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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
Andri Firmansyah
Universitas Pelita Bangsa, Bekasi
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Sistem Pendaftaran Peserta Didik Baru pada SMK Berbasis Web Menggunakan Metode Waterfall Zulham Iftikar Jamaludin; Andri Firmansyah; Ikhsan Romli
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1314

Abstract

Education is one of the critical sectors in the development of a country, and the need for an efficient new student registration process has become increasingly pressing with technological advancements. a vocational high school in North Cikarang, currently faces challenges in the manual process of enrolling new students. This creates issues for prospective students and school administrators. To address this problem, a technology-based solution is required, and the use of the Waterfall method in system development is an appropriate approach. The Information System for New Student Enrollment at Vocational High School, using the Waterfall method, aims to provide an efficient, structured, and accessible registration process. With this approach, each step in the system development process is carried out sequentially, including analysis, system design, implementation, testing, and maintenance. Prospective students and parents can access this system online, eliminating geographical limitations in the registration process. The results of this research include the development of a web-based new student registration system using PHP with the Laravel framework. This system provides a solution to address the existing manual issues at Vocational High School. In conclusion, this system offers several benefits, including simplifying the registration process for parents, providing better access to school information, and improving the efficiency and effectiveness of the new student admission process. Furthermore, data stored in the database is more secure and easily accessible to school staff.
Analisis Sentimen Penonton Terhadap Film Sore Istri dari Masa Depan pada Twitter Menggunakan Algoritma Support Vector Machine Alfaza Putra Adjie Ariefiansyah; Andri Firmansyah
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10244

Abstract

The development of social media has encouraged people to express their opinions about cinematic works openly, quickly, and in real time. Twitter, now known as X, has become one of the most widely used platforms for sharing brief film reviews, generating a large volume of opinion data that is difficult to analyze manually. This study aims to analyze audience sentiment toward the film Sore: Istri dari Masa Depan based on tweet data, apply the Support Vector Machine algorithm in the sentiment classification process, and evaluate the performance of the resulting model. The research data were collected through a tweet crawling process using keywords related to the film Sore: Istri dari Masa Depan from July 2025 to April 2026. A total of 7,467 tweets were analyzed through text preprocessing, sentiment labeling, feature weighting using Term Frequency-Inverse Document Frequency, and classification using Support Vector Machine. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics, using an 80:20 training and testing data split. The results showed that 6,822 tweets were classified as positive sentiment and 645 tweets as negative sentiment. The model achieved an accuracy of 90.75%, indicating that it was able to classify audience sentiment effectively. This study contributes by providing a computational approach to mapping public reception of Indonesian films through social media data, while also offering a basis for film industry practitioners to evaluate audience responses and develop data-driven promotional strategies.
Deteksi Port Scanning pada Jaringan IoT dengan Pemantauan Web Real-Time Bilal AlHafidz; Andri Firmansyah; Suherman Suherman
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10521

Abstract

Port scanning is a reconnaissance activity that may precede service exploitation on Internet of Things (IoT) devices. This study designs and implements a prototype for detecting TCP port scanning with real-time web monitoring in the local network of PT WIMISEC, Bekasi. The system was developed using a Research and Development approach and Waterfall stages, integrating a Laravel application, a MySQL database, and a Python detection engine. Detection applies a threshold rule based on the number of unique destination ports accessed by one source IP address within a time window. The evaluation covered functional testing, five TCP SYN scans, five TCP Connect scans, three browsing activities, and three normal SSH logins. All 10 scan trials generated alerts (an observed detection rate of 100%), while none of the six normal trials generated an alert (an observed false-positive rate of 0% in the limited sample). The mean latency from detection-engine identification to dashboard alert display was 1.24 seconds. These results demonstrate the prototype's functional feasibility for initial LAN monitoring rather than general accuracy across attack patterns. The evaluation remains limited by its small sample, the absence of mixed-traffic and stress testing, and the lack of tests involving UDP, slow, distributed, and evasive scans.
Perbandingan Kinerja Naïve Bayes dengan dan Tanpa SMOTE untuk Klasifikasi Gangguan Kecemasan Mahasiswa pada Data Tidak Seimbang Nurhadi Surojudin; Sufajar Butsianto; Andri Firmansyah
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1021

Abstract

Anxiety disorders are one of the most common mental health problems experienced by university students and may affect learning concentration and academic performance. The analysis of psychological survey data using machine learning techniques can support early detection of student anxiety conditions. However, one of the main challenges in mental health data analysis is the presence of class imbalance within the dataset. This study aims to analyze the effect of applying the Synthetic Minority Oversampling Technique (SMOTE) on the performance of the Naïve Bayes algorithm for multi-class classification of student anxiety levels, which are categorized into three classes: No Stress, Eustress, and Distress. The dataset used in this research was obtained from student questionnaire data and underwent several preprocessing steps including data cleaning, feature transformation, and dataset splitting using the hold-out method with a ratio of 80% training data and 20% testing data. Model performance was evaluated using a confusion matrix with evaluation metrics including accuracy, precision, recall, and F1-score. The results show that the Naïve Bayes model without SMOTE achieved an accuracy of 0.84, precision 0.78, recall 0.41, and F1-score 0.54. After applying SMOTE, the model achieved an accuracy of 0.82, precision 0.74, recall 0.69, and F1-score 0.71. These results indicate that SMOTE improves the model's ability to detect minority classes in multi-class classification problems, although a slight decrease in overall accuracy is observed.
Analisis Pengembangan dan Evaluasi Virtual World Bertema Lingkungan Pegunungan Menggunakan Metode Research and Development (R&D) Robby Firmansyah; Andri Firmansyah
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1146

Abstract

The development of metaverse technology and virtual worlds has created opportunities for utilizing virtual environments as media for digital exploration, simulation, and interactive learning. However, most previous studies have primarily focused on pedagogical aspects and learning simulations, while research discussing the technical development of terrain modeling, lighting, and mountainous environmental atmospheres on the Roblox Studio platform remains limited. This study aims to develop and evaluate a mountainous environment-themed virtual world using Roblox Studio and to analyze user acceptance of the resulting virtual environment. The research employed the Research and Development (R&D) method, consisting of needs analysis, design, implementation, testing, and evaluation stages. The development process utilized Terrain Editor, Future Lighting, skybox, fog, and Lua scripting features to create an interactive virtual environment. Evaluation was conducted using a Likert-scale questionnaire distributed to 67 respondents. The results showed a feasibility score of 79.88%, which falls into the good category. The highest-rated indicator was the suitability of the virtual world to the mountainous environment concept (4.15), while the lowest-rated indicator was the structured layout of the environment (3.83). These findings indicate that the developed virtual world was able to provide a positive exploration experience for users. The contribution of this research lies in establishing a systematic development framework for a mountainous environment-themed virtual world through the implementation of terrain modeling, lighting and atmospheric configuration, and user experience evaluation using the Research and Development (R&D) approach. In addition, this study provides a practical reference for the development of metaverse-based virtual environments that can be utilized for simulation, digital exploration, and interactive learning applications.
Co-Authors Alfaza Putra Adjie Ariefiansyah Bilal AlHafidz Ikhsan Romli Nurhadi Surojudin Robby Firmansyah Sufajar Butsianto Suherman Suherman Zulham Iftikar Jamaludin