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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Jurnal CoreIT Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining JMM (Jurnal Masyarakat Mandiri) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Abdimasku : Jurnal Pengabdian Masyarakat Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Innovation in Research of Informatics (INNOVATICS) Journal of Soft Computing Exploration Journal of Information System Exploration and Research Scientific Journal of Informatics
Jumanto Unjung
Department Of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Improved convolutional neural network model for leukemia classification using EfficientNetV2M and bayesian optimization Wibowo, Kevyn Alifian Hernanda; Rianto, Nur Azis Kurnia; Unjung, Jumanto
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.378

Abstract

Leukemia is a health condition in which the body produces too many abnormal white blood cells or leukocytes. Leukemia can affect both children and adults. Early diagnosis of leukemia faces significant challenges, as diagnostic methods are time consuming, require experienced medical experts, and are expensive. Previous studies have been conducted using deep learning approaches, but it is still rare to find a model that shows the best performance and uses optimization methods to classify leukemia diseases. Therefore, a Convolutional Neural Network (CNN) model with EfficientNetV2M architecture and Bayesian Optimization is proposed as the main method assisted by ImageDataGenerator in preprocessing. This study shows a significant impact of Bayesian optimization with good Accuracy, Precision, Recall and F1-Score results of 91.37%, 93.00%, 87.00%, 89.00%, respectively, which are expected to improve the performance of the model in previous studies in classifying leukemia diseases.
Enhancing costumer churn prediction with stacking ensemble and stratified k-fold Rofik, Rofik; Unjung, Jumanto; Prasetiyo, Budi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8112

Abstract

In the era of rapid technological advancement, the telecommunications industry undergoes significant changes. Factors such as the speed of technological change, high customer expectations, and changing preferences are the main obstacles that affect the dynamics of telecommunications companies. One major issue faced is the high customer churn rate, adversely impacting company revenue and profitability. Previous studies indicate that customer churn prediction remains complex in the telecommunications industry, with opportunities to optimize algorithm selection and prediction model construction methods. This research aims to improve the accuracy of customer churn prediction by employing a complex model that utilizes stacking ensemble learning techniques. The proposed model combines 6 base algorithms: extreme gradient boosting (XGBoost), random forest, light gradient boosting machine (LightGBM), support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN), with XGBoost as the meta-learner model. The research process involves preprocessing, class data balance with synthetic minority oversampling technique (SMOTE), training using stratified k-fold, and model evaluation. The model is tested using the Telecom Churn dataset. The evaluation results show that the constructed stacking model achieves 98% accuracy, 98.74% recall, 98.03% precision, and 98.38% F1 score. This study demonstrates that optimizing the stacking ensemble model with SMOTE and stratified k-fold enhances customer churn prediction accuracy.
The Asthma Classification Using an Adaptive Boosting Model with SVM-SMOTE Sampling Dullah, Ahmad Ubai; Utami, Putri; Unjung, Jumanto
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.486

Abstract

Asthma is a disease that affects the human respiratory tract, characterized by inflammation and narrowing of the respiratory tract such as wheezing, coughing, and shortness of breath. The causes of asthma can come from genetics, lifestyle, and a bad environment. Diagnosis made to asthma patients is very influential on the severity and treatment carried out. However, the diagnosis process may not be able to precisely determine asthma patients because the diagnosis is influenced by the classification of asthma based on the symptoms that appear. Therefore, this study proposes an asthma disease classification model that is optimized using a sampling method to balance the data. The proposed classification model uses the Adaptive Boosting algorithm with a sampling technique using SVM-SMOTE to help balance the data. The results obtained from the experiment achieved an accuracy of 98.60%. This result shows that the proposed model is more accurate and optimal in performing classification when compared to previous research.
Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification Dullah, Ahmad Ubai; Darmawan, Aditya Yoga; Pertiwi, Dwika Ananda Agustina; Unjung, Jumanto
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.48-62

Abstract

Heart disease is one of the leading causes of death worldwide. According to data from the World Health Organisation (WHO), the number of victims who die from heart disease reaches 17.5 million people every year. However, the method of diagnosing heart disease in patients is still not optimal in determining the proper treatment. Along with technology development, various models of machine learning algorithms and data processing techniques have been developed to find models that can produce the best precision in classifying heart disease. This research aims to create a machine learning algorithm model for categorizing heart disease, thereby enhancing the effectiveness of diagnosis and facilitating the determination of appropriate treatment for patients. This research also aims to overcome the limitations of accuracy in existing diagnosis methods by identifying models that can provide the best results in processing and analyzing health data, particularly in terms of heart disease classification. In this study, the XGBoost model was identified as the most superior, with an accuracy of 99%. These results demonstrate that the XGBoost model achieves a higher accuracy rate than previous methods, making it a promising solution for enhancing the accuracy of future heart disease diagnosis and classification.
Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE Unjung, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1627

Abstract

Parkinson's disease is one of the major neurodegenerative diseases that affect the central nervous system, often leading to motor and cognitive impairments in affected individuals. A precise diagnosis is currently unreliable, plus there are no specific tests such as electroencephalography or blood tests to diagnose the disease. Several studies have focused on the voice-based classification of Parkinson's disease. These studies attempt to enhance the accuracy of classification models. However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. This research aims to improve the accuracy of speech-based Parkinson's disease prediction by addressing class imbalance in the data and building an appropriate model. The proposed new model is to perform class balancing using SMOTE and build an ensemble voting model. The research process is systematically structured into multiple phases: data preprocessing, sampling, model development utilizing a voting ensemble approach, and performance evaluation. The model was tested using voice recording data from 31 people, where the data was taken from OpenML. The evaluation results were carried out using stratified cross-validation and showed good model performance. From the measurements taken, this study obtained an accuracy of 97.44%, with a precision of 97.95%, recall of 97.44%, and F1-Score of 97.56%. This study demonstrates that implementing the soft-voting ensemble-SMOTE method can enhance the model's predictive accuracy.
Grape leaf disease classification using efficientnet feature extraction and catboostclassifier Darmawan, Aditya Yoga; Tanga, Yulizchia Malica Pinkan; Unjung, Jumanto
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.507

Abstract

Grapes are one of the most extensively cultivated crops worldwide due to their significant economic importance. However, the productivity of grape crops is often threatened by diseases caused by bacterial, fungal, or viral infections. Traditionally, the detection of infected grape leaves has been conducted through manual visual inspections, a method that is both time-consuming and prone to biases. Recent studies have leveraged transfer learning models to classify grape leaf diseases with high accuracy. Despite this progress, there is a notable gap in research exploring the integration of transfer learning for feature extraction and machine learning for feature classification in detecting grape leaf diseases. This study introduces a novel approach that combines transfer learning using EfficientNetB0 for feature extraction with a machine learning model, specifically Categorical Boosting (CatBoost), for feature classification. The proposed model demonstrates outstanding performance, achieving an accuracy of 99.56% on the test dataset, surpassing traditional transfer learning methods reported in previous studies.
Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection Ningsih, Maylinna Rahayu; Unjung, Jumanto
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.20582

Abstract

Purpose: Mental health issues are now a concern for many people. Anxiety or often called Anxiety that is excessive and prolonged has also become the forefront of various psychological disorders that trigger impacts such as stress to suicide. People using social media platforms tend to be a medium for expressing opinions sharing information and even expressing daily emotions. Many studies have shown a correlation between expressing emotional statements on social media and mental disorders. This research aims to conduct sentiment analysis of Anxiety on social media using H2O Gradient Boosting by implementing TF-IDF Vectorization which is set to max feature. Methods: This research utilizes 6980 post data from social media. The method applied is by conducting Exploratory Data Analysis then Data preprocessing, Tf-Idf Vectoriztion with max feature experiments 100, 250, 500, 1000 and 2000, H2O Gradient Boosting Model, Cross Validation, and Model performance evaluation. Result: The results of this study show good model performance through max feature TF-IDF = 250 with an accuracy value of 99%, Specificity 99.57%, and Eror Rate of 0.0106. Novelty: So that the use of the H2O Gradient Boosting model succeeded in providing good performance in classifying anxiety sentiment.
Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings Rofik Rofik; Roshan Aland Hakim; Jumanto Unjung; Budi Prasetiyo; Much Aziz Muslim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3566

Abstract

Online job searching is one of the most efficient ways to do this, and it is widely used by people worldwide because of the automated process of transferring job recruitment information. The easy and fast process of transferring information in job recruitment has led to the rise of fake job vacancy fraud. Several studies have been conducted to predict fake job vacancies, focusing on improving accuracy. However, the main problem in prediction is choosing the wrong parameters so that the classification algorithm does not work optimally. This research aimed to increase the accuracy of fake job vacancy predictions by tuning parameters using GridSearchCV. The research method used was SVM and Gradient Boosting with parameter adjustments to improve the parameter combination and align it with the predicted model characteristics. The research process was divided into preprocessing, feature extraction, data separation, and modeling stages. The model was tested using the EMSCAD dataset. This research showed that the SVM algorithm can achieve the highest accuracy of 98.88%, while gradient enhancement produces an accuracy of 98.08%. This research showed that optimizing the SVM model with GridSearchCV can increase accuracy in predicting fake job recruitment.
Melanoma Skin Cancer Classification Using EfficientNetB7 for Deep Feature Extraction and Ensemble Learning Approach Darmawan, Aditya Yoga; Dullah, Ahmad Ubai; Qohar, Bagus Al; Unjung, Jumanto; Muslim, Much Aziz
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.12764

Abstract

Cancer is one of the deadliest diseases in the world. cancer is caused by the presence of cancer cells due to abnormal conditions during the cell turnover process. One of the dangerous types of cancer is melanoma skin cancer, this cancer attacks the outer skin of humans because skin cells are prone to damage. However, diagnosis for this disease is mostly done manually while there are previous studies that use deep learning approaches with the accuracy that can be improved. The purpose of this study is to find an effective and efficient method for melanoma cancer recognition so that it can be treated more quickly. We propose several methods that we have compared to be able to classify melanoma skin cancer with EfficientNetB7 Feature Extractor and Ensemble Learning. The results of this research model get the highest accuracy of 91.2%. When EfficientNetB7 together with ensemble learning. This research model has better and efficient results when compared to previous research.
Classification of Apple Tree Leaf Diseases Using Pretrained EfficientNetB0 and XGBoost Qohar, Bagus Al; Dullah, Ahmad Ubai; Darmawan, Aditya Yoga; Unjung, Jumanto
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.33174

Abstract

The diseases that affect apple tree leaves seriously compromise agricultural production; therefore, early and accurate diagnosis is quite important for good disease control. Machine learning's recent developments have opened fascinating possibilities for automating the detection process and enhancing methods of precision agriculture. This study aims to create a strong classification model that can accurately and efficiently identify various diseases that affect apple tree leaves. The approach combines the pre-trained EfficientNetB0 architecture for feature extraction with the XGBoost model for classification, utilizing the advantages of both deep learning and gradient-boosting methods. With high performance measures including a macro-average precision of 95.86%, recall of 95.44%, and F1 score of 95.64%, the model achieved a classification accuracy of 95.74%. Furthermore, the average ROC-AUC score of 0.9964 emphasizes how well the model differentiates the five disease categories. This work stands out due to its hybrid approach, which integrates a robust pre-trained convolutional neural network (EfficientNetB0) with the XGBoost model. This significantly improves the accuracy of disease classification. This approach presents a novel pathway for precision agriculture, providing a reliable and effective instrument for the automatic identification of diseases in apple orchards.
Co-Authors Abas Setiawan Ahmad, Kamilah Al Qohar, Bagus Alamsyah - Anisa, Devi Nurul Budi Prasetiyo Budi Prasetiyo, Budi Darmawan, Aditya Yoga Devi Ajeng Efrilianda Dimas Fanny Hebrasianto Permadi Dullah, Ahmad Ubai Endang Sugiharti, Endang Farih, Habib al Hakim, M. Faris Al Hakim, Roshan Aland Heri Ardiyanto Khalidah, Saidatul M. Faris Al Hakim M. Faris Al Hakim Maylinna Rahayu Ningsih Much Aziz Muslim Much Aziz Muslim Much Aziz Muslim Nina Fitriani Ningsih, Maylinna Rahayu Nurriski, Yopi Julia Nurul Hidayat Pertiwi, Dwika Ananda Agustina Prabowo Yudho Jayanto Purwoko, Agus Putri Utami, Putri Qohar, Bagus Al Rianto, Nur Azis Kurnia Riza Arifudin Riza Arifudin Rofik Rofik Rofik Rofik, Rofik Roshan Aland Hakim Sanjani, Fathimah Az Zahra Syamsudin, Hilmi Tanga, Yulizchia Malica Pinkan Tiara Lailatul Nikmah Utami, Annisaa Wibowo, Kevyn Alifian Hernanda Zaenal Abidin