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All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) International Journal of Artificial Intelligence in Medical Issues Management of Information System Journal Jurnal Studi Seni Visual dan Desain: Les-Guet's
Ahmad Naswin
Universitas Megarezky
Arts Humanities Computer Science & IT Control & Systems Engineering Dentistry Health Professions Medicine & Pharmacology Public Health

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Memories Of The Creativity: A Visual Criticism Of The Evolution Of Creativity In The Era Of Artificial Intelligence: Memories Of The Creativity: A Visual Criticism Of The Evolution Of Creativity In The Era Of Artificial Intelligence Ahmad Naswin; I Putu Adi Saskara; I Putu Adi Pratama
Warna Komunikasi Vol 1 No 1 (2025): Les-Guet's
Publisher : Universitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25078/lg.v1i1.4847

Abstract

Alit Kumala Dewi's "Memories of the Creativity" poster is a powerful visual response to the shifting meaning of creativity in the posthuman era, where artificial intelligence (AI) technology is beginning to redefine creative processes that were previously closely tied to human experience. By combining the image of an old man with closed eyes, a collage of manual art activities, and the symbolism of AI invasion in a contrasting visual composition, the poster highlights the existential tension between the memory of traditional creativity and the new, increasingly digitalized reality. This work is not only an aesthetic statement, but also a philosophical reflection on the changing role of humans in artistic creation. This article analyzes the poster using a theoretical approach based on the concepts of posthumanity, simulacra, creative nostalgia, and a critique of cultural automation. Through an in-depth reading, it is revealed how the poster functions as a social critique and a contemplative invitation to the position of humans in the creative landscape of the future.
Confidence-Aware Depression Severity Detection in Low-Resource Urdu Social Media Text: A Multilingual Machine Learning Approach Ahmad Naswin; Yuli Praptomo Pamungkas Hari Sungkowo; Lukman Syafie
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v4i1.434

Abstract

Depression is a major mental health concern that requires early identification and timely intervention. Social media has become an important source of user-generated text that may reflect emotional distress, hopelessness, social withdrawal, and suicidal ideation. However, most existing depression detection studies focus on English or high-resource languages, while research on low-resource languages such as Urdu remains limited. This study investigates depression severity classification in Urdu social media text using multilingual and confidence-aware natural language processing approaches. The dataset consists of 4,000 Twitter/X posts collected between January 2024 and April 2025, annotated into four severity classes: none, mild, moderate, and severe. Each post is represented in three parallel textual forms: native Urdu script, Roman Urdu transliteration, and English translation. The dataset also includes label confidence scores, human verification indicators, cultural markers, and depression-related keywords. Several text representation scenarios were evaluated, including Urdu text, Roman Urdu text, English text, and combined multilingual features. Baseline machine learning models were developed using TF-IDF features with Logistic Regression, Linear Support Vector Machine, and Multinomial Naive Bayes. Confidence-aware learning was examined by incorporating label confidence scores as sample weights and by evaluating a high-confidence subset. The experimental results showed that all baseline models achieved perfect classification performance, with accuracy, macro F1-score, weighted F1-score, and Cohen’s Kappa values of 1.000 across the evaluated scenarios. These results indicate that the dataset contains highly separable linguistic patterns among depression severity classes. However, further inspection suggests that repeated or highly similar textual patterns may contribute to overly optimistic performance. Therefore, stricter validation using duplicate-free splitting, external datasets, and transformer-based models is recommended for future work. This study provides a preliminary benchmark for multilingual depression severity classification in low-resource Urdu text and highlights the potential of AI-driven mental health informatics as a supportive early-warning tool rather than a clinical diagnostic system
Analisis Risiko Keamanan Pada Aplikasi Mobile Banking Dan Strategi Mitigasi Abdul Sakti; Ahmad Naswin; Sulkifli
Management of Information System Journal Vol 4 No 2: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i2.2566

Abstract

This study aims to analyse security risks in mobile banking applications and their mitigation strategies. A qualitative approach using descriptive methods was employed, with data collected through literature reviews, documentation, and analysis of scientific literature relating to cybersecurity and mobile banking. The study focuses on identifying the types of risks, their sources, the resulting impacts, and the mitigation strategies implemented by banking institutions. The analysis was conducted systematically through the stages of data reduction, data presentation, and drawing conclusions, and was validated through triangulation of sources. The research findings indicate that the primary threats to mobile banking applications include phishing, malware, man-in-the-middle attacks, data breaches, and the use of weak passwords. Technical factors such as system and network vulnerabilities, as well as human factors—particularly user security awareness—are the main causes of these risks. Effective mitigation strategies include the implementation of multi-factor authentication, data encryption, real-time system monitoring, user education, and the development of applications based on secure coding. The combination of security technology, system monitoring, and user literacy has been proven to enhance mobile banking security, making the service safer, more reliable, and adaptable to evolving risks.
Evaluating Service Quality Metrics with AdaBoost Classifier at Restaurant X Kadek Suarjuna Batubulan; I Putu Adi Pratama; Ahmad Naswin
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 3 (2024): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.234

Abstract

This paper explores the use of the AdaBoost classifier to evaluate service quality metrics in the restaurant industry, specifically at Restaurant X. The study focuses on how machine learning, particularly ensemble learning algorithms, can improve the understanding of customer satisfaction by analyzing various service attributes, such as food quality, staff behavior, wait times, and ambiance. By applying AdaBoost, the model combines multiple weak classifiers to create a stronger, more accurate prediction model that identifies key factors influencing customer experience. The research highlights the importance of real-time data and customer feedback in refining service quality metrics and suggests that incorporating sentiment analysis and other dynamic data sources can provide a more comprehensive view of customer satisfaction. The findings suggest that using machine learning algorithms, like AdaBoost, can enhance operational decision-making, improve customer service, and contribute to overall business success. Additionally, the study proposes the continuous updating of the model to reflect changing customer preferences and trends in the competitive food service industry. This approach can lead to better service, customer retention, and a strategic advantage for restaurants seeking to meet the evolving demands of the market.
ARIMA Model for Time Series Forecasting of Doge Coin Prices Kadek Suarjuna Batubulan; I Putu Adi Pratama; Ahmad Naswin
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 1 (2024): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.242

Abstract

The volatility and speculative nature of cryptocurrencies present significant challenges for accurate price forecasting. This study evaluates the performance of the AutoRegressive Integrated Moving Average (ARIMA) model in predicting Dogecoin (DOGE) prices based on historical data obtained from reputable cryptocurrency platforms such as Binance, Coinbase, and CoinGecko. The ARIMA(5,1,0) model demonstrated strong performance under stable market conditions, achieving a Mean Squared Error (MSE) of 0.0006656 and a Root Mean Squared Error (RMSE) of 0.0258, effectively capturing linear price trends. However, the model’s limitations in handling high volatility and non-linear dependencies—common characteristics of cryptocurrency markets—were also identified. To address these challenges, the study explores hybrid ARIMA–neural network models that integrate statistical and machine learning approaches, improving predictive accuracy during periods of market instability. The results suggest that while ARIMA provides a solid baseline for time series forecasting, hybrid and sentiment-aware models incorporating social media and blockchain metrics offer more robust and adaptive solutions for dynamic cryptocurrency markets.
Cataract Classification in Eye Images Using MobileNetV2 Kadek Suarjuna Batubulan; I Putu Adi Pratama; Ahmad Naswin
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 8 No 2 (2025): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.268

Abstract

Cataract remains one of the primary causes of visual impairment globally, with early detection being essential to prevent permanent blindness and improve patient quality of life. However, conventional diagnosis depends on ophthalmologists and clinical-grade imaging devices, which are often limited in remote or under-resourced areas. This condition highlights the need for an efficient, accessible, and automated screening solution. To address this challenge, this study utilizes the MobileNetV2 deep learning architecture to classify cataract conditions based on eye images. MobileNetV2 is selected because of its lightweight model structure and strong feature representation capabilities, making it suitable for deployment in portable or embedded medical systems. The dataset used consists of two cataract stages, namely immature and mature cataracts, with images undergoing preprocessing prior to model training. The proposed system demonstrates excellent performance, achieving an accuracy, precision, recall, and F1-score of 100% in distinguishing cataract stages. These results confirm that MobileNetV2 can effectively support cataract screening with high reliability while maintaining efficiency. Future work will involve extending the dataset to include additional cataract severity levels and non-cataract eye images, as well as integrating explainable artificial intelligence methods to provide visual diagnostic interpretations and enhance clinical trust in real-world applications.
Co-Authors Abdul Sakti I Putu Adi Pratama I Putu Adi Pratama I Putu Adi Saskara Kadek Suarjuna Batubulan Lukman Syafie Sulkifli Yuli Praptomo Pamungkas Hari Sungkowo