Article Per Year (5 Year)
p-Index From 2021 - 2026
3.283
P-Index
This Author published in this journals
All Journal Jurnal Teknik Informatika Jurnal Informatika Jurnal Algoritma Jurnal Sosioteknologi Journal of ICT Research and Applications Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika Jurnal Filsafat Civil Engineering Journal Jurnal Teknologi dan Informasi ETNOREFLIKA: Jurnal Sosial dan Budaya IJAIT (International Journal of Applied Information Technology) JIKA (Jurnal Informatika) Jurnal Instrumentasi Jurnal Filsafat Indonesia Jurnal Algoritma Jurnal Sistem Informasi Journal of Digital Literacy and Volunteering Journal of Digital Literacy and Volunteering
Dimitri Mahayana
Sekolah Teknik Elektro Dan Informatika, Institut Teknologi Bandung
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Social Sciences Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 27 Documents
Search

 1   2   3 
IDENTIFIKASI SISTEM BERBASIS DATA KE DALAM MODEL NARX-FNN DENGAN EXTENDED KALMAN FILTER DALAM KONDISI PENGUKURAN YANG NOISY UNTUK SISTEM KOLOM DISTILASI Nasution, Muhammad Alifsyah Putra; Mahayana, Dimitri; Rusmin, Pranoto Hidaya; Amalia, Hayati; Zidni, Hasan
Instrumentasi Vol 48, No 1 (2024)
Publisher : National Standardization Agency of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31153/instrumentasi.v48i1.625

Abstract

Pada penelitian ini, digunakan plant sistem kolom distilasi tipe batch untuk memisahkan larutan biner campuran etanol dan air. Agar dapat mengimplementasikan skema sistem kendali, diperlukan pemodelan sistem kolom distilasi. Namun, masalahnya plant sistem kolom distilasi tersebut adalah sistem yang highly nonlinear dengan terbatasnya jumlah sensor yang dimiliki untuk memperoleh semua informasi states. Akibatnya, sistem kolom distilasi diidentifikasi menggunakan pendekatan black-box modeling berbasis data input-output eksperimen memanfaatkan Artificial Neural Network (ANN) untuk memperoleh model Nonlinear Autoregressive with eXogenous inputs (NARX). Hasil dengan MSE terbaik dicapai pada kasus tanpa noise pengukuran menggunakan algoritma EKF dengan hyperparameter  yang menghasilkan MSE sebesar 6,9896e-05. Selanjutnya, dapat dilihat pula pada kasus dengan noise ringan, algoritma EKF dengan hyperparameter  mampu konvergen dengan paling cepat, yaitu pada sekitar instance ke-6. Untuk setiap kasus, secara umum, semakin kecil nilai  maka semakin cepat identifikasi sistemnya konvergen, namun dengan trade-off di mana MSE yang dihasilkan menjadi sedikit memburuk. Kata Kunci: sistem kolom distilasi, identifikasi sistem berbasis data, model NARX, algoritma EKF
Analisis Filsafat Ilmu Pengetahuan dalam Transformasi Digital Tridharma Perguruan Tinggi Menuju Smart Campus: An Analysis of the Philosophy of Science in Digital Transformation The Tridharma of Higher Education Towards a Smart Campus Johan, Meliana Christianti; Langi, Armein Z. R.; Mahayana, Dimitri; Abbas, Muhammad Fadhl
Jurnal Filsafat Indonesia Vol. 8 No. 3 (2025)
Publisher : Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jfi.v8i3.96905

Abstract

The technological revolution has driven the digital transformation of higher education institutions towards a Smart Campus. This process often focuses on technical aspects and neglects philosophical foundations, risking unsustainable implementation. This study analyzes digital transformation in the Tridharma of Higher Education using a philosophy of science approach. It applies interpretive qualitative content analysis to 10 Q1 journal articles from 2021 to 2024. The analysis reveals three fundamental findings. Ontologically, technology is not a neutral tool, but an active agent that shapes academic reality. Epistemologically, there is a dilemma between personalization and standardization of knowledge and the challenge of academic integrity due to Generative AI. Axiologically, operational efficiency can conflict with the humanization of education, as well as innovation and data privacy. This research produces a value-based strategic framework of eight pillars: human-centered design, ethics from the outset, pedagogical accuracy, equity and inclusion, cultural responsiveness, sustainability, governance, and capacity building. This framework guides the implementation of an ethical and sustainable Smart Campus in Indonesia, aligning with the values of Tridharma.
Membangun Kepercayaan terhadap Sistem Pendukung Pendidikan berbasis Kecerdasan Buatan: Sebuah Review Naratif Herdiani, Anisa; Mahayana, Dimitri; Rosmansyah, Yusep
Jurnal Sosioteknologi Vol. 23 No. 1 (2024): MARCH 2024
Publisher : Fakultas Seni Rupa dan Desain ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/sostek.itbj.2024.23.1.6

Abstract

A primary challenge associated with the implementation of educational support systems is the establishment of student trust in the systems themselves. Trust is a critical factor in the acceptance and use of AI-enabled systems, as it reduces uncertainty and the perception of risk associated with new technology adoption. A literature review of existing studies on trust in AI-based systems is needed to provide a solid foundation for future studies. This research aims to identify gaps in the literature regarding the establishment of user trust in AI-based educational systems by exploring the criteria of trust and the challenges of building trust in AI systems. A narrative review of the literature is conducted to synthesize the findings of selected articles, covering (1) fundamental principles of trust and the process of establishing trust in non-human entities; (2) technical issues relating to explainable AI; (3) the utilization of explainable AI to facilitate decision-making; and (4) the use of AI systems in facilitating educational activities and its influence. This article summarizes trust criteria, including reliance, transparency, affectiveness, integrity, consistency, fairness, accountability, security, and usability. Building trust in AI systems involves addressing technical, ethical, and societal challenges to ensure the responsible and beneficial use of AI for individuals and society.
DATA DRIVEN MODEL FREE SYSTEM IDENTIFICATION OF BATCH DISTILLATION COLUMN USING XGBOOST MACHINE LEARNING ALGORITHM Amalia, Hayati; Mahayana, Dimitri; Rusmin, Pranoto Hidaya
Instrumentasi Vol 47, No 2 (2023)
Publisher : National Standardization Agency of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31153/instrumentasi.v47i2.520

Abstract

The distillation process plays a crucial role in the chemical industry, enabling material separation, purification, and waste product disposal. Distillation columns, including the batch type, are widely used in industries due to their ability to produce raw materials for various applications. However, modeling and controlling batch distillation columns pose challenges due to their nonlinear and dynamic behavior. This paper presents a novel data-driven approach for system identification using XGBoost, an advanced gradient boosting algorithm, eliminating the need for explicit model equations. The proposed methodology leverages the power of XGBoost to learn the underlying system behavior directly from data. The paper provides an overview of the methodology, including data preprocessing, feature engineering, training the XGBoost model, and evaluating its performance. Techniques such as cross-validation and input feature delay tuning are also discussed to ensure robustness and optimal model performance. The effectiveness of the approach is demonstrated through various case studies and some comparisons. The results highlight the capability of the proposed model-free system identification methodology using XGBoost in accurately capturing the dynamics of batch distillation systems, offering potential for improved process control and optimization in the chemical industry.
Research on Online Hate Speech Detection from Popper and Kuhn's Philosophical Perspective Cahyana, Rinda; Fitriani, Leni; Setiawan, Yudi; Mahayana, Dimitri
Journal of Digital Literacy and Volunteering Vol. 2 No. 2 (2024): July
Publisher : Puslitbang Akademi Relawan TIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57119/litdig.v2i2.96

Abstract

The negative impact of spreading hate speech on social media has prompted various parties to intervene. Computer science researchers have conducted experiments to find solutions for automated intervention by applying artificial intelligence, such as machine learning and deep learning. The fulfillment of the theory of truth makes the machine learning paradigm considered by scientists to solve problems. However, the increasing size of social media data has shifted its paradigm to deep learning. Deep learning becomes a new normal science after completing the task of classifying hate speech well on a large amount of data. However, any approach will be an anomaly when it cannot complete the task. The accessibility of research resources makes it easier for researchers to determine the nature of their experiments, whether scientific or pseudo-science.
Historical Analysis of Interactive Gamification Research: A Literature Review with Kuhn and Lakatos’ Approaches Firmansyah, Feri Hidayatullah; Rosmansyah, Yusep; Mahayana, Dimitri
Jurnal Filsafat "WISDOM" Vol 34, No 1 (2024)
Publisher : Fakultas Filsafat, Universitas Gadjah Mada Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jf.84610

Abstract

This literature review provides an analysis of the historical development and theoretical frameworks put forth by notable philosophers such as Thomas Kuhn and Imre Lakatos in the field of interactive gamification research. By examining the contributions and perspectives of these influential figures, the authors aimed to enhance our understanding of the evolution of gamification as a research area. This review serves as a valuable resource for individuals interested in delving deeper into the concept of gamification in interactive research. Furthermore, it offers a collection of references that can be explored in the initial stages of gamification research. By studying the works of influential philosophers such as Thomas Kuhn and Imre Lakatos, researchers can gain valuable insights into the evolution of gamification  research and its theoretical underpinnings.. Kuhn's concept of paradigm shifts and Lakatos's ideas on research programs provide researchers with a framework to understand the progression of ideas and theories within the field.
Adaptive Real-Time Strain-Rate Control in CRS Consolidation Testing Using SARSA Reinforcement Learning Muhammad Fadhl 'Abbas; Hasbullah Nawir; Dimitri Mahayana; Erza Rismantojo; Dayu Apoji; M. Alifsyah Putra Nasution; Targhib Ibrahim
Civil Engineering Journal Vol. 12 No. 4 (2026): April
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-04-023

Abstract

This study presents a reinforcement-learning framework for real-time strain-rate control in Constant Rate of Strain (CRS) consolidation testing to hasten the testing process using the SARSA algorithm. The controller adaptively adjusts deformation rate based on evolving pore-pressure ratio, with a reward strategy designed to maintain an average pore-pressure ratio near 30% to ensure partially drained conditions consistent with CRS theory. Two normally consolidated clays with contrasting compressibility were modeled numerically using a 1-D CRS consolidation model to evaluate learning and testing performance. The results show that the SARSA agent autonomously learns soil-specific strain-rate policies and maintains smooth effective stress trajectories and stable pore-pressure ratio responses. Test duration reductions of 60-75% were achieved depending on soil type. The interpreted compression index (Cc) remains consistent with the baseline CRS values, confirming that reinforcement-learning-based strain-rate control can accelerate testing without compromising data integrity. The study demonstrates the feasibility of reinforcement learning for CRS testing and highlights practical potential for soil-responsive, adaptive strain-rate control. Current limitations include simulation-based evaluation, discretized action selection, and the need for multiple runs to achieve optimal convergence.
Co-Authors Abbas, Muhammad Fadhl Abdurrasyid, Abdurrasyid Ade Chandra Aditya Pradana, Aditya Agung Wahyu Setiawan Agus Nursikuwagus Akhmadi Surawijaya Amalia, Hayati Amalia, Hayati Ambarwari, Agus Anisa Herdiani Arief Ichwan Armein Z.R. Langi Arry Akhmad Arman Ayu Latifah Bryan Denov Budi Rahardjo Budi Sulistyo Carmadi Machbub Dayu Apoji Denny Hidayat Tri Nugroho Desti Madya Saputri Didik Fauzi Dakhlan Dwi Harinitha Emir Mauludi Husni Endang Darwati Erza Rismantojo, Erza Feisy Kambey, Feisy Firmansyah, Feri Hidayatullah Fitra Arifiansyah Hasbullah Nawir, Hasbullah Hasta Pratama Hikmawati, Erna Hurianti Vidyaningtyas Imelda Uli Vistalina Simanjuntak Ira Puspasari Johan, Meliana Christianti Komarudin, Agus Kridanto Surendro Kurniawan Nur Ramadhani Ledya Novamizanti Leni Fitriani, Leni M. Alifsyah Putra Nasution M. Octaviano Pratama Mohamad Idris Muhammad Fadhl 'Abbas Munggana, Wira Nasution, Muhammad Alifsyah Putra Nasy`an Taufiq Al Ghifari Nilam Fitriah Okyza Maherdy Parjuangan, Sabam Pranoto H. Rusmin Rusmin, Pranoto H. Rusmin Pranoto Hidaya Rusmin Radiant Victor Imbar Ratna Mayasari Reza Budiawan, Reza Ridha Muldina Negara Rinaldi Munir Rinaldi Munir Rinda Cahyana Rita Rismala Riza Ibnu Adam Rosmansyah, Yusep RUKMAN HERTADI Setia Juli Irzal Ismail Sulistyaningsih Sulistyaningsih Susanty, Meredita Susmini Indriani Targhib Ibrahim Tati Latifah Erawati Rajab Teguh Aryo Nugroho Toni Kusnandar Untari Novia Wisesty Wahyu Hidayat Yeni Sanovia Yoanes Bandung Yudi Setiawan Yulrio Brianorman Yusep Rosmansyah YUYUN SITI ROHMAH Zakaria, Hasballah Zidni, Hasan