Garuda - Garba Rujukan Digital

Aplikasi Absensi Berbasis Android Pada Sekolah Boarding Sebagai Transformasi Digital Bidang Pendidikan Kamila, Ahya Radiatul; Derhass, Gerry Hudera; Rabbani, Deswin Auliyaa; Andry, Johanes Fernandes; Lee, Francka Sakti
NUANSA INFORMATIKA Vol. 18 No. 2 (2024): Nuansa Informatika 18.2 Juli 2024
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v18i2.155

Boarding schools require students and staff to reside on campus for a set period, necessitating high levels of security and comfort to optimize educational outcomes through effective human resource management. Managing staff and student attendance is crucial in these settings, exemplified by the manual attendance system at Insan Cendikia Magnet School in Bogor. However, manual systems often suffer from inefficiencies, inaccuracies leading to data errors, fraud, and real-time monitoring challenges. To address these issues, this study developed a digital attendance system using FlutterFlow, employing barcode scanning for both academic and non-academic staff. Implementation of this system improved digital attendance processes, with testing confirming its reliable performance. The system effectively met user needs and conditions, integrating attendance data with real-time reporting features. These accessible reports facilitate evaluation and decision-making regarding staff attendance.

Rancang Bangun Aplikasi Member Parkir Terintegrasi dengan Kartu Tanda Mahasiswa Andry, Johanes Fernandes; Lee, Francka Sakti; Geasela, Yemima Monica; Kamila, Ahya Radiatul; Meyliana, Sintia; Winata, Samuel
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 4 No. 2 (2024): Desember 2024
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v4i2.10129

Dalam konteks universitas, teknologi smart card sering kali digunakan sebagai metode autentikasi di gerbang parkir, dengan tujuan meningkatkan efisiensi dan efektivitas. Penelitian ini berfokus pada pengembangan aplikasi dan sistem kartu member parkir yang terintegrasi dengan Kartu Tanda Mahasiswa (KTM) menggunakan metode Waterfall. Tujuan utama penelitian ini adalah untuk memfasilitasi akses parkir di kampus melalui aplikasi desktop yang terhubung dengan KTM, sehingga mahasiswa dapat dengan mudah mengelola keanggotaan parkir mereka. Hasil perancangan menunjukkan bahwa sistem ini memberikan berbagai manfaat, termasuk kemudahan dalam verifikasi identitas mahasiswa, otomatisasi proses parkir, serta optimasi sumber daya kampus. Dengan menggunakan metode Waterfall, penelitian ini memberikan solusi terstruktur dalam pengembangan aplikasi, mulai dari analisis kebutuhan hingga pengujian, yang secara signifikan meningkatkan efisiensi pengelolaan parkir di kampus. Penelitian ini berkontribusi pada pengembangan teknologi integrasi sistem di lingkungan kampus dan membuka wawasan baru tentang implementasi teknologi untuk meningkatkan layanan kampus.

Analisa Pengaruh Penambahan Fitur dengan Perbandingan Algoritma berbasis Bagging dan Boosting pada Deteksi Phishing Link Kamila, Ahya Radiatul; Adikara, Fransiskus; Sutrisno, Sutrisno; Herdian, Cevi
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 10, No 3 (2024): Volume 10 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v10i3.83366

Deteksi phishing link merupakan tantangan kritis dalam keamanan siber yang memerlukan teknik analisis canggih untuk membedakan antara link sah (legitimate link) dan link berbahaya (phishing link). Hal ini perlu dilakukan karena seiring dengan perkembangan teknologi, ancaman phishing semakin kompleks dan sulit dikenali, sehingga tidak hanya dapat menyebabkan kerugian finansial, tetapi juga dapat merusak reputasi organisasi dan menimbulkan kerentanan lebih lanjut terhadap serangan siber lainnya. Dengan peningkatan kompleksitas serangan phishing, pendekatan konvesional tidak lagi cukup efektif, oleh karena itu, diperlukan teknik yang lebih adaptif seperti machine learning untuk mengenali pola-pola dalam link yang menunjukkan potensi ancaman. Penelitian ini bertujuan untuk mendeteksi dini phishing link menggunakan algoritma machine learning dengan menganalisis pengaruh penggunaan feature engineering dengan membandingkan performa algoritma berbasis bagging dan boosting. Dalam penelitian ini, kami mengembangkan fitur baru ('Count_/_Path' dan 'path_length') yang merupakan hasil ekstraksi dari fitur yang sudah ada dan mengevaluasinya menggunakan pehitungan nilai Mutual Information untuk meningkatkan akurasi model. Hasil penelitian menunjukkan bahwa penambahan fitur 'Count_/_Path' dan 'path_length' secara signifikan meningkatkan kinerja model. Selain itu, kami membandingkan tiga algoritma machine learning, yaitu Random Forest, Gradient Boosting, dan XGBoost. Dari hasil perbandingan, algoritma XGBoost dengan penambahan fitur menunjukkan performa terbaik dengan akurasi 92%, recall 94%, dan presisi 91%. Dimana Random Forest hanya penghasilkan akurasi 91%, recall 92%, presisi 90% dan Gradient Boosing hanya menghasilkan akurasi 90%, recall 93%, presisi 88%.

Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction Kamila, Ahya Radiatul; Andry, Johanes Fernandes; Lee, Francka Sakti; Tampinongkol, Felliks F.
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1129

Employee turnover refers to the replacement of employees within an organization, which can lead to losses such as recruitment costs and decreased productivity. Predicting turnover is crucial for companies to anticipate and take appropriate actions to retain potential employees. This study aims to optimize the employee turnover prediction model by integrating hash encoding techniques and machine learning. The dataset used in this study is an open-source dataset obtained from Kaggle dataset. It consists of 14,994 rows and 10 columns (features) representing employee-related information such as satisfaction level, evaluation score, number of projects, average monthly hours, and whether the employee left the company. Among these features, some are of object data type. Since machine learning algorithms generally cannot work directly with object-type features, the use of hash encoding is proposed. This technique converts object-type data into numerical data. It is part of the preprocessing stage, aiming to reduce memory usage, speed up data preprocessing, and improve model performance. After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. The evaluation is conducted using accuracy, recall, precision, and F1-score metrics, which yielded results of 0.988, 0.961, 0.988, and 0.974, respectively. These results indicate that the integration of hash encoding techniques and machine learning can produce a well-performing model for predicting employee turnover.

Analysis Comparison of K-Nearest Neighbor, Multi-Layer Perceptron, and Decision Tree Algorithms in Diamond Price Prediction Kamila, Ahya Radiatul; Andry, Johanes Fernandes; Kusuma, Adi Wahyu Candra; Prasetyo, Eko Wahyu; Derhass, Gerry Hudera
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.532.298-311

Diamond price predictions are essential due to the high demand for these gemstones, valued as investments and jewelry. Diamonds are expensive due to their rarity and extraction process. Their prices vary depending on key factors like the diamond's inherent value and secondary factors such as marketing costs, brand names, and market trends. These variations often confuse customers, potentially leading to investment losses. This research aims to help investors determine the true price of diamonds based solely on their intrinsic value, excluding secondary factors. A machine learning approach was utilized to predict diamond prices, focusing on primary determinants. Three models such as Multi-Layer Perceptron (MLP), Decision Tree, and K-Nearest Neighbor (KNN) were compared with manual hyperparameter tuning to identify the best performing algorithm. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Among the models, KNN demonstrated the best results, achieving MAPE, MAE, and MSE values of 1.1%, 0.00038, and 〖2.687 x 10〗^(-6) respectively. This study offers valuable insights for investors by accurately predicting diamond prices based on fundamental attributes, minimizing the impact of secondary factors.

Exploring the Effectiveness of Bi-LSTM in Detecting Indonesian-Language Hoax News Kamila, Ahya Radiatul; Budiyanto, Very; Surianto, Surianto
Riwayat: Educational Journal of History and Humanities Vol 8, No 3 (2025): July, Social Studies, Educational Research and Humanities Research.
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i3.48627

This study aims to develop and evaluate a hoax detection model based on Bidirectional Long Short-Term Memory (Bi-LSTM) using a Semi-Supervised Learning approach. In the context of the increasing spread of false information on online platforms, the model is designed to automatically classify news articles as hoaxes or non-hoaxes, even when labeled data is limited. The initial model was trained on a labeled minor dataset and then used to predict labels for an unlabeled major dataset. After combining both datasets, a retraining process was conducted to improve the models generalization to various linguistic styles and sentence structures. Evaluation results show that the model achieved an accuracy of 84%, recall of 76.9%, precision of 70%, and an F1-score of 73.3%. These findings demonstrate that the semi-supervised approach, which combines labeled and unlabeled data, can significantly enhance model performance in hoax detection tasks. This study contributes to the development of an effective and adaptable automated hoax detection system that addresses linguistic challenges in online news texts.

Evaluation of Keyword Extraction using YAKE and KeyBERT in Text Preprocessing for Hoax News Detection Based on Bi-LSTM Kamila, Ahya Radiatul; Derhass, Gerry Hudera; Surianto, Surianto; Budiyanto, Very
Riwayat: Educational Journal of History and Humanities Vol 8, No 3 (2025): July, Social Studies, Educational Research and Humanities Research.
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i3.48626

The spread of hoaxes through social media presents a significant challenge to the accuracy of public information. Automated detection based on natural language processing (NLP) offers a potential solution to this issue. This study investigates the impact of keyword extraction methods on the performance of hoax classification using the Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. Two methods are evaluated: YAKE, which relies on statistical features, and KeyBERT, which utilizes semantic representations from the BERT transformer model. The IDNHoaxCorpus, an Indonesian-language dataset, serves as the experimental basis, undergoing preprocessing, keyword extraction, and model training stages. Evaluation metrics include accuracy, precision, recall, F1-score, and processing time. Results show that KeyBERT achieves higher accuracy and F1-score (82.56% and 73.30%, respectively) compared to YAKE (80.07% and 71.11%), but at the cost of significantly longer processing time (360 seconds vs. 13 seconds). These findings highlight a notable trade-off between accuracy and computational efficiency, which should be considered based on application requirements such as real-time systems or batch processing. This study underscores the importance of selecting appropriate feature extraction strategies in text-based hoax detection systems.

Implementation of Random Forest Classification and Support Vector Machine Algorithms for Phishing Link Detection Tampinongkol, Felliks Feiters; Kamila, Ahya Radiatul; Wardhana, Ariq Cahya; Kusuma, Adi Wahyu Candra; Revaldo, Danny
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1588

This research compares two machine learning methods, Support Vector Machine (SVM) and Random Forest Classification (RFC), in detecting phishing links. Phishing is an attempt to obtain sensitive information by masquerading as a trustworthy entity in electronic communications. Detecting phishing links is crucial in protecting users from this cyber threat. In this study, we used a dataset consisting of features extracted from URLs, such as URL length, the use of special characters, and domain information. The dataset was then split into training and testing data with an 80:20 ratio. We trained the SVM and RFC models using the training data and evaluated their performance based on the testing data. The results show that both methods have their respective advantages. SVM, known for handling high-dimensional data well and providing optimal solutions for classification problems, demonstrated a high accuracy rate in detecting phishing links. However, SVM requires a longer training time compared to RFC. On the other hand, RFC, an ensemble method known for its resilience to overfitting, showed performance nearly comparable to SVM in terms of accuracy but with faster training time and better interpretability. This comparison indicates that RFC is more suitable for scenarios requiring quick results and easy interpretation, while SVM is more appropriate for situations where accuracy is critical, and computational resources are sufficient. In conclusion, the choice of phishing link detection method should be tailored to specific needs and available resource constraints. This research provides valuable insights for developing more effective, efficient, and relevant phishing detection systems.

Rancang Bangun Aplikasi Member Parkir Terintegrasi dengan Kartu Tanda Mahasiswa Andry, Johanes Fernandes; Lee, Francka Sakti; Geasela, Yemima Monica; Kamila, Ahya Radiatul; Meyliana, Sintia; Winata, Samuel
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 4 No. 2 (2024): Desember 2024
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v4i2.10129

Dalam konteks universitas, teknologi smart card sering kali digunakan sebagai metode autentikasi di gerbang parkir, dengan tujuan meningkatkan efisiensi dan efektivitas. Penelitian ini berfokus pada pengembangan aplikasi dan sistem kartu member parkir yang terintegrasi dengan Kartu Tanda Mahasiswa (KTM) menggunakan metode Waterfall. Tujuan utama penelitian ini adalah untuk memfasilitasi akses parkir di kampus melalui aplikasi desktop yang terhubung dengan KTM, sehingga mahasiswa dapat dengan mudah mengelola keanggotaan parkir mereka. Hasil perancangan menunjukkan bahwa sistem ini memberikan berbagai manfaat, termasuk kemudahan dalam verifikasi identitas mahasiswa, otomatisasi proses parkir, serta optimasi sumber daya kampus. Dengan menggunakan metode Waterfall, penelitian ini memberikan solusi terstruktur dalam pengembangan aplikasi, mulai dari analisis kebutuhan hingga pengujian, yang secara signifikan meningkatkan efisiensi pengelolaan parkir di kampus. Penelitian ini berkontribusi pada pengembangan teknologi integrasi sistem di lingkungan kampus dan membuka wawasan baru tentang implementasi teknologi untuk meningkatkan layanan kampus.

Information system architecture for healthcare company based on TOGAF Christy, Vania; Andry, Johanes Fernandes; Kamila, Ahya Radiatul; Lee, Francka Sakti
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp806-813

In 2020 COVID-19 cases have entered Indonesia causing public health problems and millions of deaths. To prevent transmission of COVID-19, an air purifier is needed whose function is to remove small droplets that can carry the virus. One of them is a medical device company located in Jakarta. The purpose of this research is to produce a design that can improve business processes in the health sector and achieve company goals. The current business process is not very optimal because it is still done conventionally and the existing system has not been integrated with other divisions. To achieve business goals, it is necessary to integrate business processes with information technology (IT) and technology development that will be proposed based on the design of information system architecture that will produce a blueprint and assisted by the open group architecture framework (TOGAF) framework which is very helpful in the process of analyzing company needs. In this research, data collection through interviews with directors and direct observation of health service companies. The results of this study are recommendations given to help health.

Title

Found 11 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 11 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 11 Documents
Search