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All Journal TEKNIK INFORMATIKA Jurnal Teknologi Informasi dan Ilmu Komputer Fundamental Management Journal Jurnal Informatika Universitas Pamulang MULTINETICS Building of Informatics, Technology and Science Jurnal E-Komtek Journal of Applied Data Sciences fundamental management journal
Nenny Anggraini, Nenny
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Simulasi Pembelajaran Interaktif pada Praktikum Embedded System Berbasis Web Anggraini, Nenny
MULTINETICS Vol. 3 No. 1 (2017): MULTINETICS Mei (2017)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v3i1.1072

Abstract

Simulasi adalah suatu proses peniruan dari sesuatu yang nyata beserta keadaan sekelilingnya (state of affairs). Aksi melakukan simulasi ini secara umum menggambarkan sifat-sifat karakteristik kunci dari kelakuan sistem fisik atau sistem yang abstrak tertentu. Metode yang dipakai adalah Monte Carlo sebagai alur perancangan simulasi. Simulasi ini mendukung proses pembelajaran mata kuliah embedded system yang ada di Program Studi Teknik Informatika Fakultas Sains dan Teknologi. Simulasi pembelajaran ini menggunakan multimedia interaktif sebagai alat pengontrol yang dapat dioperasikan oleh pengguna melalui web, sehingga pengguna dapat memilih apa yang dikehendaki untuk proses selanjutnya. Dalam mata kuliah embedded system, dengan simulasi seperti ini pengguna dapat melakukan praktikum seperti di laboratorium menggunakan mikroprosesor secara digital. Tujuannya adalah agar pembelajaran tidak terbatas pada waktu, tempat dan ketersediaan alat di laboratorium. Simulasi laboratorium ini berbasis web sehingga dapat dijadikan sebagai cetak biru electronic laboratory.
A Comparative Analysis of Random Forest, XGBoost, and LightGBM Algorithms for Emotion Classification in Reddit Comments Anggraini, Nenny; Putra, Syopiansyah Jaya; Wardhani, Luh Kesuma; Arif, Farid Dhiya Ul; Hakiem, Nashrul; Shofi, Imam Marzuki
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.38651

Abstract

This research aims to compare the performance of three classification algorithms, namely Random Forest, XGBoost, and LightGBM, in classifying emotions in Reddit comments. Emotion classification in Reddit comments is a complex classification problem due to its numerous variations and ambiguities. This research utilizes the GoEmotions Fine-Grained dataset, filtered down to 7,325 Reddit comments with 5 different basic emotion labels. In this study, data preprocessing steps, feature extraction using CountVectorizer and TF-IDF, and hyperparameter tuning using GridSearchCV for each algorithm are conducted. Subsequently, model evaluation is performed using Cross-Validation and confusion matrix. The results of the study indicate that Random Forest outperforms the XGBoost and LightGBM algorithm with an accuracy of 75.38% compared to XGBoost with 69.05% accuracy and LightGBM with 66.63% accuracy.
Nearest Neighbor Interpolation and AES Encryption for Enhanced Least Significant Bit (LSB) Steganography Anggraini, Nenny; Wardhani, Luh Kesuma; Assyahid, Muhammad Hudzaifah; Hakiem, Nashrul; Yusuf, Muhammad; Setyawan, Okky Bagus
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7079

Abstract

The increasing use of digital communication raises concerns about data security, especially when transmitting sensitive information. Steganography conceals messages within digital media to prevent detection. However, conventional methods face storage limitations, leading to message truncation or distortion, making hidden messages more detectable. This study proposes a combination of Nearest Neighbor Interpolation (NNI) and Least Significant Bit (LSB) steganography to dynamically expand the cover image, allowing larger encrypted messages to be embedded while maintaining image integrity. NNI was chosen over other interpolation techniques such as Bilinear and Bicubic due to its lower computational complexity and preservation of sharp edges, which minimizes blurring artifacts that could make steganographic alterations more noticeable. AES-128 encryption ensures message confidentiality before embedding. The system was developed as a web-based application to improve usability. The research followed the Waterfall Software Development Life Cycle (SDLC), and Black Box Testing validated system functionality. Testing results showed that the method successfully embedded and extracted messages without data loss, maintaining PSNR values above 40 dB, ensuring minimal perceptual distortion. However, the maximum interpolation limit was 5310 × 5310 pixels, beyond which system constraints caused failures. The stego-images retained original aspect ratios, reducing suspicion. Despite its success, the system remains vulnerable to modifications such as color changes, cropping, rotation, and compression, which can disrupt the message.
CNN-LSTM with Multi-Acoustic Features for Automatic Tajweed Mad Rule Classification Anggraini, Nenny; Rahman, Yusuf; Hidayanto, Achmad Nizar; Sukmana, Husni Teja
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1062

Abstract

The rules of mad recitation in the Qur’an are a crucial aspect of tajwīd, governing the lengthening of vowel sounds that affect both meaning and recitational accuracy. Despite its importance, there is currently no reliable automatic system capable of classifying mad rules based on voice input. This study proposes a deep learning-based approach using a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to automatically classify mad rules from Qur’anic recitations. The research follows the CRISP-DM methodology, covering data understanding, preparation, modeling, and evaluation stages. Acoustic features were extracted from 3,816 annotated audio segments of Surah Al-Fātiḥah, combining Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Root Mean Square (RMS) to represent phonetic and prosodic attributes. The CNN layers captured spatial characteristics of the spectrum, while LSTM layers modeled temporal dependencies of the audio. Experimental results show that the combination of all four features achieved an accuracy of 97.21%, precision of 95.28%, recall of 95.22%, and F1-score of 95.25%. These findings indicate that multi-feature integration enhances model robustness and interpretability. The proposed CNN-LSTM framework demonstrates potential for practical deployment in voice-based tajwīd learning tools and contributes to the broader field of Qur’anic speech recognition by offering a systematic, ethically grounded, and data-driven approach to mad classification.
Co-Authors 1, Aprisani AA Sudharmawan, AA Achmad Nizar Hidayanto Arif, Farid Dhiya Ul Ariq Cahya Wardhana Assyahid, Muhammad Hudzaifah Br Sihaloho, Ruth Devi Yuliani Dwi Cahyanti, Dwi Fitri Mintarsih Heni Pujiastuti Husni Teja Sukmana Hutapea, Ganda T. Khairani, Dewi Ktut Silvanita Luh Kesuma Wardhani Lumbantoruan, Rutman Marchelina, Eva Muhammad Yusuf Muhammad Yusuf Nurpariz Naiborhu , Joseph Rivan Nashrul Hakiem Nurhayati Panjaitan, Robert Rizka Amalia Putri Rozy, Nurul Faizah Sari, Putu Indah Mariska Sembiring, Carolina F. Setyawan, Okky Bagus Shaeku, Muhammad Akhil Shofi, Imam Marzuki Simamora, Engelina Lawita Siti Ummi Masruroh Syopiansyah Jaya Putra Tobing, Josephine Viva Arifin Yusuf Rahman