Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal JUTIM (Jurnal Teknik Informatika Musirawas) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA
Joni Karman
Universitas Bina Insan, Lubuklinggau
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
PERANCANGAN SISTEM INFORMASI GEOGRAFIS (SIG) LOKASI PEMETAAN RUMAH PENERIMA PROGRAM KELUARGA HARAPAN (PKH) BERBASIS WEB MOBILE MENGGUNAKAN LEAFLET DI KOTA LUBUKLINGGAU A. Taqwa Martadinata; Joni Karman; Akbar Prigana
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 7 No 1 (2022): JUTIM (Jurnal Teknik Informatika Musirawas) JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v7i1.1635

Abstract

Permasalahan dalam penilitian ini adalah bagaimana memberikan kemudahan pemerintah dalam rangka mengetahui lokasi-lokasi penduduk miskin pada tiap daerah sehingga nantinya dapat menyalurkan bantuan kepada masyarakat agar lebih efektit dan efisien. Belum adanya sistem yang terstruktur dengan menggunakan computer, sehingga nantinya diharapkan dengan adanya sistem informasi geografis ini dapat membantu pengguna dalam pengelolaan lokasi pemetaan masyarakat miskin dengan lebih efektif dan efisien. Selain itu, metode penelitian ini adalah deskriptif dengan menggunakan metode waterfall yang terdiri dari beberapa tahap: analisis, perancangan, pengkodean dan pengujian. Selanjutnya, dalam mengumpulkan data, peneliti menggunakan data primer dan data sekunder. Data primer meliputi observasi, wawancara dan dokumentasi. Di sisi lain, data sekunder termasuk referensi, jurnal, artikel dan dokumentasi. Nantinya sistem informasi geografis (sig) lokasi pemetaan rumah penerima Program Keluarga Harapan (PKH) berbasis web mobile menggunakan Leaflet ini dapat diakses dengan menggunakan smartphone sehingga memberikan kemudahan dalam proses pelayanan serta penyampaian informasi.
Deteksi Berita Hoax Berbahasa Indonesia Menggunakan Multilingual-BERT pada Media Berita Online Syalma Tarissa; Muhamad Akbar; A. Taqwa Martadinata; Joni Karman
TIN: Terapan Informatika Nusantara Vol 6 No 11 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i11.9351

Abstract

The development of infoormation technology is increasing, which can trigger the spread of hoaxes in online newss media, resulting in misinformation for the public. Manual detection of hoaxes is difficult due to the large volume of online news and variety of language styles and mixed language usage. Advances in artificial intelligence technology, particularly Natural Language Processing (NLP), open up significant opportunities for automatically detecting hoax news. Transformer-based NLP models, such as Multilingual-BERT (mBERT), are emerging. Transformer-based models such as Multilngual-BERT (mBERT) are capable of understanding text context in both directions and support various languages, including Indonesian. Therefore, this study aims to apply and test the effectiveness of mBERT in detecting hoax news in online news media. The dataset used comes from TurnbackHoax on the Kaggle website for hoax news, Tempo and CNN Indonesia for non-hoax news datasets, and additional data from the Kaggle website on news coverage in 2025. The data was processed using the built-in mBERT Tokenizer with data undersampling techniques, resulting in a model with an accuracy of 0.97, a precision value of 0.96, a recall of 0.97, and F1-score of 0.97,which shows that the mBERT approach is able to provide high classification performance on the Indonesian language hoax news dataset.
Model Hybrid dalam Penentuan Stok Barang Bangunan Melalui Pendekatan Machine Learning Intan Bintang Adinda; Davit Irawan; Joni Karman; Ahmad Sobri
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i1.8065

Abstract

This study aims to develop a machine learning-based construction material stock prediction model using a hybrid approach that combines K-Means Clustering as a sales pattern grouping method and Support Vector Machine (SVM) as a classification method to predict material sales levels. This research was motivated by the problem of stock management at Toko Usaha Jaya in Lubuklinggau City, which is still done manually, thus potentially causing excess stock that increases storage costs and stock shortages that can lead to lost sales opportunities and decreased customer satisfaction. The data used includes material names, initial stock quantities, quantities sold, remaining stock, and selling prices collected during the period from January to December 2023. The results show that the hybrid model is capable of grouping materials into three categories, namely very popular, fairly popular, and less popular, with a Silhouette Score of 0.42, indicating fairly good clustering quality. Furthermore, the SVM model produced a classification accuracy rate of 99%, reflecting an increase in stock prediction accuracy compared to manual management methods. These findings indicate that the application of the K-Means and SVM hybrid model can improve inventory management efficiency and support more accurate and effective data-driven decision making.
Co-Authors A. Taqwa Martadinata Ahmad Sobri Akbar Prigana Davit Irawan Intan Bintang Adinda Martadinata, A. Taqwa Muhamad Akbar Syalma Tarissa