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All Journal Teika Infotech Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) IJIS - Indonesian Journal On Information System Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Ilmiah Media Sisfo JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Mandala Pengabdian Masyarakat Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Bulletin of Information Technology (BIT) Batara Wisnu : Indonesian Journal of Community Services PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Buletin Ilmiah Informatika Teknologi JAMU : Jurnal Abdi Masyarakat UMUS Innovative: Journal Of Social Science Research Jurnal Ilmiah Sistem Informasi Software Development Digital Business Intelligence and Computer Engineering Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Pustabiblia: Journal of Library and Information Science
Mochammad Darip
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COMPARISON OF THE EFFECTIVENESS OF WHATSAPP GROUPS AND WEBSITES AS INFORMATION MEDIA FOR TAM-BASED PPDB Nurul Chafid; Abdul Halim; Mochammad Darip
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.382

Abstract

The development of digital technology has encouraged schools to utilize various platforms to deliver information, particularly in the new student admission process, commonly referred to as PPDB. MIN 7 Tangerang uses WhatsApp Groups and the school website as the primary media for disseminating PPDB information; however, the effectiveness of these two media has not been determined. This study aims to compare the effectiveness of WhatsApp Groups and the website as media for PPDB information using the Technology Acceptance Model (TAM) approach. The study employed a quantitative method supported by qualitative data. Data were collected from 136 parents of admitted students who passed the PPDB selection for the 2024/2025 academic year, all of whom had accessed and used both media. The research instrument consisted of a Likert-scale questionnaire based on TAM constructs along with additional variables. Cronbach’s Alpha was used to test the reliability of the instrument, while validity testing was conducted using Exploratory Factor Analysis (EFA). Furthermore, a paired sample t-test was applied to compare two related conditions, and regression analysis was employed to examine the effect of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) on technology acceptance. The results indicate that WhatsApp Groups are superior in terms of ease of use, whereas the website demonstrates superiority in terms of usefulness, trust, and user satisfaction. The regression analysis confirms that both PEOU and PU significantly influence technology acceptance, with PU exerting a more effect. In conclusion, WhatsApp Groups and the website play complementary roles in delivering PPDB information.
Analisis Performa Support Vector Machine untuk Klasifikasi Risiko Kredit Nasabah pada Perbankan Daerah Asep Sapaatullah; Rahmat Rahmat; Mochammad Darip
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2603

Abstract

Credit risk assessment is a crucial component of the banking system because it directly relates to a financial institution's ability to manage potential losses due to non-performing loans. Banks often face difficulties in accurately classifying customer credit risk levels, especially when the data being analyzed is complex, nonlinear, and contains interacting variables. Conventional methods such as regression analysis often fail to capture hidden patterns in such data. Therefore, this study aims to apply the Support Vector Machine (SVM) algorithm as a solution to classify bank customers' credit risk levels based on attributes such as income, loan amount, length of employment, payment history, debt-to-income ratio, and asset ownership status. The research process begins with data collection and pre-processing, including data cleaning and normalization to ensure a uniform distribution of values. The data is then divided into training and test data with specific proportions. An SVM model is then applied using several kernel types, such as linear, polynomial, and radial basis function (RBF), to determine the best-performing kernel. Model evaluation is performed using accuracy, precision, recall, and F1-score metrics to measure classification performance. Test results show that the SVM model with the RBF kernel provided the best results, achieving an accuracy rate of over 90% and minimizing classification errors in the high-risk category. In conclusion, the application of the SVM algorithm has proven effective in classifying customer credit risk levels with high accuracy and stability, making it a reliable tool for banks in the creditworthiness analysis process and more accurate, data-driven strategic decision-making
Co-Authors Abdul Halim Abdul Halim Ali Rochman Ali Rohman Ali Rohman Anam, Aan Khoirul Anugerah Agung Auliana, Sigit Aziz, Aflahul Basuki Rakhim Setya Permana Eris Purnama Gelard Untitha Pratama Gerald Untirtha Pratama Hamdan Hamdan Hamdan Hasan Siddiq Heru Maulana, Yozie Iis Nurasiah Irvan Septiandi Makin, Samsul Malik Farid Hanafi Maman Maman Masyhuri Masyhuri, Maman Maulana, Rafli MMSI Irfan ,S. Kom Muhamad Hidayatullah Muhamad Shiraj Al-Ayyubi Nana Supiana Nova Fitriyani Novita Sari Nurul Chafid Nurul Chafid Nurul Khoer Hidayah, Nadaa Parimin Pratama, M. Arieza Purnama, Eris Rahmat Rahmat Rahmat Rahmat Rizki, Wahyu Roy, Sujan Chandra Rudianto Rudianto Sapaatullah, Asep Saputro, Ahmad Dwi Sayyidah, Arnaeta Aprilia Siti Bunga Cinta Sunardi Sunardi Sunardi Sunardi Supiana, Nana Ubaedilah, Ikbal Wahyu Amaldi Yansyah, Mardi