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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Electronics, Electromedical Engineering, and Medical Informatics Scientific Journal of Informatics
Sigit Priyanta
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Aplikasi Mobile WEB Map Service Pada Mobile Device Dengan SVGT Sigit Priyanta; Ghulam Imanuddin; Suci Karunia Prilistya
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 5, No 1 (2011): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2001

Abstract

Abstract— Teknologi informasi geografi berkembang sangat pesat dalam beberapa dekade terakhir ini. Sekarang ini, WMS (Web Map Services) tidak hanya dapat menghasilkan gambar raster tetapi juga gambar vektor. Contohnya adalah SVGT (Scalable Vector Graphics Tiny) yang merupakan bagian dari SVG (Scalable Vector Graphics) yang digunakan pada piranti mobile device. Mahalnya biaya komunikasi antara handheld device dengan jaringan internet melalui GPRS (Global Pocket Radio System) menimbulkan sebuah masalah dalam penerapan aplikasi mobile mapping. Untuk mengeliminasi masalah tersebut, harus dibuat sebuah aplikasi mobile mapping yang dapat mentransfer data sekecil mungkin. Didalam penelitian ini, dicoba untuk dibuat sebuah server pemetaan yang memanfaatkan teknologi SVGT dan XML. Kedua format data tersebut digunakan untuk menghasilkan data sekecil mungkin agar dapat menghemat biaya komunikasi antara klien dan server. Pada akhirnya, aplikasi yang dibangun akan menjadi sebuah server yang memiliki klien berupa handheld / mobile device.Aplikasi ini dibangun dengan bahasa pemrograman PHP dan memanfaatkan database PostgreSQL beserta ekstensi PostGIS-nya pada sisi server dan J2ME (Java 2 Micro Edition) pada sisi mobile client. Aplikasi yang telah dibangun mampu untuk menampung peta jalan dari sebuah daerah, menampilkannya dalam format SVGT kepada klien, serta mencari rute terpendek antara dua buah jalan dengan menggunakan algoritma Floyd-Warshall. Mobile device sebagai client mempunyai beberapa fungsi utama, yaitu download map dengan format SVGT dari server, menyimpan map ke dalam record store, menampilkan map dan juga melakukan fungsi pan dan zoom terhadap map. Fungsi lainnya adalah searching atau mencari  titik tertentu pada map sesuai dengan permintaan user, request path ke server untuk mendapatkan jalur terpendek antara dua titik, dan set mark atau menyimpan titik-titik pada peta yang telah diberi tanda oleh user.Keywords— SVG, SVGT, Mobile Mapping, Web Map Services, J2ME, Java 2 Micro Editon.
Multi-Stage CNN: U-Net and Xcep-Dense of Glaucoma Detection in Retinal Images Desiani, Anita; Priyanta, Sigit; Ramayanti, Indri; Suprihatin, Bambang; Rio Halim, Muhammat; Geovani, Dite; Rayani, Ira
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.314

Abstract

Glaucoma is a chronic neurological disease in the human eye where there is damage to the nerves which causes vision loss to blindness. Glaucoma can be detected by classifying retinal images. Several previous studies that classified glaucoma did not perform segmentation beforehand. Segmentation is needed to extract the features of the optic disc and optic cup from retinal images that are used to detect glaucoma. This study proposes two stages in the detection of glaucoma, namely the segmentation and classification stages. Segmentation is carried out using the U-Net architecture. Classification is done using a new architecture, namely Xcep-Dense. The Xcep-Dense architecture is a new architecture which is the result of a combination of the Xception and DenseNet architectures. At the segmentation stage, accuracy, recall, precision, and F1-score values are obtained above 90%. The Cohen’s kappa value has a value above 85% and loss below 20%. At the classification stage, accuracy and specification values were obtained above 85%, sensitivity and F1-score above 80%, and Cohen’s kappa above 70%. The predicted image obtained at the segmentation stage has a very similar appearance to the ground truth. Based on the results of the performance evaluation obtained, it shows that the method proposed in this study is feasible in detecting glaucoma.Glaucoma,
Optimizing Early Breast Cancer Classification Using Hybrid SVM-ANN with Ridge Embedded Feature Selection Priyanta, Sigit; Selvyana, Dita Ria; Salsabila, Aulia
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.36676

Abstract

Purpose: This study aims to enhance early breast cancer detection by systematically evaluating multiple machine learning (ML) algorithms and feature selection strategies. The goal is to identify the most effective combination of classifiers and feature selection methods for accurately distinguishing malignant from benign breast tumors, thereby improving diagnostic reliability and clinical decision support. Method: The Wisconsin Breast Cancer Dataset containing 699 samples described by nine diagnostic features was used. Tumor classes were encoded as 0 (malignant) and 1 (benign). The analysis was conducted in two stages. First, five ML algorithms—K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and a hybrid SVM–ANN—were evaluated to establish baseline performance. Second, two feature selection approaches (wrapper and embedded) were applied to four ML models and the optimized hybrid classifier. The embedded approach employed Ridge-based feature selection to identify the most discriminative attributes and improve model generalization. Results: The hybrid SVM–ANN combined with Ridge Embedded feature selection achieved the best performance, with an accuracy of 97.86%, precision of 96.5%, recall of 96.5%, and an F1-score of 96%. This configuration outperformed all other algorithms and feature selection techniques, affirming the effectiveness of hybrid integration and embedded feature optimization. Novelty: The novelty lies in the integration of an SVM–ANN hybrid model with Ridge-based embedded feature selection for breast cancer classification. Unlike prior works that rely primarily on conventional filter or wrapper techniques, this approach demonstrates superior accuracy and robustness. The proposed framework provides a promising pathway for developing more reliable ML-based diagnostic tools in oncology.
Co-Authors Anita Desiani Bambang Suprihatin Geovani, Dite Ghulam Imanuddin Ramayanti, Indri Rayani, Ira Rio Halim, Muhammat Salsabila, Aulia Selvyana, Dita Ria Suci Karunia Prilistya