Article Per Year (5 Year)
p-Index From 2021 - 2026
7.307
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Matematika dan Statistika Al-Jabar : Jurnal Pendidikan Matematika Jurnal Matematika: MANTIK Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zeta - Math Journal Teorema: Teori dan Riset Matematika Jurnal Telematika Jambura Journal of Mathematics Jambura Geoscience Review Jurnal Matematika UNAND Sains, Aplikasi, Komputasi dan Teknologi Informasi InPrime: Indonesian Journal Of Pure And Applied Mathematics Jambura Journal of Biomathematics (JJBM) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Milang Journal of Mathematics and Its Applications Jurnal Sintak Jurnal Matematika Integratif Al-Aqlu : Jurnal Matematika, Teknik dan Sains Indonesian Journal of Mathematics and Applications Jurnal Pendidikan Progresif Indonesian Journal of Mathematics and Natural Sciences MILANG Journal of Mathematics and Its Applications
Najib, Mohamad Khoirun
Department Of Mathematics, Faculty Of Science And Mathematics, IPB University, Jl. Meranti, Kampus IPB Dramaga, Bogor 16680
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology 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 Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 46 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 46 Documents
Search

 1   2   3   4   5 
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.
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.
Co-Authors Abisha, Nicholas Ade Irawan Ade Irawan Akbar, Raihan Alifah, Nayla Nur Alifah, Rifdah Nur Amalia, Rizki Nurul Andriani, Rizka D. Annisa Permata Sari, Annisa Permata Antika, Ester Ardhana, Muhammad Reza Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardiyani, Evi Aziz, Muhammad Farhan Blante, Trianty Putri Chairunisa, Ghevira Ekaputri, Dhea Elis Khatizah Endar Hasafah Nugrahani Ester Antika Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Fatmawati, Linda Leni Fauzan, Muhammad Daryl Ginting, Dini Tri Putri Br Girsang, Harley Dearmanson Gunawan, Khairuna Putri Handoyo, Sapto Mukti Hasafah Nugrahani, Endar Hayani, Nadhira Maulida Henriyansah Herlambang, Karen Hilmi, Kautsar I Wayan Mangku Imni, Salsabila F. Kasiyah Junus Kautsar Hilmi Khairiati, Alfi Khatizah, Elis Khoerunnisa, Nazwa Linda Leni Fatmawati Manuella, Nerissa Patrice Martal, David Vijanarco Maulia, Syammira Dhifa Mochamad Tito Julianto Muhammad Adam Tripranoto Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Muliawan Sebastian, Denny Nabila, Nur Nadiyah, Fadilah Karamun Nisaa Nandika Safiqri NGAKAN KOMANG KUTHA ARDHANA Noval Nugraha, Wanda Nur Fallahi, Putri Afia Nurdiari, Sri Nuzhatun Nazria Pratama, Yoga Abdi Putri, Renda S. P. Qonita, Maliha Rafhida, Syukri Arif Rahmaisty, Fathia Redytadevi, Tita Putri REFI REVINA Retno Budiarti Rohimahastuti, Fadillah Ruben Harry Valentdio Salsabila, Fitra Nuvus Salsabilla Rahmah Salsabilla, Fitra Nuvus Sanjaya, Wardah Saputra, Rika Ardiansyah Setyawati, Suci Nur Soelaiman, Aaron August Vincent Sopaheluwakan, Ardhasena Sri Nurdiati Sriwahyuni, Lilis Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Tito Julianto, Mochamad Trianty Putri Blante Triwulandari, Raden Roro Carissa Valentdio, Ruben Harry Yoga Abdi Pratama Yulianty, Sherly