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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal ULTIMA Computing Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Prosiding Konferensi Nasional PKM-CSR
Afriliana, Nunik
Universitas Multimedia Nusantara
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Nursing Public Health Social Sciences

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Contour evolution method for precise boundary delineation of medical images Friska Natalia; Hira Meidia; Nunik Afriliana; Julio Christian Young; Sud Sudirman
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14746

Abstract

Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.
Pendeteksian Ruang Kosong Parkir di dalam Ruangan Nunik Afriliana; Rosalina Rosalina; Regina Valeria
Ultima Computing : Jurnal Sistem Komputer Vol 10 No 1 (2018): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2055.885 KB) | DOI: 10.31937/sk.v10i1.888

Abstract

Menemukan tempat parkir kosong di tempat parkir dalam ruangan seperti pusat perbelanjaan menjadi kesulitan banyak pengemudi, terutama saat jam sibuk di kota-kota besar. Dalam makalah ini, sebuah sistem deteksi kekosongan tempat parkir dalam ruangan diusulkan, dengan menggunakan sistem kamera yang melibatkan OpenCV untuk mempercepat waktu dalam mencari tempat parkir bagi pengemudi kendaraan dengan memberi mereka informasi lokasi dan tempat parkir. Sistem ini menggunakan metode deteksi objek statis, yaitu Haar-Like Cascade Classifier yang dikombinasikan dengan Hough Line Detection untuk mengidentifikasi area parkir kosong dari gambar parkir yang diambil secara real time melalui kamera IP atau kamera USB. Sistem ini dirancang untuk disematkan dengan sistem manajemen parkir sebuah bangunan sebagai alat yang menyediakan tempat parkir untuk membantu pengemudi kendaraan memasuki area parkir. Index Terms—Haar-Like Cascade Classifier, Hough Line Detection, Sistem Maanajemen Parkir
Implementasi Sistem Informasi Pembangunan Daerah Di Kabupaten Tangerang PM Winarno; Adhi Kusnadi; Nunik Afriliana
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 2 (2019): Peran Perguruan Tinggi dan Dunia Usaha dalam Mempersiapkan Masyarakat Menghadapi Era I
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.136 KB) | DOI: 10.37695/pkmcsr.v2i0.500

Abstract

Sistem Informasi Pemerintah Daerah (SIPD) adalah suatu Sistem yang mendokumentasikan, mengadministrasikan, serta mengolah data pembangunan daerah menjadi informasi yang disajikan kepada masyarakat dan sebagai bahan dalam pengambilan keputusan. Data SIPD terdiri dari 8 kelompok data, yaitu (Data Umum, Sosial Budaya, Sumber Daya Alam, Infrastruktur, Ekonomi, Keuangan daerah, Politik/hukum/Keamanan dan Insidental, serta 31 jenis data dan 2691 Elemen data. Pemerintah Kabupaten Tangerang telah bekerjasama dengan Universitas Multimedia Nusantara (UMN) untuk mengembangkan SIPD Kabupaten Tangerang. Untuk selanjutnya SIPD berbasis web tersebut telah diimplmentasikan dan berhasil dioperasionalkan oleh Tim UMN. Langkah-langkah implementasi SIPD meliputi penetapan Domain Name (nama domain internet untuk diakses oleh pengguna), hosting sistem, dan pelatihan operator. Adapun Domain Name telah diputuskan menggunakan nama URL sipd.tangerangkab.go.id. Sedangkan hosting sistem telah dilakukan pada server di Dinas Komunikasi dan Informatika Kabupaten Tangerang. Pelatihan operator telah dilakukan pada Bulan Januari 2019 dalam dua kelompok, yaitu kelompok operator dari dinas-dinas di Kabupaten Tangerang, dan kelompok operator dari kecamatan-kecamatan di Kabupaten Tangerang.
Deep learning utilization in Sundanese script recognition for cultural preservation Rosalina, Rosalina; Afriliana, Nunik; Utomo, Wiranto Herry; Sahuri, Genta
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1759-1768

Abstract

This study addresses the challenge of preserving the Sundanese script, a traditional writing system of the Sundanese community in Indonesia, which is at risk of being forgotten due to technological advancements. To tackle this problem, we propose a deep learning approach using the YOLOv8 model for the automatic recognition of Sundanese characters. Our methodology includes creating a comprehensive dataset, applying augmentation techniques, and annotating the characters. The trained model achieved a precision of 95% after 150 epochs, demonstrating its effectiveness in recognizing Sundanese characters. While some variability in accuracy was observed for certain characters and real-time applications, the results indicate the feasibility and promise of using deep learning for Sundanese script recognition. This research highlights the potential of technological solutions to digitize and preserve the Sundanese script, ensuring its continued legacy and accessibility for future generations. Thus, we contribute to cultural preservation by providing a method to safeguard the Sundanese script against obsolescence.
Enhancing Aspect-Based Sentiment Analysis in Tourism Reviews Through Hybrid Data Augmentation Iswari, Ni Made Satvika; Afriliana, Nunik
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.842

Abstract

The increasing reliance on online reviews in tourism has made User-Generated Content (UGC) an invaluable resource for understanding visitor perceptions. However, extracting meaningful insights from these reviews remains challenging due to their unstructured nature, aspect imbalance, and the prevalence of code-mixing between languages such as Indonesian and English—particularly in multicultural destinations like Bali. Aspect-Based Sentiment Analysis (ABSA) offers a promising solution by associating sentiment polarity with specific aspects of tourist experiences. Yet, its performance is often constrained by limited and imbalanced datasets, especially for underrepresented aspects such as sanitation and amenities. This study proposes a hybrid data augmentation framework that integrates three complementary strategies: generative augmentation using ChatGPT, semantic filtering via Sentence-BERT (SBERT), and domain refinement through Masked Language Modeling (MLM). The framework is designed to improve ABSA performance on multilingual tourism reviews by generating synthetic aspect-relevant data while preserving semantic integrity and contextual nuance. Using 398 reviews of Kuta Beach in Bali, we evaluate the effectiveness of the proposed approach across five tourism aspects: scenery, dusk, surf, amenities, and sanitation. Results show that the hybrid strategy reduces hallucination rates from 12% (using ChatGPT alone) to 3.8%, increases F1-scores for underrepresented aspects by up to 5.1%, and improves cross-lingual alignment (Cohen’s κ = 0.78). These improvements demonstrate the synergy between generative and semantic augmentation in addressing real-world ABSA challenges. The proposed method not only advances the state of multilingual ABSA but also offers practical implications for tourism analytics, allowing destination managers to better understand and respond to aspect-specific visitor feedback. The framework is extensible to other low-resource domains, were linguistic diversity and data scarcity present similar limitations.
An AI-driven framework for efficient and accurate calibration of electricity meters using extreme gradient boosting Rosalina Rosalina; Nunik Afriliana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp154-163

Abstract

Calibration testing plays a vital role in electricity meter manufacturing to guarantee measurement accuracy and compliance with industry standards. In practice, however, conventional calibration methods are often hindered by lengthy test cycles and the high cost of expanding test bench capacity. This study proposes a data-driven approach to address these limitations by applying machine learning techniques to optimize calibration testing. An extreme gradient boosting (XGBoost) regression model, enhanced through systematic hyperparameter tuning and feature engineering, was developed to predict calibration outcomes using data obtained from existing production test benches. When evaluated under real manufacturing line conditions, the proposed method shortened calibration runtime by about 55% compared with manual procedures relying on power supply units (PSU) and standard meter calculations, while maintaining reliable measurement accuracy. The framework also achieved lower root mean square error (RMSE), demonstrating improved predictive performance. In addition to reporting these results, the study describes the preprocessing pipeline, model selection process, and optimization strategy, providing a practical and replicable framework for integrating artificial intelligence (AI) into industrial calibration processes.
Enhancing Aspect-based Sentiment Analysis in Visitor Review using Semantic Similarity Ni Made Satvika Iswari; Nunik Afriliana; Eddy Muntina Dharma; Ni Putu Widya Yuniari
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.249

Abstract

The global economy greatly depends on the tourism industry, which fosters job opportunities and stimulates economic development. With the growing reliance of tourists on online platforms for guidance, evaluations of tourist destinations have gained heightened significance. These assessments, frequently expressed through user-generated content, offer valuable perspectives on customer experiences, viewpoints, and levels of satisfaction. Nevertheless, analyzing and interpreting these reviews can pose difficulties because of the unstructured or semi-structured nature of user-generated content. Conventional sentiment analysis methods might not adequately grasp the intricacies and particular aspects of tourism encounters that users convey in their reviews. The efficacy of sentiment analysis can be augmented by integrating semantic similarity. This study explores methods to enhance aspect-based sentiment analysis within tourism reviews by utilizing semantic similarity approaches. Five aspects have been curated, representing keywords frequently reviewed by visitors to the tourist attraction. These aspects encompass scenery, dusk, surf, amenities, and sanitation. Based on the data analysis, F-Measure values with Semantic Similarity tend to increase for the scenery and dusk aspects. This is because in the sample data used, visitor reviews for the scenery and dusk categories may use other words that are semantically similar. The sample data used for these categories is also quite extensive, resulting in a better classification model for both categories. While it is valuable to analyze user-generated content data from visitor reviews, it's important to consider the limitations and potential biases associated with this data. The classification results per aspect need to be further reviewed in more depth. What aspects lead visitors to give positive reviews will certainly be maintained and even improved by stakeholders. Similarly, for negative review outcomes, it is necessary to investigate more deeply the factors contributing to visitor dissatisfaction so that they can be addressed by stakeholders.
Co-Authors Adhi Kusnadi Dharma, Eddy Muntina Hira Meidia Julio Christian Young Natalia, Friska Ni Made Satvika Iswari Ni Putu Widya Yuniari P. M. Winarno Regina Valeria Rosalina Rosalina Sahuri, Genta Sahuri Sud Sudirman Wiranto Herry Utomo