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All Journal Teknovokasi : Jurnal Pengabdian Masyarakat JRIIN :Jurnal Riset Informatika dan Inovasi JURNAL TEKNIK INFORMATIKA DAN KOMPUTER
Verina Tita Nabila
Universitas Catur Insan Cendekia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Social Sciences

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Prediksi Risiko Penyakit Parkinson Menggunakan Seleksi Fitur Algoritma Genetika dan SMOTE-XGBoost Verina Tita Nabila; Rewan Jayadi
Jurnal Riset Informatika dan Inovasi Vol 3 No 11 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Parkinson’s disease is a progressive neurodegenerative disorder that affects the central nervous system and is characterized by reduced motor control and changes in voice quality due to impaired motor function. To date, the diagnosis of Parkinson’s disease largely depends on the expertise and clinical experience of specialists. The uneven distribution of clinicians across regions remains a major challenge in providing accurate diagnosis and appropriate treatment. Therefore, this study aims to develop a machine learning–based model for predicting the risk of Parkinson’s disease by incorporating feature selection using a genetic algorithm, handling data imbalance through the SMOTE approach, and performing prediction using the XGBoost method. The results indicate that the proposed method achieves excellent performance, with an accuracy of 95%, sensitivity of 93%, specificity of 100%, precision of 100%, an F1-score of 97%, and an AUC value of 97%. Several selected features include fundamental voice frequency, pitch stability, amplitude perturbation across voice cycles averaged over five periods, harmonic-to-noise ratio, and spectral spread measure 2.
IMPLEMENTASI SISTEM INFORMASI ABSENSI BERBASIS WEB YANG TERINTEGRASI DENGAN SISTEM PENGGAJIAN DI CV. LAKSANA JAYA CIREBON Muhammad Fakhrul Rodzi; Muhammad Luthfi Rizqullah; Verina Tita Nabila
TEKNOVOKASI : Jurnal Pengabdian Masyarakat Volume 4 Issue 3: September 2026
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/5375jj91

Abstract

CV Laksana Jaya Cirebon masih menghadapi permasalahan dalam mengelola data absensi dan penggajian karyawan karena proses absensi dilakukan secara manual melalui pengiriman foto ke grup WhatsApp, sedangkan perhitungan gaji masih menggunakan Microsoft Excel. Kondisi tersebut menyebabkan proses administrasi membutuhkan waktu lebih lama, rentan terhadap kesalahan pencatatan dan perhitungan gaji, serta belum adanya integrasi antara data absensi dengan sistem penggajian. Kegiatan pengabdian kepada masyarakat ini bertujuan mengimplementasikan sistem informasi absensi berbasis web yang terintegrasi dengan sistem penggajian sebagai solusi untuk meningkatkan efektivitas pengelolaan administrasi karyawan di CV Laksana Jaya Cirebon. Pelaksanaan kegiatan dilakukan melalui lima tahapan, yaitu identifikasi kebutuhan mitra, perancangan dan pengembangan sistem menggunakan framework Laravel dan database MySQL, implementasi sistem pada lingkungan kerja mitra, pengujian dan pendampingan penggunaan sistem, serta evaluasi dan penyempurnaan berdasarkan masukan dari pihak mitra. Solusi yang dihasilkan berupa aplikasi berbasis web yang mampu mengintegrasikan proses absensi, pengelolaan data karyawan, pengajuan cuti, perhitungan gaji, serta pembuatan laporan dan slip gaji dalam satu sistem. Hasil pengujian fungsi menggunakan metode Black Box menunjukkan bahwa 100% fitur utama meliputi pengelolaan data karyawan, pencatatan absensi, pengajuan cuti, hingga kalkulasi gaji berjalan valid dan sesuai dengan kebutuhan operasional mitra. Implementasi sistem ini membantu mempercepat proses administrasi, mengurangi kesalahan pencatatan dan perhitungan gaji, serta meningkatkan efisiensi dan akurasi pengelolaan data karyawan.
IMPLEMENTASI SUPPORT VECTOR REGRESSION DAN SUPPORT VECTOR CLASSIFICATION UNTUK PREDIKSI MASA TUNGGU KERJA DAN KLASIFIKASI KESESUAIAN BIDANG KERJA ALUMNI Raden Muhamad Rama Poetra Ardiningrat; Lena Magdalena; Verina Tita Nabila
Jurnal Teknik Informatika dan Komputer Vol. 5 No. 2 (2026): Jurnal Teknik Informatika dan Komputer
Publisher : Universitas Muhammadiyah Prof. DR. HAMKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/jutikom.v5i2.24699

Abstract

Catur Insan Cendekia University (UCIC) regularly conducts tracer studies to evaluate graduate outcomes. However, UCIC has not yet implemented an integrated tracer study information system, and alumni data are still managed manually using Microsoft Excel. Consequently, tracer study data, particularly employment waiting period and job field suitability, contain a high proportion of missing values, preventing the university from obtaining a comprehensive graduate profile. This study aims to develop a web-based Tracer Study Information System capable of predicting alumni employment waiting periods, classifying job field suitability for incomplete tracer study records, and analyzing annual trends. The proposed system implements Support Vector Regression (SVR), Support Vector Classification (SVC), and Linear Regression. The dataset consists of 314 tracer study records from the Information Systems Study Program, covering graduation years 2016–2026, and is divided into 270 training records, 28 testing records, and 16 prediction records using a temporal split approach. The SVR model achieved an MAE of 0.8318 months, RMSE of 1.0478 months, and R² of 0.5798, while the SVC model achieved an accuracy of 78.57%, precision of 81.82%, recall of 69.23%, and F1-Score of 75.00%. Linear Regression identified a decreasing trend in both the average employment waiting period (0.2765 month per year) and job field suitability (3.2877% per year). The prediction results were further converted into the Average Graduate Waiting Period (RMT) and Job Field Suitability Percentage (PBS) indicators based on the LAM-INFOKOM 1.0 accreditation instrument. In addition, Black Box Testing confirmed that all system features functioned as expected.
Co-Authors Lena Magdalena Muhammad Fakhrul Rodzi Muhammad Luthfi Rizqullah Raden Muhamad Rama Poetra Ardiningrat Rewan Jayadi