Article Per Year (5 Year)
p-Index From 2021 - 2026
1.034
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Jurnal Dedikasi Jurnal Teknik Industri TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JURNAL NASIONAL TEKNIK ELEKTRO Cyberspace: Jurnal Pendidikan Teknologi Informasi Jurnal Tekno Kompak Jurnal Manajemen Informatika dan Sistem Informasi
Irfan, Muhammad
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
PERANCANGAN PROTOTYPE INTERFACE SISTEM INFORMASI KEBERADAAN DOSEN Evi Dwi Wahyuni; Muhammad Irfan
Jurnal Tekno Kompak Vol 14, No 1 (2020): Februari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v14i1.461

Abstract

Teknik Informatika merupakan salah satu program studi yang ada di Universitas Muhammadiyah Malang. Program studi Teknik Informatika memiliki bagian civitas akademika yaitu staff administrasi. Masalah yang sering dihadapi staff administrasi jurusan Teknik Informatika adalah menjawab pertanyaan mahasiswa terkait keberadaan dosen. Hal seperti itu dapat mengganggu kinerja dan fokus kerja staff administrasi. Untuk mengatasi masalah tersebut, staff administrasi jurusan Teknik Informatika memerlukan sebuah sistem informasi keberadaan dosen. Sebelum membangun sebuah sistem informasi dibutuhkan sebuah perancangan prototype interface web. Penelitian ini menggunakan perancangan User Experience Design Process untuk meningkatkan kepuasan pengguna dalam berinteraksi dengan produk dan pada tahap akhir dilalukan evaluasi Usability Testing untuk uji kebergunaan produk. Hasil dari penelitian ini berupa rancangan prototype interface sistem informasi keberadaan dosen dan hasil dari evaluasi pengolahan data kuesioner terdapat beberapa pertanyaan yang memiliki nilai skala rendah, oleh karena itu diberikan rekomendasi perbaikan interface dari prototype tersebut untuk pengembangan selanjutnya. Secara umum perbaikan interface terletak pada bagian informasi status keberadaan dosen, simbol icon pada interface web dan desain warna web pada prototype interface. Saran rekomendasi perbaikan dapat digunakan dalam pengembangan lebih lanjut prototype interface ke tahap implementasi coding.
A comparative analysis of transfer learning models on suicide and non-suicide textual data Merinda Lestandy; Abdurrahim Abdurrahim; Amrul Faruq; Muhammad Irfan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.25654

Abstract

The rise of social media has allowed individuals to express themselves freely, increasing the visibility of mental health concerns, including suicidal tendencies. This issue is particularly significant, as suicide is one of the leading causes of death globally. The objective of this study is to develop a model capable of accurately detecting suicide-related textual data using advanced natural language processing techniques. To achieve this, we applied transfer learning models, including bidirectional encoder representations from transformers (BERT), robustly optimized bidirectional encoder representations from transformers (RoBERT), a lite BERT (ALBERT), and decoding-enhanced BERT with disentangled attention (DeBERTa). the dataset used in this research includes 232,074 posts from Reddit, categorized into suicide and non-suicide labels. Preprocessing steps such as removing HTML tags, special characters, and punctuation were applied, followed by stopword removal and lemmatization. The models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. Among the models tested, DeBERTa demonstrated superior performance, achieving an accuracy of 98.70% and an F1-score of 98.70%. These findings suggest that transfer learning models, particularly DeBERTa, are effective in identifying suicidal ideation in textual data.
Co-Authors Abdurrahim Abdurrahim Afirda Desember Riawati Agus Eko Minarno Amir Syarifuddin Amrul Faruq Bambang Yudi Ariadi Basuki, Setio Evi Dwi Wahyuni Ilham Pakaya Khaeruddin Khaeruddin Lailis Syafa’ah Machmud Effendy Merinda Lestandy Mochammad Arif Hidayatullah Nur Alif Sumadi, Fauzi Dwi Setiawan Suwignyo . Syafa'ah, Lailis Syafaah, Lailis Yufis Azhar