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All Journal Jurnal Pekommas JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Tekno Kompak METIK JURNAL JUTIS : Jurnal Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics JIKA (Jurnal Informatika) Informatics and Digital Expert (INDEX) Jurnal Ilmiah Wahana Pendidikan Journal of Informatics and Communication Technology (JICT) Jurnal Locus Penelitian dan Pengabdian Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Algoritma Jurnal Global Ilmiah INOVTEK Polbeng - Seri Informatika
Ashaury, Herdi
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Religion Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Social Sciences Other

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Sistem Keamanan Otentikasi Pengguna Pada Modul Single Sign On Menggunakan OAuth 2.0 dan One Time Password Arianto, Ilham Gumeraruloh; Witanti, Wina; Ashaury, Herdi
Jurnal IT UHB Vol 6 No 1 (2025): Jurnal Ilmu Komputer dan Teknologi
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/ikomti.v6i1.1768

Abstract

Keamanan informasi menjadi prioritas utama dalam melindungi data sensitif pada sistem yang menangani transfer data. Penelitian ini mengembangkan sistem Technical Support Assistance (TSA) dengan keamanan yang ditingkatkan melalui kombinasi modul Single Sign-On (SSO) berbasis Open Authentication (OAuth 2.0) dan metode One-Time Password (OTP) berbasis waktu. Pendekatan ini menciptakan autentikasi dua faktor (2FA) yang efektif dalam menghadapi risiko serangan seperti sniffing, brute force attacks, dan man-in-the-middle (MITM). Hasil pengujian menunjukkan bahwa tanpa OTP, tingkat keberhasilan serangan adalah 63% untuk brute force, 50% untuk sniffing, dan 65% untuk MITM. Setelah penerapan Oauth 2.0 dan OTP, angka ini turun signifikan menjadi masing-masing 25%, 5%, dan 10%, membuktikan bahwa kombinasi OAuth 2.0 dan OTP meningkatkan perlindungan sistem secara signifikan. Dibandingkan metode autentikasi terdahulu, TSA menawarkan keunggulan berupa keamanan berbasis token dinamis, pengurangan risiko serangan secara drastis, integrasi yang lebih mudah dengan layanan lain, serta efisiensi autentikasi yang lebih tinggi. Penelitian ini memberikan solusi inovatif untuk meningkatkan keamanan data sensitif dan relevan bagi organisasi yang memerlukan perlindungan tingkat tinggi dalam sistem mereka.
Prediksi Pengagguran Menggunakan Decision Tree Dengan Algoritma C5.0 Pada Data Penduduk Kecamatan Caringin Kabupaten Bogor Kahfi, Muhammad Dzatul; Umbara, Fajri Rakhmat; Ashaury, Herdi
Informatics and Digital Expert (INDEX) Vol. 4 No. 2 (2022): INDEX, November 2022
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v4i2.913

Abstract

Tingkat kesejahteraan dalam kehidupan bermasyarakat dapat dilihat dari tingkat penganggurannya. Pemerintah daerah biasanya mengadakan sebuah program untuk membantu mengurangi jumlah pengangguran, entah itu dengan mengadakan sebuah pelatihan atau hal lain yang dapat mendorong kreativitas masyarakat dan meningkatkan kemampuan hardskill agar dapat bersaing di dunia kerja. Ada banyak penelitian yang memprediksi tingkat pengangguran dan juga ada penelitian yang menggunakan algoritma C5.0 untuk melakukan prediksi, namun belum ada penelitian yang menggabungkan subjek dan metode tersebut. penelitian ini bertujuan untuk membuat sebuah model prediksi menggunakan algoritma C5.0 terhadap data penduduk kecamatan caringin dan mencari skenario dengan hasil akurasi yang paling tinggi. namun terdapat beberapa permasalahan yang harus dihadapi seperti bagaimana tingkat akurasi Model klasifikasi Decision Tree dengan algoritma C5.0 terhadap dataset penduduk Kecamatan Caringin dan Bagaimana resio data latih data uji dan penggunaan pruning memengaruhi tingkat akurasi prediksi yang akan dilakukan. Penelitian ini dievaluasi menggunakan beberapa skenario rasio data latih dan data uji yang berbeda beda dan penggunaan pruning yang berbeda. Hasil dari penelitian ini adalah model prediksi pengangguran berhasil dibuat dengan tingkat akurasi paling tinggi yaitu pada skenario data latih 70% dan data uji 30% dengan menerapkan teknik post pruning.
Analisis Ketahanan Web Application Firewall Terhadap Serangan SQL Injection Humaira, Hana Nazla; Hadiana, Asep Id; Ashaury, Herdi
Jurnal Ilmiah Wahana Pendidikan Vol 10 No 5 (2024): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10526246

Abstract

The ever-advancing technological transformation has brought benefits to our daily lives. Thanks to these technological advances, it is very easy to get access to information, communicate with platforms, and conduct transactions in an increasingly sophisticated digital environment. Web application services are one of the positive impacts of the development of the digital world. However, behind the ease of access offered by web applications, it is often targeted by cyber criminals to obtain sensitive data within it. The application of Web Application Firewall (WAF) as a web application security from SQL injection attacks can be a solution to security issues. This research involves several different WAF solution selections. The results show that the effectiveness of WAF in protecting applications from SQL injection attacks varies depending on the type of attack. From the attacks performed, Naxsi was able to filter out 99% of the attacks and ModSecurity 100%.
Klasifikasi Tuberkulosis (TBC) dengan Metode Random Forest Menggunakan Teknik Re-Sampling ADASYN-Tomek Links Nurhaliza, Nabillah; Sabrina, Puspita Nurul; Ashaury, Herdi
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2458

Abstract

Data imbalance is a common challenge in medical classification, including in the diagnosis of Tuberculosis (TB), where the number of positive cases is significantly lower than that of negative cases. This condition can reduce model performance, particularly in detecting the minority class. This study aims to evaluate the performance of the Random Forest method in classifying imbalanced TB data by applying a combination of the ADASYN and Tomek Links re-sampling techniques. The dataset used was obtained from the Cisarua Public Health Center (Puskesmas), Bogor, consisting of 1,069 patient records with 15 features and one target label. The research process included data preprocessing, one-hot encoding, data splitting, the use of ADASYN to generate synthetic samples for the minority class, and the application of Tomek Links to remove ambiguous data in overlapping class regions. The evaluation employed accuracy, precision, recall, and F1-score metrics using both hold-out and k-fold cross-validation schemes. The results show that the combination of ADASYN and Tomek Links improved the F1-score for the positive class from 0.67 to 0.71 in the hold-out evaluation, and reached 0.9129 in the cross-validation evaluation. These findings indicate that the proposed approach is effective in addressing data imbalance and has the potential to be integrated into clinical decision-support systems in community health centers (Puskesmas) to aid in early detection of TB cases.
Computational Analysis of Student Stress on Social Media using Support Vector Machine and Latent Dirichlet Allocation Fauzan, Mochammad; Ashaury, Herdi; Ramadhan, Edvin
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/8jcvxk45

Abstract

This study develops a two-stage machine-learning framework to identify academic stressors among Indonesian university students using Twitter data. A Support Vector Machine (SVM) classifier was trained on manually annotated tweets and benchmarked against Naïve Bayes, logistic regression, and random forest, achieving an accuracy of 0.91 and a macro F1-score of 0.914, outperforming all baselines. Tweets classified as stress-related with ≥75% confidence were subsequently analyzed using Latent Dirichlet Allocation (LDA), which generated six coherent stressor categories. The framework reveals both structural academic pressures and culturally specific patterns, including references to “dosen killer” and emerging mental-health vocabulary. Contributions include the first Indonesia-focused stressor map derived from large-scale social media discourse and the integration of confidence filtering to enhance topic quality. While results demonstrate the feasibility of social-media–based stress detection, limitations remain regarding temporal drift, annotation bias, and demographic representativeness. Future research should incorporate real-time streaming pipelines, multimodal annotation, and longitudinal evaluation to enhance robustness and early-warning potential.
Co-Authors Arianto, Ilham Gumeraruloh Athallah, Sulthan Cepi, Gan Edvin Ramadhan Fauzan, Mochammad Gerliandeva, Alfin Gikaldi Arbiyan Septuri Gunawan Abdillah, Gunawan Hadiana, Asep Id Humaira, Hana Nazla Kafi, Moch. Nurul Kahfi, Muhammad Dzatul Kharis Pratama, Adam Khoirullah, Debri Krisdianto Sitanggang, Sari Mulianti, Adhani Nurhaliza, Nabillah Nurhasanah, Wulandari Nurul Sabrina , Puspita Nurul Sabrina, Puspita Oliviana Zabka, Arthur Permana, Hary Puspita Nurul Sabrina, Puspita Nurul Putra Permana, Daffa Rahayu, Ania Rakhmat Umbara, Fajri Ramadhani, Desnantia Eka Ria Andriani Sutialisna, Susan Trianggara, Dhani Umbara, Fajri Rakhmat Wina Witanti Wirianto, Rahmadi Dimas Yulison Herry Chrisnanto