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All Journal Rotasi JUTI: Jurnal Ilmiah Teknologi Informasi Emerging Science Journal Jurnal Ilmiah Administrasi Bisnis dan Inovasi Darmabakti Cendekia: Journal of Community Service and Engagements Indonesian Applied Physics Letters Medika Respati : Jurnal Ilmiah Kesehatan Jurnal Ilmu Administrasi: Media Pengembangan Ilmu dan Praktek Administrasi Influence: International Journal of Science Review ENDLESS : International Journal of Future Studies
Soegianto Soelistiono
Study Program Of Biomedical Engineering, Department Of Physics, Faculty Of Science And Technology, Universitas Airlangga University, Surabaya-Indonesia
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Nursing Physics Public Health Social Sciences

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Embracing the Era of Artificial Intelligence: The Transformation of Education in Indonesia from Primary School to Higher Education Soelistiono, Soegianto; Hamid, Rabiatul Adawiyah; Wahidin
ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES Vol. 7 No. 3 (2024): ENDLESS: International Journal of Future Studies
Publisher : Global Writing Academica Researching & Publishing

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Abstract

The digital revolution driven by the rapid advancement of artificial intelligence (AI) compels the education system in Indonesia to undergo transformation in order to prepare future generations who not only understand technology but also possess the ability to utilize it wisely and innovatively. Indonesian education must adopt approaches that are relevant to the demands of the times, emphasizing the development of skills that enable effective interaction with this intelligent technology. This article examines various strategic steps to prepare Indonesian education for the AI era, ranging from primary school to higher education. The primary focus of this discussion is the formulation of curricula that align with future needs, enhancement of foundational skills necessary for interacting with AI, and fostering ethical understanding in the use of this technology. This study also recommends policies that could improve access to AI-based education, particularly in remote areas, by optimizing teacher and student training to harness the potential of AI in supporting more efficient and inclusive learning processes. By effectively integrating technology at every educational level, Indonesia can produce a generation better prepared to face global challenges and contribute to the development of technology-based solutions.
Education Beyond AI: Building Integrity Through Authentic Assessment Wahidin; Soelistiono, Soegianto; Tanjung, Rona
ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES Vol. 8 No. 2 (2025): ENDLESS: International Journal of Future Studies
Publisher : Global Writing Academica Researching & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/endlessjournal.v8i2.338

Abstract

The advancement of artificial intelligence (AI) has transformed the paradigm of higher education assessment, challenging the effectiveness of traditional examination methods such as essays and multiple-choice tests. AI technologies, like ChatGPT, enable students to generate high-quality responses rapidly, triggering an authenticity crisis in academic assessments. Research indicates that 56% of global students, including those in Indonesia, use AI for assignments or exams, with 54% considering it a form of cheating (BestColleges, 2023). This paper aims to analyze the weaknesses of traditional exams in the AI era and propose authentic evaluation approaches through a conceptual discussion based on the analysis of challenges and new strategies. Key findings include recommendations for project-based exams, assessments of learning processes, hands-on practical exams, and the integration of AI literacy and ethics into the curriculum. This transformation demands that lecturers serve as facilitators and authentic evaluators while developing curricula focused on competence, creativity, and soft skills. Consequently, higher education can produce graduates who are not only academically proficient but also critical, creative, and responsible in leveraging AI.
Odor Profiling of Blood Shells Using TGS Gas Sensor and PCA-SVM Analysis Astuti, Suryani Dyah; Funabiki, Nobuo; Soelistiono, Soegianto; Winarno; Arifianto, Deny; Ramadhani, Nadia Nur; Permatasari, Perwira Annissa Dyah; Yaqubi, Ahmad Khalil; Susilo, Yunus; Syahrom, Ardiyansyah
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-017

Abstract

Blood cockles (Andara granulosa) are among the most popular animal protein sources due to their rich nutritional content and high economic value. The storage period and temperature are two critical factors that significantly influence the freshness of blood cockles. One key indicator of blood cockle quality is the odor they emit. An unpleasant or inappropriate odor can indicate contamination or a decline in quality, posing potential food safety risks. However, conventional methods of odor quality testing are often subjective, require specialized skills, and may not always be reliable. To address the limitations of human olfaction, advancements in gas sensor technology, specifically gas array sensors (also known as the electronic nose), have been developed. This research aims to profile the freshness of blood cockles by identifying their odor under different storage conditions using electronic nose technology. The study used fresh blood cockle meat, which was stored under varying temperature conditions: at room temperature, in a cooler, and in a freezer. The storage periods for the samples were 1, 2, 3, 4, and 5 days. The samples were placed in sealed bottles and tested using a gas array sensor. The data collected from this process were in the form of voltage readings, which were analyzed using machine learning techniques, specifically Principal Component Analysis (PCA). The data were then classified using a Support Vector Machine (SVM) model. The study results showed that the gas array sensor successfully classified the odor profiles, with PCA explaining 93.83% of the variance in the data. The SVM model achieved an accuracy of 89.66% for PCA-reduced data and 91.44% for non-PCA data.
Deep Physics-Informed Neural Network (D-PINN) for Real-Time Dynamic Electrical Impedance Tomography (EIT) Reconstruction with Geometrical Uncertainty Robustness Soelistiono, Soegianto
Indonesian Applied Physics Letters Vol. 6 No. 1 (2025): Volume 6 No. 1 – December 2025
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v6i1.84060

Abstract

Electrical Impedance Tomography (EIT) is a vital non-invasive imaging technique for dynamic monitoring, such as lung ventilation. The primary challenge in EIT lies in the inverse problem, which is non-linear, ill-posed, and computationally slow, especially when high accuracy and real-time speed are simultaneously required. Conventional EIT reconstruction algorithms often yield blurred images and are highly susceptible to measurement noise and geometrical uncertainties, such as variations in electrode placement and unknown boundary shapes. This research proposes the Deep Physics-Informed Neural Network (D-PINN), an extended deep learning framework, to achieve accurate and real-time dynamic EIT reconstruction. Unlike purely data-driven methods, our D-PINN integrates the governing Laplace’s Equation directly into the network’s loss function, providing a strong physical constraint to significantly enhance image quality. The innovative focus of this study is addressing the critical gap in model uncertainty robustness. We develop a stochastic D-PINN training scheme that not only solves the conventional inverse problem (predicting conductivity) but also simultaneously accounts for small variations in boundary geometry or electrode positions. Initial simulation results are expected to show that D-PINN consistently:1. Reduces the reconstruction inference time to the millisecond scale, enabling true real-time monitoring. 2. Significantly improves the spatial resolution and image contrast (measured by the Structural Similarity Index / SSIM) compared to standard iterative methods. 3. Maintains high accuracy even when the input measurement data is noisy and the assumed forward geometrical model is intentionally perturbed, which is crucial for real-world instrumentation applications. This work is expected to advance EIT into a more reliable and robust real-time imaging tool.
Co-Authors Abidah Alfi Maritsa Amillia Kartika Sari Amirul Amalia Anggrek Citra Nusantara, Anggrek Citra Arifianto, Deny Augustinah, Fedianty David Buntoro Kamandjaja Endah Purwanti Endah Purwanti Fadhlin A Arif, Fadhlin A Fedianty Augustinah Firman Isma Isma Serdana Hamid, Rabiatul Adawiyah Handayani, Vitria Wuri Hanif Assyarify Khusnul Ain Miranda Syafira Widyananda Muhammad Fadhel Maulama Nobuo Funabiki, Nobuo Permatasari, Perwira Annissa Dyah Pramulen, Aji Sapta Rahmatillah, Akif Ramadhani, Nadia Nur Riries Rulaningtyas Suryani Dyah Astuti Susilo, Yunus Syahrom, Ardiyansyah Sylvaranto, Teguh Sylvaranto, Teguh tanjung, rona Teguh Sylvaranto Trilaksana, Herry Wahidin Weni Endahing Warni WINARNO Yaqubi, Ahmad Khalil Yusrinourdi Muhammad Zuchruf