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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) CogITo Smart Journal Journal of Information Systems and Informatics Konstelasi: Konvergensi Teknologi dan Sistem Informasi Nuansa Informatika
Kamila, Ahya Radiatul
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
Co-Authors Adikara, Fransiskus Derhass, Gerry Hudera EKO WAHYU PRASETYO Francka Sakti Lee Geasela, Yemima Monica Herdian, Cevi Johanes Fernandes Andry Kusuma, Adi Wahyu Candra Meyliana, Sintia Rabbani, Deswin Auliyaa Samuel Winata Sutrisno, Sutrisno Tampinongkol, Felliks F.