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All Journal VALENSI Voteteknika (Vocational Teknik Elektronika dan Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) Jurnal Kartika Kimia JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology JATI (Jurnal Mahasiswa Teknik Informatika) Informatics and Digital Expert (INDEX) Journal of Applied Computer Science and Technology (JACOST) International Journal of Quantitative Research and Modeling Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Algoritma IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Jurnal Matematika Integratif Jurnal Sains dan Kesehatan
Melina Melina
Jurusan Teknik Informatika, Fakultas Sains Dan Informatika, Universitas Jenderal Achmad Yani
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation

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Prediksi Curah Hujan Menggunakan Metode Bi-LSTM dan GRU Berbasis Data Iklim Abdillah, Fajrul; Hadiana, Asep Id; Melina, Melina
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.22-2.2305

Abstract

As a tropical country, Indonesia faces great challenges in predicting rainfall due to increasingly dynamic climate change. This study aims to predict rainfall in an urban area in West Java with tropical climate characteristics using deep learning methods, namely Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) based on climate data collected from local meteorological stations. The results show that the Bi-LSTM method provides more stable prediction performance with a Mean Absolute Error (MAE) value of 0.0108 and a Root Mean Squared Error (RMSE) of 0.0158. In contrast, the GRU method produced variable performance with higher MAE and RMSE values in some test scenarios. The main findings of this study indicate that the BiLSTM model has a higher level of accuracy, making it an effective information technology solution to support disaster mitigation and agricultural sector planning in climatically complex regions.
Klasifikasi Penyakit Monkeypox dengan XGBoost dan SMOTE untuk Penanganan Data Tidak Seimbang Illawati, Adinda Rahma; Hadiana, Asep Id; Melina, Melina
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.22-2.2349

Abstract

Monkeypox merupakan penyakit menular yang penyebarannya cepat dan memerlukan sistem deteksi dini yang akurat. Penelitian ini bertujuan mengembangkan model klasifikasi penyakit monkeypox dengan mengatasi permasalahan ketidakseimbangan data. Metode yang digunakan adalah Extreme Gradient Boosting (XGBoost) yang dikombinasikan dengan teknik Synthetic Minority Over-sampling Technique (SMOTE). Evaluasi model menggunakan Confusion Matrix dengan hasil akurasi 69%, presisi sebesar 0.69, recall sebesar 0.93, dan F1-score sebesar 0.79. Selain itu, nilai Area Under Curve - Receiver Operating Characteristic (AUC-ROC) mencapai 0.68. Penelitian ini menunjukkan bahwa kombinasi SMOTE dan XGBoost dapat mengatasi ketidakseimbangan data dan meningkatkan deteksi kelas minoritas, sehingga memberikan kontribusi dalam pengembangan sistem deteksi dini penyakit menular secara lebih akurat dan efisien.
Evaluasi Kualitas Klaster Wilayah Rawan Bencana Menggunakan K-Means dengan Silhouette dan Elbow Method Sudrajat, Risqi; Hadiana, Asep Id; Melina, Melina
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.22-2.2379

Abstract

Natural disasters such as floods, earthquakes, and landslides are recurring threats in Cirebon City, West Java. This study aims to classify disaster-prone areas using the K-Means algorithm based on 1,144 incident data from Open Data Jabar. The data were grouped into three clusters, namely safe, moderate, and dangerous. Cluster quality was evaluated using the Silhouette Score and Elbow Method. The results of this study show that the model without normalization produced a score of 0.6804, reflecting good cluster separation. Conversely, the application of MinMaxScaler normalization significantly reduced the model's performance, with a score of 0.3900. The main contribution of this study is to show that data normalization can disrupt the natural pattern of risk distribution, thereby reducing the quality of clustering. Therefore, the selection of pre-processing techniques needs to be adjusted to the characteristics of local data. It is hoped that this study can be the basis for the development of a more adaptive and data-driven disaster mitigation decision support system.
QUESTION BANK SECURITY USING RIVEST SHAMIR ADLEMAN ALGORITHM AND ADVANCED ENCRYPTION STANDARD Taris Monica; Asep Id Hadiana; Melina Melina
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 3 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8654

Abstract

Data security is essential. Educational question banks at vocational high schools (SMK) contain confidential information that could be misused if not properly secured. This research aims to ensure students question bank data and develop a responsive web platform for Pusdikhubad Cimahi Vocational School by implementing the integration of the Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA) cryptographic algorithms through the encryption and decryption process. AES is a symmetric key cryptography algorithm, while RSA is an encryption algorithm based on using public keys to encrypt the keys required by AES-256. The integration of these two algorithms aims to ensure data confidentiality, prevent manipulation, and facilitate access to exam materials by authorized parties. This research shows that the process of encrypting and decrypting question data using a combination of RSA and AES was successfully carried out on the question bank system. Avalanche Effect testing shows that the RSA and AES 256-bit encryption has an Avalanche Effect level of 49.99%. Apart from that, the system feasibility test using black box testing results shows that the SIFILE system has a percentage level of 100%. It is hoped that the results of this research can serve as a data security system at Pusdikhubad Cimahi Vocational School and other educational institutions to secure the question bank from unauthorized access
Perbandingan IBAC dan ABAC Sebagai Strategi Keamanan Siber Berbasis Zero Trust Architecture (ZTA) Erwin Maulana; Herdi Ashaury; Melina
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3127

Abstract

The rapid development of web technologies has increased exposure to various types of cyberattacks, making traditional perimeter-based security approaches less effective. Zero Trust Architecture (ZTA), with its principle of “never trust, always verify,” offers a more adaptive approach to addressing modern threats. This study aims to analyze and compare the effectiveness of two access control models within the ZTA framework—Identity-Based Access Control (IBAC) and Attribute-Based Access Control (ABAC)—in enhancing web application security. The research employs a quantitative experimental approach by developing two ZTA-based web application prototypes implementing IBAC and ABAC. Three common cyberattacks—SQL Injection, Cross-Site Scripting (XSS), and Brute Force—are tested using Detection Rate as the evaluation parameter. The results show that both models achieve a 100% Detection Rate against all simulated attacks, indicating that they provide an equivalent level of protection within the context of this experiment. Nevertheless, conceptual analysis reveals that ABAC offers advantages in terms of granularity and contextual awareness, as it is capable of dynamically evaluating user attributes, object attributes, and environmental conditions. These characteristics make ABAC more aligned with the principle of continuous verification in Zero Trust, particularly for long-term implementation in modern web applications.
Co-Authors Abdillah, Fajrul Ahmad Marjan Akbar Mulya Hadi Ali, Moch. Dzikri Azhari Ardiyansyah, Muhamad Salman Asep Rizaludin Azhari, Moch Dzikri Benedictus Benny Sihotang Cecep Jamaludin Devitri Amisa Dewi Novianti Diana Lupitasari Dudi Darmawan Eddie Khrisna Putra Eina, Muhammad Fikri Emia Rosta Br. Sebayang Endang Rosdiana Erwin Maulana Fahmi Yusron Fiddin Fajri Rakhmat Umbara Farhan Fadilah Fuji Astari, Dhea Hadiana, Asep Id Herdi Ashaury Herlina Napitupulu Hernandi Sujono Illawati, Adinda Rahma Ilyas, Rid Kasyidi, Fatan Komarudin, Agus Levi Sabili, Naufal Lia Dewi Juliawaty Lia Dewi Juliawaty Monica, Taris Muthmainah, Sekar Ghaida Nabilla, Ulya Nia Sri Hardianti Norizan Mohamed Octo Emerald Siregar Oktaviani, Ayu Nur Putra, Eddie Krishna Rahmat Awaludin Salam Razaki, Adam Restianim, Vivien Reza Fauzi Iskandar Rezky Yuniarti Ridwan Ilyas Senadi Budiman Sudrajat, Risqi Sukono Sukono Sukrido Sukrido Sumantri, Fithra Aditya Tanjung, Irvan Taris Monica Tia Setiawati Valentina Adimurti Kusumaningtyas Wina Witanti Yana Maolana Syah Yana Maolana Syah Yuliana, Trisna Yulison Herry Chrisnanto