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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi Sistem Informasi dan Aplikasi JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Malcom: Indonesian Journal of Machine Learning and Computer Science Science and Technology: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science
Jazman, Muhammad
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Social Sciences Other

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Analisis Penerimaan Pengguna E-Wallet DANA Menggunakan Metode TAM dan Delone Mclean Sari, Gusmelia Puspita; Salisah, Febi Nur; Rozanda, Nesdi Evrilyan; Afdal, M; Jazman, Muhammad; Marsal, Arif
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5334

Abstract

The industrial revolution 4.0 motivates advances in information technology through the idea of ​​the internet of things (IoT). One form of implementing the internet of things is the use of e-wallets as a payment medium. One e-wallet that is popular among users is the DANA application. The DANA application helps users make non-cash or cardless payments, making transactions easier and more practical. Despite the advantages offered, there are several problems in its implementation, such as delays when making transfers, not being able to top up and losing balance. Therefore, using the TAM and Delone Mclean approach, this research aims to analyze user acceptance of the DANA application as an effort to see what factors make the DANA application able to be accepted and used by many users. This research was conducted on DANA application users who live in Pekanbaru City with a sample size of 100 respondents. The research uses quantitative methods by distributing questionnaires online. The data that has been collected is processed first using Microsoft Excel, then continued using SmartPLS 4 to analyze PLS-SEM. From hypothesis testing, the results obtained were that seven hypotheses were accepted and declared positive and significant, while one hypothesis was rejected because it did not show a significant relationship.
Integrating Support Vector Machines and Geospatial Analysis for Enhanced Tuberculosis Case Detection and Spatial Mapping Jannah, Miftahul; Jazman, Muhammad; Afdal, M; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7158

Abstract

Tuberculosis (TB) remains a significant global health problem, with Indonesia ranking third in the world in terms of TB burden. Riau Province recorded 13,007 notified TB cases in 2022 with a Case Notification Rate (CNR) of 138 per 100,000 population, still far from the national target. This study aims to develop a TB case classification system using Support Vector Machine (SVM) integrated with geospatial analysis to identify TB positive cases from screening data and visualize their spatial distribution in Riau Province. The research data was sourced from the Tuberculosis Information System (SITB) of the Riau Provincial Health Office for the period January-December 2024, covering 350 samples with demographic information, clinical symptoms, and patient risk factors. The research process includes data collection, preprocessing with Min-Max and Z-Score methods, feature extraction, modeling with SVM using various kernels (RBF, Linear, Polynomial, and Sigmoid), and geospatial visualization using Google Earth Engine (GEE). The results showed that the SVM model with Linear kernel achieved the highest accuracy of 80%, sensitivity of 100%, and specificity of 80% in detecting TB cases. Geospatial analysis successfully identified clusters of TB cases in several districts in Riau Province, with Pekanbaru City (112 cases) and Rokan Hulu (89 cases) as the main hotspots. The integration of machine learning and geospatial analysis proved effective in improving TB detection and providing a comprehensive understanding of disease spread patterns in Riau Province.
Co-Authors Adam, A Ahsyar, T. Khairil Ahsyar, Tengku Khairil Am, Andri Nofiar Andaranti, Arifah Fadhila Angraini Angraini Anisa Nirmala, Fitri Anmi, Fauzi Hidayatul Anofrizen Anofrizen Aprinastya, Rachell Aqilla, Nabila Fadia Arif Marsal Ariyansir, Sigit Butar Butar, Febiola Siska Dermawan, Tri Dharmawan, Muhammad Rizky Eki Saputra Fahmi Rifai Fandi, Ridho Arif Febi Nur Salisah Fenty Kurnia Oktorina Fina Nasari Fitri Hamdi, Fathan Surya Hamzah, Muhammad Luthfi Herliza, Herliza Inggih Permana Khairil Ahsyar, Tengku Kusuma, Gathot Hanyokro M. Afdal MAULANA, BIMA Maulana, Rizki Azli Megawati - Miftahul Jannah Mona Fronita, Mona Muhammad Naufal, Muhammad Muhammad Ridwan Muttakin, Fitriani Novita Sari Nurhadi, Muhammad Permata, Nur Fitria Mita Prananda, Yaldri Oktra Praniffa, Anisya Caty Pribadi, Antoni Putri, Runi Aulia Rahman, Eman Rahmawita, Medyantiwi Raihan, Muhammad Renaldi, Rengga Rianda, Rahmad Didho Rice Novita Rika Aryati Rozanda, Nesdi Evrilyan Saputra , Eki Sari, Gusmelia Puspita Setiawati, Elsa Siregar, Syafril Syaifullah Syaifullah Syaifullah Yulianti, Nelvi Zarnelly Zarnelly