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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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Life Expectancy Prediction Using Decision Tree, Random Forest, Gradient Boosting, and XGBoost Regressions Chairunisa, Ghevira; Najib, Mohamad Khoirun; Nurdiati, Sri; Imni, Salsabila F.; Sanjaya, Wardah; Andriani, Rizka D.; Henriyansah; Putri, Renda S. P.; Ekaputri, Dhea
JURNAL SINTAK Vol. 2 No. 2 (2024): Vol. 2 No. 2 (2024): MARET 2024
Publisher : LPPM-ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62375/jsintak.v2i2.249

Abstract

Angka harapan hidup menggambarkan rata-rata lamanya waktu seseorang hidup sejak lahir di dunia. Angka harapan hidup menjadi salah satu aspek dalam menentukan indeks pembangunan manusia. Semakin tinggi Angka harapan hidup maka akan semakin tinggi nilai IPM. Tujuan penelitian ini adalah memprediksi angka harapan hidup melalui model yang paling akurat dengan menggunakan model decision tree regression, random forest regression, gradient boosting regression, dan XGBoost regression, serta analisis variabel penjelas yang paling mempengaruhi angka harapan hidup. Data yang digunakan dalam penelitian ini adalah dataset Global Country Information Dataset 2023. Data diperoleh dari situs Kaggle. Berdasarkan analisis diperoleh bahwa model random forest regression menunjukkan kinerja yang lebih unggul dalam memprediksi hasil, yang ditunjukkan dengan nilai RMSE yang lebih rendah dan nilai R² yang lebih tinggi. Kematian bayi dan rasio kematian ibu secara konsisten diidentifikasi sebagai prediktor yang signifikan di semua model, sedangkan populasi merupakan prediktor yang kurang memprengaruhi angka harapan hidup.
Deteksi Penyakit Jantung Menggunakan Metode Klasifikasi Decision Tree dan Regresi Logistik Bukhari, Fahren; Nurdiati, Sri -; Najib, Mohamad Khoirun; Amalia, Rizki Nurul
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 5, No 1 (2023): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v5i1.10780

Abstract

Penyakit jantung merupakan salah satu penyakit paling umum dan kritis yang membahayakan kehidupan manusia. Selain diagnosis klinis, pembelajaran mesin dan pendekatan berbasis pembelajaran mendalam sangat penting dalam diagnosis penyakit jantung, seperti decision tree dan regresi logistik. Penelitian ini bertujuan membandingkan kedua metode klasifikasi tersebut untuk mendeteksi adanya penyakit jantung berdasarkan beberapa indikator. Data yang digunakan adalah data penyakit jantung yang dikeluarkan oleh University of California, Irvine (UCI) Machine Learning Repository.  Berdasarkan hasil yang diperoleh, model decision tree yang terbentuk menempatkan variabel thal (tipe detak jantung pasien) sebagai simpul akar, dikarenakan nilai entropy yang paling tinggi. Model decision tree memiliki akurasi terhadap data uji sebesar 75%. Sementara itu, model regresi logistik menempatkan variabel sex, cp_3, slope_1, ca, dan thal_2 sebagai variabel-variabel yang berpengaruh nyata. Model regresi logistik memiliki akurasi terhadap data uji sebesar 87%. Dari akurasi dari kedua model tersebut, regresi logistik lebih akurat untuk mendeteksi adanya penyakit jantung dibandingkan model decision tree.
Sensitivity and feature importance of climate factors for predicting fire hotspots using machine learning methods Hasafah Nugrahani, Endar; Nurdiati, Sri; Bukhari, Fahren; Khoirun Najib, Mohamad; Muliawan Sebastian, Denny; Nur Fallahi, Putri Afia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2212-2225

Abstract

Every year, Indonesia experiences a national crisis due to forest fires because the resulting impacts and losses are enormous. Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still few research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares machine learning methods to predict hotspots in Kalimantan based on local and global climate factors in 2001-2020. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Four methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, measures of sensitivity and feature importance used are variance, density, and distributionbased sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the machine learning model concluded that the Bayesian linear regression model outperformed other models with an RMSE of 750 hotspots and an explained variance score of 68.96% on testing data. Meanwhile, tree-based models show signs of overfitting, including gradient boosting and random forest. Based on the results of sensitivity analysis and feature importance of the Bayesian linear regression model, the number of dry days is the most important feature in predicting fire hotspots in Kalimantan.
Copula in Wildfire Analysis: A Systematic Literature Review Najib, Mohamad Khoirun; Nurdiati, Sri; Sopaheluwakan, Ardhasena
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 3, No 2 (2021)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v3i2.22131

Abstract

AbstractCopula model is a method that can be implemented in various study fields, including analyzing wildfires. The copula distribution function gives a simple way to define joint distribution between two or more random variables. This study aims to review the application of copula in the analysis of wildfires using a Systematic Literature Review (SLR) and provide insight into research opportunities related to the application in Indonesia. The results show there are very few articles using the copula model in the analysis of wildfires. However, the increasing number of article citations each year shows the importance of such article research and has contributed to wildfire analysis development. In that article, 50% of studies applied the copula model to direct wildfire analysis (using fire data) in Canada, Portugal, and the US. Meanwhile, the other 50% use the copula model for indirect wildfire analysis (not using fire data) in Canada and the European region. The outcome of the presented review will provide the latest research positions and future research opportunities on the application of copula in the analysis of wildfires in Indonesia.Keywords: copula; wildfire; systematic literature review. AbstrakModel copula merupakan metode yang dapat diimplementasikan pada berbagai bidang penelitian, salah satunya pada analisis kebakaran hutan. Fungsi sebaran copula memberikan cara yang mudah untuk mendefinisikan sebaran peluang bersama antara dua peubah acak atau lebih. Tujuan penelitian ini mengulas penerapan model copula tersebut pada analisis kebakaran hutan dalam studi literatur menggunakan Systematic Literature Review (SLR) serta memberikan peluang riset ke depan terkait implementasinya pada analisis kebakaran hutan di Indonesia. Hasil penelitian menunjukkan bahwa model copula pada analisis kebakaran hutan masih sangat sedikit. Namun, peningkatan jumlah sitasi artikel tiap tahun menunjukkan pentingnya penelitian tersebut dan memiliki kontribusi pada perkembangan analisis kebakaran hutan. Pada artikel tersebut, sebanyak 50% penelitian menerapkan model copula pada analisis kebakaran secara langsung (menggunakan data kebakaran) di Kanada, Portugal, dan Amerika. Sementara, sebanyak 50% lainnya menerapkan model copula pada analisis kebakaran secara tak langsung (tidak menggunakan data kebakaran), yaitu di Kanada dan kawasan Eropa. Hasil tinjauan memberikan posisi riset terkini serta usulan riset ke depan mengenai penerapan model copula untuk analisis kebakaran hutan dan lahan di Indonesia.Kata kunci: copula; kebakaran hutan; studi literatur sistematik. 
Cattle weight prediction model using convolutional neural network and artificial neural network Yulianingsih, Yulianingsih; Nurdiati, Sri; Sukoco, Heru; Sumantri, Cece
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp441-449

Abstract

The weight of livestock is a crucial metric for evaluating management efficacy, informing policy decisions, and determining the market value of animals. In certain scenarios, conventional methods such as physical weighing and measurement calculations can prove challenging, including the absence of livestock health records or weighing equipment. This research aims to develop a predictive model for estimating the live weight of cattle through visual assessments and metadata, including age and pixel count, utilizing a combination of convolutional neural network (CNN) and artificial neural network (ANN) methodologies. A total of 223 data were obtained from a local farm before augmentation. The model's predictive capability was successfully demonstrated, with its performance quantified by an average mean absolute percentage error (MAPE) of 10% on test data. This study demonstrates that through the combination of CNN and ANN, as well as optimal parameter tuning, efficient prediction of cattle weight can be achieved.
Grade Classification of Agarwood Sapwood Using Deep Learning Hatta, Heliza Rahmania; Nurdiati, Sri; Hermadi, Irman; Turjaman, Maman
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2257

Abstract

The agarwood tree (Aquilaria sp.) is a tree that produces agarwood, which is a black resin that has a distinctive fragrant smell. In Indonesia, one that is commonly traded is sapwood agarwood. Agarwood sapwood is black or brownish-black wood obtained from the parts of the agarwood-producing tree containing a strong aromatic mastic. Based on the Indonesian National Standard (SNI) 7631:2018, agarwood sapwood has three classes: Super Double, Super A, and Super B. However, many agarwood farmers need to learn to differentiate and classify the agarwood sapwood classes, and traders exploit this to buy cheap. So, deep learning can be used to classify the agarwood sapwood class. One of the uses of deep learning is in image processing. Image processing is used to help humans recognize or classify objects quickly and precisely and can process many data simultaneously. One of the deep learning algorithms used in image processing is the Convolutional Neural Network (CNN). In this study, it is proposed that the deep learning model used is CNN with batch normalization. The dataset used is 72 agarwood sapwood images with a white background, each consisting of 24 Super A, 24 Super B data, and 24 Super Double data. The dataset is divided into 80% training and 20% testing data. The evaluation results of the proposed method at 100 epochs show an accuracy of 87.5%. The research implications will help agarwood tree farmers differentiate and classify agarwood sapwood so that farmers get the right price from buyers.
Deep Learning Approaches for Plant Disease Diagnosis Systems: A Review and Future Research Agendas Riyanto, Verry; Nurdiati, Sri; Marimin, Marimin; Syukur, Muhamad; Neyman, Shelvie Nidya
Journal of Applied Agricultural Science and Technology Vol. 9 No. 2 (2025): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v9i2.308

Abstract

To identify novel advancements in plant diseases detection and classification systems employing Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL), this research compiled 111 peer-reviewed papers published between 2019 and early 2023. The literature was sourced from databases such as Scopus and Web of Science using keywords related to deep learning and leaf disease. A structured analysis of various plant disease classification models is presented through tables and graphics. This paper systematically reviews the model approaches employed, datasets utilized, countries involved, and the validation and evaluation methods applied in plant disease identification. Each algorithm is annotated with suitable processing techniques, such as image segmentation and feature extraction, along with standard experimental metrics, including the total number of training/testing datasets utilized, the quantity of disease images considered, and the classifier type employed. The findings of this study serve as a valuable resource for researchers seeking to identify specific plant diseases through a literature-based approach. Additionally, the implementation of mobile-based applications using the DL approach is expected to enhance agricultural productivity.
Long Short Term Memory-Based Marine Data Prediction with Pearson Correlation Mukhlis, Mukhlis; Jaya, Indra; Nurdiati, Sri; Priandana, Karlisa; Hermadi, Irman
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10731

Abstract

Marine data prediction plays a vital role in supporting decision-making in the field of marine environment and resources. However, the complexity of marine data, which is nonlinear and dynamic, is a significant challenge in producing accurate predictions. This study aims to explore the role of Long Short-Term Memory (LSTM) models in computer systems to predict marine data, focusing on Pearson Correlation analysis. The methods applied include collecting historical marine data, implementing LSTM models for prediction, and evaluating performance using metrics such as Mean Absolute Error (MAE). In addition, Pearson Correlation analysis is used to understand the relationship between variables in marine data. The results show that the LSTM model is able to produce predictions with a low error rate with a composition of training data and testing data of 80:20, resulting in Sea Surface Temperature (SST) = 0.0053, Sea Surface Salinity (SSS) = 0.0026, sea Surface Height (SSH) = 0.0061 and CHL-a = 0.0002 and shows a significant relationship between variables through Multivariate correlation analysis. This research contributes to the development of marine data-based prediction systems and provides implications for the world of marine resource research and management.
JOINT DISTRIBUTION AND PROBABILITY DENSITY OF CLIMATE FACTORS IN KALIMANTAN USING NESTED COPULAS Nurdiati, Sri; Mas’oed, Teduh W.; Najib, Mohamad K.; Rahmawati, Dewi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1203-1216

Abstract

In this study, we investigate the joint distribution of local and global climate factors in Kalimantan, Indonesia, using fully and partially nested copula models. The analysis focuses on capturing the dependencies between local factors (precipitation and the number of dry days) and global indices (ENSO and IOD). The methodology involves estimating the marginal distributions of each variable using goodness-of-fit tests, and then modeling the dependence structure between variables with a range of copulas. We used both one-parameter copulas, including Gaussian, Clayton, Gumbel, Joe, and Frank, as well as two-parameter copulas, such as BB1, BB7, and BB8, with rotations of 90°, 180°, and 270° applied to account for negative dependencies. Nested copula structures were employed to model multivariate dependencies, with fully nested and partially nested approaches used to capture interactions between all four variables. The results show that global climate indices, particularly ENSO and IOD, have a more substantial influence during the dry season, impacting drought conditions in Kalimantan. The copula method offers a flexible and efficient way to construct multivariate joint distributions, better representing complex climate relationships than traditional models. Future work could extend this approach to include additional climate variables and use real-time data for forest fire risk prediction.
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 (IJMA)
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.
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