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All Journal Sistemasi: Jurnal Sistem Informasi JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Nasional Komputasi dan Teknologi Informasi KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Academic Journal of Computer Science Research (AJCSR) Al-MIKRAJ: Jurnal studi Islam dan Humaniora
Andrias Darmayadi
Universitas Komputer Indonesia
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Neuroscience Social Sciences Transportation

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PENERAPAN CONVOLUTIONAL NEURAL NETWORK UNTUK PENGENALAN BAHASA ISYARAT INDONESIA: STUDI KASUS DAN TINJAUAN FILSAFAT SAINS Hernalom Sitorus; Ucu Nugraha; Sri Titi Handayani; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 12 No. 2 (2026)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jitter.vol12.iss2.2026.3448

Abstract

The low literacy of Indonesian Sign Language (BISINDO) in the general public remains a barrier to communication with the Deaf community, while research on AI-based sign language recognition generally focuses solely on technical achievements. This study aims to develop a BISINDO alphabet recognition system based on Convolutional Neural Network (CNN) and evaluate it through a philosophy of science perspective. The methods used include collecting a BISINDO alphabet hand image dataset, data augmentation, and transfer learning-based CNN training with the MobileNetV2 architecture and a stepwise training scheme, then deployed to Android using TensorFlow Lite. Test results show the system is able to achieve an accuracy of around 93% on controlled test data with stable real-time inference performance. The scientific contribution of this research is not only in the development of applied AI systems, but also in providing a reflective ontological, epistemological, and axiological framework to assess the validity and social implications of BISINDO recognition technology.
Open Government Data Analytics of Tourist Visits In West Java 2014–2024: A Data Science and Philosophy of Science Perspective Ucu Nugraha; Hernalom Sitorus; Sri Titi Handayani; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
SISTEMASI Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6175

Abstract

Open Government Data (OGD) in tourism provides opportunities for data-driven analytics to support destination management policies. In policy practice, tourism OGD is often accepted at face value as a direct representation of real-world conditions, even though such data are constructed through definitions, recording procedures, and measurement choices. Therefore, a philosophy of science perspective is essential in data governance. This article analyzes an Open Data Jabar dataset on the number of tourist visits by visitor type and district/city in West Java Province for the period 2014–2024 (n = 565; 27 districts/cities; two visitor categories: domestic and international). The data science approach includes data quality auditing (completeness and consistency), time-series aggregation, spatial concentration measurement using the Gini coefficient, and a comparison of shock–recovery patterns in tourist visits before and after the pandemic. The results indicate a decline in total visits of -50.6% in 2020 compared to 2019, with international visits experiencing the sharpest drop (-82.8%). By 2024, total visits reached 64,517,298, dominated by domestic tourists (63,963,443; international share 0.9%). Spatial concentration in 2024 is reflected by a Gini coefficient of 0.429, with the top five regions accounting for 44.2% of total visits. The discussion emphasizes that visitor counts are epistemic representations shaped by definitions, reporting practices, and data cleaning processes. Therefore, policy recommendations should be accompanied by data provenance, metadata, and explicit uncertainty annotations to avoid the reification of indicators.
Epistemologi Artificial Intelligence: Kebenaran, Validitas, dan Otoritas Algoritmik Sri Nurhayati; Diana Effendi; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
AL-MIKRAJ Jurnal Studi Islam dan Humaniora (E-ISSN 2745-4584) Vol. 6 No. 1: Al-Mikraj, Jurnal Studi Islam dan Humaniora
Publisher : Pascasarjana Institut Agama Islam Sunan Giri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/almikraj.v6i1.8530

Abstract

The development of artificial intelligence (AI) has brought fundamental changes in the way knowledge is produced, validated, and accepted in various sectors of life. Algorithmic models, especially deep learning, generate predictions and recommendations that are often treated as operational truths even though the inference process is not fully explainable. This study analyzes how AI changes the understanding of truth, validity, and epistemic authority from the perspective of the philosophy of science, and links it to the ontological and axiological dimensions in modern knowledge production. A qualitative approach based on philosophical analysis is used to integrate the thoughts of Popper, Kuhn, Lakatos, Van Fraassen, and Floridi. The results show that AI shifts knowledge from rational justification to performative and statistical validity, and challenges the position of humans as the primary epistemic agents. This study asserts that the epistemic transformation triggered by AI requires ontological and axiological reflection so that the development of knowledge remains in line with humanitarian principles and ethical responsibility
Epistomologi Sains di Era Kecerdasan Buatan: Menimbang Kebenaran Prediktif Popon Dauni; Rizal Rachman; Sri Erina Damayanti; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
AL-MIKRAJ Jurnal Studi Islam dan Humaniora (E-ISSN 2745-4584) Vol. 6 No. 1: Al-Mikraj, Jurnal Studi Islam dan Humaniora
Publisher : Pascasarjana Institut Agama Islam Sunan Giri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/almikraj.v6i1.8879

Abstract

The development of artificial intelligence (AI), particularly machine learning and deep learning, has brought significant changes to contemporary scientific practices. AI no longer functions solely as a computational tool, but plays an active role in the production, validation, and evaluation of scientific knowledge through data modelling and probabilistic inference. This development raises fundamental questions in the philosophy of science, particularly regarding the shift in the concept of scientific truth from the paradigm of empirical verification and causal explanation towards an approach based on prediction, mathematical approximation, and the management of uncertainty. This research aims to re-evaluate the status of scientific truth in the age of AI by philosophically analysing the relationship between uncertainty, computational knowledge, and scientific truth claims generated by AI models. The research method used is a qualitative study based on literature review and conceptual analysis of contemporary science and technology philosophy literature. The study results indicate that the integration of AI into scientific practice is driving a shift in the epistemology of science from a verifiative orientation towards a predictive epistemology that emphasises model reliability and instrumental validity. This research concludes that scientific truth in the AI era is more contextual and pragmatic, thus demanding an adaptive, reflective, and interdisciplinary framework for the epistemology of science. Theoretically, scientific truth in the age of artificial intelligence is more contextual, thus requiring an adaptive, reflective, and interdisciplinary framework for the epistemology of science as its main theoretical contribution.
Studi Pedagogis dan Etika Teknologi pada Penggunaan Sistem Penilaian Esai Berbasis AI di Pendidikan Tinggi Rizki Adha; Lusianto Lusianto; Dodi Syaripudin; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
Academic Journal of Computer Science Research Vol 8, No 2 (2026): Academic Journal of Computer Science Research (AJCSR)
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/ajcsr.v8i2.16265

Abstract

Penggunaan kecerdasan artifisial (AI) dalam penilaian esai semakin berkembang di pendidikan tinggi karena kemampuannya meningkatkan efisiensi dan konsistensi evaluasi. Namun, penerapannya menimbulkan tantangan pedagogis dan etika terkait keadilan, transparansi, serta legitimasi keputusan akademik. Penelitian ini bertujuan menganalisis implikasi pedagogis dan etika penggunaan sistem penilaian esai berbasis AI dengan mengintegrasikan persepsi mahasiswa dan dosen. Penelitian menggunakan pendekatan mixed-methods exploratory melalui survei kuantitatif terhadap 36 mahasiswa dan 24 dosen, dilengkapi analisis kualitatif. Hasil menunjukkan mahasiswa memiliki persepsi positif terhadap kegunaan, kemudahan, dan nilai pedagogis, namun tetap berhati-hati terhadap aspek kepercayaan dan transparansi. Sementara itu, dosen menunjukkan tingkat penerimaan sangat tinggi selama sistem diterapkan dalam kerangka terkontrol. Integrasi temuan menunjukkan bahwa pendekatan Human-in-the-Loop merupakan faktor kunci menjaga keseimbangan antara efisiensi teknologi, nilai pedagogis, dan legitimasi akademik. Penelitian ini berkontribusi dengan menegaskan bahwa sistem penilaian esai berbasis AI paling tepat diterapkan dalam paradigma AIassisted assessment, di mana dosen tetap memegang kendali utama dalam pengambilan keputusan akademik, serta memberikan rekomendasi praktis bagi institusi dalam merancang kebijakan penilaian berbasis AI yang bertanggung jawab.
Pengembangan Model Dynamic Naive Bayes untuk Pemodelan Risiko Akademik Mahasiswa: Integrasi Filsafat Ilmu Kuhn dan Popper dalam Analisis Risiko Pendidikan Tinggi Ridwan Zulkifli; Dony Waluya Firdaus; Muhamad Nawawi; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi

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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10409

Abstract

Abstrak - Pemodelan risiko akademik mahasiswa serta prediksi kemungkinan kegagalan studi berdasarkan pola perilaku historis merupakan fokus utama institusi pendidikan tinggi. Model prediksi statis seperti Naive Bayes konvensional sering kali gagal menangkap perubahan perilaku mahasiswa sepanjang waktu pada data longitudinal, sehingga menghasilkan anomali prediksi. Penelitian ini, melalui Tinjauan Literatur Sistematis (Systematic Literature Review/SLR), mengkaji pengembangan Dynamic Naive Bayes (DNB) sebagai perluasan Naive Bayes yang memasukkan dimensi temporal dan transisi antar periode (misalnya perubahan status risiko antar semester), serupa dengan prinsip Dynamic Bayesian Networks. DNB diasumsikan mampu meningkatkan akurasi prediksi sebesar 10–15% dibandingkan model statis. Secara metodologis, pengembangan DNB didasarkan pada kerangka filsafat ilmu Thomas Kuhn dan Karl Popper. Mengacu pada paradigma Kuhn, kegagalan Naive Bayes statis dalam menangani data dinamis mencerminkan tahap crisis dan anomaly dalam normal science pemodelan prediktif risiko akademik, yang mendorong scientific revolution melalui adopsi model dinamis seperti DNB. Sementara itu, dari perspektif Rasionalisme Kritis Popper, hipotesis bahwa DNB memberikan prediksi yang lebih akurat harus terus-menerus diuji secara ketat dan terbuka terhadap falsifikasi empiris menggunakan data baru. Integrasi kedua kerangka ini memastikan bahwa pengembangan DNB tidak hanya bersifat teknis, melainkan juga didasari pada pertumbuhan pengetahuan ilmiah yang kritis, rasional, dan berkelanjutan.Kata kunci: pemodelan risiko akademik; dynamic naive bayes; prediksi kegagalan studi; data longitudinal; tinjauan literatur sistematis; filsafat ilmu,;thomas kuhn; karl popper; Abstract - Student academic risk modeling, the study of predicting the likelihood of failure based on historical behavioral patterns, is a major focus for higher education institutions. Although predictive models have been developed, statistical approaches such as standard Naive Bayes often fail to capture changes in student behavior over time, which are characteristic of longitudinal data. This failure, known as anomalies, highlights the need for new models capable of addressing dynamic and temporal aspects, such as Dynamic Naive Bayes (DNB). This study, through a Systematic Literature Review (SLR), examines the development of a DNB capable of processing or accounting for time factors, such as changes in risk status between semesters. Methodologically, DNB is an extension of Naive Bayes by incorporating transition time, similar to Dynamic Bayesian Networks, which is assumed to improve predictive accuracy by 10-15% compared to statistical models. Philosophically, the DNB development model is interpreted through the framework of Thomas Kuhn and Karl Popper. According to Kuhn, the failure of statistical Naive Bayes on dynamic data led to a crisis and anomaly in normal science (the use of standard predictive models), triggering the need for a scientific revolution (the adoption of DNB). Meanwhile, Popper's perspective (Critical Rationalism) demands that DNB predictive hypotheses must always be open to falsification through aggressive testing against new data. This integration ensures that the DNB development model is based on the critical and continuous growth of scientific knowledge.Keywords: Scientific Revolution; Dynamic Naive Bayes (DNB); Student Academic Risk; Longitudinal Data; Naive Bayes; Philosophy of Science;
Model Blockchain Pada Rekam Medis Terdistribusi : Tinjauan Ontologi dan Epistemologi  Terhadap Integritas Data Sufa Atin; Hani Irmayanti; Andri Heryandi; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.18639

Abstract

Data integrity is a fundamental epistemological foundation for ensuring the validity and reliability of clinical knowledge. However, conventional Electronic Medical Record (EMR) systems still face structural and trust-related challenges due to centralized data management. This approach often leads to information fragmentation, limited interoperability among healthcare facilities, and low patient autonomy in controlling access to medical data. Philosophically, these conditions expose the limitations of an epistemology grounded in single authority, which is vulnerable to data manipulation, opacity, and centralized failures.This study examines, from a philosophical perspective, the role of distributed EMR systems based on permissioned blockchain—particularly Hyperledger Fabric—in strengthening guarantees of medical record data integrity. The research applies a philosophical conceptual analysis combined with a systematic literature review on EMR systems, blockchain technology, and data integrity. The analysis highlights the epistemological limitations of conventional EMRs, the defining features of permissioned blockchain architectures, and the ontological and epistemological implications of distributed consensus mechanisms. The findings indicate that, ontologically, blockchain redefines medical record data as distributed truth that is persistent and resistant to manipulation. Epistemologically, trust shifts from single authority to cryptographic validation and collective consensus, marking a paradigm shift in the legitimation of clinical knowledge in the digital era.
Co-Authors Agus Nursikuwagus Andri Heryandi Diana Effendi Dodi Syaripudin Firdaus, Dony Waluya Hani Irmayanti Hernalom Sitorus Lusianto Lusianto Muhamad Nawawi Popon Dauni Rizal Rachman Rizki Adha Sri Erina Damayanti Sri Nurhayati Sri Titi Handayani Sufa Atin Ucu Nugraha Usep Mohamad Ishaq zulkifli, ridwan