dalam mengolah dan menganalisis data. Hal ini ditunjukkan dari hasil pre-test dan post-test yang dilakukan sebelum dan sesudah kegiatan. Kegiatan pengabdian ini tidak hanya memberikan pengetahuan baru, tetapi juga membangun kepercayaan diri para guru dalam menggunakan teknologi untuk mendukung pengajaran. Kesimpulan dari pengabdian ini menekankan pentingnya pelatihan berkelanjutan dalam literasi data untuk meningkatkan kualitas pendidikan
Klasterisasi Penyakit pada Data Klaim Rujukan Tingkat Lanjut BPJS Kesehatan Menggunakan Algoritma Density-Based Spatial Clustering of Application with Noise Rivai, Muklas; Huda, Misbahul; Rosni; Dewi, Karina Sylfia
Jurnal Informatika Vol 25 No 2 (2025): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jurnalinformatika.v25i12

Abstract

Over time and with the advancement of technology, an increasing number of disease-claim submissions have been received by Badan Penyelenggara Jaminan Sosial (BPJS) for Health, causing data accumulation to the point that the dataset can now be categorized as Big Data. One of the challenges of Big Data is that it cannot be processed using conventional methods, thus requiring specialized approaches such as data clustering. The purpose of this study is to determine the optimal number of clusters and to analyze the characteristics of the cluster groups. The type of data used is secondary data obtained from the BPJS Health database. The data used consists of claim data from Fasilitas Kesehatan Rujukan Tingkat Lanjutan (FKRTL) under BPJS Health from January 2019 to December 2020. The variables used include childbirth, accidents, catastrophic diseases, and other diseases. The stages of the clustering process include data normalization, parameter determination, application of the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm, and evaluation of cluster results using the silhouette index. The results of the clustering analysis on FKRTL claim data based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10), show that there are three clusters and one noise cluster, with an average silhouette index of 0.6595942, indicating that the model has a medium structure. Cluster 1 consists of two members with dominant claim categories being accidents and other diseases, cluster 2 consists of 27 members with childbirth as the dominant claim category, cluster 3 consists of four members with catastrophic diseases and other diseases as the dominant claim categories, and the noise cluster consists of one member with childbirth as the dominant claim category.
Segmentasi Kecamatan di Kota Bandung Berdasarkan Statistik Penyandang Disabilitas Menggunakan Algoritma K-Means Clustering Rosni, Rosni; Fredella, Erinna; Rivai, Muklas
Mandalika Mathematics and Educations Journal Vol 8 No 1 (2026): Edisi Maret
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v8i1.11430

Abstract

People with disabilities continue to experience discrimination, stigmatization, and unfulfilled rights. The number of people with disabilities in Bandung City is the second highest in West Java Province. Various factors, including a disability-unfriendly environment, lack of accessibility to public infrastructure, and insufficient social support. This study aims to analyze and segment subdistricts in Bandung City based on disability statistics using the K-Means clustering algorithm. The types of disabilities analyzed include mental disabilities, physical disabilities, physical and mental disabilities, visual impairments, hearing impairments, and other types of disabilities. The results of the study showed that the optimal number of clusters, as determined by the Elbow method, is 2 clusters. A total of 18 subdistricts are classified into Cluster 1, characterized by a relatively lower number of persons with disabilities, while the remaining 12 subdistricts are classified into Cluster 2, which has a relatively higher number of persons with disabilities. The results of this segmentation are expected to support the realization of Bandung City as an inclusive city and to assist the government in developing more targeted policies and programs.
Pemanfaatan Looker Studio untuk Mengembangkan Kompetensi Analisis dan Visualisasi Data Siswa SMKS Nurul Huda Pringsewu Rosni, Rosni; Madonna, Nora; Fitriawati, Andi; Al Mahkya, Dani; Irawan, Agus; Simanjuntak, Erica Grace; Hayati, Ma’rufah; Nasrullah, Nasrullah; Irfan, Miftahul; Mahrani, Dwi; Sofia, Ayu; Yulita, Tiara; Rivai, Muklas
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 1 (2026): Maret
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v4i1.4194

Abstract

Kemampuan dalam memanfaatkan teknologi, khususnya dalam eksplorasi dan visualisasi data, menjadi tantangan signifikan dalam dunia pendidikan di era digital saat ini. SMKS Nurul Huda Pringsewu sebagai mitra kegiatan pengabdian menunjukkan adanya keterbatasan kompetensi siswa dalam mengolah data dan menyajikannya dalam bentuk visual yang informatif. Menanggapi permasalahan tersebut, tim Pengabdian kepada Masyarakat (PkM) melaksanakan pelatihan menggunakan aplikasi Looker Studio untuk eksplorasi data dan pembuatan dashboard statistik. Looker Studio merupakan platform berbasis web yang memudahkan pengguna dalam mengolah data numerik dan menyajikannya secara visual, sehingga proses pembacaan data tidak lagi dilakukan secara manual. Pelatihan ini bertujuan untuk meningkatkan kemampuan siswa dalam mengolah dan memvisualisasikan data menggunakan teknologi terkini. Hasil dari kegiatan ini menunjukkan peningkatan kompetensi siswa dalam mengeksplorasi data statistik serta peningkatan kualitas pembuatan dashboard yang lebih informatif dan menarik. Kegiatan ini diharapkan dapat menjadi langkah awal dalam membekali siswa dengan keterampilan digital yang relevan dengan kebutuhan industri saat ini.
Co-Authors Agus Irawan Andi Fitriawati Andirasdini, Indah Gumala Ayu Sofia Azzanina, Nanda Baiti, Putri Isnaini Cahyaning Berliana Ratam, Aldila Nur Indah Dani Al Mahkya Dewi, Karina Sylfia Dwi Mahrani Fredella, Erinna Gustina K. S, Annisa Hevita Hayati MT, Ma’rufah Irfan, Miftahul Irwan, Sri Efrinita Ismail Madonna, Nora Marisa . Misbahul Huda Nainggolan, Lorena Ulitara Nasrullah Nasrullah Rivai, Muklas S, Putriyani Simanjuntak, Erica Grace Yulita, Tiara