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All Journal ILKOM Jurnal Ilmiah Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Data and Science Ilmu Komputer untuk Masyarakat Jurnal Ilmiah Teknik Informatika dan Komunikasi International Journal of Artificial Intelligence in Medical Issues Masyarakat Mandiri: Jurnal Pengabdian Dan Pembangunan Lokal
Yudha Islami Sulistya
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health Other

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Bimbingan Teknis Pemanfaatan xSIA untuk Pelaporan Akademik Siswa di SDN No. 133 Kabupaten Takalar Poetri Lestari Lokapitasari Belluano; Purnawansyah Purnawansyah; Yudha Islami Sulistya; La Saiman; Kasmira Kasmira
Ilmu Komputer untuk Masyarakat Vol 2, No 1 (2021)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (202.406 KB) | DOI: 10.33096/ilkomas.v2i1.1001

Abstract

Sistem Informasi Akademik (xSIA) adalah sistem yang dibangun untuk mengelola data-data peserta ajar sehingga memberikan kemudahan kepada pengguna dalam hal ini adalah Guru dalam kegiatan administrasi akademik secara online. Sekolah perlu menyediakan layanan sistem informasi akademik dalam bentuk web application dimana Guru secara mandiri dapat melaksanakan pelaporan akademik siswa untuk kebutuhan sinkronisasi data Pelaporan Kinerja Guru (PKG) DAPODIK. Kemudahan dalam mengakses sistem informasi akademik mulai dari level Guru, Operator Sekolah sampai Kepala Sekolah diperlukan, sehingga pengembangkan xSIA untuk tingkat Pendidikan Dasar dan Menengah diterapkan sesuai spesifikasi User Experience (UX) dan Developer Experience (DX). Program Kemitraan Masyarakat (PKM) berupa bimtek pemanfaatan xSIA yang diikuti oleh Guru dilaksanakan dengan model latihan Preceptorship dan Partisipatif. sedangkan tahap peran DAPODIK dengan aplikasi digunakan model Prototyping untuk merepresentasikan secara grafis alur kerja sistem. Target luaran berupa aplikasi berbasis web xSIA untuk pelaporan data akademik siswa.
CNN Ensemble Learning Method for Transfer learning: A Review Yudha Islami Sulistya; Elsi Titasari Br Bangun; Dyah Aruming Tyas
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1541.45-63

Abstract

This  study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning ensemble, and transfer learning is growing every year. In 2022, there will be 35 papers reviewed related to this topic in this study. Some datasets contain apparent information starting from the dataset name, total data points, dataset splitting, target dataset availability, and type classification. ResNet-50, VGG-16, InceptionV3, and VGG-19 are used in most papers as pre-trained models and transfer learning processes. 50 (90.1%) papers use ensemble learning, and 5 (9.1%) do without ensemble learning. The reviewed paper summarizes several performance measurements, including accuracy, precision, recall, f1-score, sensitivity, specificity, training accuracy, validation accuracy, test accuracy, training losses, validation losses, test losses, training time, and AUC, DSC. In the last section, 49 papers produce the best model performance using the proposed model, and 6 other papers use DenseNet, DeQueezeNet, Extended Yager Model, InceptionV3, and ResNet-152.
Analysis of the Ensemble Method Classifier's Performance on Handwritten Arabic Characters Dataset Abdul Rachman Manga'; Anik Nur Handayani; Heru Wahyu Herwanto; Rosa Andrie Asmara; Yudha Islami Sulistya; Kasmira Kasmira
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1357.186-192

Abstract

Arabic character handwriting is one of the patterns and characteristics of each person's writing. This characteristic makes Arabic writing more challenging if the letter recognition process is based on a dataset of Arabic scripts. This Arabic script has been presented in a dataset totaling 16800, each representing a class of hijaiyah letters starting from alif to yes, consisting of 600 data for each class. The accuracy of the data used can be increased using the ensemble method. By using multiple algorithms at simultaneously, the ensemble technique can raise the level or result of a score in machine learning. This study's primary goal is to evaluate the ensemble method classifier's performance on datasets of handwritten Arabic characters. The classifier uses the ensemble method by applying the proposed soft voting to provide a multiclass classification of three machine learning algorithms, namely, SVM, Random Forest, and Decision Tree for classification. This research process produces an accuracy value for the voting classifier of 0.988 and several other SVM algorithms with an accuracy of 0.103, a random forest with an accuracy of 1.0, and a decision tree with an accuracy of 0.134. The test results used the confusion matrix evaluation model, including accuracy, precision, recall, and f1-score of 0.99.
Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques Sulistya, Yudha Islami; Musdholifah, Aina; Sapuletea, Chrissandy; Br Bangun, Elsi Titasari; Hamda, Hizbullah; Anjani, Sarah; Septiadi, Abednego Dwi
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.1948.115-124

Abstract

This research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest yields. The results show that ensemble learning techniques significantly improve prediction performance. For instance, the ensemble model for predicting area harvested, combining Model 6 (linear regression) and Model 10 (ARIMA), achieved  of coefficient of determination outperforming the individual models. Similarly, for predicting yield, the ensemble model combining Model 4 (linear regression) and Model 9 (ARIMA) achieved  of coefficient of determination indicating superior prediction accuracy. For predicting production, the ensemble model combining Model 2 (linear regression) and Model 8 (ARIMA) achieved  of coefficient of determination. These results demonstrate the effectiveness of ensemble learning in enhancing prediction accuracy with lower MSE and RMSE values. By analyzing various factors influencing rice yields, this research provides valuable insights for increasing rice production and yield, supporting efforts to improve the efficiency and effectiveness of rice farming, and contributing to achieving the United Nations Sustainable Development Goals (SDGs).
Analisis perbandingan Reduction Technique dengan metode Dimentional Reduction dan Cross Validation pada dataset Breast Cancer Sulistya, Yudha Islami; Danuputri, Chyquitha
Indonesian Journal of Data and Science Vol. 3 No. 2 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i2.41

Abstract

Machine learning (ML) merupakan bidang ilmu yang memungkinkan komputer dalam mengembangkan sebuah sistem yang dapat belajar dari data. Dalam ML sendiri banyak teknik sangat berperan penting dalam pengembangan machine ML salah satunya adalah teknik reduksi yang dimana membuat sistem lebih baik dari data yang telah di reduksi. Penelitian ini bertujuan membandingkan performa teknik reduksi dengan metode dimentional reduction dan cross validation pada dataset breast cancer. Dimentional reduction merupakan teknik yang menyederhanakan feature atau mengurangi dimensi pada dataset sedangkan cross validation merupakan metode yang digunakan untuk memaksimalkan hasil dari prediksi pada suatu model. Setalah melakukan tahapan-tahapn dalam pengujian dengan dimentional reduction dan cross validation menggunakan algoritma K-Nearest Neighbors dengan dataset breast cancer berjumlah 500. Hasil yang diperolah untuk dimentional reduction akurasi rata-rata pada model 95.2%, sedangkan pada cross validation 96.6%.
Analisis Performa Algoritma Stochastic Gradient Descent (SGD) Dalam Mengklasifikasi Tahu Berformalin Admojo, Fadhila Tangguh; Sulistya, Yudha Islami
Indonesian Journal of Data and Science Vol. 3 No. 1 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i1.42

Abstract

Tahu berformalin adalah salah satu jenis makanan yang sering mengandung bahan-bahan kimia yang dapat mengawetkan daripada tahu tanpa formalin. Pada tahu berformalin dapat memberikan tekstur lebih kenyal dan berwarna putih bersih. Penelitian ini bertujuan untuk mengklasifikasikan tahu berformalin dan tahu tidak berformalin. Pada paper ini menggunakan algoritma Stochastic Gradient Descent atau dalam penerapannya lebih dikenal dengan SGD Classifier yang merupakan bagian dari algoritma machine learning untuk klasifikasi, regresi maupun jaringan syaraf tiruan serta algoritma ini sangat efisien pada dataset berskala besar. Penelitian ini mencoba menerapkan algoritma SGD pada dataset tahu berformalin dengan jumlah dataset yakni 11000 yang dimana 5500 data tahu berformalin dan 5500 data tahu tidak berformalin. Setelah dilakukan beberapa tahapan dalam pengujian dengan algoritma SGD maka diperolah hasil akurasi, presisi, recall, f1-score pada model yang masing-masing 82.6% untuk akurasi, 81.7% untuk presisi, 84.1% untuk recall, 83.5% untuk f1-score dan dilakukan pengujian menggunakan 10 data yang tidak termasuk dalam data latih memperoleh performansi rata-rata akurasi sebesar 70%, presisi 71%, recall 70% dan f1-score 70%.
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.
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.
Co-Authors Abdul Rachman Manga' Abednego Dwi Septiadi Admojo, Fadhila Tangguh Aina Musdholifah Amrullah, Arif Anik Nur Handayani Anjani, Sarah Arif Riyandi Ariq Cahya Wardhana Br Bangun, Elsi Titasari Danuputri, Chyquitha Dyah Aruming Tyas Elsi Titasari Br Bangun Galih Putra Pamungkas Hamda, Hizbullah Heru Wahyu Herwanto Kasmira Kasmira La Saiman Laurensius Windy Octanio Haryanto Lokapitasari Belluano, Poetri Lestari Maie Istighosah Maryona Septiara Nisrina Hanifa Setiono Purnawansyah Purnawansyah Rosa Andrie Asmara Sapuletea, Chrissandy