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All Journal International Journal of Advances in Applied Sciences Jurnal Edukasi dan Penelitian Informatika (JEPIN) Computatio : Journal of Computer Science and Information Systems CogITo Smart Journal Technologia: Jurnal Ilmiah Journal of Information Systems and Informatics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Infotech: Journal of Technology Information Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Konstelasi: Konvergensi Teknologi dan Sistem Informasi JURNAL POLI-TEKNOLOGI Nuansa Informatika Riwayat: Educational Journal of History and Humanities Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal Ragam Pengabdian
Ahya Radiatul Kamila
Universitas Bunda Mulia
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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
Publisher : Universitas Syiah Kuala

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

Abstract

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

Abstract

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

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.
Aplikasi Absensi Berbasis Android Pada Sekolah Boarding Sebagai Transformasi Digital Bidang Pendidikan Ahya Radiatul Kamila; Gerry Hudera Derhass; Deswin Auliyaa Rabbani; Johanes Fernandes Andry; Francka Sakti Lee
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.
Predictive Maintenance of Heavy Equipment Machines using Neural Network Based on Operational Data Ahya Radiatul Kamila; Derhass, Gerry Hudera; Andry, Johanes Fernandes; Lee, Francka Sakti; Budiyanto, Very; Anatasia, Velly
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.555.229-241

Abstract

Preventive maintenance is a routine maintenance strategy that aims to maximize equipment life cycle and prevent unplanned downtime which causes increased repair costs. When carrying out this maintenance, error in selecting machines need to be anticipated to avoid company losses. This research aims to reduce human error in machine selection for preventive maintenance using deep learning. The dataset used in this research is operational data of heavy equipment machine dataset from one of the palm oil companies in Indonesia with 9 independent features and 1 dependent feature. Dependent feature is a target feature contain two target classes representing effective and ineffective machines. The dataset in this study contains outlier, feature scales that are very different, and imbalanced data class. To handle outlier and standardise data scale, the Z-score method is used. Meanwhile, the over sampling method is used to handle imbalanced data classes. To obtain the best model performance, the number of epochs and two types of optimizers (adam&adamax) of neural network are selected. In selecting the number of epochs, experiments were carried out using 100 epochs. This research obtained the linearity relationship between the number of epochs and accuracy with the accuracy values using Adam and Adamax optimizers were 94.82% and 93.11% at the 100th epoch.
Information system architecture for healthcare company based on TOGAF Vania Christy; Johanes Fernandes Andry; Ahya Radiatul Kamila; Francka Sakti Lee
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.pp810-817

Abstract

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.
PENINGKATAN PERFORMA MODEL MACHINE LEARNING UNTUK DETEKSI DINI POLYCYSTIC OVARY SYNDROME MELALUI KOMBINASI METODE PREPROCESSING Ahya Radiatul Kamila; Francka Sakti Lee; Johanes Fernandes Andry
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.448

Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most common hormonal disorders experienced by women of reproductive age and can lead to various health problems, including menstrual irregularities, infertility, and an increased risk of metabolic diseases. Early detection of PCOS is essential to minimize long-term impacts and improve the quality of life for patients. This study aims to identify effective data preprocessing strategies to enhance the performance of classification models for PCOS detection. The dataset used is open source, consisting of 541 participants with 45 clinical and laboratory features. The main challenges encountered include the presence of many missing values, an imbalanced target class distribution, and a large number of independent features. To address these issues, a series of preprocessing steps were applied, including missing value imputation, data balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and dimensionality reduction using Principal Component Analysis (PCA). A classification model was built using the Random Forest algorithm, and its performance was compared before and after applying PCA. The evaluation results show that before PCA, the model achieved an accuracy of 87.5%, precision of 86%, recall of 86%, and an F1-score of 86%. After applying PCA, performance improved to an accuracy of 90%, precision of 89%, recall of 89%, and an F1-score of 89%. These findings indicate that the right combination of preprocessing strategies, particularly SMOTE and PCA, can significantly improve the efficiency and effectiveness of models in detecting PCOS, thereby supporting the development of more reliable medical decision support systems.
Optimalisasi Strategi Pemasaran Laptop dengan Association Rule Mining pada Data Prosesor dan Rating Pelanggan: - Johanes Fernandes Andry; Ahya Radiatul Kamila; Joel Calvary
Computatio : Journal of Computer Science and Information Systems Vol. 10 No. 1 (2026): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v10i1.35703

Abstract

Penelitian bertujuan menganalisis hubungan antara spesifikasi prosesor laptop dengan tingkat kepuasan pelanggan melalui metode Association Rule Mining menggunakan algoritma FP-Growth. Meningkatnya penggunaan big data analytics dalam memahami preferensi konsumen, khususnya di industri laptop. Prosesor sebagai “otak” laptop memiliki pengaruh signifikan terhadap performa dan penilaian pengguna, hubungan kuantitatifnya masih jarang dianalisis secara komprehensif. Metode penelitian dimulai dengan pengumpulan data laptop dari berbagai sumber, termasuk basis data Kaggle. Data meliputi atribut prosesor, RAM, SSD, dan rating pengguna. Proses Cleaning dan transformasi data dianalisis menggunakan FP-Growth untuk menemukan pola keterkaitan antara spesifikasi perangkat keras dan penilaian pelanggan. Hasil menunjukkan adanya korelasi kuat antara jenis prosesor dan rating laptop. Laptop dengan prosesor berkinerja tinggi serta dukungan SSD memiliki kecenderungan memperoleh rating lebih tinggi. Analisis aturan asosiasi yang diperoleh memiliki tingkat kepercayaan tinggi, sehingga dapat menjadi indikator penting dalam strategi pengembangan produk.
Eye-Gaze sebagai Indikator Beban Kognitif pada Tugas Bahasa Indonesia: Studi Diagnostik dalam Intelligent Tutoring System Nisrina, Alaniah; Prasetyo, Eko Wahyu; Kamila, Ahya Radiatul; Lee, Francka Sakti
Jurnal Ragam Pengabdian Vol. 3 No. 2 (2026): Mei-Agustus, Sustainable Development Goals (SDGs): Multidisciplinary Perspectiv
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/sqmn2d94

Abstract

Most classroom assessments of Indonesian-language proficiency still rely on a single outcome measure—whether the selected answer is correct or incorrect while largely overlooking how a learner's attention moves before arriving at that answer. This study aims to identify and compare eye-gaze scanpath patterns between high- and low-performing learners across three Indonesian-language task types: interpreting figurative language (majas), extracting the gist of a short reading passage, and applying spelling and punctuation conventions (Ejaan yang Disempurnakan/EYD). The goal is to reveal how cognitive load manifests differently across these task types. Unlike most prior eye-gaze research that treats cognitive load as a single, undifferentiated construct, this study tests the hypothesis that cognitive load varies systematically with the underlying language skill being exercised: figurative-language tasks emphasize semantic inference, reading comprehension emphasizes main-idea extraction, and orthography tasks emphasize rule-checking. To this end, gaze heatmap data were collected via real-time eye-movement recording through each participant's webcam while they completed the three tasks on an Intelligent Tutoring System (ITS) application. The article concludes by discussing the implications of this task-sensitive approach for designing an adaptive, AI-assisted Intelligent Tutoring System capable of responding to cognitive load in a task-specific manner.
Studi Kasus Feature Engineering Untuk Data Teks: Perbandingan Label Encoding dan One-Hot Encoding Pada Metode Linear Regresi Cevi Herdian; Ahya Kamila; I Gusti Agung Musa Budidarma
Technologia : Jurnal Ilmiah Vol 15 No 1 (2024): Technologia (Januari)
Publisher : Fakultas Teknologi Informasi, Universitas Islam Kalimantan Muhammad Arsyad Al Banjari, di bawah koordinasi UPT Publikasi dan Pengelolaan Jurnal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v15i1.13457

Abstract

Di dalam pemodelan pembelajaran mesin (Machine Learning), data terbagi menjadi jenis data numerik dan jenis data teks. Tetapi Machine Learning lebih cenderung efektif dalam mengenali pola pada jenis data numerik karena algoritma Machine Learning, terutama yang berbasis statistik dan matematika, dirancang untuk memproses dan menganalisis data numerik. Sehingga bentuk data teks harus dirubah ke dalam bentuk data numerik yang merupakan bagian dari Feature Engineering. Pada penelitian ini, peneliti membanding sebuah hasil akurasi dari prediksi Machine Learning yaitu linear regresi pada teks label data yang telah dilakukan perubahan menjadi numerik dengan metode Feature engineering Label Encoding dan juga Feature Engineering One-Hot Encoding. Pada penelitian ini didapatkan hasil R-Square untuk Label Encoding 0.54 dan R-Squared untuk One-Hot Encoding 0.85 (hasil One-Hot Encoding lebih baik). Sehingga tentu saja yang harus dipilih untuk model yang dibuat adalah Feature Engineering One-Hot Encoding. Untuk kedepannya bisa dilakukan pengujian dengan metode lain untuk merubah data teks menjadi numerik seperti Bags of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), dan yang lainnya.
Co-Authors Adikara, Fransiskus Agung Stefanus Kembau Anatasia, Velly Ariq Cahya Wardhana Benny Benny Cevi Herdian Derhass, Gerry Hudera Deswin Auliyaa Rabbani EKO WAHYU PRASETYO Felliks Tampinongkol, Felliks Francka Sakti Lee Geasela, Yemima Monica Gerry Hudera Derhass Herdian, Cevi I Gusti Agung Musa Budidarma Budidarma Joel Calvary Johanes Fernandes Andry Johanes Fernandes Andry Kusuma, Adi Wahyu Candra Meyliana, Sintia Nisrina, Alaniah Purnomo, Yunianto Revaldo, Danny Rozali Ilham Samuel Winata Surianto Surianto Sutrisno, Sutrisno Tampinongkol, Felliks F. Tampinongkol, Felliks Feiters Teguh Tri Sugiono Vania Christy Very Budiyanto Virginia, Stella