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All Journal Jurnal Pengabdian UntukMu NegeRI Jurnal Ilmiah FIFO Bina Insani ICT Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics Applied Industrial Engineering Journal Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika INTI Nusa Mandiri JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Teknika Jurnal Bumigora Information Technology (BITe) Jurnal Kajian Ilmiah Jurnal AbdiMas Nusa Mandiri Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Mahasiswa Sistem Informasi (JMSI) Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Media Informatika Reputasi: Jurnal Rekayasa Perangkat Lunak Informatics and Computer Engineering Journal Jurnal Inkofar Journal of Artificial Intelligence and Engineering Applications (JAIEA) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Aruna Mengabdi (ARMI) Jurnal Sains dan Teknologi Informasi Jurnal Ilmiah Sistem Informasi Informasi interaktif : jurnal informatika dan teknologi informasi Prawara Jurnal Pengabdian Masyarakat
Syifa Nur Rakhmah, Syifa Nur
Universitas Nusa Mandiri
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Prediksi Risiko Kesehatan Bayi Berbasis Parameter Tumbuh Kembang dengan Menggunakan Gradient Boosting Hulu, Astatia; Aimar, Juan Sebastian; Nabilah, Firyal Aufa; Rakhmah, Syifa Nur; Sariasih, Findi Ayu; Sutoyo, Imam
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.  
SISTEM REKOMENDASI MAKANAN MULTI – KRITERIA UNTUK KONSUMEN DENGAN ANGGARAN TERBATAS MENGGUNAKAN ALGORITMA CONTENT BASED FILTERING Azhar, Raniah; Shidqin, Dhuha Shobiyan; Prakoso, Azzam Ade; Rakhmah, Syifa Nur; Sariasih, Findi Ayu; Sutoyo, Imam
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 10 No. 1 (2026): Volume 10, Nomor 1, Januari 2026
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v10i1.1186

Abstract

ABSTRACTThe primary challenge in current digital recommendation services is aligning product quality with the economic constraints of the user. This study focuses on the development and implementation of a Food Recommendation System operating on Multi-Criteria, namely Maximum Budget (Price) and Quality (Predicted Rating). The methodology applied is Content-Based Filtering, where the system analyzes nutritional content data and the estimated ingredient cost of each menu to determine the level of compatibility with the user’s preference profile. The processing flow begins with receiving a price limit set by the consumer, followed by a strict filtering phase to exclude menus outside the budget, and subsequently ranking the qualified menus based on the quality score generated by a Machine Learning model. This implementation successfully delivers ordered and cost-efficient menu recommendations, demonstrating its high potential as an effective assistant in supporting food purchasing decisions for consumers facing financial limitations.Keywords: Recommendation System, Multi-Criteria, Budget Constraint, Content-Based Filtering, Predicted Rating.
Sistem Rekomendasi Destinasi Wisata Menggunakan Content-Based Filtering dan Analisis Fitur Geospasial Widika, Arya; Susilo, Putri Salsabila; Ramadhan, Andhika Ibnu; Rakhmah, Syifa Nur; Sariasih, Findi Ayu; Sutoyo, Imam
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol 11 No 1 (2026): Bahasa Indonesia
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study develops a tourism destination recommendation system based on Content-Based Filtering integrated with geospatial feature analysis to enhance the relevance and contextual accuracy of recommendations for users. The system addresses the limitations of existing tourism recommendation platforms that primarily focus on popularity and ratings without considering users’ location proximity and personal preferences. The dataset used in this research originates from Tourism in Indonesia (Kaggle), focusing on the Jakarta and Bandung regions. Text features are extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the similarity between destinations is measured using Cosine Similarity. Additionally, geographic distances are analyzed through the Haversine formula to strengthen the spatial context of the recommendations. The system was developed using the Agile (Scrum) methodology to ensure an iterative and adaptive development process aligned with user needs. Evaluation results indicate strong system performance, achieving a Precision of 0.63, Recall of 0.90, and an F1-Score of 0.73. These findings demonstrate that integrating content-based and spatial analysis approaches effectively improves the accuracy and personalization of tourism recommendations based on users’ preferences and location context.
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 Klasifikasi Citra AI Dan Human Menggunakan CNN Multi-Modal Berbasis Web Ardiyansyah, Oscar; Muhammad ‘Aziz Hidayatullah; Derrylen Fernanda; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 5 No. 1 (2026): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v5i1.10543

Abstract

Penelitian ini mengembangkan sistem klasifikasi citra berbasis web menggunakan arsitektur Convolutional Neural Network (CNN) multi-modal untuk membedakan citra buatan manusia dan hasil generasi AI. Sistem yang diajukan menggabungkan tiga jenis input, yaitu citra asli, Error Level Analysis (ELA), dan Residual Noise Map (RDM) guna memperkaya representasi fitur pada proses klasifikasi. Model dibangun dengan backbone VGG16 pre-trained dan diuji pada 2.102 data citra yang terbagi seimbang antara dua kelas. Hasil eksperimen menunjukkan akurasi validasi sebesar 91% dan nilai macro F1-score sebesar 0,91, mengungguli pendekatan unimodal pada tugas serupa. Sistem diimplementasikan menggunakan framework Flask yang memungkinkan uji keaslian citra secara real-time, sehingga sangat relevan diterapkan di bidang forensik digital, verifikasi hak cipta, dan mitigasi disinformasi visual.
Sistem Prediksi Kecelakaan Lalu Lintas Menggunakan Deep Learning Convolutional Neural Network (CNN) untuk Pencegahan Efektif: Indonesia Faza, Sausan; Wulandari, Rafika Puteri; Sariasih, Findi Ayu; Sutoyo, Imam; Rakhmah, Syifa Nur
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.
Co-Authors Adam Maulana Putra Aimar, Juan Sebastian Amelianda Mega Putri Andre Kurniawan Anis Rahmawati Anna Mukhayaroh Anna Mukhayaroh, Anna Ardiyansyah, Oscar Arif Ismail Husin Azhar, Raniah Bagus Nurrahman Cahyani, Ainun Dea Kartika Aulia Arawi Derrylen Fernanda Dhefine Armelsa Dian Yunita sari Endang Zulfikar Fajri Khairullah Fauziah Mk, Noha Noor Faza, Sausan Feru Adiningrat Findi Ayu Sariasih Findi Ayu Sariasih Galih Khrisna Rangga Ganda Wijaya Hakim, Dimas Lukman Hani Harafani Henny Leidiyana Hulu, Astatia Icuk Maris Kuncoro Ikhtiar Adli Wicaksono Imam Sutoyo Imam Suyoto Irfan Rizki Izzudin Ijtara’a Kardinal Novel Khoirun Nisa Lusa Indah Prahartiwi Maulidana Athur Fadilla Mochammad Imron Awalludin Muhammad Iqbal Maulana Muhammad Zaky Muhammad ‘Aziz Hidayatullah Murni Cahyati Musyaffa, Nu’man Nabilah, Firyal Aufa Nisa , Khoirun Norma Yunita Norma Yunita Norma Yunita Nu’man Musyaffa Prakoso, Azzam Ade Putri, Amelianda Mega Raden Aldhi Rajendra Yusuf Ramadhan, Andhika Ibnu Ratna Fitria Reza Reza Rizki Aulianita Rizki Aulianita Rizki Aulianita, Rizki Rizki, Irfan Shidqin, Dhuha Shobiyan Susilo, Putri Salsabila Sutikno, Wahyu Sutoyo, Imam Upi Dwi Desi Yanti Widiarina Widika, Arya Widyastuty, Wiwiek Wulandari, Rafika Puteri Yoraeni, Ani Yuni Eka Achyani, Yuni Eka Yunita, Norma