Article Per Year (5 Year)
p-Index From 2021 - 2026
2.013
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Desimal: Jurnal Matematika JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Telematika Jambura Journal of Mathematics JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Al-Aqlu : Jurnal Matematika, Teknik dan Sains MILANG Journal of Mathematics and Its Applications Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Khatizah, Elis
School Of Data Science, Mathematics, And Informatics, Bogor Agricultural University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Physics Public Health Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
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.
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
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.
The Similarity of COVID-19-Related Profiles and Pandemic Conditions in ASEAN Countries Elis Khatizah
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 22 No. 2 (2025): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v22i2.35

Abstract

Abstract Abstract This paper examines the potential relationship between countries’ profiles and COVID-19conditions in 10 ASEAN countries, focusing on new confirmed cases and deaths. We initially clusteredthe countries based on demographic profiles and then observed the pandemic situation within each cluster.The samples of genome sequence, particularly the Omicron variant collected from each country, are alsoclustered to investigate the spread of the virus. Furthermore, we created a simple algorithm to identifycountries with profiles similar to ASEAN nations. Our findings show that countries with similardemographic profiles and geographically proximate are more likely to experience similar pandemicconditions even though they have no identical genome sequence of the virus. Five of the 10 ASEANcountries have similar demographic profiles to countries within the ASEAN region and as assumed,experienced similar COVID-19 situations. Conversely, the remaining ASEAN countries, which shareprofiles with countries outside the region, demonstrate fairly different COVID-19 conditions, particularlywith regard to the timing of the spread of the virus. With comparable resources at hand, insights fromcountries with similar profiles provide valuable information for effective pandemic management effortsin the future.Keywords: ASEAN profiles; clustering; COVID-19; demographic; pandemic management
Comparison of Non-linear Autoregressive Neural Network (NARNN) and Holt–Winters Methods for Antam Gold PricePrediction Akbar, Raihan; Saputra, Rika Ardiansyah; Najib, Mohamad Khoirun; Khatizah, Elis; Nurdiari, Sri
Desimal: Jurnal Matematika Vol. 9 No. 1 (2026): Desimal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v9i1.30260

Abstract

The high volatility and nonlinear dynamics of Antam gold prices present significant challenges for accurate time series forecasting, particularly within emerging financial markets. This study aims to develop and evaluate a comparative forecasting framework by examining the performance of the Nonlinear Autoregressive Neural Network (NARNN) and the Holt–Winters exponential smoothing method. A quantitative approach was applied using daily gold price data from January 4, 2010, to January 4, 2025. Data preprocessing included linear interpolation for missing values, Box–Cox transformation for variance stabilization, and time series decomposition to identify structural patterns. The dataset was partitioned into training and testing sets using an 80:20 ratio. Model performance was assessed using the Mean Absolute Percentage Error (MAPE). The results demonstrate that the NARNN model significantly outperforms the Holt–Winters approach, achieving a MAPE of 0.44%, compared to 11.43% and 11.90% for the additive and multiplicative variants, respectively. These findings highlight the limitations of classical linear smoothing methods in capturing abrupt structural changes and confirm the superiority of nonlinear neural network models in modeling complex financial time series. This study provides a robust empirical contribution by establishing a comparative modeling framework that enhances forecasting accuracy in volatile commodity markets.
Modeling Monthly Rainfall Data Using the Alpha Power Transformed X-Lindley Distribution in the Toba Lake Region Mohamad Khoirun Najib; Sri Nurdiati; Elis Khatizah; Aulia Rizki Firdawanti; Hendri Irwandi; Mirza Farhan Azhari; David Vijanarco Martal; Nicholas Abisha
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.25692

Abstract

Modeling rainfall is crucial for hydrological studies and climate adaptation, especially in regions with complex topography such as the Toba Lake area, North Sumatra. Classical probability distributions often struggle to represent skewness, heavy tails, and variability observed in tropical rainfall. This study explores APTXL distribution as a flexible two-parameter model. Through the alpha power transformation, APTXL extends the X-Lindley distribution by introducing an additional shape parameter, allowing better accommodation of asymmetrical and extreme values while maintaining analytical tractability. Statistical properties are derived, and parameters are estimated using maximum likelihood. The model is applied to a long-term dataset from 13 meteorological stations, covering 408 monthly observations per station. Comparative analysis against Gamma, Lognormal, and Generalized Extreme Value distributions using multiple goodness-of-fit criteria indicates that APTXL provides consistently improved performance. These results suggest APTXL as a practical tool for rainfall modeling and water-resource applications in climate-sensitive regions.
Transforming DNA Sequences into Musical Patterns Via A 3-mer Classification Abel Prayoga; Elis Khatizah
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5258

Abstract

DNA can be viewed as a symbolic sequence with patterns that vary across species. This study explores DNA sequences through two complementary approaches: species classification using simple machine learning methods and transformation of DNA into musical note representations. In the first task, DNA sequences from five organisms with different evolutionary distances are represented using 3-mer and 6-mer features. These k-mers form a vocabulary whose frequency counts are converted into feature vectors. Random Forest (RF) and Support Vector Machine (SVM) models are then applied for five-class classification. Using an 80:20 train-test split and 10-fold cross-validation, the SVM model achieved average accuracies above 0.90 for 3-mer features, with low standard deviation, indicating stable performance. In the second approach, 3-mer motifs are mapped to musical notes to generate species-based musical patterns. The resulting musical representations exhibit distinct structural differences across species, reflecting variations in underlying sequence composition. Overall, the results demonstrate that 3-mer features are effective for species discrimination and that musical transformation provides an alternative and intuitive way to visualize DNA sequence patterns.Keywords – DNA Classification, DNA-to-Music, Random Forest, SVM.
Handling Missing Values using Weighted Linear Combination of KNN-SVD: A Case Study of Rainfall Data in West Java Rizkian Agung Jamaesa; Sri Nurdiati; Elis Khatizah; Mohamad Khoirun Najib; Lilis Sri Wahyuni
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.36708

Abstract

This study is an experimental and comparative quantitative research that evaluates missing value imputation methods for daily rainfall data in West Java. Rainfall data are crucial for environmental policies, particularly in flood control and water resource management. Daily rainfall records from five BMKG stations in West Java were used in this study. Although these stations provide accurate data through direct measurement, missing values often occur due to human error or equipment problems. To solve this, we introduce an integrated imputation method that combines K-Nearest Neighbors (KNN) and Singular Value Decomposition (SVD) with a Weighted Linear Combination (WLC) approach. This method represents a significant improvement over the single-model imputation methods employed in earlier research. We split the dataset into training and testing sets using five different ratios (95:5%, 90:10%, 80:20%, 70:30%, and 64:40%) to test the model's performance. We measured effectiveness using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results show that the combined KNN–SVD method outperforms KNN or SVD alone in all cases. The best results were obtained from the 95:5% split, with the lowest MAE and RMSE values of 7.35 and 13.22, respectively. These results suggest that the integrated KNN–SVD imputation model enhances the reliability of rainfall datasets, thereby improving climate information for hydrological studies, disaster risk reduction, and policy-making in West Java.
APPLICATION OF A GENETIC ALGORITHM FOR SOLVING TRAVELING SALESMAN PROBLEM IN ORGANIC PORRIDGE DISTRIBUTION Hauralia Rahmadanti Finan; Mochamad Tito Julianto; Elis Khatizah
MILANG Journal of Mathematics and Its Applications Vol. 21 No. 2 (2025): 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.21.2.101-116

Abstract

This study focuses on determining an optimal distribution route for organic porridge products produced by a company and delivered to multiple outlets. Each outlet is visited exactly once, and the delivery process starts and ends at the same outlet. A total of 44 outlets are considered, which are initially divided into nine distribution routes. To improve distribution efficiency, this study proposes reorganizing the outlets into only three distribution routes. Each route formulation is modeled as a Traveling Salesman Problem (TSP). The optimization of the three TSP cases is carried out using a Genetic Algorithm (GA). In the GA implementation, the order of outlets along a route is encoded as a chromosome consisting of a sequence of genes. The fitness function is defined based on the total travel distance, where a smaller value indicates a better solution. The results show that increasing the number of iterations and the size of population, which is the number of candidate routes considered at each step, can reduce the total travel distance up to a certain point. The exact routes and their sequence of outlets can be visualized in a map depicting each of the three optimized paths. Keywords: Genetic Algorithm, distribution routing, total distance, Traveling Salesman Problem
Co-Authors Abel Prayoga Abisha, Nicholas Akbar, Raihan Alifah, Nayla Nur Aulia Rizki Firdawanti David Vijanarco Martal Fatmawati, Linda Leni Hauralia Rahmadanti Finan Hendri Irwandi Lilis Sri Wahyuni Linda Leni Fatmawati Mirza Farhan Azhari Mochamad Tito Julianto Muhammad Faza Hanifan Nadiyah, Fadilah Karamun Nisaa Najib, Mohamad Khoirun Nicholas Abisha Nurdiari, Sri Redytadevi, Tita Putri Rizkian Agung Jamaesa Roma Rio Simbolon Saputra, Rika Ardiansyah Sri Nurdiati Wanda Nugraha