Article Per Year (5 Year)
p-Index From 2020 - 2025
8.197
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Agromet IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Veteriner Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Lingua Jurnal Bahasa dan Sastra PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika Journal of the Indonesian Mathematical Society Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Seminar Nasional Informatika (SEMNASIF) Widyariset Indonesian Journal of Science and Technology Jurnal Sains Matematika dan Statistika Al-Jabar : Jurnal Pendidikan Matematika JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika: MANTIK MAJALAH ILMIAH GLOBE Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Teorema: Teori dan Riset Matematika Jambura Journal of Mathematics Jambura Geoscience Review SALINGKA Jurnal Matematika UNAND Building of Informatics, Technology and Science Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science InPrime: Indonesian Journal Of Pure And Applied Mathematics Widyariset Jambura Journal of Biomathematics (JJBM) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Pijar MIPA Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian Journal of Applied Agricultural Science and Technology Milang Journal of Mathematics and Its Applications Jurnal Sintak Jurnal Matematika Integratif Indonesian Journal of Mathematics and Applications Jurnal Pendidikan Progresif Indonesian Journal of Mathematics and Natural Sciences MILANG Journal of Mathematics and Its Applications Majalah Ilmiah Bahasa dan Sastra
Sri Nurdiati
Department Of Mathematics, Faculty Of Science And Mathematics, IPB University, Jl. Meranti, Kampus IPB Dramaga, Bogor 16680
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture 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 Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Veterinary Other

Published : 71 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 71 Documents
Search

 3   4   5   6   7 
Mathematical Study for Proving Correctness of the Serial Graph-Validation Queue Scheme Salsabila, Fitra Nuvus; Bukhari, Fahren; Nurdiati, Sri
Journal of the Indonesian Mathematical Society Vol. 31 No. 2 (2025): JUNE
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.v31i2.1592

Abstract

Numerous studies have been conducted to develop concurency control schemes that can be applied to client-server systems, such as the Validation Queue (VQ) scheme, which uses object caching on the client side. This scheme has been modified into the Serial Graph-Validation Queue (SG-VQ) scheme, which employs validation algorithms based on queues on the client side and graphs on the server side. This study focuses on verifying the correctness of the SG-VQ scheme by using serializability as a mathematical tool. The results of this study demonstrate that the SG-VQ scheme can execute its operations correctly, in accordance with Theorem 4.16, which states that every history (H) of SG-VQ is serializable. Implementing a cycle-free transaction graph is a necessary and sufficient condition to achieve serializability. To prove Theorem 4.16, mathematical statements involving ten definitions, two propositions, and three lemmas have been formulated.
Systematic Literature Review on the Application of Mathematics, Statistics, and Computer Science in Wildfire Analysis Najib, Mohamad Khoirun; Nurdiati, Sri
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.31000

Abstract

Wildfires pose a significant threat to ecosystems, human settlements, and air quality, making accurate prediction and analysis crucial for disaster mitigation. Traditional statistical methods often struggle with the vast and complex nature of wildfire data, necessitating advanced mathematical, statistical, and computational approaches. This study presents a systematic literature review of wildfire analysis techniques, focusing on trends from 2000 to 2025. By analyzing 6,498 articles using the PRISMA framework, we identify the most widely applied methods, such as correlation, regression, classification, clustering, and artificial neural networks, while highlighting underutilized yet promising techniques such as copula, fuzzy inference, image recognition, quantile mapping, and empirical orthogonal function (EOF). The findings reveal an increasing shift toward interdisciplinary, data-driven approaches, with a significant increase in high-impact publications over the last decade. We emphasize the need for further exploration of advanced methodologies to enhance wildfire prediction models and improve decision-making in fire-prone regions. This review bridges computational innovations with environmental challenges, this study provides a roadmap for future research in wildfire analysis and management.
Performance Comparison of Gradient-based Optimizer for Classification of Movie Genres Najib, Mohamad Khoirun; Irawan, Ade; Salsabilla, Fitra Nuvus; Nurdiati, Sri
Indonesian Journal of Mathematics and Applications Vol. 3 No. 1 (2025): Indonesian Journal of Mathematics and Applications
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2025.003.01.1

Abstract

In this digital era, artificial intelligence has become very popular due to its very wide scope of application. Various models and methods in artificial intelligence are developed with their respective purposes. However, each model and method certainly requires a reliable optimizer in the training process. Many optimizers have been developed and are increasingly reliable lately. In this article, we classify the synopsis texts of several movies into nine different genre classes, leveraging Natural Language Processing (NLP) with Long Short Term Memory (LSTM) and Embedding to build models. Models are trained using several optimizers, including stochastic gradient descent (SGD), AdaGrad, AdaDelta, RMSProp, Adam, AdaMax, Nadam, and AdamW. Meanwhile, various metrics are used to evaluate the model, such as accuracy, recall, precision, and F1-score. The results show that the model structure with embedding, lstm, double dense layer, and dropout 0.5 returns satisfactory accuracy. Optimizers based on Adaptive moments provide better results when compared to classical methods, such as stochastic gradient descent. AdamW outperforms other optimizers as indicated by its accuracy on validation data of 89.48%. Slightly behind it are several other optimizers such as Adam, RMSProp, and Nadam. Moreover, the genres that have the highest metric values are the drama and thriller genres, based on the recall, precision and F1-score values. Meanwhile, the horror, adventure and romance genres have low recall, precision and F1-score values. Moreover, by applying Random Over Sampling (ROS) to balance the genre dataset, the model’s testing accuracy improved to 98.1%, enhancing performance across all genres, including underrepresented ones. Additional testing showed the model’s ability to generalize well to unseen data, confirming its robustness and adaptability.
A Systematic Literature Review on Machine Learning Techniques for Skin Disease Classification Nadiyah, Fadilah Karamun Nisaa; Alifah, Nayla Nur; Nurdiati, Sri; Khatizah, Elis; Najib, Mohamad Khoirun
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12696

Abstract

Skin diseases are health problems that require accurate diagnosis to evaluation and ultimately leading to treatment decisions. One of the crucial roles in the diagnostic process is medical imaging. Machine learning technology can assist in classifying skin diseases using image data and achieving high levels of accuracy in diagnosis. The purpose of this research is to review machine learning algorithms that can be utilized to develop image-based skin disease classification systems. The methodology employed is a Systematic Literature Review (SLR), which can be used to provide a comprehensive review of the application of machine learning in the classification of skin diseases. The literature search strategy was based on the Boolean technique, applied to the Scopus database. The selected articles were screened using predefined inclusion and exclusion criteria. The results indicate that the most used machine learning algorithm with achieved the highest classification accuracy is the Convolutional Neural Network (CNN). Keywords - Skin Disease, Machine Learning, Classification, CNN.
Statistical bias correction on the climate model for el nino index prediction Nurdiati, Sri; Sopaheluwakan, Ardhasena; Pratama, Yoga Abdi; Najib, Mohamad Khoirun
Al-Jabar: Jurnal Pendidikan Matematika Vol 12 No 2 (2021): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v12i2.8884

Abstract

El Nino can harm many sectors in Indonesia by reducing precipitation levels in some areas. The occurrence of El Nino can be estimated by observing the sea surface temperature in Nino 3.4 region. Therefore, an accurate model on sea surface temperature prediction in Nino 3.4 region is needed to optimize the estimation of the occurrence of El Nino, such as ECMWF. However, the prediction model released by ECMWF still consists of some systematic errors or biases. This research aims to correct these biases using statistical bias correction techniques and evaluate the prediction model before and after correction. The statistical bias correction uses linear scaling, variance scaling, and distribution mapping techniques. The results show that statistical bias correction can reduce the prediction model bias. Also, the distribution mapping and variance scaling are more accurate than the linear scaling technique. Distribution mapping has better RMSE in December-March, and variance scaling has better RMSE in April-June also in October and November. However, in July-September, prediction from ECMWF has better RMSE. The application of statistical bias correction techniques has the highest refinement in January-March at the first lead time and in April at the fifth until the seventh lead time. 
El nino index prediction model using quantile mapping approach on sea surface temperature data Nurdiati, Sri; Khatizah, Elis; Najib, Mohamad Khoirun; Fatmawati, Linda Leni
Desimal: Jurnal Matematika Vol. 4 No. 1 (2021): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v4i1.7595

Abstract

El Nino is a global climate phenomenon caused by the warming of sea surface temperatures in the eastern Pacific Ocean. El Nino has a powerful effect on the intensity of rainfall in several areas in Indonesia. El Nino impacts can be minimized by predicting the El Nino index from the sea surface temperature in the Nino 3.4 area. Therefore, many researchers have tried to predict sea surface temperature, and many prediction data are available, one of which is ECMWF. But, in reality, the ECMWF data still contains systematic errors or bias towards the observations. Consequently, El Nino predictions using ECMWF data are less accurate. For that reason, this study aims to correct the ECMWF data in the Nino 3.4 area using statistical bias correction with a quantile mapping approach. This method uses ECMWF data from 1983-2012 as training data and 2013-2018 as testing data. For this case, the results showed that 60% of El Nino's predictions on the testing data had improved the mean value. Also, all of El Nino's predictions on the testing data have improved the standard deviation value. Moreover, data testing's expected error can be corrected for all months in the 1st to 4th lead times. But, in the 5th to 7th lead times, only November-June can be corrected.
A Lightweight CNN for Multi-Class Classification of Handwritten Digits and Mathematical Symbols Abisha, Nicholas; Redytadevi, Tita Putri; Nurdiati, Sri; Khatizah, Elis; Najib, Mohamad Khoirun
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13138

Abstract

Recognizing handwritten digits and mathematical symbols remains a nontrivial challenge due to handwriting variability and visual similarity among classes. While deep learning, particularly Convolutional Neural Networks (CNNs), has significantly advanced handwriting recognition, many existing solutions rely on deep, resource-intensive architectures. This study aims to develop a lightweight and efficient CNN model for multi-class classification of handwritten digits and mathematical symbols, with an emphasis on deployability in resource-constrained environments such as educational platforms and embedded systems. The proposed model, implemented in Julia using the Flux.jl library, features a compact architecture with only two convolutional layers and approximately 55,000 trainable parameters significantly smaller than typical deep CNNs. Trained and evaluated on a publicly available dataset of over 10,000 grayscale 28×28-pixel images across 19 symbol classes, the model achieves a test accuracy of 91.8% while maintaining low computational demands. This work contributes to the development of practical handwritten mathematical expression recognition systems and demonstrates the feasibility of using Julia for developing lightweight deep learning applications.   Keywords - Digits, Mathematical Symbol, Classification, CNN
Pengenalan Wajah Menggunakan Dekomposisi Nilai Singular Najib, Mohamad Khoirun; Nurdiati, Sri; Blante, Trianty Putri; Ardhana, Muhammad Reza
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13645

Abstract

Pengenalan wajah (face recognition) merupakan suatu pengembangan dari teknologi deteksi wajah. Pengenalan wajah manusia merupakan salah satu turunan dari sistem biometrik yang menggunakan pola wajah manusia sebagai objek identifikasi. Sistem tersebut menggunakan pola wajah manusia yang terdapat dalam sistem basis data sebagai penyimpanan, kemudian akan melakukan perbandingan dengan gambar yang akan diuji. Sistem pengenalan wajah memiliki beberapa kendala, seperti sulit untuk mengenali objek dengan tingkat pencahayaan berbeda pada saat proses pengambilan gambar. Untuk mengatasi permasalahan yang terjadi akibat variasi tingkat cahaya, dikembangkan perangkat lunak dengan menerapkan metode Singular Value Decomposition (SVD). Pada projek ini metode eigenface cukup baik dalam melakukan pengenalan wajah. Bahkan dengan ukuran foto wajah yang cukup kecil (48 × 48), metode ini masih mampu untuk mengenali wajah dua orang yang sama. Proses pelatihan dan pengujiannya juga relatif singkat. Teknik ini dinilai efektif dalam mengenali foto wajah dengan ukuran yang kecil dan jumlah yang banyak.   Kata Kunci - Dekomposisi Nilai Singular, Eigenface, Pengenalan Wajah
PERFORMANCE COMPARISON OF GRADIENT-BASED CONVOLUTIONAL NEURAL NETWORK OPTIMIZERS FOR FACIAL EXPRESSION RECOGNITION Nurdiati, Sri; Najib, Mohamad Khoirun; Bukhari, Fahren; Revina, Refi; Salsabila, Fitra Nuvus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1086.562 KB) | DOI: 10.30598/barekengvol16iss3pp927-938

Abstract

A convolutional neural network (CNN) is one of the machine learning models that achieve excellent success in recognizing human facial expressions. Technological developments have given birth to many optimizers that can be used to train the CNN model. Therefore, this study focuses on implementing and comparing 14 gradient-based CNN optimizers to classify facial expressions in two datasets, namely the Advanced Computing Class 2022 (ACC22) and Extended Cohn-Kanade (CK+) datasets. The 14 optimizers are classical gradient descent, traditional momentum, Nesterov momentum, AdaGrad, AdaDelta, RMSProp, Adam, Radam, AdaMax, AMSGrad, Nadam, AdamW, OAdam, and AdaBelief. This study also provides a review of the mathematical formulas of each optimizer. Using the best default parameters of each optimizer, the CNN model is trained using the training data to minimize the cross-entropy value up to 100 epochs. The trained CNN model is measured for its accuracy performance using training and testing data. The results show that the Adam, Nadam, and AdamW optimizers provide the best performance in model training and testing in terms of minimizing cross-entropy and accuracy of the trained model. The three models produce a cross-entropy of around 0.1 at the 100th epoch with an accuracy of more than 90% on both training and testing data. Furthermore, the Adam optimizer provides the best accuracy on the testing data for the ACC22 and CK+ datasets, which are 100% and 98.64%, respectively. Therefore, the Adam optimizer is the most appropriate optimizer to be used to train the CNN model in the case of facial expression recognition.
PROVING THE CORRECTNESS OF THE EXTENDED SERIAL GRAPH-VALIDATION QUEUE SCHEME IN THE CLIENT-SERVER SYSTEM Salsabila, Fitra Nuvus; Bukhari, Fahren; Nurdiati, Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1359-1368

Abstract

Numerous studies have been conducted to develop concurrency control schemes that can be applied to client-server systems, such as the Extended Serial Graph-Validation Queue (SG-VQ) scheme. Extended SG-VQ is a control concurrency scheme in client-server system which implements object caching on the client side and locking strategy on the server side. This scheme employs validation algorithms based on queues on the client side and graphs on the server side. This research focuses on the mathematical analysis of the correctness of the Extended SG-VQ scheme using serializability as the criterion that needs to be achieved. Implementing a cycle-free transaction graph is a necessary and sufficient condition to achieve serializability. In this research, the serializability of the Extended SG-VQ scheme has been proven through the exposition of ten definitions, two propositions, three lemmas, and one theorem.
Co-Authors AA Gede Rai Gunawan Abisha, Nicholas Ade Irawan Ade Irawan Agah D. Garnadi Agung Widyo Utomo Agus Buono Aldri Frinaldi Alifah, Nayla Nur Alifah, Rifdah Nur Amalia, Rizki Nurul Amanah, Ayu Anak Agung Gede Rai Gunawan Andriani, Rizka D. Annisa Permata Sari, Annisa Permata Antika, Ester Ardhana, Muhammad Reza Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardiyani, Evi Ayu Amanah Aziz, Muhammad Farhan Bib Paruhum Silalahi Blante, Trianty Putri Budiarti, Retno Cece Sumantri Chairunisa, Ghevira Deni Suwardhi DEWI RAHMAWATI Edi Santosa Ekaputri, Dhea Elis Khatizah Endar Hasafah Nugrahani Eragilang Muhammad Hastapatria Ester Antika Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Fajar Delli Wihartiko Fatmawati, Linda Leni Fauzan, Muhammad Daryl Ginting, Dini Tri Putri Br Hanief, Hafzal Hany Savitry Hasafah Nugrahani, Endar Heliza Rahmania Hatta, Heliza Rahmania Henny Nuraini Henriyansah Herlambang, Karen Hilmi, Kautsar I Wayan Mangku Imni, Salsabila F. Indra Jaya Irman Hermadi Irmanida Batubara Jauhari, Muhammad Fakhri Junus, Kasiyah Karlisa Priandana Kasiyah Junus Kautsar Hilmi Khatizah, Elis Khoerunnisa, Nazwa Komariah . Lana Syakina Linda Leni Fatmawati M. Syamsul Maarif Maman Turjaman Marimin Marimin Mas’oed, Teduh W. Maulia, Syammira Dhifa Mochamad Tito Julianto Mohamad Khoirun Najib Muhamad Syukur Muhammad Adam Tripranoto Muhammad Fikri Isnaini Muhammad Ilyas Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Mukhlis Mukhlis Muliawan Sebastian, Denny Nadiyah, Fadilah Karamun Nisaa Najib, Mohamad K. Najib, Mohamad Khoirun Najib, Mohamad Khoirun Nandika Safiqri NGAKAN KOMANG KUTHA ARDHANA Niswati, Za'imatun Noval Nur Fallahi, Putri Afia Nurwegiono, Muhammad Nuzhatun Nazria Pandu Septiawan Pratama, Yoga Abdi Prihasuti Harsani Putri, Renda S. P. Rachma Fauziah Krismayanti Rafhida, Syukri Arif Rafhida, Syukri Arif Redytadevi, Tita Putri REFI REVINA Retno Budiarti Rika Kusumawati Rohimahastuti, Fadillah Ruben Harry Valentdio Salsabila, Fitra Nuvus Salsabilla Rahmah Salsabilla, Fitra Nuvus Sanjaya, Wardah Septian Dhimas Setyawati, Suci Nur Shelvie Nidya Neyman Sony Hartono Wijaya Sopaheluwakan, Ardhasena Sri Hartati Sri Mulatsih Srihadi Agungpriyono Sriwahyuni, Lilis SUHARINI, YUSTINA SRI Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Trianty Putri Blante Triwulandari, Raden Roro Carissa Valentdio, Ruben Harry Verry Riyanto Vicky Zilvan Wisnu Ananta Kusuma Yandra Arkeman Yasin Yusuf Yoga Abdi Pratama