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All Journal TEKNIK INFORMATIKA Sinkron : Jurnal dan Penelitian Teknik Informatika IJIES (International Journal of Innovation in Enterprise System) Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Data and Science Jurnal Ilmiah Teknik Informatika dan Komunikasi International Journal of Artificial Intelligence in Medical Issues
Maie Istighosah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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Systematic Literature Review of Trend and Characteristic Agile Model Liana Trihardianingsih; Maie Istighosah; Ariel Yonatan Alin; Muhammad Ryandy Ghonim Asgar
JURNAL TEKNIK INFORMATIKA Vol 16, No 1 (2023): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v16i1.28995

Abstract

Agile is a methodology and engineering approach for software development that encourages change in collaboration through tasks carried out at various stages of the software development life cycle. Scaled Agile Framework, Kanban, Scrum, Lean, Extreme Programming, Crystal, Dynamic System Development Method, and Feature Driven Development are a few of the approaches that go along with agile. Each of these approaches has distinct traits and qualities of its own. Every engineer and researcher needs to be aware of the benefits and characteristics of each method before deciding to use one. In order to assist engineers and researchers who will use one of these methods, this research will analyze it. The method used in this paper is a systematic literature review, which involved at 52 papers published in the previous eight years, from 2018 to 2022. This method is carried out by determining research questions, determining library initiation and selection, determining inclusion and exclusion criteria, and finally performing data extraction. This essay seeks to establish: (i) Study trends on each agile technique from 2018 to 2022 and (ii) Each agile method's characteristics. The results of this literature review indicate that Scrum and Extreme Programming have overtaken other agile methodologies as the most popular agile techniques over the last eight years. Through an analysis of the characteristics of each methodology, namely the development approach, suggested iteration time period, team communication, project size, project documentation, design, workflow approach, project coordinator, role assignment, coding, testing, and the nature of customer interaction, it is found that Scrum and Extreme Programming do have several advantages over other methodologies.
Breast Cancer Detection in Histopathology Images using ResNet101 Architecture Istighosah, Maie; Sunyoto, Andi; Hidayat, Tonny
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12948

Abstract

Cancer is a significant challenge in many fields, especially health and medicine. Breast cancer is among the most common and frequent cancers in women worldwide. Early detection of cancer is the main step for early treatment and increasing the chances of patient survival. As the convolutional neural network method has grown in popularity, breast cancer can be easily identified without the help of experts. Using BreaKHis histopathology data, this project will assess the efficacy of the CNN architecture ResNet101 for breast cancer image classification. The dataset is divided into two classes, namely 1146 malignant and 547 benign. The treatment of data preprocessing is considered. The implementation of data augmentation in the benign class to obtain data balance between the two classes and prevent overfitting. The BreaKHis dataset has noise and uneven color distribution. Approaches such as bilateral filtering, image enhancement, and color normalization were chosen to enhance image quality. Adding flatten, dense, and dropout layers to the ResNet101 architecture is applied to improve the model performance. Parameters were modified during the training stage to achieve optimal model performance. The Adam optimizer was used with a learning rate 0.0001 and a batch size of 32. Furthermore, the model was trained for 100 epochs. The accuracy, precision, recall, and f1-score results are 98.7%, 98.73%, 98.7%, and 98.7%, respectively. According to the results, the proposed ResNet101 model outperforms the standard technique as well as other architectures.
Pemanfaatan Alat Berbasis Web untuk Otomatisasi Pengambilan Data Publikasi dari Google Scholar Sulistya, Yudha Islami; Wardhana, Ariq Cahya; Istighosah, Maie; Riyandi, Arif
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 4 (2024): Oktober 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i4.1604

Abstract

Here’s the revised abstract in English: The rapid growth of academic publications requires efficient tools for publication data extraction and management, especially from widely used platforms like Google Scholar. To address this need, an automated web-based tool was developed, designed to simplify the processes of data crawling, extraction, and publication data management, allowing researchers to handle large volumes of academic publications more effectively. The tool supports both simple and detailed crawling modes, enabling users to input multiple Google Scholar URLs and neatly organize the extracted data into CSV files. For multiple URLs, the data is compiled into a ZIP file containing separate CSV files for each source, ensuring organized and accessible publication data management. The tool was tested with various dataset sizes. When processing 41 entries, the simple mode completed extraction in 9.054 seconds, while the detailed mode took 71.898 seconds. For smaller datasets of 5 entries, the simple mode executed in 3.283 seconds, while the detailed mode required 11.908 seconds. These results indicate that the tool is efficient and performs well with both small and large datasets. The differences in execution time between the simple and detailed modes offer users flexibility in balancing speed and depth of data extraction according to their research needs. This web-based tool not only automates the data extraction process from Google Scholar but also enhances the organization and accessibility of publication data, making it an asset for researchers and institutions in managing publication data.
Classification of Noni Fruit Ripeness Using Support Vector Machine (SVM) Method Yudha Islami Sulistya; Istighosah, Maie; Septiara, Maryona; Septiadi, Abednego Dwi; Amrullah, Arif
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.180

Abstract

The classification of Noni fruit (Morinda citrifolia) ripeness is essential for maximizing its medicinal benefits and ensuring product quality. This research aimed to classify Noni fruit ripeness using the Support Vector Machine (SVM) method, comparing three kernel functions: linear, Radial Basis Function (RBF), and polynomial. A dataset consisting of images of ripe and unripe Noni fruits was utilized, with preprocessing steps including the extraction of color and texture features. Performance evaluation revealed that the RBF kernel achieved the highest accuracy at 86.18%, followed by the polynomial kernel with 84.55%, and the linear kernel with 81.30%. These results suggest that the RBF kernel is the most effective for this classification task, showing superior capability in capturing non-linear patterns and complexities within the dataset.
Obesity Prediction with Machine Learning Models Comparing Various Algorithm Performances Sulistya, Yudha Islami; Istighosah, Maie
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 1 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i1.181

Abstract

Obesity poses a significant global health risk due to its links to conditions such as diabetes, cardiovascular disease, and various cancers, underscoring the need for early prediction to enable timely intervention. This study evaluated the performance of seven machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, ExtraTrees, Gradient Boosting, AdaBoost, and XGBoost—in predicting obesity using health and lifestyle data. The models were assessed based on accuracy, precision, recall, and F1-score, with hyperparameter tuning applied for optimization. The results confirmed that the ExtraTrees Classifier was the best performer, achieving an accuracy of 92.6%, precision of 92.7%, recall of 92.8%, and F1-score of 92.7%. Both Random Forest (91.3% accuracy) and XGBoost (89.9% accuracy) also exhibited strong predictive abilities. In contrast, models like Logistic Regression (74.3% accuracy) and AdaBoost (73.0% accuracy) showed lower effectiveness, emphasizing the advantages of ensemble methods such as ExtraTrees in delivering accurate obesity predictions. These findings suggest that ensemble models provide a promising approach for early diagnosis and targeted healthcare interventions.
Image Augmentation for BreaKHis Medical Data using Convolutional Neural Networks Istighosah, Maie; Sunyoto, Andi; Hidayat, Tonny
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12878

Abstract

In applying Convolutional Neural Network (CNN) to computer vision tasks in the medical domain, it is necessary to have sufficient datasets to train models with high accuracy and good general ability in identifying important patterns in medical data. This overfitting is exacerbated by data imbalances, where some classes may have a smaller sample size than others, leading to biased predictive results. The purpose of this augmentation is to create variation in the training data, which in turn can help reduce overfitting and increase the ability of the model to generalize. Therefore, comparing augmentation techniques becomes essential to assess and understand the relative effectiveness of each method in addressing the challenges of overfitting and data imbalance in the medical domain. In the context of the research described, namely a comparative analysis of augmentation performance on CNN models using the ResNet101 architecture, a comparison of augmentation techniques such as Image Generator, SMOTE, and ADASYN provides insight into which technique is most suitable for improving model performance on limited medical data. By comparing these techniques' accuracy, recall, and overall performance results, research can identify the most effective and relevant techniques in addressing the challenges of complex medical datasets. This provides a valuable guide for developing better CNN models in the future and may encourage further research in developing more innovative augmentation methods suitable for the medical domain.
Analisis Komparatif VGG19 pada Data Kanker Payudara Berbasis Augmentasi Maie Istighosah; Yudha Islami Sulistya
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1643

Abstract

Class imbalance in breast cancer imaging often leads to models prioritizing the majority class, reducing sensitivity to actual cancer cases. This study evaluates data augmentation as a class balancing strategy for breast cancer classification using VGG19 with transfer learning. The model was trained and tested in two settings: before and after augmentation, to measure performance improvement. The results show a clear improvement after balancing, with accuracy rising from 94.63% to 97.59%, recall and specificity increasing from about 85.60% to 97.58%, and the F1 score rising from 0.8933 to 0.9759, indicating better balance between precision and recall. Interpretability analysis using Grad-CAM supports this improvement, with activations before augmentation being spread out and sometimes focusing on background artifacts, while the heatmap after augmentation concentrated on the lesion region, indicating that the network learned clinically meaningful features. Overall, the findings demonstrate that targeted augmentation effectively addresses class imbalance, enhances generalization, and improves lesion detection with VGG19. This approach enhances cancer sensitivity while reducing false alarms, supporting its potential for adoption in computer-aided diagnostic pipelines to provide more reliable breast cancer detection in clinical practice.
E-Farm Livestock Platform Requirements Engineering Using Loucopoulos and Karakostas Iterative Process Model Liana Trihardianingsih; Maie Istighosah; Ariel Yonatan Alin; Muhammad Ryandy Ghonim Asgar
International Journal of Innovation in Enterprise System Vol. 8 No. 1 (2024): International Journal of Innovation in Enterprise System
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v8i01.206

Abstract

Global human population growth has forced farms to evolve in order to produce more livestockproducts more efficiently while also paying attention to public health, environmental sustainability,and animal welfare. However, problems arise when some diseases appear to affect farm animals andlarge companies providing livestock products dominate the market. It is necessary to develop aplatform or application that can be used to solve these two problems, especially for breeders who havefarms on a small scale. This study aims to outline the process of understanding engineeringrequirements by utilizing the Loucopoulos and Karakostas Requirements Engineering Process Modelmethod, which consists of elicitation of requirements, specification of requirements, as well asvalidation and verification of requirements. The development process is carried out by hiring breedersand potential customers to determine the priority needs of the platform. The results showed that of the25 defined functional needs, there were 22 final functional needs that were validated with valuesabove 50%. The E Farm platform should be further developed based on the defined demands since atotal of 22 validated needs have been determined to be able to represent 88% of the needs required byusers.
Co-Authors Abednego Dwi Septiadi Amrullah, Arif Andi Sunyoto Ariel Yonatan Alin Arif Riyandi Ariq Cahya Wardhana Liana Trihardianingsih Maryona Septiara Muhammad Ryandy Ghonim Asgar TONNY HIDAYAT Yudha Islami Sulistya