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All Journal International Journal of Health, Engineering and Technology Sevaka: Hasil Kegiatan Layanan Masyarakat
Yusril Iza Mahendra Hasibuan
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Dentistry Education Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Implementation of Password Validation using a Combination of Letters, Numbers and Symbols in the New Student Registration Application Sentosa Pohan; Putri Ramadani; Riszki Fadillah; Yusril Iza Mahendra Hasibuan; Baginda Restu Al Ghazali
International Journal of Health Engineering and Technology Vol. 3 No. 1 (2024): IJHET May 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v3i1.282

Abstract

This research aims to evaluate the implementation of password validation using a combination of letters, numbers and symbols in new student registration applications in increasing the level of application security. This research method involves implementing a password validation system with strict criteria, as well as testing password strength using brute force attacks. The test results show that passwords that meet the criteria take time 150 seconds to be broken using brute force, while passwords that only use letters only take time 10 seconds. Surveys of users show that 70% feel comfortable with this validation system, though 40% find it difficult to create a valid password. As much 85% users consider this system to improve application security. This research suggests that new student registration applications adopt a strict password validation system to increase the protection of users' personal data, while providing solutions for users to create more secure passwords.complex but easy to remember. The implementation of this system is expected to strengthen application security and increase user confidence in the protection of their personal data.
Simulation and Detection of Phishing Attacks on Student Academic Emails Using Social Engineering Techniques Santosa Pohan; Desi Irfan; Intan Nur Fitriyani; Yusril Iza Mahendra Hasibuan; Indah Chayani
International Journal of Health Engineering and Technology Vol. 2 No. 4 (2023): IJHET NOVEMBER 2023
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v2i4.283

Abstract

Phishing attacks on student academic emails are a serious threat to information security. Social engineering techniques are often used in these attacks to manipulate victims into divulging sensitive information, such as passwords and other personal data. This research aims to analyze and detect phishing attacks that use social engineering techniques on student academic emails. In this research, a phishing attack simulation was carried out with the scenario of falsifying the identity of an academic institution and creating fake emails that appear legitimate. Students as simulated subjects were tested to see how they reacted to deceptive phishing emails, such as clicking on malicious links or downloading infectious attachments. The detection methods used include heuristic analysis and machine learning techniques, where the system is trained to recognize suspicious patterns in emails, including elements such as unusual subjects, links and attachments. The research results show that phishing attacks that utilize social engineering are effective in manipulating victims. On the other hand, detection using machine learning and heuristic analysis can achieve a high level of accuracy in identifying phishing attacks. This research also underscores the importance of increasing awareness about cyber security among students as well as the need to develop more effective phishing detection tools.
Penyuluhan Penerapan Naive Bayes Untuk Identifikasi Keterlambatan Perkembangan Anak Berdasarkan Data Kesehatan Pada Program Studi Kebidanan Fahruzi Sirait; Eka Ramadhani Putra; Nailatun Nadrah; Rika Handayani; Yusril Iza Mahendra Hasibuan
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 2 No. 4 (2024): November: Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v2i4.525

Abstract

Child developmental delay is a public health issue that needs to be identified early to prevent long-term impacts on children’s quality of life. In Rantau Prapat Sub-district, cases are still found among toddlers with undernutrition, incomplete immunizations, and suboptimal developmental stimulation, which may pose risks of growth and developmental delays. This study aims to apply the Naive Bayes method in identifying child developmental delays based on health data collected through medical records and questionnaires. The research method includes data collection, pre-processing (cleaning, transformation, and normalization), classification using the Naive Bayes algorithm, and model validation with the k-fold cross-validation technique. The results showed that out of 150 toddler data samples, 30.7% experienced developmental delays, with the dominant influencing factors being nutritional status and immunization completeness. The Naive Bayes algorithm achieved an accuracy rate of 87.3% with a precision of 84.1%, recall of 85.7%, and F1-score of 84.9%. These findings demonstrate that Naive Bayes can be used as a decision support system in the early identification process of child developmental delays. Therefore, the results of this study are expected to assist healthcare workers, particularly midwives, in improving the quality of early detection and delivering more targeted interventions for children in the Rantau Prapat area.
Pelatihan Deteksi Risiko Hipertensi Dengan Analisis Data Riwayat Medis Berbasis Random Forest Untuk Tenaga Kesehatan Masyarakat Desi Irfan; Evri Ekadiansyah; Halimah Tusakdiyah Harahap; Novica Jolyarni Dornik; Yusril Iza Mahendra Hasibuan
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 1 No. 4 (2023): November: Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v1i4.527

Abstract

Hypertension is one of the most prevalent non-communicable diseases and a major risk factor for heart disease, stroke, and kidney disorders. The high prevalence of hypertension cases in the community, particularly in the working area of Puskesmas Kota Rantau Prapat, highlights the urgent need for more effective early detection efforts to prevent severe complications in the future. However, the limited capacity of healthcare workers in utilizing data analysis technologies has resulted in hypertension risk detection being dominated by conventional methods, which are often less accurate and inefficient. To address this issue, this community service program was conducted through training on the application of the Random Forest algorithm to analyze patients’ medical history data in order to detect hypertension risks. The training method included an introduction to the fundamentals of machine learning, data pre-processing stages, implementation of the Random Forest algorithm, and interpretation of prediction results. The outcomes of the program demonstrated that healthcare workers were able to understand the use of data analysis technologies to support more accurate early detection of hypertension. Furthermore, the participants gained practical skills in utilizing medical datasets to produce predictions that can serve as a decision-support tool for preventive medical actions.Thus, this training contributed to enhancing the capacity of community healthcare workers in integrating machine learning-based technologies into preventive healthcare services. This program is expected to serve as an initial step toward developing more effective, efficient, and sustainable data-driven health systems.
Co-Authors Baginda Restu Al Ghazali Desi Irfan Eka Ramadhani Putra Evri Ekadiansyah Fahruzi Sirait Halimah Tusakdiyah Harahap Indah Chayani Intan nur Fitriyani Nailatun Nadrah Novica Jolyarni Dornik Putri Ramadani Rika Handayani Riszki Fadillah Santosa Pohan Sentosa Pohan