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All Journal Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat Journal of Information Systems and Business Technology Journal of Information Technology and Informatics Engineering
Abdul habib Hasibuan
Universitas Pamulang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Other

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Peningkatan Cyber Security dan Penggunaan Sosial Media dalam Teknologi Informasi di Era Digital di SMK Media Informatika Muhamad Firly; Muhamad Ridwan Nurrulloh; Maulana Farras Fathurrahman; Abdul habib Hasibuan
Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat Vol 1 No 1 (2025): Jurnal Sinergi Sistem Informasi Pengabdian Masyarakat
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

This community service program aims to improve the knowledge and skills of students at SMK Media Informatika in cybersecurity and responsible use of social media in the digital age. The training consisted of interactive sessions covering fundamental information security concepts, awareness of cyber threats, and ethical social media practices. Evaluation results indicated significant improvements in students’ understanding, threat recognition skills, and responsible social media conduct. Furthermore, the training effectively raised awareness of digital security within the school and positively influenced students’ daily behavior. It isrecommended to integrate cybersecurity and social media literacy topics into the curriculum and to conduct ongoing training programs to continuously enhance student competencies.
Pengujian Fungsionalitas dan Keandalan Sistem Laundry Berbasis Web Amar Naufal; Abdul Habib Hasibuan
Journal of Information Systems and Business Technology Vol 1 No 1 (2025): Journal of Information Systems and Business Technology
Publisher : PT Jurnal Cendekia Indonesia

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Abstract

This study aims to test the quality of a web-based clothing system from the aspects of functionality and reliability. As the need for efficient and easily accessible clothing services increases, the development of a web-based system becomes a relevant solution. The system was tested using the Dark Box Testing method to evaluate the performance of each feature based on real-world usage scenarios. The development approach used was Rapid Application Development (RAD), which allows rapid iteration through the creation and refinement of prototypes based on user feedback. Testing focused on key features such as login, customer management, clothing transactions, and revenue reporting. The test results showed that most features ran as expected and supported clothing operations effectively, although one case failed in recording transactions. These findings provide an important picture of the advantages of the system and the range that needs improvement. This study is expected to be a reference in the development of similar service systems in the future.
Analisis Kinerja Algoritma Naive Bayes dalam Klasifikasi Data Kategorikal Prediksi Keputusan Bermain Tenis Berdasarkan Cuaca Feriandri Lesmana; Athila Defian Rizkimu; Muhamad Ridwan Nurrulloh; Maulana Farras Fathurrahman; Abdul Habib Hasibuan; Maulana Fansyuri
Journal of Information Technology and Informatics Engineering Vol 1 No 1 (2025): Journal of Information Technology and Informatics Engineering (JITIE)
Publisher : PT Jurnal Cendekia Indonesi

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Abstract

Decision-making based on weather factors is often subjective and inconsistent. This research applies data mining classification methods to build an objective predictive model regarding the decision to play tennis based on weather conditions. The objective of this study is to analyze the performance of the Naive Bayes algorithm in predicting this decision. The methodology involves applying the Naive Bayes algorithm to the classic "Play Tennis" dataset, which consists of 14 instances with four categorical predictor attributes: outlook, temperature, humidity, and wind. The modeling and evaluation process was conducted visually using the Altair AI Studio (RapidMiner) platform, employing the cross-validation technique to test model stability. The test results show an average model accuracy of 57.14%. A deeper analysis of the confusion matrix reveals that the model has a strong bias towards predicting the 'Yes' class, yet is very weak in identifying the 'No' class (20.00% recall). Specifically, the model exhibits a high number of False Positive errors, where 4 out of 5 'No' cases were misclassified. In conclusion, the Naive Bayes model in its current configuration is not yet fully reliable for practical application due to its biased performance. This study recommends further optimization, such as applying data balancing techniques or using more complex alternative algorithms, to significantly improve predictive performance.
Co-Authors Amar Naufal Athila Defian Rizkimu Feriandri Lesmana Maulana Fansyuri Maulana Farras Fathurrahman Muhamad Firly Muhamad Ridwan Nurrulloh