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All Journal Informatics for Educators and Professional : Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) Infotek : Jurnal Informatika dan Teknologi Jurnal Media Informatika Informatics and Computer Engineering Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Syifa Nur Rakhmah
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Perancangan Sistem Prediksi Deteksi Alzheimer Berbasis Random Forest Menggunakan Metode Scrum Daira Syahfitri; Dian Rahayuningtyas; Raihano Garcia; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/gkep7058

Abstract

Alzheimer's disease is a neurodegenerative characterized by a gradual decline in memory and cognitive function, with a prevalence that continues to increase globally and in Indonesia. Constraints in early detection, such as limited healthcare facilities and the high cost of conventional diagnosis, drive the need for easily accessible technology-based solutions. This research aims to develop a web system named MindCare that integrates the Random Forest algorithm to predict the risk of Alzheimer's based on clinical and lifestyle data. The system development method uses the Agile Scrum approach with four sprint cycles, covering needs analysis, model training, web system integration, as well as testing and refinement. The model was trained using Alzheimer's and mental health datasets from Kaggle, with evaluation results showing perfect accuracy and AUC (100%). The features FamilyHistoryAlzheimers, Age, and PhysicalActivity proved to be the most influential in prediction. The resulting web system provides risk prediction features, result visualization, personalized prevention recommendations, and education about Alzheimer's. Black-box testing showed all functions worked as expected. The conclusion of this research is that the MindCare system is suitable for use as an easily accessible medium for early detection and education on Alzheimer's, with recommendations for further development through database expansion, exploration of other algorithms, and the addition of consultation and monitoring features
Pengembangan Sistem Prediksi Risiko Gangguan Mental Remaja Menggunakan Support Vector Machine (SVM) Anisya Septianur; Elsya Bani Aulia; Nugroho Fathul Aziz; Findi Ayu Sariasih; Syifa Nur Rakhmah; Imam Sutoyo
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zh47p731

Abstract

Adolescent mental health has become an increasingly critical issue due to the rising prevalence of emotional and behavioral disorders among young individuals. Social pressure, academic demands, and psychological changes often trigger stress, anxiety, and even depression, which affect learning activities and social interactions. This study aims to develop a web-based system to detect mental disorder risk in adolescents using a machine learning approach with the Support Vector Machine (SVM) algorithm. Three open datasets from the Kaggle platform—Big Five Personality Test Dataset, Symptom2Disease Dataset, and Mental Health in Tech Survey Dataset—were utilized to integrate personality traits, physical conditions, and mental health indicators. The data underwent preprocessing involving duplicate removal, missing value imputation, standardization, and categorical-to-numerical transformation before being split into 70% training and 30% testing sets. The system was developed using the Agile Scrum methodology in an iterative and adaptive manner based on user feedback. The experimental results show that the SVM model with an RBF kernel achieved 91.3% accuracy, 89.7% precision, and 91.9% F1-score. The resulting system, can classify mental disorder risk levels and provide prevention recommendations according to the assessment results. With an interactive interface, this system is expected to assist adolescents in recognizing their mental conditions early, increase awareness of psychological well-being, and serve as a technologybased educational tool for mental health prevention. 
ASSESSING USER EXPERIENCE OF SITURAWA GEDE TOURISM WEBSITE USING PSSUQ AND HEURISTIC Anie Yoraeni; Cep Adiwiharja; Syifa Nur Rakhmah; Sinta Rukiandari; Sari Hartini
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7332

Abstract

The Siturawagede platform is an information site built to support services, promotions, and interactions with the public. Therefore, the quality of the user experience (UX) is a crucial factor in ensuring the site's effective, efficient, and satisfying use. This study aims to analyze the user experience of the Siturawagede website, measure user satisfaction with ease of use, efficiency, and information quality using the Post-Study System Usability Questionnaire (PSSUQ), assess the site's compliance with Nielsen's usability principles through a heuristic evaluation, and provide recommendations for improving the site's user-friendliness. The study involved 100 respondents who completed the PSSUQ questionnaire and three expert evaluators who conducted the heuristic assessment. The results showed that the average PSSUQ score of 2.6 was categorized as "good." This is based on the PSSUQ 1–7 scale, where a score closer to 1 indicates a positive experience (Strongly Agree) and a score closer to 7 indicates a negative experience (Strongly Disagree), indicating that users were quite satisfied with the system. The heuristic evaluation obtained a score of 1.49, identifying several minor navigation issues, but the system was generally good and needed only minor improvements. These findings provide guidance for improving the quality of the Siturawagede website to make it more informative and optimal in supporting tourism management.
Prediksi Risiko Kesehatan Bayi Berbasis Parameter Tumbuh Kembang dengan Menggunakan Gradient Boosting Astatia Hulu; Juan Sebastian Aimar; Firyal Aufa Nabilah; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Informatics and Computer Engineering Journal Vol 6 No 1 (2026): Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v6i1.11066

Abstract

Kesehatan bayi merupakan indikator penting kualitas generasi masa depan, namun deteksi dini risiko kesehatan sering terkendala keterbatasan tenaga medis dan sistem pemantauan efektif. Penelitian ini mengembangkan sistem prediksi risiko kesehatan bayi berusia 0-30 hari menggunakan algoritma Gradient Boosting berdasarkan parameter tumbuh kembang. Metode pengembangan sistem menggunakan Agile Scrum dengan dataset "Infant Wellness and Risk Evaluation" yang melalui tahap pra-pemrosesan data dan feature engineering. Hasil evaluasi menunjukkan model mencapai akurasi 94%, recall 84% untuk kelas berisiko, dan precision 71%. Analisis feature importance mengidentifikasi age_days, oxygen_saturation, dan heart_rate_zscore sebagai fitur paling berpengaruh. Sistem prediksi berbasis web yang dihasilkan ini nantinya diharapkan dapat menjadi alat bantu yang efektif bagi tenaga medis. Infant health is an important indicator of future generation quality, but early detection of health risks is often constrained by limitations of medical personnel and effective monitoring systems. This research develops a health risk prediction system for infants aged 0-30 days using Gradient Boosting algorithm based on growth and development parameters. The system development method uses Agile Scrum with "Infant Wellness and Risk Evaluation" dataset through data preprocessing and feature engineering stages. Evaluation results show the model achieves 94% accuracy, 84% recall for at-risk class, and 71% precision. Feature importance analysis identifies age_days, oxygen_saturation, and heart_rate_zscore as the most influential features. The resulting web-based system has potential as an effective assistance tool for medical personnel.  
Brute-Force Attack Detection on Computer Networks Using Artificial Neural Network Ikhtiar Adli Wicaksono; Muhammad Iqbal Maulana; Bagus Nurrahman; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1804

Abstract

This research aims to develop a brute-force attack detection system on computer networks using the Artificial Neural Network (ANN) algorithm. This security problem is crucial, especially in the banking sector because it can threaten login systems and sensitive customer data. The research methods include data cleansing, feature selection using the Wrapper method, ANN model training, and performance evaluation using datasets from Kaggle which include four classes of network traffic, namely Normal, Brute-force FTP, Brute-force SSH, and Web Attack Brute-force. The test results showed that the ANN model achieved an accuracy of 95%, precision of 91%, and the best performance in the Brute-force FTP class with an accuracy of 98.3%. This system has proven to be effective in detecting brute-force attack patterns and can improve the security of banking networks adaptively. This research broadens the insights of the application of ANN in network security and provides a basis for the development of systems that are more responsive to cyber threats.
Sistem Prediksi Kualitas Air Konsumsi Machine Learning Menggunakan Algoritma Random Forest Fredyo Eltanin Lumban Raja; Daniel Purba; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.33035

Abstract

This study aims to design and implement a clean water quality prediction system based on machine learning using the Random Forest algorithm. The background of this research is the limited public access to fast information regarding water feasibility, while laboratory testing requires significant time and cost. The data used is synthetic data constructed based on the value ranges and threshold limits of water quality in SNI 3553:2015, SNI 3553:2023, and the Ministry of Health Regulation No. 2 of 2023, so that it remains representative and aligned with real conditions. This dataset was created because field data is difficult to obtain completely and in a standardized form, but it still imitates real condition variations according to national standards, with feasibility labels determined based on official regulations. The system development uses the Agile method through the stages of dataset creation, preprocessing, training, evaluation, and application implementation. The Random Forest model is used to classify water into suitable, moderately suitable, and unsuitable categories. The test results show that the model with three physical parameters achieved an accuracy of 96.67%, while the model with ten chemical parameters achieved an accuracy of 100%, confirming that adding more parameters can improve prediction accuracy. This system is expected to help the public and environmental officers in conducting an initial assessment of water quality quickly before laboratory testing and can still be further developed to become more applicable in various regions.
Sistem Prediksi Kecelakaan Lalu Lintas Menggunakan Deep Learning Convolutional Neural Network (CNN) untuk Pencegahan Efektif: Indonesia Sausan Faza; Rafika Puteri Wulandari; Findi Ayu Sariasih; Imam Sutoyo; Syifa Nur Rakhmah
Jurnal Media Informatika Vol. 7 No. 1 (2026): Edisi Januari - Februari
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v7i1.7659

Abstract

Kecelakaan lalu lintas merupakan masalah global yang memerlukan pendekatan inovatif berbasis teknologi visi komputer. Penelitian ini bertujuan mengembangkan aplikasi web yang mampu mengidentifikasi probabilitas terjadinya kecelakaan kendaraan menggunakan citra dashcam dengan pendekatan deep learning berbasis Convolutional Neural Network (CNN). Pengembangan aplikasi dilakukan menggunakan metodologi Feature Driven Development (FDD) untuk memastikan integrasi fitur yang modular dan berorientasi pada kebutuhan pengguna. Dataset yang digunakan bersumber dari Kaggle Car Crash Dataset sebanyak 10.000 citra yang dibagi menjadi data training (7.000), validation (1.500), dan testing (1.500). Hasil penelitian menunjukkan bahwa model CNN berhasil mencapai akurasi pelatihan sebesar 83,66% dan akurasi validasi sebesar 82,13%. Meskipun demikian, terdapat tantangan pada ketidakseimbangan data yang menyebabkan nilai recall untuk kelas kecelakaan berada di angka 37,79%. Implementasi sistem pada antarmuka web memungkinkan pengguna mengunggah citra dan menerima hasil klasifikasi risiko berupa "High Risk" atau "Low Risk" secara real-time. Sistem ini diharapkan dapat menjadi prototipe awal bagi pengembangan teknologi keselamatan berkendara yang lebih responsif di masa depan.
Analisis Kepuasan Pengguna Aplikasi E-Learning Ruangguru Di Kalangan Pelajar SMA Menggunakan Model EUCS Feroz Fairus Ananda; Arif Ismail Husin; Syifa Nur Rakhmah
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 2 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Desember
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i2.3715

Abstract

This study aims to analyze the level of satisfaction of Ruangguru e-learning application users among high school students. This study uses the End User Computing Satisfaction (EUCS) model, which consists of five main aspects, namely content, accuracy of information, format, ease of use, and timeliness. The research was conducted quantitatively by distributing questionnaires to 397 high school students who use the Ruangguru application. The data obtained was then analyzed using various tests such as validity, reliability, t-test, F-test, and coefficient of determination tests with the help of SPSS version 30. The results showed that all five variables in the EUCS model had a significant effect on the level of satisfaction of Ruangguru app users. This was evident from the calculated F value being greater than the F table value and the significance value being less than 0.05. In addition, the determination test results showed that these variables collectively contributed significantly to user satisfaction with the app. Thus, it can be concluded that the Ruangguru application is able to meet the expectations of high school students in terms of content, accuracy of information, ease of use, presentation format, and speed of service.
Co-Authors Anie Yoraeni Anisya Septianur Arif Ismail Husin Astatia Hulu Bagus Nurrahman Cep Adiwiharja Daira Syahfitri Daniel Purba Dian Rahayuningtyas Elsya Bani Aulia Feroz Fairus Ananda Findi Ayu Sariasih Firyal Aufa Nabilah Fredyo Eltanin Lumban Raja Ikhtiar Adli Wicaksono Imam Sutoyo Juan Sebastian Aimar Muhammad Iqbal Maulana Nugroho Fathul Aziz Rafika Puteri Wulandari Raihano Garcia Sari Hartini Sausan Faza Sinta Rukiandari