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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

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EXTENDED SERIAL GRAPH-VALIDATION QUEUE SCHEME WITH LOCKING STRATEGY Jauhari, Muhammad Fakhri; Bukhari, Fahren; Nurdiati, Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1899-1908

Abstract

In today's digital landscape, collaborative work in real-time is on the rise, allowing individuals to connect across different locations through applications facilitated by client-server architecture, enabling users to access and work on the same project simultaneously. However, clients' simultaneous access and modifications to the database can result in data inconsistencies, underscoring the importance of concurrency control. Managing concurrent transactions can introduce complexities and potentially adversely impact server performance. Object caching emerges as a viable solution as an alternative approach to handling transaction traffic. Extended Serial Graph-Validation Queue (Extended SG-VQ) is a control concurrency scheme that operates within the client-server architecture framework and incorporates object caching. The cache component implements a queue-based validation algorithm as part of its validation process. At the same time, the server-side employs a graph-based validation algorithm with locking strategies. Through a series of hypothetical transaction scenarios across three cases, this study validates the effectiveness of the Extended SG-VQ, demonstrating its ability to utilize serial graphs, resolve conflicts, and identify cyclic patterns.
Pemodelan Deret Waktu Menggunakan Non-linear Autoregressive Neural Network: Studi Kasus Prediksi Harga Saham Mandiri Najib, Mohamad Khoirun; Nurdiati, Sri
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.33397

Abstract

Accurate stock price forecasting is critical for investment decision-making, yet the nonlinear and complex nature of time series data poses significant challenges. This study investigates the application of the Nonlinear Autoregressive Neural Network (NARNN) for modeling the monthly stock price time series of PT Bank Mandiri (Persero) Tbk (BMRI) from January 2011 to December 2023. The model is constructed by exploring combinations of feedback delays and hidden neurons to identify the optimal configuration based on the root mean squared error. The dataset is divided into training, validation, and testing. Evaluation results show that the configurations 8–12 and 8–13 yield the best testing accuracy with a MAPE of 4.71%. An ensemble mean strategy is also employed, producing competitive and stable performance. These findings demonstrate that the NARNN approach effectively captures nonlinear patterns in stock data and holds promise for financial forecasting applications.
Performance Comparison of VGG16, MobileNetV2, and InceptionV3 Convolutional Neural Networks in Classifying Facial Dermatological Conditions Nadiyah, Fadilah Karamun Nisaa; Alifah, Nayla Nur; Nurdiati, Sri; Khatizah, Elis; Najib, Mohamad Khoirun
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.33082

Abstract

This study investigates the performance of three convolutional neural network (CNN) architectures (VGG16, MobileNetV2 and InceptionV3) in classifying two common facial dermatological conditions: acne and dark spots. A dataset of 235 facial skin images was augmented, then used to train and evaluate each model using standard classification metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that MobileNetV2 achieved the highest classification accuracy of 93.13% while maintaining a relatively low computational cost. The model exhibited perfect precision (1.00) for the acne class and a high recall of 0.99 for the dark spots class, indicating its strong capability in accurately and sensitively identifying both lesion types. All three models demonstrated acceptable classification performance for both acne and dark spots classes, as evidenced by their precision, recall, and F1-scores exceeding 70%. This indicates that each model was capable of capturing relevant discriminative features of both lesion types.
Time-varying Distribution Analysis for Rainfall and Air Temperature Data in Jakarta in Response to Future Climate Change Setyawati, Suci Nur; Nurdiati, Sri; Mangku, I Wayan; Najib, Mohamad Khoirun
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.32780

Abstract

AbstractIndonesia is vulnerable to climate change (rainfall and air temperature), which can increase the chances of climatic disasters. An organized risk analysis is a strategic plan to minimize the impact. The purpose of this research is to estimate time-varying distribution parameters for normal, generalized extreme value (GEV), and lognormal distributions using fminsearch and MLE algorithms on rainfall and air temperature data in Jakarta, as well as visualize and analyze the best time-varying distribution. The maximum likelihood estimation (MLE) method is used for stationary distribution parameter estimation. The fminsearch algorithm is used for stationary and nonstationary distribution parameter estimation. The highest difference value of stationary distribution parameter results from both methods is 5.3768 mm for rainfall data and 0.2670°C for air temperature data. The results of the best distribution based on the AIC value are the 3-parameter lognormal distribution for rainfall data and the 4-parameter GEV distribution for air temperature data. Over time, the variance of rainfall increases, and the average air temperature increases with a fixed variance.
IMPUTATION OF MISSING DAILY RAINFALL DATA USING CONVOLUTIONAL NEURAL NETWORKS (CNN) WITH SPATIAL INTERPOLATION Sriwahyuni, Lilis; Nurdiati, Sri; Nugrahani, Endar Hasafah; Sukmana, Ihwan; Najib, Mohamad Khoirun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2921-2936

Abstract

Accurate rainfall estimation is crucial in climate analysis and water resource planning. Observational data from weather stations play a vital role in climatological analysis as they represent actual conditions at specific locations. However, many observation stations in Indonesia need more complete data, hindering analysis and data-driven decision-making. To address this issue, this study aims to impute missing rainfall data for BMKG stations in East Java using the Convolutional Neural Network (CNN) method. Satellite data used in this study include ERA5 without interpolation and ERA5 with interpolation. The study employs a spatial interpolation approach. Data were split into training and testing datasets with various ratios: 95:5%, 90:10%, 80:20%, 70:30%, and 50:50%. The results show that the CNN method with spatially interpolated satellite data yields better results, with a Mean Absolute Error (MAE) of 7.50 on the training data and 7.05 on the testing data, indicating better generalization capability than the method without interpolation. The combination of CNN and ERA5 with interpolation was chosen for imputing missing rainfall data at BMKG stations in East Java due to its lower MAE.
Milk Production Estimation Model for Cattle Based on Image Processing using Random Forest, XGBoost, and LightGBM Niswati, Za'imatun; Nurdiati, Sri; Buono, Agus; Sumantri, Cece
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7585

Abstract

Milk is a livestock product consumed by individuals of all ages. Therefore, it is essential to increase milk production in Indonesia to meet domestic demand. The growth of dairy cattle populations and milk production has not been able to keep up with rising consumption, resulting in a reliance on imports for most dairy products and their derivatives, with imports steadily increasing over the years. Therefore, alternative solutions are needed to enhance the milk production. One approach is to develop a milk production estimation model to determine the optimal number of dairy cattle to be cultivated by farmers and livestock companies to meet domestic demand. The objective of this study was to create a dairy milk production estimation model through image analysis using the Random Forest, XGBoost, and LightGBM algorithms. The milk production estimation model used in this study used CLAHE for contrast enhancement and VGG-16 for feature extraction. The results showed that XGBoost provided the best performance, explaining 74% of the data variation in the Y variable with a relatively small estimation error of 0.92. After parameter tuning using Grid Search, an improvement was observed, where XGBoost explained 86% of the data variation in the Y variable, and the estimation error decreased to 0.72. Image processing and machine learning technologies are part of precision agriculture that aims to improve the efficiency, productivity, and sustainability of livestock operations.
Evaluation of Best-Fit Probability Distribution Models for Monthly Rainfall in the Lake Toba Region Rafhida, Syukri Arif; Nurdiati, Sri; Budiarti, Retno; Najib, Mohamad Khoirun
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.25688

Abstract

Understanding rainfall’s statistical distribution is crucial for effective water resource management, disaster mitigation, and climate adaptation in tropical regions. This study identifies the best-fit probability distributions for monthly rainfall in the Lake Toba region, Indonesia, based on long-term data from 34 rain gauge stations. Ten commonly used probability distributions were evaluated, with parameters estimated via Maximum Likelihood Estimation (MLE). The Kolmogorov–Smirnov (KS) test was applied to assess model goodness-of-fit at each station and month. Results indicate that the Generalized Extreme Value (GEV), Gamma, and Weibull distributions consistently provide the best fit for most stations and regencies, while Exponential and Inverse Gaussian distributions perform poorly. Spatial analysis reveals notable variation in best-fit models among regencies, emphasizing the influence of local topography and microclimate. These results highlight the need to select flexible probability models for hydrological planning and climate risk assessment in complex tropical regions. The findings provide valuable references for rainfall modeling and bias correction elsewhere.
PREDIKSI MASA STUDI MAHASISWA MATEMATIKA IPB BERDASARKAN INDEKS PRESTASI KUMULATIF MENGGUNAKAN JARINGAN SYARAF TIRUAN Nurdiati, Sri; Bukhari, Fahren; Najib, Mohamad Khoirun; Hilmi, Kautsar
MILANG Journal of Mathematics and Its Applications Vol. 18 No. 1 (2022): MILANG Journal of Mathematics and Its Applications
Publisher : School of Data Science, Mathematics and Informatics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.18.1.1-13

Abstract

Akreditasi sebuah program studi sangat dipengaruhi oleh masa studi dan Indeks Prestasi Kumulatif (IPK) lulusannya. Beberapa penelitian menunjukkan adanya keterkaitan antara kelulusan dengan IPK mahasiswa. Namun, model prediksi lama masa studi berdasarkan IPK masih sedikit. Oleh karena itu, penelitian ini bertujuan untuk memprediksi masa studi mahasiswa berdasarkan IPK menggunakan model jaringan syaraf tiruan (JST) berbasis backpropagation. Beberapa fungsi pelatihan diterapkan, meliputi gradient descent, Nesterov accelerated gradient descent, Adaptive moment estimation (Adam), dan Nesterov Adam (Nadam). Data yang digunakan dalam penelitian ini adalah data masa studi dan IPK semester 1-6 mahasiswa S1 Matematika IPB. Hasil penelitian menunjukkan bahwa model JST terbaik dihasilkan oleh jaringan dengan jumlah input node 6 yang dinormalisasi dengan batch normalization (BatchNorm), hidden node 10 dan output node 1. Parameter jaringan terbaik diperoleh dari percobaan menggunakan fungsi pelatihan gradient descent dan laju pembelajaran 0.5 dengan MAE sebesar 1.887 pada data testing. Fungsi pelatihan gradient descent memperlihatkan adanya penurunan nilai MAE ketika nilai laju pembelajaran meningkat. Sementara itu, pada fungsi pelatihan lainnya, terdapat tren bahwa semakin kecil nilai laju pembelajaran maka semakin kecil pula nilai MAE yang dihasilkan. Berdasarkan model JST terpilih, nilai IPK yang paling berpengaruh pada masa studi mahasiswa matematika IPB adalah nilai IPK pada semester 3, yaitu masa mahasiswa matematika IPB pertama kali menerima mata kuliah mayor dari Departemen Matematika secara keseluruhan. Kepentingan dari fitur ini sangat tinggi, mencapai 75.62%. Model JST terpilih menghasilkan MAPE sebesar 3.8% dan RMSPE sebesar 4.9% pada data testing.
IMPLEMENTASI PENYELESAIAN PERSAMAAN BURGERS DENGAN METODE BEDA HINGGA DALAM BAHASA PEMROGRAMAN JULIA Bukhari, Fahren; Nurdiati, Sri; Julianto, Mochamad Tito; Najib, Mohamad Khoirun; Valentdio, Ruben Harry
MILANG Journal of Mathematics and Its Applications Vol. 19 No. 1 (2023): MILANG Journal of Mathematics and Its Applications
Publisher : School of Data Science, Mathematics and Informatics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.19.1.1-9

Abstract

Burgers equation is a partial differential equation used to modelling several events related to fluids. Burgers equation was firstly introduced by Harry Bateman in 1915 and later studied by Johannes Martinus Burgers in 1948. This study discusses solving Burgers equations with finite difference method. In this study, several parameters have been known for the Burgers equation and several cases of partitions are used in finite difference method. The result shows that the more partitions used, the numerical result obtained will be closer to the exact values. In this study, calculations are numerically carried out with the help of Julia programming language.
KONSTRUKSI ATURAN PENGGABUNGAN DUA GRAF KALIMAT Amanah, Ayu; Nurdiati, Sri; Bukhari, Fahren
Salingka Vol 11, No 01 (2014): SALINGKA, EDISI JUNI 2014
Publisher : Balai Bahasa Sumatra Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26499/salingka.v11i01.2

Abstract

Knowledge Graph merupakan hal baru yang berguna untuk menggambarkan bahasa manusia yang lebih berpusat pada aspek semantik daripada aspek sintetik. Representasi makna teks berbahasa Indonesia ke dalam bentuk graf dapat dilakukan dengan menggunakan Knowledge Graph. Representasi tersebut bertujuan mengurangi ambiguitas. Representasi makna teks diperoleh melalui beberapa penelitian. Penelitian representasi makna kata, makna frasa, dan makna klausa telah dilakukan sehingga penelitian ini bertujuan mengkaji representasi makna kalimat ke dalam graf kalimat dan menggabungkan dua graf kalimat. Hasil penelitian ini berupa aturan pembentukan graf kalimat dan aturan penggabungan dua graf kalimat. Kedua aturan tersebut dikonstruksi agar setiap orang memiliki representasi kalimat dan penggabungan dua graf kalimat yang sama
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