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All Journal Jurnal Teknologi Dan Sistem Informasi Bisnis Abdimasku : Jurnal Pengabdian Masyarakat
Usman Sudibyo
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Pelatihan Penggunaan Aplikasi Screen Reader JAWS Bagi Tunanetra Untuk Meningkatkan Kemampuan Dalam Pengelolaan Administrasi Cinantya Paramita; Usman Sudibyo; Muljono Muljono; Catur Supriyanto
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.68 KB) | DOI: 10.33633/ja.v2i2.46

Abstract

Salah satu permasalahan yang dihadapi Perkumpulan Penyandang Disabilitas Indonesia (PPDI), Dewan Pengurus Cabang (DPC) Kota Semarang yakni terbatasnya media dan prasarana untuk mendukung kegiatan belajar mereka dalam mengoperasikan komputer serta tingkat perekonomian yang hanya cukup untuk memenuhi kebutuhan hidup, meraka pun belum sepenuhnya paham dalam perkembangan teknologi hingga sampai saat ini semua masih diolah dalam bentuk manual yakni melaporkan dengan lisan dan dengan cara mengingat. Untuk mengatasi permasalahan tersebut, pengabdian ini mengusulkan untuk mengadakan pelatihan penggunaan Job Access with Speech (JAWS) bagi para tuna netra. Pengabdian dilakukan dengan mengajarkan penggunaan dasar keyboard yang didukung oleh aplikasi JAWS dan pengetahuan dasar Microsoft Excel.
Optimasi Algoritma Random Forest menggunakan Principal Component Analysis untuk Deteksi Malware Fauzi Adi Rafrastaraa; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Etika Kartikadarma; Usman Sudibyo
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 3 (2023): July 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i3.854

Abstract

Malware is a type of software designed to harm various devices. As malware evolves and diversifies, traditional signature-based detection methods have become less effective against advanced types such as polymorphic, metamorphic, and oligomorphic malware. To address this challenge, machine learning-based malware detection has emerged as a promising solution. In this study, we evaluated the performance of several machine learning algorithms in detecting malware and applied Principal Component Analysis (PCA) to the best-performing algorithm to reduce the number of features and improve performance. Our results showed that the Random Forest algorithm outperformed Adaboost, Neural Network, Support Vector Machine, and k-Nearest Neighbor algorithms with an accuracy and recall rate of 98.3%. By applying PCA, we were able to further improve the performance of Random Forest to 98.7% for both accuracy and recall while reducing the number of features from 1084 to 32.
Co-Authors Catur Supriyanto Cinantya Paramita Dwi Puji Prabowo Etika Kartikadarma Fauzi Adi Rafrastara Muljono Muljono Ricardus Anggi Pramunendar