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A Comparative Analysis of Classification Algorithms for Cyberbullying Crime Detection: An Experimental Study of Twitter Social Media in Indonesia Muzakir, Ari; Syaputra, Hadi; Panjaitan, Febriyanti
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.35149

Purpose: This research aims to identify content that contains cyberbullying on Twitter. We also conducted a comparative study of several classification algorithms, namely NB, DT, LR, and SVM. The dataset we use comes from Twitter data which is then manually labeled and validated by language experts. This study used 1065 data with a label distribution, namely 638 data with a non-bullying label and 427 with a bullying label.Methods: The weighting process for each word uses the bag of word (BOW) method, which uses three weighting features. The three-word vector weighting features used include unigram, bigram, and trigram. The experiment was conducted with two scenarios, namely testing to find the best accuracy value with the three features. The following scenario looks at the overall comparison of the algorithm's performance against all the features used.Result: The experimental results show that for the measurement of accuracy weighting based on features and algorithms, the SVM classification algorithm outperformed other algorithms with a percentage of 76%. Then for the weighting based on the average recall, the DT classification algorithm outperformed the other algorithms by an average of 76%. Another test for measuring overall performance (F-measure) based on accuracy and precision, the SVM classification algorithm, managed to outperform other algorithms with an F-measure of 82%.Value: Based on several experiments conducted, the SVM classification algorithm can detect words containing cyberbullying on social media.

Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture Saputra, Tommy; Nurmaini, Siti; Roseno, Muhammad Taufik; Syaputra, Hadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1976

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.

Metode Klasifikasi Gejala Penyakit Coronavirus Disease 19 (COVID-19) Menggunakan Algoritma Neural Network Rahmi, Rahmi; Antoni, Darius; Syaputra, Hadi; Fatoni, Fatoni; Kurniawan, Tri Basuki
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 1 (2023): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i1.1406

Coronavirus Disease 19 (COVID-19) adalah virus baru yang dapat menyebabkan infeksi saluran pernafasan. Virus ini berasal dari hewan yang dapat menular ke manusia melalui percikan ludahnya. Menurt data epidemiologis, rata-rata penderita virus ini berusia 15-80 tahun. Virus ini memiliki masa inkubasi 3-14 hari yang memiliki gejala awal yaitu demam tinggi, sesak napas, batuk dan pilek. Indonesia mwmiliki 2 kasus pertama pada 2 Maret 2020, Covid-19 meningkat secara teratur pada 29 Desember 2020 data menunjukkan 719.219 ribu orang dipastikan terjangkit Covid-19. Masalah yang diangkat dalam penelitian ini adalah bagaimana mengklasifikasikan risiko tertular virus Covid-19 dari gejala yang ditimbulkan. Tujuan dari penelitian ini adalah untuk mengetahui nilai akurasi dari klasifikasi resiko tertular virus Covid-19 berdasarkan instrument yang digunkan dari metode Cross Industry Standard Process for Data Mining (CRISP-DM). Dataset yang digunakan peniliti diambil dari website http://github.com/nshomron/covidpred. Penelitian ini menggunakan Algoritma Neural Network (NN) dengan bantuan alat Phyton, akurasi Algoritma Neural Ntwork (NN) diperoleh nilai sebesar 95%, artinya telah menunjukkan hasil klasifikasi yang baik. Peneliti juga menguji dengan Algoritma Logistic Regression namun nilai akurasi yang diperoleh tidak jauh berbeda dengan Algoritma NN, Algoritma Logistic Regression diperoleh akurasi nilai sebesar 94%.

Optimisasi Hyperparameter pada Model Klasifikasi Jenis Bunga dengan arsitektur MobileNetV3-Small Syaputra, Hadi; Zahro, Lailatuz; Oktarina, Serly; Roseno, M. Taufik; Supratman, Edi
Jurnal Nasional Ilmu Komputer Vol. 5 No. 3 (2024): Jurnal Nasional Ilmu Komputer
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

This study aims to classify various types of flowers using the deep learning method with the Convolutional Neural Networks MobileNetV3-Small architecture. The research stages include data collection and sharing, model training using the MobileNetV3-Small architecture, and testing with evaluation using the Confusion Matrix. Eight models with hyperparameter variations were tested to find the model with the highest accuracy. Model five achieved the highest validation accuracy of 99.25%. The evaluation showed that model five achieved the highest accuracy of 92%. These results indicate that increasing the amount of data and parameter settings can significantly improve model accuracy, indicating that the model can achieve near-perfect performance with more training data and optimal parameter settings. This study uses open-access data from Kaggle covering five types of flowers: daisy, dandelion, rose, sunflower, and tulip.

Dilatasi Inkremental Menggunakan Metode CNN Untuk Klasifikasi Tumor Otak Dengan Arsitektur VCG16 dan Resnet50 Saputra, Tommy; Roseno, Muhammad Taufik; Syaputra, Hadi
Generic Vol 16 No 2 (2024): Vol 16, No 2 (2024)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v16i2.190

Klasifikasi tumor otak adalah tugas yang menantang di bidang pemrosesan citra medis. Teknologi kini telah memungkinkan dokter medis untuk memiliki bantuan tambahan untuk diagnosis. Penelitian ini bertujuan untuk mengklasifikasikan tumor otak menggunakan gambar MRI, yang dikumpulkan dari pasien anonim dan simulator otak buatan. Baru-baru ini, teknik berbantuan komputer seperti menggunakan deep learning sebagai ekstraksi fitur, dan teknik klasifikasi digunakan secara intensif untuk mendiagnosis otak pasien untuk memeriksa apakah ada tumor. Dalam penelitian ini diusulkan model klasifikasi tumor otak menggunakan Convolutional Neural Network yang dapat menklasifikasikan tumor otak secara akurat. Data yang digunakan berupa data MRI tumor otak sebanyak 253 data tumor otak. Dataset yang dugankan dibagi menjadi data pelatihan dan pengujian. Penelitian menghasilkan model klasifikasi tumor otak dengan menggunakan arsitektur VCG16 dan Resnet50. Model menghasilkan nilai rata-rata akurasi sebesar 80%, Recall 85% dan Presisi 70%. Penelitian menunjukkan kinerja Resnet50 menunjukkan kemampuan model untuk mengklasifikasikan tumor otak secara akurat.

Residual pixel-wise semantic segmentation for assessing enlarged fetal heart: a preliminary study Roseno, Muhammad Taufik; Nurmaini, Siti; Rini, Dian Palupi; Saputra, Tommy; Mirani, Putri; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Syaputra, Hadi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9244

The four-chamber view is a crucial scan plane routinely employed in both second-trimester perinatal screening and fetal echocardiographic examinations. Sonographers typically measure biometrics in this plane, such as the cardiothoracic ratio (CTR) and heart axis, to diagnose fetal heart anomalies. However, due to the echocardiographic artifacts, the assessment not only suffers from low efficiency but also inconsistent results depending on the operators’ skills. This study proposes a residual pixel-wise semantic segmentation, which segmented the fetal heart and thoracic contours in a 4-chamber view for assessing an enlarged fetal heart condition. The accuracy of intersection-over-union (IoU) and dice coefficient similarity (DCS) is used for model validation to further regulate the evaluation procedure. We use 1174 US images, comprising about 560 enlarged heart images, and about 614 normal heart images. Out of these data, 248 images are used for unseen data, and the remaining for training/validation processes. The performance of the proposed model, when tested on unseen data, achieved satisfactory results with 97.71% accuracy, 90.36% IoU, and 94.93% DCS. These metrics collectively demonstrate the satisfactory performance of the proposed model compared to existing segmentation models. The outcomes underscore that the proposed model establishes a state-of-the-art standard for enlarged fetal heart detection.

Multiclass instance segmentation optimization for fetal heart image object interpretation Syaputra, Hadi; Nurmaini, Siti; Partan, Radiyati Umi; Roseno, Muhammad Taufik
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4137-4150

This research aims to develop a multi-class instance segmentation model for segmenting, detecting, and classifying objects in fetal heart ultrasound images derived from fetal heart ultrasound videos. Previous studies have performed object detection on fetal heart images, identifying nine anatomical classes. Further, these studies have conducted instance segmentation on fetal heart images for six anatomical classes. This research seeks to expand the scope by increasing the number of classes to ten, encompassing four main chambers left atrium (LA), right atrium (RA), left ventricle (LV), right ventricle (RV); four valves tricuspid valve (TV), pulmonary valve (PV), mitral valve (MV), and aortic valve (AV); one aorta (Ao), and the spine. By developing an instance segmentation method for segmenting ten anatomical structures of the fetal heart, this research aims to make a significant contribution to improving medical image analysis in healthcare. It also aims to pave the way for further research on fetal heart diseases using AI. The instance segmentation approach is expected to enhance the accuracy of segmenting fetal heart images and allow for more efficient identification and labeling of each anatomical structure in the fetal heart.

Pelatihan Penggunaan AutoCAD untuk Kontruksi Bangunan Sipil Nurmeyliandari, Revianty; Panjaitan, Febriyanti; Bastam, Mukhlis Nahriri; Syaputra, Hadi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 2 (2023): Mei 2023
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v6i2.1296

Kegiatan pengabdian Pelatihan Penggunaan Software AutoCAD untuk proyek konstruksi bangunan sipil bertujuan untuk meningkatkan pemahaman dan pemanfaatan teknologi dalam bidang proyek konstruksi bangunan sipil. Dalam pelaksanaan proyek, kemampuan teknis dan penggunaan software yang tepat sangat penting untuk memastikan kualitas dan efisiensi proyek. Software AutoCAD adalah alat yang sangat penting dalam bidang pembangunan sipil. Namun, masih ada mararakat atau pelaku pembangunan yang belum memahami dan belum mampu menggunakan software ini, sehingga menyebabkan masalah-masalah dalam pelaksanaan proyek. Pelatihan penggunaan software AutoCAD untuk proyek konstruksi bangunan sipil diharapkan dapat membantu peserta memahami dan menguasai penggunaan software ini dengan baik. Pelatihan akan memfokuskan pada pemahaman dan aplikasi software dalam bidang proyek konstruksi bangunan sipil, sehingga peserta dapat memahami bagaimana software ini dapat membantu dalam pelaksanaan proyek. Luaran dari pengabdian ini adalah peserta memiliki kapasitas dan pemahaman yang lebih baik dalam menggunakan software AutoCAD dan dapat memastikan hasil proyek konstruksi bangunan sipil yang baik dan sesuai dengan yang diharapkan. Hasil kegiatan pengabdian menunjukkan hasil positif, dimana 83% peserta mampu mengerjakan soal gambar teknik menggunakan aplikasi AutoCAD. Implementasi pelatihan sangat penting untuk membantu peserta memahami dan memiliki keterampilan dalam menggunakan software AutoCAD untuk proyek konstruksi bangunan sipil. Dukungan pihak terkait dan motivasi peserta merupakan faktor pendorong, sedangkan keterbatasan waktu, sumber daya, dan kendala teknis merupakan faktor penghambat. Oleh karena itu, perlu adanya upaya untuk mengatasi faktor penghambat agar pelaksanaan program dapat berjalan dengan baik dan efektif.

Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture Saputra, Tommy; Nurmaini, Siti; Roseno, Muhammad Taufik; Syaputra, Hadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1976

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.

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