Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Syntax Literate: Jurnal Ilmiah Indonesia SAINTEK
M. Syaibani Anwar
Universitas Pelita Bangsa
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perancangan Sistem Informasi Kependudukan Menggunakan Metode Waterfall Pada Desa Jatibaru Berbasis Web Ivan Reynata; M. Syaibani Anwar; Edora Edora
Prosiding Sains dan Teknologi Vol. 1 No. 1 (2022): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 1 - Juli 2022
Publisher : DPPM Universitas Pelita Bangsa

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

Abstract

In daily operational activities, the Jatibaru Village government performs several ways of archiving files, namely by conventional archiving for document files and using Microsoft Word for making correspondence. Meanwhile, the process of managing these files is done by manual writing on the agenda book, both physical document files and files in the form of files. In addition, if the village archive officer is not present, the letter request process is disrupted because the archive data is on a computer that can only be opened by the archive officer. This study uses the waterfall method to design a population data information system and request letters to web-based villages using PHP and MySQL. With the development of this system, the village office can record data on its villagers, especially Jatibaru Village, so that with accurate citizen data in the database it can be used for further information system development. Requests for letters to villages such as Certificates of Incapacity, Domicile Letters, Birth Certificates and others can be requested online by residents who have been registered at the village office, thereby speeding up the service process and facilitating the performance of village archive officers in serving the community, especially requests for letters from Village residents. Jatibaru, so that services at the Village Office can be maximized and fast. Keywords: Population data, Documents, Mail request system, Waterfall, PHP Mysql
Perbandingan Metode Machine Learning Klasik (Naive Bayes dan SVM) dan IndoBERT untuk Analisis Sentimen Ulasan Produk pada Platform Tokopedia Endah Kurnia Fitri; Abdul Halim Anshor; M. Syaibani Anwar
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v11i6.64746

Abstract

Penelitian ini dilatarbelakangi oleh meningkatnya jumlah ulasan produk pada platform e-commerce yang menghasilkan data teks dalam jumlah besar dan tidak terstruktur sehingga memerlukan metode analisis sentimen yang efektif. Tujuan penelitian ini adalah untuk membandingkan kinerja metode machine learning klasik (Naive Bayes dan Support Vector Machine) dengan model deep learning IndoBERT dalam klasifikasi sentimen ulasan produk Tokopedia. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksperimen komparatif. Data yang digunakan merupakan dataset sekunder dari Kaggle yang terdiri dari ulasan produk Tokopedia. Proses analisis dilakukan melalui tahap preprocessing, ekstraksi fitur TF-IDF untuk model klasik, serta fine-tuning IndoBERT untuk model berbasis transformer. Evaluasi model menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa IndoBERT memberikan performa terbaik dengan accuracy 97,80% dan F1-score 97,66%, diikuti oleh SVM dan Naive Bayes. Meskipun perbedaan akurasi relatif kecil, IndoBERT menunjukkan kemampuan lebih baik dalam mengenali kelas sentimen minoritas seperti negatif dan netral. Kesimpulannya, model berbasis transformer lebih unggul dalam menangkap konteks bahasa dibandingkan metode klasik, terutama pada dataset yang tidak seimbang.
Co-Authors Abdul Halim Anshor Edora Edora Endah Kurnia Fitri Ivan Reynata