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All Journal Brilliance: Research of Artificial Intelligence Informatics and Enginering Dedication
Sasongko, Randie
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Pelatihan Pembuatan Website Portofolio Sederhana Putra Darmansius, Albertus Dwi Andhika; Hartati, Ery; Candra, Candra; Chandra, Kelvin William; nicholas, nicholas; Sasongko, Randie
FORDICATE Vol 3 No 1 (2023): November 2023
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v3i1.5069

Abstract

Saat ini rata-rata siswa/i banyak yang hanya dapat menggunakan aplikasi ataupun website saja tanpa mengetahui proses pengerjaannya, maka dari itu penulis mengharapkan agar siswa/i tersebut dapat sedikit memahami tentang proses pembuatan website sehingga dapat menumbuhkan rasa ingin tahu mereka untuk membuat website. Dari hal tersebut, penulis memutuskan untuk mengedukasi para siswa/i dengan memberikan materi berupa HTML dan CSS disekolah. Dari peserta pelatihan, sekitar 80% dapat menjalankan materi dengan baik, sedangkan 20% mengalami kendala dalam mengerjakannya. Pelatihan ini bertujuan untuk mengenalkan HTML dan CSS kepada siswa/siswi agar mereka tertarik dan memiliki keinginan untuk terjun di dunia programmer. Dalam pelatihan ini, penulis memberikan solusi dan bantuan kepada siswa/siswi yang mengalami kesulitan dalam mengerjakan materi.
Software to Predict Computer Component Failures using Multinomial Naïve Bayes Based On User Information Sasongko, Randie; Devella, Siska
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7194

Abstract

Based on data released by the International Data Corporation (IDC), the global market for personal computing devices experienced a year-over-year growth of 7.3% in the second quarter of 2024. This marks the second consecutive quarter of positive growth after nine quarters of decline. The trend suggests an increase in computer usage intensity, which is directly associated with a higher likelihood of hardware failures, particularly in components such as hard drives, RAM, and motherboards. This research aims to create a mobile-based application that can automatically classify types of computer component damage based on user-reported issues. The approach utilizes machine learning, specifically the Multinomial Naïve Bayes algorithm, combined with the Term Frequency–Inverse Document Frequency (TF-IDF) method for text feature extraction. The training data consists of categorized complaint texts according to the type of hardware problem. The resulting model was integrated into a mobile application to enable automated damage prediction. Experimental findings indicate that the proposed model performs effectively, achieving an accuracy rate of 78% in identifying computer damage categories. In summary, the developed application can help both technicians and general users diagnose potential hardware problems more accurately and efficiently. This not only speeds up the troubleshooting process but also reduces diagnostic errors and unnecessary part replacements. Moreover, the integration of Natural Language Processing (NLP) and machine learning enables the system to continuously improve its accuracy and adaptability as it learns from new data over time.
Co-Authors Candra candra Chandra, Kelvin William Devella, Siska Hartati, Ery Nicholas, Nicholas Putra Darmansius, Albertus Dwi Andhika