Article Per Year (5 Year)
p-Index From 2020 - 2025
1.512
P-Index
This Author published in this journals
All Journal Jurnal Informatika Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Nusantara Hasana Journal RENATA Jurnal Pengabdian Masyarakat Kita Semua Indonesian Journal of Applied Mathematics TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Indonesian Actuarial Journal
Rivai, Muklas
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 Environmental Science Mathematics Nursing Physics Social Sciences Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
Analisis Jumlah Kecelakaan Lalu Lintas, Korban, dan kerugian di Provinsi Lampung Tahun 2021 Dengan Metode K-Means Cluster Rivai, Muklas; Hutabarat, Aulia Khairani; Halawa, Doni Hardian; Satrio, Ilham; Tarmizi, Muhammad Syauki; Frasiska, Nadia; Pratiwi, Niken; Alma, Zalikha
Indonesian Journal of Applied Mathematics Vol. 4 No. 1 (2024): Indonesian Journal of Applied Mathematics Vol. 4 No. 1 April Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v4i1.1772

Abstract

Abstract: Provinsi Lampung is facing a serious challenge regarding the high rate of traffic accidents, triggered by the increasing number of vehicles and inadequate infrastructure. Low awareness of traffic regulations also serves as a major cause of accidents. This research employs the Cluster Analysis method within the Geographic Information System (GIS) to categorize traffic accident data in Lampung Province for the year 2021. The data encompasses the number of accidents, fatalities, serious injuries, minor injuries, and financial losses across 14 districts/cities. The cluster analysis results identify two main groups with distinct characteristics. The first cluster exhibits a low accident rate, while the second cluster shows a high accident rate, contributing significantly to the total number of accidents, fatalities, serious injuries, minor injuries, and financial losses. Recommendations for interventions are tailored to the characteristics of each cluster, emphasizing either fatal injury prevention or road infrastructure improvements. The findings of this research provide a foundation for designing more effective strategies to reduce accident risks and enhance traffic safety in Lampung Province Keywords: K-means, Accidents, Lampung Province
Pelatihan Pengolahan dan Analisa Data Statistik Untuk Meningkatkan Kompetensi Guru SMPN di Kalianda, Lampung Selatan Andirasdini, Indah Gumala; Sofia, Ayu; Lestari, Fuji; Listiani, Amalia; Yulita, Tiara; Julianty, Dila Tirta; Rivai, Muklas
TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Vol 4 No 2 (2024): TEKNOKREATIF : Jurnal Pengabdian kepada Masyarakat Volume 4 No 2
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M), Institut Teknologi Sumatera, Lampung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/teknokreatif.v4i2.1875

Abstract

Salah satu wujud pengembangan diri seorang guru adalah dengan melakukan dan menulis laporan penelitian. Penelitian dan penulisan laporan hasil penelitian terkait kinerja pembelajaran seorang guru merupakan salah satu upaya evaluasi (refleksi) terhadap kinerja seorang guru di dalam kelas. Evaluasi ini dilakukan dengan melaksanakan Penelitian Tindakan Kelas (PTK). Permasalahan yang terjadi pada PTK adalah kurangnya kompetensi guru dalam melakukan pengolahan data, menggunakan tools, dan menerapkan model-model statistika yang cocok untuk kasus-kasus yang terjadi di kelas. Pada jurnal ini membahas peningkatan kompetensi guru-guru SMPN melalui pelatihan olah data dan analisis data statistik menggunakan JASP. Tingkat pemahaman dan kompetensi guru diukur menggunakan kuesioner yang diberikan pada sebelum dan setelah melakukan pelatihan. Pelatihan ini memberikan perubahan tingkat pemahaman dan kompetensi yang signifikan berdasarkan bidang kompetensi yang ditanyakan. Hal ini dapat dilihat dari gap responden yang diperoleh saat sebelum pelatihan dan setelah pelatihan dilakukan.
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
ANALISIS FAKTOR YANG MEMPENGARUHI KESEHATAN KEUANGAN PERUSAHAAN ASURANSI PRUDENTIAL Ratam, Aldila Nur Indah Berliana; Azzanina, Nanda; Rivai, Muklas; Selvira, Natalia Aisa; Lase, Marie Vivien; Rohmah, Iffah Nurul; Situmorang, Anita Grace Pretty; Rinaldi, Fanny Dwi Putri; Antika, Siluh Putu Dela; Nadeak, Natasha Putri
Nusantara Hasana Journal Vol. 5 No. 3 (2025): Nusantara Hasana Journal, August 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i3.1595

Abstract

The financial health of an insurance company is a crucial aspect that must be ensured to maintain its ability to manage risks. The insurance industry plays a significant role in economic growth through risk transfer mechanisms. This study aims to analyze the factors influencing the financial health of Prudential Insurance Company during the 2023–2024 period. The method used is factor analysis, a multivariate statistical method designed to identify the underlying structure among financial variables. The analysis results show that two main factors explain 99.2% of the data variance. The first factor is influenced by premium income (X1), claims and benefits paid (X2), and operating expenses (X5). The second factor is influenced by technical reserves (X3) and investment income (X4). These findings highlight the importance of managing premiums, improving operational efficiency, controlling claims, and optimizing assets and investments to maintain the company's financial health. With the right approach, Prudential is expected to maintain its financial stability and competitive position in the insurance industry.  
Pelatihan Olah Data dan Visualisasi Data Statistik dalam Peningkatan Kompetensi Perangkat Desa Badran Sari, Lampung Selatan Sofia, Ayu; Lestari, Fuji; Rivai, Muklas; andirasdini, indah Gumala; Julianty, Dila Tirta
TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Vol 5 No 1 (2025): TEKNOKREATIF : Jurnal Pengabdian kepada Masyarakat Volume 5 No 1
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M), Institut Teknologi Sumatera, Lampung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/teknokreatif.v5i1.1873

Abstract

The use of data processing skills is something that is very important in various fields. The data processing process can use various applications, one of which is the number/data processing application which is commonly known as the Ms. Excel application. Ms. Excel is a software program that allows users to process and calculate numerical data so that calculations and reading data are no longer done manually. The problem with partners is the lack of competence of village officials regarding the use of technology in processing village data. Based on the problems faced by partners, the PkM team offers a solution, namely providing training in processing and analyzing statistical data using Ms. Excel which aims to help village officials to be able to process data and be able to visualize the data into images/graphs that are more attractive to the community so that able to improve the quality of data processing contained in village officials.
Pelatihan Pembuatan Laporan Keuangan Pelaku Usaha Mikro, Kecil, Dan Menengah (UMKM) Di Desa Badran Sari Julianty, Dila Tirta; Listiani, Amalia; Lestari, Fuji; Sofia, Ayu; Rivai, Muklas; Mahrani, Dwi; Fitriawati, Andi
RENATA: Jurnal Pengabdian Masyarakat Kita Semua Vol. 3 No. 3 (2025): Renata - Desember 2025
Publisher : PT Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/1.renata.140

Abstract

Hingga saat ini terdapat sekitar 15 Usaha Mikro, Kecil, dan Menengah yang ada di Desa Badran Sari. Kegiatan usaha yang dilakukan oleh UMKM tidak terlepas dari transaksi keuangan baik transaksi masuk maupun keluar. Apabila transaksi tersebut diolah dengan baik maka dapat menghasilkan laporan keuangan yang tepat untuk berbagai periode waktu. Dengan adanya laporan keuangan yang tepat, maka bisa didapatkan informasi yang tepat mengenai kondisi keuangan dari suatu usaha sehingga bisa membuat keputusan yang tepat untuk masa yang akan datang. Dengan rendahnya kemampuan masyarakat desa dalam mengolah informasi sehingga menjadikan para pelaku UMKM tidak dapat memanfaat informasi keuangan yang mereka miliki sebagai dasar dalam pengambilan keputusan dan pengembangan usahanya. Oleh karena itu perlu diadakannnya suatu pelatihan terkait pembuatan laporan keuangan untuk meningkatkan kemampuan masyarakat Desa Badran Sari yang memiliki UMKM sehingga tercapainya masyarakat informasi dan berbasi pengetahuan. Hal ini diharapkan dapat meningkatkan ukuran usaha UMKM dan meningkatkan kegiatan perekonomian di Desa Badran Sari, Kecamatan Punggur, Kabupaten Lampung Tengah. Setelah dilaksanakannya pelatihan mengenai penyusunan laporan keuangan, jumlah peserta yang memiliki tingkat pemahaman rendah mengalami penurunan dari semula 68,18% menjadi 36,36% dan jumlah responden dengan tingkat pemahaman sedang mengalami peningkatan lebih dari dua kali lipat dari semula 27,27% menjadi 59,09%
ANALISIS PERAMALAN HARGA SAHAM MENGGUNAKAN TEMPORAL CONVOLUTIONAL NETWORK: STUDI KASUS PT LIPPO GENERAL INSURANCE TBK Rivai, Muklas; Nugraha, Ongky Setya
Jambura Journal of Probability and Statistics Vol 6, No 2 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i2.26817

Abstract

The stock market has an important role in the Indonesian economy, but share price fluctuations are often difficult to predict accurately. The machine learning algorithm for forecasting stock price movement trends uses a Temporal Convolutional Network (TCN). This method uses a more comprehensive dataset and advanced analysis techniques to capture non-linear and dynamic patterns in stock price data. This research aims to predict the share price of PT Lippo General Insurance Tbk using Temporal Convolutional Network (TCN) to provide a more accurate and reliable forecasting model. The research method uses a quantitative approach with daily historical stock data from 2011 to 2023 which is processed through several stages, including data collection, pre-processing, model development, and performance evaluation.  The results of the study show that the stock price forecasting of PT Lippo General Insurance Tbk using the Temporal Convolutional Network (TCN) method produces values that are relatively close to the actual ones with MSE, RMSE, MAE, and MAPE indicators, respectively, being 11,076.8214; 105.2464; 63.5915; and 2.2369\%. This indicates that the TCN model is able to capture complex temporal patterns in the stock price data of PT Lippo General Insurance Tbk. The forecasting results that have been projected for the next 60 days, that the stock price of PT Lippo General Insurance for the next 60 days will tend to decrease from August 31 to November 23.  
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

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.
Analysis of the Health Social Security Administration (BPJS Kesehatan) Claim Amount using Random Forest Regression Andirasdini, Indah Gumala; Saputra, Desta; Rivai, Muklas; Putra, Septia Eka Marsha
Indonesian Actuarial Journal Vol. 1 No. 1 (2025): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Claims paid by hospitals need to be identified to verify the accuracy of health services, maintain service quality, and optimize services provided to the Health Social Security Administration (BPJS Kesehatan) participants. This aligns with the third goal of the Sustainable Development Goals (SDGs), which is to ensure healthy lives and promote well-being for all ages, particularly in the context of universal health coverage. The difference in tariffs set by BPJS Kesehatan (INA-CBGs) compared to the amount paid by hospitals has led to a problem that can harm health facilities, such as delayed claim payments. This study aims to analyze the amount of claims paid by a regional hospital to BPJS Kesehatan participants using machine learning with the Random Forest Regression method. Based on this modeling, it was found that the severity of patients, length of stay, and type of illness are the most significant factors in determining the amount of claims. This study has an accuracy value of 81.89%, an adjusted R-square value of 80.4%, and a Mean Absolute Percentage Error (MAPE) of 18.11% in estimating the amount of claims.
Co-Authors Aldila Nur Indah Berliana Ratam Alma, Zalikha Andi Fitriawati Andirasdini, Indah Gumala Antika, Siluh Putu Dela Azzanina, Nanda Baiti, Putri Isnaini Cahyaning Berliana Ratam, Aldila Nur Indah Dewi, Karina Sylfia Dila Tirta Julianty Dwi Mahrani Frasiska, Nadia Fuji Lestari Fuji Lestari Gustina K. S, Annisa Hevita Halawa, Doni Hardian Hutabarat, Aulia Khairani Irwan, Sri Efrinita Lase, Marie Vivien Listiani, Amalia Marisa Marisa Misbahul Huda Nadeak, Natasha Putri Nainggolan, Lorena Ulitara Niken Pratiwi, Niken Nugraha, Ongky Setya Rinaldi, Fanny Dwi Putri Rohmah, Iffah Nurul Rosni Rosni, Rosni Saputra, Desta Satrio, Ilham Selvira, Natalia Aisa Septia Eka Marsha Putra Situmorang, Anita Grace Pretty Sofia, Ayu Tarmizi, Muhammad Syauki Yulita, Tiara