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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JMM (Jurnal Masyarakat Mandiri) Mandalika Mathematics and Educations Journal Griya Journal of Mathematics Education and Application Journal of Mathematics, Computation and Statistics (JMATHCOS)
Adriani, Ika Reskiana
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Tren, Inovasi, dan Keberlanjutan dalam Mathematical Modelling untuk Food Science: Analisis Bibliometrik 2014–2024 Afriansyah, Dilla; Perdhana, Firman Fajar; Zahra, Khoiruz; Adriani, Ika Reskiana
Mandalika Mathematics and Educations Journal Vol 6 No 2 (2024): Edisi Desember
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v6i2.8078

Abstract

Mathematical Modelling is an essential tool in various aspects of Food Science, particularly in addressing complex challenges such as production process optimization, shelf-life prediction, food waste management, and food safety assurance. This article aims to provide an in-depth analysis of research trends in Mathematical Modelling within Food Science during the 2014–2024 period, based on 350 documents indexed in Scopus. A bibliometric approach was employed using VOSviewer to map keyword relations, collaboration patterns among researchers, and geographical distribution of studies. The results revealed four main clusters of research topics: food safety and disease (Blue Cluster), sustainability and environmental issues (Red Cluster), prediction and process optimization (Yellow Cluster), and technology and innovation in food processing (Green Cluster). These findings underline the critical role of Mathematical Modelling in tackling global food challenges. This article provides recommendations to expand international collaborations and explore artificial intelligence integration in Mathematical Modelling research for food in the future. Keywords: Bibliometrix, Food Science, Mathematical Modelling
The Agricultural Insurance: Explore Trends and Advances Over the Last Two Decades ADRIANI, IKA RESKIANA; Ekasasmita, Wahyuni; Afriansyah, Dilla; Zahra, Khoiruz
Mandalika Mathematics and Educations Journal Vol 7 No 2 (2025): Edisi Juni
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i2.9018

Abstract

The agricultural sector faces major risks due to extreme climate change, market uncertainty and economic fluctuations, which can destabilize global food production. Agricultural insurance, despite its importance as a risk mitigation tool for farmers, still has limited article reviews compared to other insurance sectors such as health, life, and property. This study aims to conduct a bibliometric analysis of global research on agricultural insurance over the past two decades, focusing on publication trends, the most influential countries and journals, keyword analysis, and providing directions for future research. The study used data from Scopus with a total of 643 documents. Analysis was conducted using VOS viewer, RStudio, and Tableau to visualize collaboration patterns, dominant keywords, and global publication trends. The analysis shows a significant increase in the number of studies on agricultural insurance, which reinforces the urgency of insurance in supporting global food stability. The implications of this research point to the need to develop insurance models that are technology-based and more adaptive to climate change, especially to expand access for smallholder farmers in developing countries. Recommendations for future research are to strengthen cross-sector collaboration and technological innovation to support the sustainability and resilience of the agricultural sector in the future.
Matematika dalam Penelitian Kopi: Visualisasi Jaringan dan Klasterisasi Topik Berdasarkan Data Scopus Afriansyah, Dilla; Fajar Perdhana, Firman; Zahra, Khoiruz; Reskiana Adriani, Ika
Griya Journal of Mathematics Education and Application Vol. 5 No. 2 (2025): Juni 2025
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v5i2.657

Abstract

This study aims to explore the scientific landscape of mathematical applications in coffee-related research using a bibliometric approach. By analyzing 99 documents retrieved from the Scopus database, this study identifies global research trends, productive authors and institutions, country-level contributions, and dominant subject areas. The analysis includes publication patterns, keyword co-occurrence networks, and author collaboration clusters visualized using VOSviewer. The findings show a significant rise in scientific interest since 2016, with the United States and Indonesia leading in publication volume. Although the research spans multiple disciplines such as mathematics, engineering, agriculture, and computer science, collaboration among researchers remains limited, with many author clusters operating independently. The keyword clustering reveals six major themes ranging from bioinformatics and plant disease modeling to chemical composition and teaching philosophy. These findings underscore the growing role of mathematical methods in coffee research and highlight the need for stronger interdisciplinary and international collaboration to support innovation in coffee science and production.
OPTIMALISASI MANAJEMEN KEHADIRAN DENGAN SISTEM ABSENSI IOT BERBASIS RFID DAN ANALISIS AKTUARIA Utomo, Andri Dwi; Akbar, Andi Taufiqurrahman; Syafaat, Muhammad; Jeffry, Jeffry; A Suyuti, Muh Zulfadli; Adriani, Ika Reskiana
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i2.29748

Abstract

Abstrak: Di era digital, salah satu tantangan organisasi masyarakat adalah pengelolaan data kehadiran yang masih dilakukan secara manual, sehingga kurang mendukung analisis berbasis data. Untuk mengatasi masalah ini, program pengabdian kepada masyarakat ini bertujuan memberikan pelatihan mengenai sistem absensi berbasis Internet of Things (IoT) dengan data logger, serta analisis data menggunakan pendekatan aktuaria. Pelatihan ini bertujuan untuk meningkatkan hard-skills peserta dalam hal pemahaman dan penerapan teknologi IoT, konfigurasi perangkat, serta analisis data. Selain itu, pelatihan juga berfokus pada peningkatan soft-skills peserta dalam hal pemecahan masalah, kolaborasi tim, dan pengambilan keputusan berbasis data, yang akan berguna dalam implementasi sistem absensi secara mandiri. Kegiatan ini melibatkan Study Club Informatika Parepare sebagai mitra, dengan 22 peserta. Metode yang digunakan mencakup pengenalan teknologi IoT, praktik langsung, dan analisis data. Pelatihan terdiri dari pemahaman dasar IoT, konfigurasi perangkat, serta integrasi sistem dengan Google Spreadsheet untuk pencatatan data absensi secara otomatis. Hasil evaluasi menunjukkan peningkatan signifikan dalam pemahaman peserta, dengan rata-rata nilai pretest 61,52% meningkat menjadi 92,61% pada posttest. Implementasi sistem ini membantu organisasi dalam digitalisasi proses absensi, meningkatkan efisiensi administrasi, dan membuka peluang penerapan lebih luas di komunitas lainnya.Abstract: In the digital era, one of the challenges faced by community organizations is attendance data management, which is still done manually and does not adequately support data-driven analysis. To address this issue, this community service program aims to provide training on Internet of Things (IoT)-based attendance systems using data loggers, along with data analysis using an actuarial approach. This training aims to enhance the participants' Hard-Skills in understanding and applying IoT technology, device configuration, and data analysis. Additionally, the training focuses on improving the participants' soft skills in problem-solving, teamwork, and data-driven decision-making, which will be useful in the independent implementation of the attendance system. This program involves Study Club Informatika Parepare as a partner, with 22 participants. The methods used include IoT technology introduction, hands-on practice, and data analysis. The training covers basic IoT concepts, device configuration, and system integration with Google Spreadsheet for automated attendance recording. Evaluation results indicate a significant improvement in participants' understanding, with an average pretest score of 61.52% increasing to 92.61% in the posttest. The implementation of this system helps organizations digitize attendance processes, improve administrative efficiency, and expand its potential applications to other communities. 
Deepfake Image Classification Using ResNet50 Feature Extraction and XGBoost Learning Model Kusnaeni, Kusnaeni; Adriani, Ika Reskiana; Hafid, Mega Sartika; Andy B, Afif Budi; Rizal, Muhammad Edy
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8387

Abstract

Deepfake is an artificial intelligence-based media manipulation technology that realistically fabricates a person's face, voice, and movements in both video and audio formats. The increasing use of deepfakes in the creation of various forms of deceptive content, including pornography, fake news, and fraud, has led to an urgent need for effective detection methods. One of the main challenges in detecting deepfakes is the high quality and realism of synthetic media, which renders conventional detection techniques less effective. Therefore, machine learning techniques capable of recognizing subtle patterns in visual data that are imperceptible to the human eye are required. This study aims to develop a deepfake image detection system using a hybrid machine learning approach that combines ResNet50 for feature extraction and XGBoost for classification. The pre-trained ResNet50 model, originally trained on the large-scale ImageNet dataset, is utilized to extract visual representations from images in the form of feature vectors. These features are then classified using XGBoost to distinguish between authentic and AI-generated images based on subtle patterns embedded within the extracted features. The results demonstrate that this hybrid approach achieves an accuracy of 94.6% in detecting deepfake images by leveraging the deep representation power of CNNs and the advanced classification capabilities of XGBoost. This method is not only computationally efficient but also highly relevant for integration into adaptive digital security systems.
ASYMPTOTIC DISTRIBUTIONS OF ESTIMATORS FOR THE MEAN AND THE VARIANCE OF A COMPOUND CYCLIC POISSON PROCESS Adriani, Ika Reskiana; Mangku, I Wayan; Budiarti, Retno
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0453-0464

Abstract

A stochastic process has an important role in modeling various real phenomena. One special form of the stochastic process is a compound Poisson process. A compound Poisson process model can be extended by generalizing the corresponding Poisson process. One of them is using a cyclic Poisson process. Our goals in this research are to determine the asymptotic distribution of the estimator for the mean and the variance of this process. In this paper, the problems of estimating the mean function and the variance function of a compound cyclic Poisson process are considered. We do not assume any parametric form for the intensity function except that it is periodic. We also consider the case when only a single realization of the cyclic Poisson process is observed in a bounded interval. Consistent estimators for the mean and variance functions of this process have been proposed in respectively. This paper introduces a set of novel theorems that, to the best of our knowledge, are not available in the existing literature and contribute original results to the field. Asymptotic distributions of these estimators are established when the size of the observation interval indefinitely expands. Asymptotic distributions of and are, respectively and as .
Co-Authors A Suyuti, Muh Zulfadli Andi Taufiqurrahman Akbar Andri Dwi Utomo Andy B, Afif Budi Dilla Afriansyah Firman Fajar Perdhana Hafid, Mega Sartika I Wayan Mangku Jeffry Jeffry Kusnaeni Kusnaeni, Kusnaeni Muhammad Edy Rizal Retno Budiarti Syafaat, Muhammad Wahyuni Ekasasmita Zahra, Khoiruz