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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika Creative Information Technology Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JUTEI (Jurnal Terapan Teknologi Informasi) JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Telematika JIEMS (Journal of Industrial Engineering and Management Systems) JOISIE (Journal Of Information Systems And Informatics Engineering) Zonasi: Jurnal Sistem Informasi JISA (Jurnal Informatika dan Sains) Journal of Innovation Information Technology and Application (JINITA) Jurnal Algoritma, Logika dan Komputasi Jurnal Minfo Polgan (JMP)
Thenata, Angelina P.
Universitas Bunda Mulia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Analysis of Public Sentiment Towards President Prabowo's Work Program Using The CNN Thenata, Angelina Pramana; Saputra, Dimas Sakti Reka
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9394

Abstract

Digital media has now become the primary means for Indonesians to receive and respond to information, including the work programs presented by Prabowo Subianto. One of the programs that is widely discussed by the public is related to efforts to improve the national economy. Public responses to this issue are widespread on social media, reflecting diverse sentiments. Therefore, this study aims to analyze the sentiment of comments from social media users X regarding President Prabowo's work programs in the economic sector, using a deep learning approach based on the Convolutional Neural Network (CNN) architecture. The methods employed include data collection, text preprocessing, and training a CNN model. The dataset used consisted of 2,467 data points, with 1,086 labeled as positive and 1,381 labeled as negative. The test results showed that the model achieved an accuracy of 87.45% and an Area Under the Curve (AUC) score of 0.9373, indicating excellent classification performance in distinguishing between positive and negative sentiments. This study proves that the combination of CNN and FastText is a practical approach to understanding text-based public opinion from social media.
Klasifikasi Terawasi Anomali Suara Kipas Industri Menggunakan Jaringan Saraf Tiruan dan Fitur Akustik Rekayasa Thenata, Angelina Pramana; ., Ranny; Hakim, Bhustomy; Kaunang, Fergie Joanda
Jurnal Telematika Vol. 20 No. 1 (2025)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v20i1.772

Abstract

Penelitian ini menggunakan pendekatan supervised learning berbasis jaringan saraf untuk deteksi anomali pada sistem kipas industry. Dengan subset data FAN dari MIMII (malfunctioning industrial machine investigation and inspection) dataset dengan 530 rekaman berlabel (383 normal dan 147 abnormal), penelitian ini mengekstraksi fitur akustik yang meliputi mel-frequency cepstral coefficients (MFCC), spectral descriptor (centroid, roll off), serta temporal measures (zero-crossing rate, autocorrelation). Uji statistik univariat menunjukkan sejumlah koefisien MFCC dan fitur domain waktu berbeda signifikan antar kelas (p < 0,05). Model jaringan saraf feed-forward dengan dua lapisan tersembunyi berukuran 64 unit (aktivasi ReLU) dan regularisasi dropout dilatih menggunakan stratified cross validation dengan 5-fold sehingga menghasilkan nilai F1 rata-rata sebesar 89,9%. Penggunaan beberapa nilai ambang (τ ∈ {0,3–0,7}) menegaskan kekokohan model yang terlihat pada hasil data uji dengan nilai ambang terpilih adalah τ = 0,5 yang mencapai precision sebesar 100%, recall = 93,10%, F1 = 96,43%, dan akurasi = 98,11% (hasil identik diperoleh pada τ = 0,6–0,7; sementara τ = 0,3 memberikan recall lebih tinggi). Model juga menghasilkan nilai AUC-ROC sebesar 0,9978 yang mendekati ideal dan menunjukkan daya diskriminasi lintas-ambang yang sangat baik. Temuan ini memperlihatkan bahwa penggabungan fitur akustik yang dapat diinterpretasikan dengan pengklasifikasi saraf yang ringkas memungkinkan deteksi anomali non-invasif yang akurat untuk penerapan Industri 4.0 dengan kebutuhan perangkat keras minimal.
Data Pipeline Architecture with Near Real-Time Streaming Multiple Source Indonesian Online News Data Lake Thenata, Angelina Pramana
JISA(Jurnal Informatika dan Sains) Vol 3, No 1 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i1.657

Abstract

The rapid development of information has made online news increasingly needed. Online news attracts readers' attention by providing convenience and speed in presenting news from various fields. However, the large amount (volume) of online news that spreads in a short time (velocity) and the public's need to consume news in various references (variety) can affect people's lives. Therefore, the government as the regulator and news agencies need to monitor online news circulating. Based on these problems, the researcher proposes a data lake architectural design that is suitable for online news and can run in real-time. Data lakes can solve the main problems of Big Data (volume, velocity, variety). In proposing this data lake architecture, the researcher conducted a literature study and analyzed the flow of the data lake architecture according to online news. Furthermore, the researcher will use this architecture to combine and uniform the online news data structure from several online news channels and then stream it in real-time to fill the data lake. The results of using the data lake architecture for online news will be stored on MongoDB which functions as a database to store all data for both the short and long term. Finally, this data lake will be a means to accommodate, dive into, and analyze the circulating online news data. Keywords – Data Lake, Online News, Real-Time
Classification of Facial Acne Types Based on Self-Supervised Learning using DINOv2 Chardaputeri, Gantari; Thenata, Angelina Pramana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11856

Abstract

Acne is a common inflammatory skin condition that can affect an individual’s psychological well-being and overall quality of life. The inability to independently recognize specific types of acne often leads to the use of inappropriate skincare products. This situation highlights the need for an image-based classification system that can provide accurate visual identification. The self-supervised learning method Distillation with NO Labels, version 2 (DINOv2), is employed as a feature extractor to classify four types of acne—Acne fulminans, Acne nodules, Papules, and Pustules—using the “skin-90” dataset. The fine-tuning process is conducted through a Parameter-Efficient Fine-Tuning (PEFT) approach using Low-Rank Adaptation (LoRA) to adjust the model’s visual representations to the acne domain without updating all parameters in full, followed by integration with a classification head. The results show that the model achieves an accuracy of 90.70%, with precision, recall, and F1-score values of 90.64%, 90.68%, and 90.57%, respectively. The findings suggest that the proposed architectural design and training configuration are suitable for capturing relevant visual patterns of acne, while further validation is required to assess robustness across more diverse data distributions.
Dual-Domain Transformer-Based Video Deepfake Detection Kesya Wangsa; Angelina Pramana Thenata
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3023

Abstract

Deepfake-enabled fraud caused nearly US$900 million in global losses during 2025, yet existing detection methods struggle with high-quality synthetic videos. Traditional CNN-based approaches fail to capture subtle global manipulations, while transformer-based models focus predominantly on spatial features, missing frequency-domain artifacts where manipulation traces are most evident. This research developed a dual-domain deepfake detection system combining spatial RGB analysis with Discrete Cosine Transform (DCT) frequency features, integrated into  Data-Efficient Image Transformer (DeiT-Small) architecture. The proposed DeiT-DCT model processes four-channel inputs (RGB + DCT) enabling simultaneous learning of spatial textures and frequency anomalies. Training employed the AdamW optimizer with Cosine Annealing Warm Restart, Mixup augmentation with SoftTargetCrossEntropy loss, and domain-balanced sampling via WeightedRandomSampler. Evaluated on a combined dataset of five benchmarks (Celeb-DF v2, DeeperForensics-1.0, FaceForensics++, Korean Deepfake and Indonesia, totaling 3000 videos), the model achieved 92.54% accuracy, 91.69% precision, 93.33% recall, and 92.50% F1-score on the test set. These findings demonstrate that integrating dual-domain representation with data-efficient transformer architectures produces a robust deepfake detection system deployable in real-world scenarios where manipulation techniques continuously evolve.
KLASIFIKASI ENGINE FAILURE BERDASARKAN BUNYI MOBIL MENGGUNAKAN MFCC DAN STFT DENGAN MACHINE LEARNING ; Bhustomy Hakim; Fergie Joanda Kaunang; Angelina Pramana Thenata; Ranny Ranny
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol. 9 No. 1 (2025)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v9i1.4936

Abstract

Deteksi kerusakan mobil sangat penting untuk meningkatkan keselamatan berkendara dan mengurangi biaya perbaikan. Salah satu metode yang umum digunakan oleh mekanik adalah mendiagnosis kerusakan melalui suara yang dihasilkan oleh komponen kendaraan. Namun, proses ini masih bergantung pada keahlian manual dan dapat menyebabkan kesalahan atau keterlambatan diagnosa. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan model klasifikasi kerusakan mobil berbasis suara menggunakan teknik data science, terutama machine learning. Data suara dikumpulkan dari berbagai sumber dari kerjasama bengkel-bengkel di Indonesia berupa file suara mesin dengan format wav dengan label berbeda, kemudian diolah menggunakan metode Mel Frequency Cepstral Coefficients (MFCC) dan Short-Time Fourier Transformation (STFT) untuk mengekstrak fitur penting dari sinyal suara. Selanjutnya, dengan algoritma Multilayer Perceptron diimplementasikan untuk membangun model prediksi kerusakan. Penelitian ini mengevaluasi model berdasarkan nilai akurasi dari confusion matrix untuk menemukan model terbaik dalam mendeteksi jenis kerusakan berdasarkan suara. Model dengan MFCC sebagai metode ekstraksi fitur terbukti menghasilkan akurasi tertinggi sebesar 83,35% ketimbang STFT yang hanya memiliki akurasi sebesar 78,82% dengan konfigurasi fungsi aktivasi ReLU dan layer dengan 512 neuron.
Indonesian Hate Speech Detection Across Diverse Domains Using Parameter-Efficient Fine-Tuning with IndoBERT and LoRA Fergie Joanda Kaunang; Bhustomy Hakim; Angelina Pramana Thenata
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16581

Abstract

The rapid proliferation of digital connectivity in Indonesia has catalyzed an unprecedented surge in harmful online content, necessitating robust automated systems for hate speech detection that can generalize across diverse digital platforms. Traditional models often struggle with domain shift and the linguistic complexities of Indonesian social media discourse, including informal slang and code-mixing. This research proposes a multi-domain detection framework leveraging the IndoBERT-base-p1 architecture integrated with Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning (PEFT) strategy. The study utilizes a multi-source corpus from Instagram, Twitter, and news portals, employing back-translation to augment scarce Instagram data and stratified downsampling to ensure domain equilibrium. By training only 1–2% of the total 110 million parameters, specifically targeting the query and value attention modules, the model achieves significant computational savings with a training loss of 0.345. Experimental results demonstrate high robustness, with the framework attaining F1-scores of 0.83 for both Instagram and Twitter, and 0.81 for news portals, while maintaining accuracies between 0.81 and 0.85. Qualitative validation through word cloud analysis further confirms the model's ability to distinguish between aggressive sociopolitical triggers and neutral functional discourse. This study contributes a scalable and resource-efficient solution for real-time content moderation, proving effective across both formal journalistic Indonesian and informal digital dialects. The findings indicate that IndoBERT+LoRA provides a promising and resource-efficient approach for multi-domain Indonesian hate and abusive speech classification, while stricter leave-one-domain-out evaluation remains an important direction for future work.
Co-Authors ., Ranny Agus, Agus Andhika Galuh Prabawati Arron, Rivaltino Asvian Sulaeman Bhustomy Hakim Chardaputeri, Gantari Fergie Joanda Kaunang Kaunang, Fergie Joanda Kesya Wangsa Lim Yong Teck Matheus Alvian Wikanargo Muh Rizky Ramadhan Muhammad Syukri Mustafa Novian Adi Prasetyo Ranny Ranny Saputra, Dimas Sakti Reka Sinata, Frans Wikanargo, Matheus Alvian