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All Journal Jurnal Simetris Kompak : Jurnal Ilmiah Komputerisasi Akuntansi E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING Krea-TIF: Jurnal Teknik Informatika EDUMATIC: Jurnal Pendidikan Informatika EVOLUSI : Jurnal Sains dan Manajemen JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknologi Informasi dan Komunikasi Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Dinamika Jurnal Teknik Informatika dan Teknologi Informasi JURNAL AKUNTANSI DAN BISNIS Community: Jurnal Pengabdian Pada Masyarakat Journal of Technology Informatics and Engineering Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Scientific Journal of Informatics Switch: Jurnal Sains dan Teknologi Informasi Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan Inovasi Sosial: Jurnal Pengabdian Masyarakat Journal of Engineering, Electrical and Informatics Jurnal Teknik Informatika dan Teknologi Informasi
Sindhu Rakasiwi
Universitas Sains Dan Teknologi Komputer
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Pelatihan Penggunaan Mendeley dan MS. Word Untuk Otomatisasi Penulisan Karya Ilmiah Untuk Guru dan Dosen Pada PPMULTINDO Cahaya Jatmiko; Sindhu Rakasiwi; Deddy Award Widya Laksana; Lalang Erawan; Candra Irawan
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 3 (2024): November : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/kfzphr34

Abstract

Pelatihan penggunaan Mendeley dan MS. Word untuk otomatisasi penulisan karya ilmiah diadakan untuk meningkatkan efisiensi dan akurasi dalam penulisan referensi dan format dokumen ilmiah. Pelatihan ini melibatkan 137 peserta menggunakan media zoom yang terdiri dari guru dan dosen dari Perkumpulan Profesi Multimedia dan Teknologi Informasi. Mendeley merupakan alat manajemen referensi yang membantu dalam pengorganisasian dan penyusunan kutipan secara otomatis, sementara MS. Word menyediakan fitur otomatisasi format yang mempermudah penulisan karya ilmiah sesuai dengan standar akademik. Studi yang relevan menunjukkan bahwa pelatihan semacam ini dapat meningkatkan keterampilan teknis dan produktivitas peserta dalam penulisan ilmiah. Hasil kegiatan pelatihan para peserta guru dan dosen mampu menghasilkan karya ilmiah yang lebih terstruktur dan berkualitas tinggi dalam waktu yang lebih singkat. Dengan demikian, pelatihan ini menjadi langkah penting dalam mendukung profesionalisme dan kualitas publikasi ilmiah di kalangan guru dan dosen.
Pelatihan Pengembangan Media Pembelajaran Menggunakan Animaker Untuk Guru dan Dosen Pada PPMULTINDO Sindhu Rakasiwi; Heru Lestiawan; Suprayogi; Feri Agustina; Daurat Sinaga
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 3 (2024): November : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/vmqpnx34

Abstract

Dalam era digital saat ini, guru dan dosen diharapkan mampu mengintegrasikan teknologi dalam proses pembelajaran. Pelatihan pengembangan media pembelajaran menggunakan Animaker diadakan untuk membantu mereka dalam menciptakan konten pembelajaran yang menarik dan interaktif. Pelatihan ini dilaksanakan tim dosen UDINUS Semarang yang melibatkan 125 peserta yang terdiri dari guru dan dosen dari PPMULTINDO. Tujuan pelatihan ini agar bisa meningkatkan literasi digital dan keterampilan teknis peserta dalam membuat bahan ajar yang interaktif. Hasil dari kegiatan ini, para guru dan dosen mampu menghasilkan bahan ajar yang lebih inovatif, sehingga dapat meningkatkan motivasi belajar siswa. Dengan demikian, pelatihan ini menjadi langkah penting dalam mendukung transformasi pendidikan di era digital.
Diagnosis Dini Penyakit Mata: Klasifikasi Citra Fundus Retina dengan Convolutional Neural Network VGG-16 Putri, Chana Amelinda; Rakasiwi, Sindhu
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29571

Abstract

Retinal fundus image-based eye disease classification is important to support early diagnosis of vision disorders such as cataracts, glaucoma, and diabetic retinopathy. This study aims to diagnose early eye diseases with retinal fundus image classification using Convolutional Neural Network VGG-16. The model was developed to detect cataract, glaucoma, and diabetic retinopathy to support early diagnosis. The dataset used comes from Kaggle, including 4,217 retinal fundus images consisting of 1,038 cataract, 1,007 glaucoma, 1,098 diabetic retinopathy, and 1,074 normal images. The images were processed through normalization, augmentation, and resizing to 224×224 pixels, with the dataset divided in a ratio of 80:10:10 for training, validation, and testing. Results showed that the VGG-16 model with transfer learning achieved 88% accuracy, a 10% increase from the previous 75% in the CNN model without transfer learning. This model has the potential to be integrated in clinical decision support systems or mobile applications to improve the speed and accuracy of diagnosis. Limitations of the study include the limited dataset size and potential data bias that may affect the accuracy of the model in detecting eye diseases early, so future research is recommended to use larger and more diverse datasets, as well as explore other deep learning architectures to improve classification performance.
Pelatihan Desain Layout Buku Monograf dengan Canva untuk Guru dan Dosen pada PPMULTINDO Cahaya Jatmoko; Sindhu Rakasiwi; Feri Agustina; Daurat Sinaga; Heru Lestiawan
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 2 No. 4 (2025): November : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v2i4.2362

Abstract

In the midst of the demand to actively publish scientific papers, the ability to design is a significant added value for teachers and lecturers. This report outlines a monograph book layout design training with Canva held for academics at PPMULTINDO. The main purpose of this activity is to provide practical skills so that participants can independently produce professional book layouts. This training uses an interactive workshop method, where participants are guided from the introduction of Canva's features, the application of design principles, to the practice of preparing layout chapters by chapter. As a result, participants demonstrated a significant improvement in their ability to operate Canva for publication design needs. They are able to produce a structured, consistent, and visually appealing layout. Thus, this training has succeeded in becoming a practical solution for academics to efficiently improve the visual quality of their monograph books
Pelatihan Membangun Media Evaluasi Pembelajaran Menggunakan Quizziz pada Guru dan Dosen Perkumpulan Profesi Multimedia dan Teknologi Informasi (PPMULTINDO) Sindhu Rakasiwi; Cahaya Jatmoko; Candra Irawan; Lalang Erawan; Suprayogi Suprayogi; Deddy Award Widya Laksana
Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan Vol. 2 No. 4 (2025): November: Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/unggulan.v2i4.2365

Abstract

This community service activity aims to provide insight and training to teachers and lecturers who are members of the Multimedia and Information Technology Professional Association (PPMULTINDO) regarding the use of the Quizizz application as a learning evaluation medium. The background of this activity is that there is a challenge for educators to continue to innovate in the digital era, but there are still obstacles in the form of a lack of introduction and insight into Quizizz, as well as the assumption that the development of technology-based evaluation media is complicated. This training was carried out online through the Zoom application with tutorial, guidance, and consultation methods. The results achieved are that the trainees gain basic understanding and skills in using Quizizz to support a more effective and interactive learning process. This activity is expected to motivate teachers and lecturers to develop technology-based evaluation media and contribute to the advancement of education in the future.
Enhancing Interpretable Multiclass Lung Cancer Severity Classification using TabNet Norman, Maria Bernadette Chayeenee; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11417

Abstract

Lung cancer poses a significant global mortality challenge, with early clinical detection hindered by non-specific symptoms making accurate diagnosis dependent on extracting subtle patterns from often complex medical tabular data. Traditional machine learning approaches often fall short in capturing intricate patterns within such heterogeneous datasets, hindering effective clinical decision support. This research introduces TabNet, an interpretable deep learning architecture, for multiclass lung cancer severity prediction (low, medium, high). Utilizing the Kaggle Lung Cancer dataset, our methodology leverages TabNet's unique attention-based feature selection for end-to-end processing of tabular data, enabling adaptive identification of key predictors and crucial model interpretability. To effectively assess its predictive capabilities and ensure robust performance, the model was trained with default configurations and validated through stratified 5-fold cross-validation, achieving outstanding performance on the test set: 98.50% accuracy, a 0.98 F1-score, and a 0.9996 macro-AUC-ROC. Beyond its robustness, confirmed by stable learning curves, interpretability analysis highlighted 'Genetic Risk' and 'Shortness of Breath' as dominant factors. Our results underscore TabNet's efficacy as a reliable, robust, and inherently interpretable solution, offering significant potential to improve the precision and transparency of lung cancer severity assessment in clinical practice.
Utilization of E-money for School Payments Using Web-Based RFID Sensors Rakasiwi, Sindhu; Kusumo, Haryo
Advance Sustainable Science, Engineering and Technology Vol 3, No 2 (2021): May-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v3i2.9721

Abstract

School payments are a very important issue to support a school that is used to meet infrastructure and other needs. Therefore, if there is an error in writing or the loss of payment data, a difficult problem will arise. Because the payment process is still manual using a notebook.By utilizing E-money and RFID Sensors in the school payment process, it will be very helpful to create an effective and efficient payment process. That is by using school student cards as self-identity in searching for data that will be linked to a web as a basis. In paying school fees, you can also use E-money for the payment process. Therefore, the author will use electronic money to process the payment. That is by utilizing server-based electronic money in the form of applications such as OVO and DANA. Because it is easier and has been registered with Bank Indonesia, it will be guaranteed safe. The way it works is the first by installing an RFID sensor on the student card which functions as a means of finding personal data or identity so there is no need to search for data one by one manually. Furthermore, it will be linked to the website as a payment basis, where when the RFID sensor is successful in scanning student data, a payment option will appear, which can be paid directly or through the available OVO and DANA electronic money.
Information System Supply Chain Management with FIFO Pertetual Method Kusumo, Haryo; Rakasiwi, Sindhu
Advance Sustainable Science, Engineering and Technology Vol 3, No 2 (2021): May-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v3i2.9722

Abstract

Inventory is one of the assets that has an important role for the activities of a company. Problems that often occur in inventory are at the time of recording and evaluating inventory. Generally, the recording is not detailed or even manual, thus making inventory reports unclear and not good. . This study applies an information system using Supply Chain Management (SCM) at PT. Von Mustika sejahtera, a company engaged in the distributor and retail of ornamental plants in the form of orchids. This system is built using the PHP programming language and MySQL database. This study resulted in an Accounting Information System using the FIFO Pertetual method which functions for inventory management and report presentation more effectively and efficiently.
Penerapan Model SVM dengan Ekstraksi Fitur ResNet50 untuk Identifikasi Sel Darah Terinfeksi Malaria Adhesyah Putra, Maulana Damar; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8750

Abstract

Malaria remains a major public health challenge in Indonesia, with 279,865 reported cases in 2023 and an Annual Parasite Incidence (API) of 0.99 per 1,000 population. Although microscopic examination is still considered the gold standard for malaria diagnosis, it has several limitations, including dependency on trained experts, subjective interpretation, and relatively lengthy processing time. To address these challenges, this study aims to analyze the performance of a Support Vector Machine (SVM) classifier with feature extraction based on ResNet50 in a Computer-Aided Diagnosis (CAD) system for automatic detection of malaria-infected blood cells.ResNet50 was selected for its transfer learning capability to generate high-level feature representations from medical images, while SVM was chosen due to its strong performance on high-dimensional data and limited datasets. A feature vector of 2048 dimensions produced from the global average pooling layer was classified using SVM with a Radial Basis Function (RBF) kernel. The dataset used in this study was obtained from the National Institutes of Health (NIH) and consists of 27,558 microscopic blood cell images (Parasitized and Uninfected classes). The data were partitioned using stratified sampling with an 80:20 ratio for training and testing. Preprocessing steps included pixel normalization, resizing to 224×224 pixels, and basic augmentation to improve model generalization. Experimental results show that the proposed model achieved an accuracy of 93.94%, precision of 94%, recall of 93.43% (Parasitized) and 94.46% (Uninfected), and an average F1-score of 94%. The confusion matrix indicates 2,575 true positives, 2,606 true negatives, 153 false positives, and 181 false negatives, with a false negative rate of 6.57% and a false positive rate of 5.54%. These findings demonstrate that the combination of ResNet50 and SVM has strong potential as a fast and accurate image-based malaria detection method and is suitable for implementation in healthcare settings with limited resources.
Pendekatan Ensemble Multi-Arsitektur Convolutional Neural Network melalui Soft Voting untuk Klasifikasi Citra Histopatologi Kanker Payudara Fitriyani, Shelomita; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8797

Abstract

Breast cancer is one of the leading causes of mortality among women, creating a strong need for diagnostic methods that are accurate, consistent, and capable of handling the morphological variations present in histopathological images. This study aims to improve the stability and accuracy of breast cancer histopathology image classification through an ensemble multi-architecture Convolutional Neural Network approach. The BreakHis dataset, which consists of four magnification levels 40×, 100×, 200×, and 400× was used in this research. Three architectures, VGG19, ResNet50, and EfficientNetB0, served as the base models. All images underwent preprocessing, including resizing to 224×224 pixels, pixel-intensity normalization, and data augmentation. Each model was trained independently, and their probability outputs were combined using a soft voting mechanism to generate the final predictions. The experimental results show that the ensemble method provides the most stable and superior performance across all magnification levels. At 40× magnification, the ensemble achieved an accuracy of 92.00%, recall of 99.03%, and F1-score of 94.44%. At 100× magnification, the accuracy increased to 94.56%, with a recall of 99.07% and an F1-score of 96.18%. The 200× level produced an accuracy of 94.03%, recall of 97.61%, and an F1-score of 95.77%. Meanwhile, at 400× magnification, the model reached an accuracy of 90.11%, recall of 95.14%, and an F1-score of 92.88%. These consistently high recall and F1-score values highlight the model’s strong ability to detect malignant cases while maintaining balanced predictive performance. Overall, the findings demonstrate that combining multiple CNN architectures enhances feature representation and shows strong potential as a decision-support system for breast cancer diagnosis using histopathological images.
Co-Authors Abu Salam Adhesyah Putra, Maulana Damar Agus Cahyo Pangestu Agustinus Budi Santoso Albastomi, Taqius Shofi Andi Dharu Permana Andriana, Myra Ardytha Luthfiarta Arifin, Muhammad Farhan Ariyanto, Noval Arya Erlangga Astuti, Yani Parti budi hartono Cahaya Jatmiko Cahaya Jatmoko Cahyo Pangestu , Agus Candra Irawan Catur Supriyanto Daurat Sinaga Deddy Award Widya Laksana Dewantoro, Eustachius Dito Dewi Agustini Santoso Dzaky, Azmi Abiyyu Edi Sugiarto Edwin Zusrony Edy Mulyanto Egia Rosi Subhiyakto Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Erna Zuni Astuti Erna Zuni Astuti Erwin Yudi Hidayat Etika Kartikadarma Febryantahanuji Febryantahanuji Feri Agustina Fikri Budiman Fitriyani, Shelomita Guruh Fajar Shidik Haresta, Alif Agsakli Haryo Kusumo Haryo Kusumo Haryo Kusumo Heribertus Himawan Heru Lestiawan Ifan Rizqa Ika Novita Dewi Indra Laila Intan Nurul Alfiani Isnaini Khusnul Khotimah Jarot Dian Susatyono Jarot Dian Susatyono Jatmiko, Cahaya Junta Zeniarja Khani, Nadia Ifti Kurniawan, Defri Kusumo , Haryo Kusumo, Haryo Lalang Erawan Lalang Erawan Lutfi Ubaidillah Marjuni, Aris Moh Muthohir Mulyanto, Edy Munifah Murwoko, F Iwan Setyo Myra Andriana Norman, Maria Bernadette Chayeenee Nova Rijati Nur Rokhman Octaviani, Dhita Aulia Paramita, Cinantya Pulung Nurtantio Andono Putri, Chana Amelinda Rafsanjani, Muhammad Ivan Rahardian, Farhan Rifal Winazar Rifal Winazar Roymon Panjaitan Saputra, Ahmad Bintang Savicevic, Anamarija Jurcev Septiani, Karlina Dwi Shier Nee Saw Sinaga, Daurat Sri Wahyuning Suprapti suprayogi Suprayogi Suprayogi Syah Putra, Fernanda Mulya T.Sutojo Tantik Sumarlin . Taqius Shofi Albastomi Taufik Kurnialensya Triginandri, Rifqi Ubaidillah , Lutfi Utomo, Danang Wahyu Widya Laksana, Deddi Award Winarsih, Nurul Anisa Sri Yani Parti Astuti Yuli Fitrianto