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All Journal Media Jurnal Informatika Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Irma Amelia Dewi
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences Other

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Sistem Pengenalan Aksara Sunda Menggunakan Metode Modified Direction Feature dan Learning Vector Quantization rizki rahmat riansyah; Youllia Indrawaty Nurhasanah; Irma Amelia Dewi
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 1 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i1.651

Abstract

Sundanese script is one revised regional Indonesia which is the work of the sundanese have orthographic peculiarities in terms of how the writing system by using a non-latin character as well as in terms of the unique pronunciation, therefore his presence should be conserved. One of its preservation efforts is to build an sundanese script recognition systems, which in the process is to identify a pattern of characters can make use of a technique of feature extraction and classification of characteristics, one of which was modified direction featured (MDF) which is the hallmark of the extraction method based on the shape of the patterns on the image, whereas the methods used for the process of classification of characteristics of an image is learning vector quntization (LVQ). This system will accept input in the image of sundanese script and image patterns revised its characteristics will be taken and entered into the database that will be used as training data, then done in the result of the extraction of characteristics are grouped into classes to the nearest value identified on a class derived from the test image, its function is to support the introduction of this aksara can be combined with text to speech. Text to speech system (TTS) is a system that can turn text into speech, so that the output can be showing the introduction of aksara sunda examples of their pronunciation. The level of accuracy of the test results of the 300 samples data of aksara sunda verified correctly between the suitability of image characters with names and their pronunciation is of 78.67%.
Peningkatan Akses Informasi Jemaat Melalui Digitalisasi Website GMI Bahtera Bandung Timur Hasibuan, Beril Berekhya Mutia; Irma Amelia Dewi
Darma Abdi Karya Vol. 4 No. 2 (2025): Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM POLITEKNIK LP3I

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/darmaabdikarya.v4i2.2768

Abstract

Penyebaran informasi di GMI Bahtera Bandung Timur selama ini masih mengandalkan metode konvensional seperti warta jemaat cetak dan pengumuman lisan, yang memiliki keterbatasan jangkauan dan biaya operasional tinggi. Tujuan dari kegiatan pengabdian masyarakat ini adalah merancang bangun website informasi gereja sebagai solusi digitalisasi untuk mempermudah akses jadwal ibadah dan warta jemaat secara real-time. Metode pelaksanaan kegiatan meliputi tahapan observasi kebutuhan mitra, perancangan sistem (prototyping), implementasi, dan evaluasi kepuasan pengguna. Hasil dari kegiatan ini adalah tersedianya website "GMI Bahtera Bandung Timur" yang dapat diakses secara publik. Berdasarkan evaluasi terhadap 45 responden jemaat, website ini memperoleh tingkat penerimaan yang sangat tinggi dengan skor rata-rata kemudahan penggunaan (usability) sebesar 4,6 dan kebermanfaatan sebesar 4,5 dari skala 5. Digitalisasi ini terbukti efektif meningkatkan efisiensi penyebaran informasi dan memperluas jangkauan pelayanan gereja kepada jemaat maupun masyarakat umum.
Parkinson's Disease Classification Using Vocal Biomarkers, XGBoost, and SHAP Wafiq Mariatul Azizah; Irma Amelia Dewi
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6458

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting more than 11.77 million people worldwide. Voice signal analysis has gained attention as a non-invasive screening approach because nearly 90% of PD patients experience measurable speech impairments. However, previous machine learning studies on PD voice datasets commonly face several limitations, including class imbalance that may lead to data leakage, the use of accuracy as the primary evaluation metric, and limited utilization of model interpretability methods. This study proposes a PD classification pipeline integrating SMOTE, XGBoost, and SHAP using the UCI Parkinson dataset, which consists of 195 samples and 22 acoustic features. A quantitative experimental approach was employed using 5-fold stratified cross-validation, where SMOTE was applied only to the training data within each fold to prevent data leakage, while SHAP was used for feature analysis and feature reduction experiments. The results showed that SMOTE improved the F1-Score from 0.9400 to 0.9527 and the Accuracy from 0.9077 to 0.9282. The final model achieved a mean AUC-ROC of 0.9614 and a Recall of 0.9592 across five folds. SHAP analysis showed differences between SHAP feature rankings and XGBoost built-in importance, with MDVP:Shimmer exhibiting the largest ranking change. In addition, the top-8 SHAP-ranked features achieved performance comparable to the full 22-feature model, obtaining an Accuracy of 0.9282 and an AUC of 0.9612. These findings indicate that the proper application of SMOTE and SHAP-based feature selection can improve model evaluation and provide additional information for feature analysis in Parkinson's disease classification.
Co-Authors Hasibuan, Beril Berekhya Mutia Nurhasanah, Youllia Indrawaty rizki rahmat riansyah Wafiq Mariatul Azizah