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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 Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Information System Exploration and Research Journal of Electronics Technology Exploration (JOeTEx) 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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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.
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.
Lepidoptera Classification Using Convolutional Neural Network EfficientNet-B0 Syamsudin, Hilmi; Khalidah, Saidatul; Unjung, Jumanto
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.24586

Abstract

Butterflies and moths are insects that have many different species. Butterflies and moths have considerable aesthetic, ecosystem, health, economic, health, and scientific values. However, because there are so many different varieties and patterns, it is vital to divide them by type for better identification. By creating a Convolutional Neural Network (CNN) algorithm that produces accurate results, a deep learning approach can be used to classify the types of butterfly and moth species. This paper offer an Lepidoptera including butterfly and moth classification model based on convolutional neural networks.  3390 images of 25 different butterfly and moth species were acquired with various images orientations, angles, distance, and background.   Using the EfficientNet-B0 CNN architecture, different types of butterflies and moths are classified and input into the EfficientNet-B0 model. EfficientNet-B0 performs feature extraction on the image, so that it can be used to perform classification and then combined through a pooling process and connected to the final layer to produce a classification probability. The probability indicates how likely the image is to belong to a particular type or class of butterfly or moth.  In comparison to earlier studies, the test results indicate an improvement in butterfly and moth classification. Increased accuracy was seen with values of 97.91% accuracy, 97% recall,  97% precision, and 97% F1-Score. This paper novelty is the enhancement of the CNN architecture EfficientNet-B0 used in image classification, which results in improved image classification accuracy.
Sign Language Detection System Using YOLOv5 Algorithm to Promote Communication Equality People with Disabilities Ningsih, Maylinna Rahayu; Nurriski, Yopi Julia; Sanjani, Fathimah Az Zahra; Hakim, M. Faris Al; Unjung, Jumanto; Muslim, Much Aziz
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.6007

Abstract

Purpose: Communication is an important asset in human interaction, but not everyone has equal access to this key asset. Some of us have limitations such as hearing or speech impairments, which require a different communicative approach, namely sign language. These limitations often present accessibility gaps in various sectors, including education and employment, in line with Sustainable Development Goals (SDGs) numbers 4, 8, and 10. This research responds to these challenges by proposing a BISINDO sign language detection system using YOLOv5-NAS-S. The research aims to develop a sign language detection model that is accurate and fast, meets the communicative needs of people with disabilities, and supports the SDGs in reducing the accessibility gap. Methods: The research adopted a transfer learning approach with YOLOv5-NAS-S using BISINDO sign language data against a background of data diversity. Data pre-processing involved Super-Gradients and Roboflow augmentation, while model training was conducted with the Trainer of SuperGradients. Result: The results show that the model achieves a mAP of 97,2% and Recall of 99.6% which indicates a solid ability in separating sign language image classes. This model not only identifies sign language classes but can also predict complex conditions consistently. Novelty: The YOLOv5-NAS-S algorithm shows significant advantages compared to previous studies. The success of this performance is expected to make a positive contribution to efforts to create a more inclusive society, in accordance with the Sustainable Development Goals (SDGs). Further development related to predictive and real-time integration, as well as investigation of possible practical applications in various industries, are some suggestions for further research.
Co-Authors Abas Setiawan Ahmad, Kamilah Al Qohar, Bagus Alamsyah - Anisa, Devi Nurul Budi Prasetiyo Budi Prasetiyo, Budi Ceorido Ghalib Wibowo DAA Pertiwi Darmawan, Aditya Yoga Devi Ajeng Efrilianda Dimas Fanny Hebrasianto Permadi Dullah, Ahmad Ubai Dwika Ananda Agustina Pertiwi Endang Sugiharti, Endang Farih, Habib al Franki SW Hakim, M. Faris Al Heri Ardiyanto Khalidah, Saidatul M. Faris Al Hakim M. Faris Al Hakim Maylinna Rahayu Ningsih Much Aziz Muslim 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 Shabaha, Achmad Rozin Syamsudin, Hilmi Tanga, Yulizchia Malica Pinkan Tiara Lailatul Nikmah Utami, Annisaa Wibowo, Kevyn Alifian Hernanda Yana, Natasya Siska Fitri Zaenal Abidin