Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

4.959

P-Index

This Author published in this journals

All Journal Jurnal Simetris JUITA : Jurnal Informatika Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Terpadu Jurnal Teknologi Komputer dan Sistem Informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication MDP Student Conference INOVTEK Polbeng - Seri Informatika JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)

Devella, Siska

Unknown Affiliation

Author-ID : 3350901

Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 36 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4

Penerapan Speeded-Up Robust Feature pada Random Forest Untuk Klasifikasi Motif Songket Palembang Yohannes Yohannes; Siska Devella; Ade Hendri Pandrean
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 3 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i3.1978

Songket is a historical heritage in the city of Palembang. Where Songket has many different types and motifs. Besides having historical value, Palembang's original Songket has high quality and complexity in the manufacturing process. As known Palembang Songket has a lot of motives, one of the ways to recognize Palembang Songket is through its motives, so that research was conducted for the classification of Palembang Songket motifs. The method used to extract features is the Speeded-Up Robust Feature (SURF), while the classification method is Random Forest. The process of forming the SURF feature is divided into two stages, the first stage is Interest Point Detection, which consists of Integral Images, Hessian Matrix Based Interest Points, Scale Space Representation and Interest Point Localization, the second stage of Interest Point Description consists of Orientation Assignment and Descriptor Based on Sum Haar Wavelet Responses. The resulting feature is used for the Random Forest classification. This study used 345 images of Palembang Songket motifs, among others, Bunga Cina, Cantik Manis and Pulir. The images taken are based on 5 colors from each Palembang Songket motif. For the separation of data there are 300 images used as data train and 45 images for testing data. From the tests that have been done the results of the overall overall accuracy are 68.89%, per class accuracy 79.26%, precision 69.27, and recall 68.89%.

Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih Yohannes Yohannes; Siska Devella; William Hadisaputra
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i2.3707

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.

PELATIHAN PENGGUNAAN WORDPRESS UNTUK MEDIA INFORMASI KPCDI PALEMBANG Al Rivan, Muhammad Ezar; Irsyad, Hafiz; Meiriyama, Meiriyama; Yohannes, Yohannes; Devella, Siska; Wijaya, Novan; Rachmat, Nur
FORDICATE Vol 4 No 2 (2025): April 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i2.11572

Penguasaan teknologi informasi menjadi kebutuhan penting bagi organisasi berbasis komunitas dalam menyebarluaskan informasi secara cepat dan terstruktur. Komunitas Pasien Cuci Darah Indonesia (KPCDI) Palembang membutuhkan sarana digital yang dapat menunjang komunikasi dan edukasi antaranggota. Kegiatan pengabdian ini bertujuan untuk memberikan pelatihan penggunaan WordPress sebagai media informasi komunitas. Pelatihan dilaksanakan di Rumah Sakit RK Charitas Palembang dengan metode ceramah, demonstrasi, dan praktik langsung. Materi pelatihan mencakup pengelolaan konten situs, pengunggahan media, dan pengaturan tampilan dasar website. Peserta dibimbing secara bertahap agar mampu memahami penggunaan platform meskipun berasal dari latar belakang non-teknis. Hasil kegiatan menunjukkan bahwa peserta antusias dan mampu mengikuti alur pelatihan dengan baik. Kegiatan ini diharapkan dapat memperkuat kapasitas digital KPCDI Palembang dalam pengelolaan media informasi secara mandiri dan berkelanjutan

Comparison of LVQ and RBFNN Algorithms for Identification of Glaucoma and Diabetic Retinopathy on Fundus Image Oktavius, Kevin; Devella, Siska
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Penyakit mata merupakan salah satu masalah kesehatan utama pada semua orang terutama pada kaum lansia, penyakit mata yang paling umum menyerang lansia diantaranya adalah glaukoma dan retinopati diabetes. Penyakit glaukoma dan diabetes retinopati dapat diketahui melalui citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma Learning Vector Quantization dengan Radial Basis Function Neural Network untuk klasifikasi penyakit glaukoma dan diabetes retinopati (accuracy, precision, recall) berdasarkan citra fundus resolusi tinggi. Dataset yang digunakan berjumlah 45 citra fundus yang terdiri dari 15 citra fundus terjangkit glaukoma, 15 citra fundus terjangkit diabetes retinopati dan 15 citra fundus mata normal. Pada perhitungan dengan confusion matrix hasil tertinggi didapatkan pada algoritma radial basis function neural network dengan spread=20 dan MN=10 menghasilkan rata-rata accuracy sebesar 81,06%, precision sebesar 80,83% dan recall sebesar 73,33% jika dibandingkan dengan algoritma learning vector quantization dengan lvqnet=50 dan epoch=45 menghasilkan rata-rata accuracy sebesar 80,85%, precision sebesar 73,33% dan recall sebesar 77,14%.

Rancang Bangun Aplikasi Permainan EscapeMenggunakan Logika Fuzzy Dan Algoritma Floyd Warshall Prabowo, Adrianus; Devella, Siska; Yohannes, Yohannes
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 2 (2021): April 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Aplikasi permainan ESCAPE merupakan permainan yang mengandalkan player untuk keluar dari labirin tersebut. Penelitian ini menggunakan Logika Fuzzy untuk membuat perilaku komputer menjadi susah ditebak dan Floyd Warshall untuk membuat item jebakan menghalangi player saat bermain. Aplikasi permainan ini dibangun dan dirancang dengan menggunakan Unity 3D dan menggunakan metodologi prototype. Hasil uji dari data sampel menunjukkan bahwa logika fuzzy berhasil diterapkan dalam menentukan perilaku NPC. Hasil uji dari data sampel yang dilakukan menunjukkan bahwa kemunculan item jebakan berhasil diterapkan pada aplikasi permainan ESCAPE.

Penggunaan Fitur HOG Dan HSV Untuk Klasifikasi Citra Sel Darah Putih Prasthio, Rial; Yohannes, Yohannes; Devella, Siska
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 2 (2022): April 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Sel darah putih (leukosit) merupakan sel pembentuk komponen darah yang diproduksi oleh sumsum tulang dan disebarkan ke seluruh tubuh melalui aliran darah. Sel darah putih merupakan bagian penting dari sistem kekebalan tubuh yang berfungsi untuk menghasilkan antibodi yang dapat membantu tubuh manusia dalam melawan berbagai penyakit. Sel darah putih dibagi menjadi 5 jenis, yaitu neutrofil, limfosit, monosit, eosinofil, dan basophil. Analisis sel darah putih masih dilakukan secara manual yang memakan waktu yang lama dan memiliki tingkat ketelitian dan keakuratan yang rendah. Solusi yang dapat dilakukan salah satunya menggunakan machine learning yaitu SVM (support vector machine) dengan menggunakan fitur HOG dan HSV. Penelitian ini menggunakan dataset hasil mikroskop sel darah putih dari Kaggle yang bersifat public. Jumlah dataset yang digunakan dalam penelitian berjumlah 12.392 gambar dari 4 jenis sel darah putih (Eosinophil, Lymphocyte, Monocyte, dan Neutrophil). Pada perhitungan confusion matrix hasil tertinggi didapatkan oleh Neutrophil dengan accuracy sebesar 88,55%, precision sebesar 100%, dan recall sebesar 54,19%.

Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network Dengan Arsitektur VGG-19 Marcella, Dewi; Yohannes, Yohannes; Devella, Siska
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i1.3331

This study raised a topic related to the classification by using eye diseases in humans. This study uses two optimizing options, namely SGD and Adagrad. The data used are 601 images consisting of 430 training images, 50 validation images, and 121 test images with a total of 4 classes. The method used in this study is the Convolutional Neural Network (CNN) method with the VGG-19 architecture, with input in the form of images that have gone through a preprocessing process, namely resizing and the CLAHE (Contrast Limited Adaptive Histogram Equalization) method of eye disease images. The test scenario consisted of 8 scenarios with different Optimizer and ClipLimit. The highest test results were obtained in the first scenario using the Adagrad optimizer and clipLimit of 1.0 with an accuracy value of 65.29%, precision of 66.53%, recall of 65.29%, and f1-score of 65. 40%.

Pengenalan Wajah Untuk Presensi Menggunakan Metode Naive Bayes Sanders, Carmel Edra; Alamsyah, Derry; Devella, Siska
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i3.13593

Automation of the attendance process has become a necessity nowadays to facilitate the process of recording and recapitulating precise attendance data compared to conservative (manual) attendance. This process is carried out through the recognition of biometric information, namely faces, using the Naive Bayes method with Gaussian distribution and pre-trained VGG16 feature extraction. In this study, the model developed based on this method uses the public CASIA WebFace dataset which has high variation and a private dataset which has low variation. The results show that the proposed method is able to work well on datasets with low variation, with accuracy results reaching 97% supported by feature dimension reduction using the PCA method.

Classification Of Ulos Fabric Motifs Using MobileNetV3-Small Architecture Sihombing, Mecha Bella Permata; Devella, Siska
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/d2sfn245

Ulos fabric is an important cultural heritage of the Batak people in North Sumatra, characterized by diverse motifs and philosophical meanings that support social and ritual life. Public knowledge of Ulos motifs is declining due to lifestyle changes and limited use of digital technology for cultural education, so accurate image-based motif classification is needed for preservation and wider utilization. This study evaluates the performance of the lightweight MobileNetV3-Small architecture with a transfer learning approach for classifying Ulos motif images, positioning it as one of the earliest uses of MobileNetV3-Small for Ulos motif classification compared to previous Ulos studies that relied on heavier CNN or earlier MobileNet variants. The dataset consists of 906 images split into 80% training, 10% validation, and 10% testing, and the model is trained using the Adam optimizer with a batch size of 32 and learning rates of 0.001 and 0.0001. On the test data, the model achieves accuracies of 98.96% and 97.92%, with consistently high precision, recall, and F1-scores, demonstrating the effectiveness of MobileNetV3-Small for Ulos motif classification as a digital educational medium to support Batak cultural heritage preservation.

Performance Analysis of MobileNetV2 and GhostNetV2 in Classifying Cervical Cancer Images in the SIPaKMeD Dataset Shela; Siska Devella
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/62samp73

Cervical cancer remains a significant global health burden, largely due to limited screening coverage and the reliance on manual cytological interpretation. The intrinsic complexity of cervical cell morphology and constraints in clinical resources necessitate automated classification systems that are both accurate and computationally efficient. This study aims to evaluate and compare the performance of two lightweight CNN architectures, MobileNetV2 and GhostNetV2, for cervical cell image classification using the SIPaKMeD dataset. The dataset comprises 4,049 cell images, which were preprocessed through normalization, augmentation, and partitioning into training, validation, and testing sets. Both models were implemented using transfer learning and trained under comparable hyperparameter settings with basic data augmentation. Model performance was assessed using confusion matrices and standard evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that MobileNetV2 achieved superior performance with an accuracy of 98.50%, outperforming GhostNetV2, which attained a maximum accuracy of 97.60%. The consistent performance across metrics indicates robust and balanced classification capability. These findings suggest that MobileNetV2 offers an optimal trade-off between accuracy and computational efficiency, making it a promising candidate for deployment in resource-constrained and edge-based cervical cancer screening systems. Nevertheless, further external validation and clinical evaluation are required prior to real-world implementation.

Co-Authors Ade Hendri Pandrean Ambarwati, Rizki Andhika Rizky Cahya Putra Cahyati, Imelia Dwinora Celvine Adi Putra Derry Alamsyah Farisi, Ahmad Firda Novia Rahmawati Gracivo Elsion Victory Hadisaputra, William Hafiz Irsyad Inayatullah, Inayatullah Jocelyn, Jennifer Julian Rusli Tee Baldi Juliana Nasution Kelvin Arianto M Ezar Al Rivan Marcella, Dewi Meiriyama, Meiriyama Molavi Arman Ningrum Larasati Novan Wijaya Nur Rachmat Nur Rachmat Oktavius, Kevin Pandrean, Ade Hendri Prabowo, Adrianus Prasthio, Rial Randie Sasongko RR. Ella Evrita Hestiandari Rusbandi Rusbandi Sanders, Carmel Edra Sandra Mareza Adelfi Santoti, Jennifer Velensia Saputra, Jordi Shela Sihombing, Mecha Bella Permata Valentino Ruben Ho Wijaya, Albert Kusuma William Hadisaputra Wiwik Handayani Yohannes, Yohannes

Title

Found 36 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 36 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 36 Documents
Search