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All Journal Jurnal Konseling dan Pendidikan Jurnal Media Infotama Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IJIS - Indonesian Journal On Information System JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Cerdas JSAI (Journal Scientific and Applied Informatics) Jurnal Teknologi Informasi dan Pendidikan Journal of Technopreneurship and Information System (JTIS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) SINTA Journal (Science, Technology, and Agricultural) Djtechno: Jurnal Teknologi Informasi Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Sistem Informasi Dan E-Bisnis JURNAL BESEMAH Mestaka: Jurnal Pengabdian Kepada Masyarakat PEDAMAS (Pengabdian Kepada Masyarakat) El-Mujtama: Jurnal Pengabdian Masyarakat Jurnal Al-Maun Jurnal Pengabdian Masyarakat Mentari Jurnal Riset Multidisiplin Edukasi Jurnal TAM (Technology Acceptance Model) JCOSIS (Journal Computer Science and Information Systems) Jurnal Komputer, Informasi dan Teknologi
Guntur Alam
Universitas Muhammadiyah Bengkulu
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Journal : Jurnal TAM (Technology Acceptance Model)

 1 
EVALUATION OF INFORMATION SECURITY USING THE INFORMATION SECURITY METHOD (KAMI) Case Study: UPT TIK UNIVERSITY MUHAMMADIYAH BENGKULU Khairullah, Khairullah; Alam, RG Guntur; Mahfuzi, AR. Walad; Abdullah, Dedy; Fransiska, Nora
Jurnal TAM (Technology Acceptance Model) Vol 16, No 1 (2025): Jurnal TAM (Technology Acceptance Model)
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v16i1.1802

Abstract

In the rapidly evolving digital era, educational institutions increasingly rely on information technology to manage data. However, information security is a critical issue because institutional data is an asset that is vulnerable to threats. This research aims to analyse how the OUR Index Method works in evaluating and measuring the level of information security at UPTIK. The methods used in this research include literature study to understand the concept of information security, primary data collection through interviews and questionnaires, and secondary data from policy documents and audit reports. Data analysis was conducted to compare the condition of information security at UPTIK with the standards set by OUR Index, which is based on SNI ISO/IEC 27001. The expected result of this research is a deeper understanding of the maturity level of information security at UPTIK, as well as the identification of weaknesses and potential risks. With this evaluation, it is expected that institutions can improve their information security posture, prepare for threats, and ensure that the security system implemented is in accordance with applicable standards. This research is expected to make a significant contribution to the management of information security in the educational environment, as well as a reference for other institutions that want to conduct similar evaluations.
COMPARATIVE ANALYSIS OF SHOPEE AND TIKTOK SHOP USER SENTIMENT USING NAÏVE BAYES ALGORITHM Lestari, Putri Dwi; Hidayah, Agung Kharisma; Saputera, Surya Ade; Erwadi, Yetman; Alam, RG Guntur
Jurnal TAM (Technology Acceptance Model) Vol 16, No 1 (2025): Jurnal TAM (Technology Acceptance Model)
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v16i1.1796

Abstract

The rapid growth of e-commerce has influenced changes in people's online shopping behaviour. Shopee and TikTok Shop are two popular e-commerce platforms in Indonesia, each offering unique features and shopping experiences. This research aims to analyse and compare user sentiment towards the two platforms by applying Naïve Bayes algorithm. User review data was obtained from Google Play Store through web scraping technique, then processed using Knowledge Discovery in Database (KDD) approach, which includes data collection, preprocessing, transformation, modelling, and evaluation stages. Naïve Bayes algorithm was used to classify the sentiment of the reviews into positive or negative categories. The results show that the majority of reviews for both platforms are positive, with Shopee having a higher proportion of positive sentiment than TikTok Shop. The model used shows good accuracy in classifying sentiment, although there is still a tendency to bias towards positive sentiment. The findings are expected to provide insights for e-commerce platform managers in improving service quality and better understanding user preferences.
Co-Authors AA Sudharmawan, AA Abdullah, Dedy Abid Aprialdi Adinda Trisista Akfarelta, Akfarelta Amrul Faruq Andi Lala Andilala, Andilala ANDRI KURNIAWAN Apridiansyah, Yovi Arbeiansah Pratama Putra Ariyanti, Widya Caca Andika Cahyo Prihantoro Dedy Abdullah Dia Komalla ewika dwi wulandari Fikri Agnesa Putra Fransiska, Nora Geri Rizki Ramadani Gufron, Muhammad Ale Gunawan Gunawan Gunawan Gunawan Gunawan Handayani, Sri Harianto, Ozi Harry Witriyono Hary Witriyono Helen Anggraini Heri Sulasono Hidayah, Aditia Hidayah, Agung Karisma hidayah, agung kharisma Ibrahim, Huda Jaka Rapino Juhardi, Ujang Karnedi, Gunawan Khairullah Khairullah, Khairullah Kirman Kirman Kirman Kirman, Kirman Lena Utami Lestari, Putri Dwi Lety Lestari Lingga, Ravi Putra Lorensya, Cintia Novita Machmud Effendy Mahendra, Yusril Mahfuzhi, A.R Walad Mahfuzi, AR. Walad Marissa Utami mawanti, desti melzan sabri sabri Monsya Juansen muhammad fikri Muntahanah, Muntahanah Mutiara Hikmah Nathania, Nabela Hermy Nengsi, Elta Putri Setia Netra Ayu Nita, Taura Puji Rahayu Kurniasih Putra, Arbeiansah Pratama Putra, Fikri Agnesa Rahmadani, Reza Nur Rajes Andika Putra Ramayanti, Imelia Okta Ramzi, Reja Muhamad Randi Riski Ananda Rexcy Elsan Rido Syahputra Rifqo, Muhammad Husni Rojandra, Hajar Saputra, Ahmat Fahry Saputra, Kreshna Ady Selta Jaya Putra selviani, pebi Sigit Muryono Sri Handayani Sulasono, Heri Supriadi Supriadi Surya Ade Saputera Tauhid, Muhammad Ikhsan Toyib, Rozali Tri Rahayu Trisista, Adinda ujang juhardi Vendy Handoyo Veronika, Adelia Wicaksono, Rama Iqbal Wijaya, Ardi Wulandari, Ardeya Yetman Erwadi Yunus Hidayat Yusril Mahendra Yuza Reswan Zhonata, Jerry Ario