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All Journal International Journal of Electrical and Computer Engineering Jurnal Informatika Jurnal Teknik Elektro Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Informatika Komputer Jurnal Manajemen Teknologi Jurnal Momentum TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Jurnal Teknobisnis Seminar Nasional Informatika (SEMNASIF) Journal of Information Systems Engineering and Business Intelligence The Asian Journal of Technology Management (AJTM) Indonesian Journal on Computing (Indo-JC) Proceeding of the Electrical Engineering Computer Science and Informatics JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Filsafat Indonesia Jurnal Teknik Informatika (JUTIF) International Journal of Social Service and Research Media Riset Akuntansi Auditing & Informasi Jurnal Komputer Bisnis
Kridanto Surendro
Institut Teknologi Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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COMPARATIVE ANALYSIS OF MULTI-CRITERIA DECISION MAKING METHODS IN DETERMINING REDD+ PROJECT LOCATION Irawan, Aditya Putra; Surendro, Kridanto
International Journal of Social Service and Research Vol. 4 No. 8 (2024): International Journal of Social Service and Research
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/ijssr.v4i8.879

Abstract

The Reducing Emissions from Deforestation and Forest Degradation (REDD+) mechanism is aimed at reducing global greenhouse gas (GHG) emissions. This study aims to develop a multi-criteria decision-making (MCDM) model specifically designed to prioritize locations for REDD + projects. The proposed research design focuses on developing a MCDM framework for determining the priority locations for projects using criteria such as climate impact reduction, contributions to local communities, and biodiversity conservation. The study utilized the Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to determine the prioritization of alternatives based on compromise solutions. The success of this research demonstrates that a systematic approach to determining priority locations can be effectively carried out using MCIM. This research is expected to aid policymakers and stakeholders in making more informed and effective decisions for environmental conservation and climate change mitigation.
Enhancing the Comprehensiveness of Criteria-Level Explanation in Multi-Criteria Recommender System Rismala, Rita; Maulidevi, Nur Ulfa; Surendro, Kridanto
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 2 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.2.160-172

Abstract

Background: The explainability of recommender systems (RSs) is currently attracting significant attention. Recent research mainly focus on item-level explanations, neglecting the need to provide comprehensive explanations for each criterion. In contrast, this research introduces a criteria-level explanation generated in a content-based pardigm by matching aspects between the user and item. However, generation may fall short when user aspects do not match perfectly with the item, despite possessing similar semantics.  Objective: This research aims to extend the aspect-matching method by leveraging semantic similarity. The extension provides more detail and comprehensive explanations for recommendations at the criteria level.    Methods: An extended version of the aspect matching (AM) method was used. This method identified identical aspects between users and items and obtained semantically similar aspects with closely related meanings.   Results: Experiment results from two real-world datasets showed that AM+ was superior to the AM method in coverage and relevance. However, the improvement varied depending on the dataset and criteria sparsity.  Conclusion: The proposed method improves the comprehensiveness and quality of the criteria-level explanation. Therefore, the adopted method has the potential to improve the explainability of multi-criteria RSs. The implication extends beyond the enhancement of explanation to facilitate better user engagement and satisfaction.  Keywords: Comprehensiveness, Content-Based Paradigm, Criteria-Level Explanation, Explainability, Multi-Criteria Recommender System
Development of an AI Governance Model for Higher Education Using the Capability Maturity Model Integration (CMMI) Walhidayah, Irfan; Surendro, Kridanto
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4709

Abstract

The increasing adoption of Artificial Intelligence (AI) in higher education presents strategic opportunities for institutional transformation, while introducing complex challenges related to ethics, accountability, transparency, and regulatory compliance. Responding to the growing complexity of AI implementation in academic environments , this study proposes a governance model for AI named GOVAIHEI (Governance of Artificial Intelligence for Higher Education Institutions), conceptualized using the Capability Maturity Model Integration (CMMI) framework. The model was developed using the Design Research Methodology (DRM), which consists of four stages: Research Clarification, Descriptive Study I, Prescriptive Study, and Descriptive Study II. GOVAIHEI encompasses five primary domains: Data and Information, Technology and Infrastructure, Ethics and Social Responsibility, Regulation and Compliance, and Monitoring and Evaluation. Each domain is articulated into capability areas and measurable practices, assessed using the tiered NPLF scale (Not, Partial, Largely, Fully Achieved) to determine institutional capability and maturity levels. The model was validated through expert judgment by three domain specialists, confirming its relevance, methodological soundness, and alignment with CMMI principles. A web-based evaluation system was also developed using Laravel, PostgreSQL, Redis, and Nginx, enabling structured, efficient, and automated assessments. Implementation in a case study at Institute XYZ revealed an initial maturity level (Level 1) with development goals toward Level 3 (Defined). The findings demonstrate a practical foundation for navigating the multifaceted nature of AI adoption in higher education through a structured and adaptable governance approach, which aligns with the increasing demand for robust digital governance frameworks in technology-driven environments.  
Deteksi Cyberbullying dengan Mesin Pembelajaran Klasifikasi (Supervised Learning): Peluang dan Tantangan Setiawan, Yudi; Maulidevi, Nur Ulfa; Surendro, Kridanto
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022976747

Abstract

Perkembangan teknologi media sosial tidak hanya memberikan kemudahan dalam berkomunikasi antar individu, akan tetapi juga dapat mengancam kehidupan sosial individu seperti tidakan cyberbullying. Bervariasinya pola dan karakteritik cyberbullying mengakibatkan sulitnya proses deteksi cyberbullying, yang dilakukan oleh pelaku cyberbullying. Penelitian deteksi pola dan karakteristik cyberbullying banyak dilakukan dengan berbagai metode, seperti dengan mengimplementasikan Machine Learning, Natural Language Processing (NLP), dan Sentiment Analysis yang memiliki variasi akurasi yang berbeda, dengan keunggulan dan kelemahan dari masing-masing metode. Implementasi Machine Learning untuk deteksi cyberbullying dapat dilakukan dengan berbagai algoritma, seperti algoritma probabilistik (Naïve Bayes) maupun supervised learning (Support Vector Machine, k-Nearest Neighbour, Decission Tree), dan metode lainnya yang hingga saat ini terus dikembangkan dengan berbagai pendekatan untuk meningkatkan akurasi deteksi cyberbullying atau non-cyberbullying. Adapun peluang dan tantangan penelitian deteksi cyberbullying seperti penerapan pada variasi domain bahasa, dan bentuk ekspresi yang dilakukan pada suatu lingkungan atau budaya, yang masih terdapat ruang untuk dikembangkan dan dijelajah secara luas. Pada artikel ini menjabarkan penelitian berikutnya berupa mengimplementasikan metode pembelajaran klasifikasi (Supervised Learning) dengan modifikasi tahapan untuk meningkatkan akurasi klasifikasi.
A System Dynamics Model of 5G Low-Band Spectrum Management Shalahuddin, Muhammad; Sunindyo, Wikan Danar; Effendi, Mohammad Ridwan; Surendro, Kridanto
Journal of ICT Research and Applications Vol. 19 No. 1 (2025)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2025.19.1.3

Abstract

The fifth-generation (5G) mobile communication system represents a major advancement in wireless technology, relying on effective radio spectrum management to ensure optimal performance. Among the available frequency ranges, the 5G low-band spectrum provides extensive coverage but limited capacity, making its efficient management a critical challenge. This study presents a predictive model based on the system dynamics approach to analyze the management of the 5G low-band spectrum. The model captures the interrelationships between technical and economic variables that influence spectrum allocation and service adoption over time. Three simulation scenarios—low, medium, and high allocation rates—were developed to examine allocation patterns and their effects on 5G service diffusion. The results revealed that spectrum management in 5G exhibits goal-seeking behavior constrained by spectrum scarcity, with service adoption showing a growth-to-saturation pattern. The findings demonstrate that appropriate low-band spectrum management can significantly enhance 5G deployment efficiency. The proposed model serves as a decision-support tool for policymakers and regulators, enabling evaluation of alternative management strategies prior to policy implementation and promoting evidence-based decision-making in future 5G spectrum policies.
Unsupervised Anomaly Detection in Hospital Wastewater Effluent Using Convolutional Autoencoder Hibban, Daffa Maulana; Surendro, Kridanto
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Background: Hospital wastewater treatment plants (WWTPs) play a crucial role in maintaining environmental sustainability. However, conventional monitoring has difficulty identifying minor differences in effluent quality, leading to non-compliance. While machine learning is increasingly applied in water quality analysis, the specific application of deep representation learning in hospital effluent analysis, focusing on identifying anomalies within stable and low variation factors, is not much explored. Objective: This study aims to evaluate the effectiveness of a proposed Convolutional Autoencoder (Conv-AE) for anomaly detection in the effluent of hospital WWTP. To ensure the efficacy of the algorithm, it is compared with two popular statistical algorithms: Isolation Forest (IF) and One-Class Support Vector Machine (OCSVM). Methods: Internet of Things (IoT) sensor data covering pH, temperature, Total Dissolved Solids (TDS), and ammonia gas parameters were collected from the effluent tank of a hospital WWTP. The Conv-AE model was designed to learn the latent nonlinear representations of normal effluent patterns. The model’s performance was evaluated using precision, recall, F1-score, accuracy, and inference time metrics. Results: The proposed Conv-AE model performed best in terms of detection, having the best values ​​for all three metrics, with a recall of 0.980, an F1 score of 0.960, and an accuracy of 0.980. This indicates a robust ability to identify subtle deviations that statistical baselines often miss. In terms of operational feasibility, while the Isolation Forest baseline exhibited the fastest inference time of 0.000014 seconds, the Conv-AE remained highly efficient for real-time applications with a inference time of 0.000348 seconds. Conclusion: In conclusion, the Conv-AE algorithm offers an optimal trade-off between high detection sensitivity and operational feasibility. By prioritizing the minimization of false negatives, this deep learning approach provides a more reliable solution for safety-critical hospital effluent monitoring compared to traditional statistical partitioning methods.   Keywords: Anomaly Detection, Hospital Wastewater Treatment Plant (WWTP) Effluent, Convolutional Autoencoder, Deep Learning
Co-Authors , Ronaldi Adelia Adelia, Adelia Adiska Fardani Aradea Ari Fajar Santoso Ayu Purwarianti Basuki Sugiharto Basuki Sugiharto Cicely Martini Asihwardji Dicky Prima Satya Dimitri Mahayana Dwi H Widyantoro Dwi H. Widyantoro Effendi, Mohammad Ridwan Erwin B. Setiawan Erwin B. Setiawan Erwin S. Panjaitan Farrell Yodihartomo Febriantoro, Wicaksono Heru Nugroho Heru Nugroho Hibban, Daffa Maulana Indra Lukmana Sardi Iping S Suwardi Iping Supriana Iping Supriana Iping Supriana Iping Supriana Suwandi Irawan, Aditya Putra Kristian Telaumbanua Ma'mun, Nurhayati Maniah Maniah Maria Irmina Prasetiyowati Megawati . Muhammad Shalahuddin Nur Ulfa Maulidevi Nurhajati Ma'mun Olivia Olivia Ririn Dwi Agustin Rita Rismala Ronaldi Ronaldi Roni Yunis Roni Yunis Roni Yunis Sakam, R. Djunaedy Sarifah Putri Raflesia Setiawan, Erwin B. Soni Fajar Surya G Sri Agustina Rumapea Sri Handayaningsih Stevanus Wisnu Wijaya Sugondo Hadiyoso Sukma Nirad, Dwi Welly Sunindyo, Wikan Danar Tati Latifah Erawati Rajab Wahyu Hidayat Walhidayah, Irfan Wawan Dhewanto Widyantoro, Dwi H Yudi Setiawan Yusep Rosmansyah