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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System JURNAL MEDIA INFORMATIKA BUDIDARMA JTERA (Jurnal Teknologi Rekayasa) INOVTEK Polbeng - Seri Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Community: Jurnal Pengabdian Pada Masyarakat Jurnal Rekayasa elektrika INOVTEK Polbeng - Seri Informatika Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
Abu Salam
Universitas Dian Nuswantoro
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Pengembangan Sistem Deployment Deteksi untuk Kista Ginjal pada Citra Ct Scan dengan Metode Yolo Salam, Abu; Pawidya, Novandra Putra
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4232

Abstract

Kidney cysts are a medical condition characterized by the formation of fluid-filled sacs on the kidneys, where CT scan image analysis is crucial for diagnosis and management. This study aims to develop a YOLOv5-based object detection model to identify kidney cysts in CT scan images. The research methodology involved training the model with a public dataset from Kaggle and validating it using private clinical data, with manual annotation conducted by a radiographer to ensure data accuracy. The results indicate that the YOLOv5 model achieved high performance with a Mean Average Precision (mAP) of 99.3%, a precision of 97.4%, and a recall of 99.1%. The model was successfully integrated into a Flask-based application, facilitating real-time kidney cyst detection in clinical practice. Consequently, this study demonstrates that the use of YOLOv5 can effectively support medical diagnosis, enhancing the accuracy and speed of kidney cyst detection, and offering a practical and innovative diagnostic tool for healthcare professionals. These findings open up opportunities for applying similar deep learning technologies to other medical conditions, significantly contributing to technological advancements in healthcare.
Analisis Topic-Modelling Menggunakan Latent Dirichlet Allocation (LDA) Pada Ulasan Sosial Media Youtube Alpiana, Vika; Salam, Abu; Alzami, Farrikh; Rizqa, Ifan; Aqmala, Diana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7127

Abstract

This research explores the role of Micro, Small, and Medium Enterprises (MSMEs) in the Indonesian economy, focusing on sales and marketing challenges in the era of social media, especially YouTube. With millions of individuals using this platform to share product insights, reviews, and experiences, MSMEs need to receive relevant feedback. This study applies text mining, particularly the topic modeling analysis method with Latent Dirichlet Allocation (LDA), to analyze user comments on MSME videos, with an emphasis on Lumpia Gang Lombok Semarang on YouTube. Through the application of LDA, the identification of ten main topics is conducted, with the highest coherence value reaching 0.414027. The visualization of the intertopic distance map provides an understanding of the relationships between topics and dominant words. Comment analysis provides valuable insights into user preferences and perceptions of products, supporting MSMEs in understanding customer satisfaction and enhancing value for those enterprises. These findings also affirm the effectiveness of YouTube as a relevant data source for understanding public preferences for MSME products. This research details text processing methods, including extraction, cleaning, tokenization, normalization, removal of stopwords, and stemming. With this approach, the research not only provides insights into topic analysis in the context of social media but also makes a valuable contribution to the development and marketing of MSMEs through a better understanding of social media data, especially on the YouTube platform.
Evaluasi Performa Oversampling dan Augmentasi pada Klasifikasi Penyakit Kulit Menerapkan Convolutional Neural Network Iskandar, Deo Andrianto; Salam, Abu
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7119

Abstract

The skin is the largest outer part of the human body. Maintaining skin is very important. The appearance of unusual things on the skin will raise concerns because it is possible that the skin could be affected by fatal diseases. Limited specialist doctor examinations in Indonesia add to the difficulty in preventing skin diseases. Therefore, this research was conducted to facilitate the classification of skin diseases. Skin disease classification must have good accuracy or precision in classifying each type. This study classifies skin diseases accurately and precisely by evaluating the performance of Oversampling and Augmentation techniques. This research uses the Convolutional Neural Network (CNN) approach. Using the HAM10000 dataset which contains dermoscopic images with a total of 10015 images. This study applies Oversampling to overcome data imbalance and applies image augmentation to improve model training performance. The performance of the model is evaluated using accuracy, recall, precision, f1-score, specificity, sensitivity, gmean. Comparisons are obtained from testing the original dataset, the dataset with oversampling and various augmentation techniques. The evaluation results show that the third test, namely classification using the CNN approach with oversampling and augmentation rotation, zoom, width, height, vertical_flip, gets the best results, namely accuracy 0.98, recall 0.98, precision 0.98, f1-score 0.98, specificity 0.99, sensitivity 0.98, gmean 0.98.
Teknik Random Undersampling untuk Mengatasi Ketidakseimbangan Kelas pada CT Scan Kista Ginjal Ramadhan, Irfan Surya; Salam, Abu
Techno.Com Vol. 23 No. 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9738

Abstract

Kista ginjal adalah pertumbuhan jaringan berbentuk kantong yang berisi carian pada sekitar ginjal. Seringkali kista ginjal tidak menimbulkan gejala, sehingga memerlukan pantauan reguler dokter. Dokter dapat melakukan pemeriksaan dan merencanakan tindakan penelitian lebih lanjut. Penelitiaan ini fokus pada model klasifikasi kista menggunakan model Deep Learning dengan arsitektur Convolution Neural Network (CNN), jenis jaringan syaraf tiruan untuk analisis gambar CT scan kista ginjal. Selain itu, penggunaan teknik pre-processing untuk meningkatkan performa model dengan memperbanyak varisasi data. Dalam membuat model klasifikasi perlu memperhatikan pemahaman data, tingkat interpretabilitas model, dan penanganan overfitting. Overfitting terjadi ketika model terlalu fokus pada data latih, sehingga tidak dapat memproses data uji dengan baik. Solusi untuk menangani masalah distribusi kelas adalah dengan penyeimbang kelas (resampling). Resampling dibagi menjadi dua jenis yaitu, undersampling dan oversampling. Undersampling merupakan metode sampling secara acak memilih di kelas mayoritas dan menambahkannya di kelas minoritas. Dan oversampling merupakan menggandakan sampel di kelas minoritas secara acak. Pada hasil pengujian model yang dilakukan dapat ditarik kesimpulan bahwa penggunaan teknik undersampling RUS memiliki tingkat akurasi tertinggi dengan nilai 30,82% untuk klasifikasi kista ginjal pada dataset tidak seimbang.
Deployment of Kidney Tumor Disease Object Detection Using CT-Scan with YOLOv5 Kahingide, Hastyantoko Dwiki; Salam, Abu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7771

Abstract

Image processing plays a crucial role in identifying kidney tumors through CT-Scan images. Object detection technology, particularly YOLO, stands out for its speed and accuracy in facilitating more detailed analysis. Using Flask as a web framework offers optimal responsiveness, providing adaptive ease of use, especially in medical image processing. Evaluation of the model shows impressive results, with a mean Average Precision (mAP) of 0.987 for the 'kidney tumor' label. Detection on public data demonstrated high performance with accuracy, precision, recall, and F1-Score of 98.56%, 98.66%, 99.66%, and 99.16%, respectively. This study also utilized clinical data comprising 62 CT-Scan images. Evaluation of the clinical data revealed that YOLOv5 produced an accurate detection model with accuracy, precision, recall, and F1-Score of 95.16%, 96.72%, 98.33%, and 97.52%, respectively. The research shows that both public and clinical data models can accurately detect kidney tumors based on CT-Scan images. The deployment process using the Flask web-based platform allows direct interaction with users through an intuitive interface, enabling users to upload their CT-Scan images and quickly obtain detection results. These test results provide evidence that object detection using YOLOv5 achieves high accuracy in detecting both public and clinical datasets.
Classification of Brain Tumors by Using a Hybrid CNN-SVM Model Nabila, Talitha Safa; Salam, Abu
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8277

Abstract

Brain tumors are diseases that involve the growth of brain cells, causing abnormalities in the brain region. An MRI scan is a useful tool for tumor detection. Researchers can process the obtained image data to conduct research capable of detecting brain tumor disease. Classifying brain tumors facilitates effort, planning, and accurate diagnosis, enabling the formulation and evaluation of treatment options for a patient with a brain tumor. The research was conducted to classify whether or not there was a tumor in the brain by using a combination of algorithms, namely CNN, to extract features from image data and then use SVM as a classification. CNN is a popular algorithm that deals very effectively with the complexity and variation of image data, whereas SVM is an algorithm for classification that maximizes margins and generalizations to produce accurate classifications. The project's goal is to create a hybrid model that can classify two labels based on image preprocessing processes, feature extraction, and brain tumor image data classification. In this study, the results of the CNN-SVM hybrid were able to obtain the highest score with Adam optimization and learning rate 0.001, accuracy of 98.92%, precision 98.92%, recall 98.92%, and f1-score 98.92%.
Pengembangan Sistem Deployment Chatbot Dengan Teknologi LSTM untuk Customer Service Industri Batik di Karanganyar Damaswara, Silvester Aditya; Salam, Abu
JTERA (Jurnal Teknologi Rekayasa) Vol 9, No 2: December 2024
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v9.i2.2024.53-62

Abstract

Batik, warisan budaya Indonesia yang telah diakui oleh UNESCO, merupakan seni yang merefleksikan nilai-nilai filosofis budaya Indonesia. Pengakuan global ini telah mendorong pemerintah Indonesia untuk menerapkan kebijakan yang mendorong penggunaan batik, terutama di sekolah-sekolah, sebagai bagian dari upaya pelestarian budaya. Maksud dari penelitian ini yaitu mengembangkan chatbot yang memuat segala informasi mengenai batik yang bertujuan untuk memberikan pengetahuan dan mengenalkan makna serta keunikan batik terhadap pengguna. Chatbot ini mengimplementasikan Artificial Intelligence Project Cycle dengan deep learning dengan mengikut sertakan pengembangan flask. Penelitian ini mengeksplorasi pemanfaatan chatbot berbasis LSTM yang diharapkan dapat meningkatkan aksesibilitas informasi tentang batik, termasuk jenis, bahan, dan warna batik, sehingga masyarakat dapat lebih mengenali dan menghargai seni batik. Studi ini menunjukkan potensi chatbot berbasis LSTM dalam meningkatkan pengetahuan dan apresiasi terhadap batik di masyarakat, sekaligus memberikan dukungan kepada industri batik lokal. LSTM menjadikan model untuk mencadangkan dan memasukan informasi yang relevan dalam jangka panjang, arsitektur ini dapat sangat berguna dalam menyumbangkan informasi yang relevan dan memahami tujuan dari pertanyaan user.
Pendampingan Penggunaan Software Aplikasi Sebagai Pendukung Menjalankan Perilaku Hidup Sehat di Lingkungan Panti Asuhan Nurul Istiqomah Al Hira’ Salam, Abu; Rakasiwi, Sindhu; Paramita, Cinantya; Supriyanto, Catur; Octaviani, Dhita Aulia; Mulyanto, Edy
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2709

Abstract

The Clean and Healthy Lifestyle Program (PHBS) is an important program to encourage the implementation of a healthy lifestyle in maintaining, preserving, and improving health. Many diseases can be avoided if the community implements a healthy lifestyle. PHBS is ideal for implementation in school-age children, because they are included in the group at risk for health problems due to several factors. Technology in education has been proven to significantly change the way interaction and learning in the classroom, more efficiently, more easily accessible, and can build the skills needed in the current digital era and in the future. The use of digital applications as one of the products of technology has been widely used in both health and education, and are interrelated with each other where they complement each other. Information on health problems certainly requires the field of education to convey it, and vice versa, education cannot run smoothly if the environment is unhealthy. Thus, the role of technology in both fields is very important. Based on the things mentioned above, it is necessary to provide knowledge to students about PHBS. In addition to being given knowledge, students also need to be given guidance when practicing the PHBS material and including the role of technology in the form of digital applications so that learning can be more enjoyable and effective, where previously it was necessary to conduct socialization and training first regarding the use of the application to the caretakers of the Islamic boarding school. Based on the reasons stated, this time the team took the initiative to hold an activity in the form of Community Service with the theme of PHBS Assistance for Students with Digital Application Socialization, with a predetermined location, namely at the Nurul Istiqomah Al Hira 'Islamic Boarding School, so that PHBS can become a habit for students in their daily lives and can transmit these good habits to their environment.
Penyusunan Analisis Kebutuhan Perangkat Lunak untuk Web Profil SMP Negeri 7 Semarang Utomo, Danang Wahyu; Kurniawan, Defri; Zeniarja, Junta; Dewi, Ika Novita; Salam, Abu; Muljono, Muljono
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2700

Abstract

Penggunaan web profil sebagai alat penyebaran informasi telah banyak digunakan pada institusi Pendidikan utamanya sekolah. SMP N 7 Semarang menggunakan web profil untuk menyampaikan informasi terkait identitas sekolah seperti visi dan misi sekolah, kurikulum serta kegiatan siswa dalam sekolah. Namun web tersebut masih terdapat kekurangan dan perlu diperbaiki menyesuaikan dengan perkembangan saat ini. Pemahaman tentang analisis kebutuhan perangkat lunak penting bagi para guru dan tenaga pendidik untuk mengetahui kebutuhan pengguna dan kebutuhan sistem yang harus disediakan dalam sistem. Program pengabdian Masyarakat dilaksanakan dalam bentuk pelatihan kepada para guru dan tenaga pendidik. Para peserta diberikan materi analisis kebutuhan termasuk kebutuhan pengguna, kebutuhan sistem, kebutuhan fungsional dan non-fungsional. Selain itu, para peserta juga menerima pelatihan tentang desain antarmuka pengguna dan tata letak konten situs web. Hasil dari program ini, para peserta dapat mengidentifikasi perbaikan yang diperlukan untuk situs web profil SMP N 7 Semarang. Fitur berita diidentifikasi sebagai kebutuhan fungsional yang perlu ditambahkan pada situs web profil. Untuk kebutuhan non-fungsional, para peserta menyarankan desain ulang tata letak konten web
Optimalisasi Model BioBERT untuk Pengenalan Entitas pada Teks Medis dengan Conditional Random Fields (CRF) Nafanda, Cynthia Dwi; Salam, Abu
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7042

Abstract

This research evaluates the performance of various models in the Named Entity Recognition (NER) task for medical entities, focusing on imbalanced datasets. Six BioBERT model configurations were tested, incorporating optimization techniques such as Class Weight, Conditional Random Fields (CRF), and Hyperparameter Tuning. The evaluation was conducted using Precision, Recall, and F1-Score metrics, which are particularly relevant in the context of NER, especially for addressing class imbalance in the data. The dataset used is BC5CDR, which targets chemical and disease entities in unstructured medical texts from PubMed. The data was divided into three parts: a training dataset for model training, a validation dataset for model tuning, and a test dataset for performance evaluation. The dataset was split evenly to ensure unbiased model testing, leading to more accurate results that can serve as a reference for developing more efficient medical NER systems. The evaluation results indicate that BioBERT + CRF is the model with an F1-Score that reflects an optimal balance between Precision (ranked 3rd, 0.6067 for B-Chemical, 0.5594 for B-Disease, 0.4600 for I-Disease, and 0.5083 for I-Chemical) and Recall (ranked 3rd, 0.5580 for B-Chemical, 0.4491 for B-Disease, 0.5718 for I-Disease, and 0.3840 for I-Chemical) compared to other models. This model proved to be more accurate in detecting medical entities without compromising prediction precision. The model's stability is also enhanced by a smaller gap between Precision and Recall, making it the best choice for NER in medical texts. The application of early stopping techniques effectively prevented overfitting, ensuring the model learned optimally without losing generalization. With better balance in recognizing medical entities from unstructured texts, this model presents the most effective approach for NER systems in the medical domain.
Co-Authors Adhitya Nugraha Alan Ma’ruf, Farda Alpiana, Vika Alzami, Farrikh Anisatawalanita Ukhifahdhina Ardin, Akbar Ilham Ardytha Luthfiarta Arifianti, Fidela Putri Arifin, Muhammad Farhan Astuti, Yani Parti Candra Irawan Catur Supriyanto Catur Supriyanto Damaswara, Silvester Aditya Debrina Luna Arghata Mangkawa Diana Aqmala Dimas Pratama, Yohanes Diyan Adiatma Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erwin Yudi Hidayat Erwin Yudi Hidayat Fahmi Amiq Farda Alan Ma'ruf Ferry Bintang Nugroho Haresta, Alif Agsakli Ifan Rizqa Ika Novita Dewi Iskandar, Deo Andrianto Juli Ratnawati Junta Zeniarja Kahingide, Hastyantoko Dwiki Khafiizh Hastuti Kurniawan, Defri Kurniawan, Wira Adi Kusmiyati Kusmiyati L. Budi Handoko Lesmarna, Salsabila Putri Maulana, Fadhli Faqih Megantara, Rama Aria Moh. Sholik Muhammad Jamhari Mukti, David Ramantya Muljono Muljono Mulyanto, Edy Muzaki, Rizki Nuzul Nabila, Talitha Safa Nafanda, Cynthia Dwi Norman, Maria Bernadette Chayeenee Octaviani, Dhita Aulia Pamungkas, Tahta Arya Paramita, Cinantya Pawidya, Novandra Putra Prinantyo, Gilang Djati Putra, Aditya Herdiansyah Ramadhan Rakhmat Sani Ramadhan, Irfan Surya Restu Agung Pamuji Ricardus Anggi Pramunendar Riyan Ardiansyah Sidiq, Syaiful Rizal Sindhu Rakasiwi Utomo, Danang Wahyu Verdian Putra Wicaksana Wibowo, Isro' Rizky Yonismara, Arvie Arvearie