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All Journal Cakrawala Pendidikan Jurnal Buana Informatika PROCEEDING IC-ITECHS 2014 SMATIKA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Scientific Journal of Informatics Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Instructional Media and Model Jurnal Pengabdian Masyarakat Asia Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) Jurnal Masyarakat Madani Indonesia Jurnal Informatika Polinema (JIP) Journal of Information Technology and Cyber Security Jeskovsia (Jurnal Desain Komunikasi Visual Asia) Jurnal ilmiah teknologi informasi Asia Cendekia: Jurnal Pengembangan Kurikulum dan Pendidikan Smatika Jurnal : STIKI Informatika Jurnal
Suastika Yulia Riska
Institut Teknologi Dan Bisnis Asia Malang
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Optimisasi Algoritma A* untuk Pencarian Rute Menggunakan Media Roblox Restu Andra Ahmad Saeroji; Suastika Yulia Riska
Jurnal Informatika Polinema Vol. 12 No. 2 (2026): Vol. 12 No. 2 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i2.9232

Abstract

Pengembangan Non-Player Character (NPC) yang realistis dalam platform metaverse seperti Roblox membutuhkan sistem yang efisien. Namun, permasalahan utama yang dihadapi pengembang adalah tingginya biaya komputasi dan kurangnya data mengenai kinerja algoritma A* pada bahasa pemrograman Luau. Penelitian ini bertujuan untuk mengevaluasi kinerja algoritma A* pada Roblox dengan bahasa pemrograman Luau melalui analisis komparatif dengan memvariasikan fungsi heuristik (Manhattan, Euclidean, Chebyshev, dan Octile) dan metode sorting (Quick Sort dan Min-Heap Priority Queue). Penelitian dilakukan dengan pendekatan eksperimental kuantitatif di dalam Roblox Studio. Pengujian dilaksanakan pada tiga skenario labirin statis dengan ukuran grid 64x64, 128x128, dan 256x256 studs. Evaluasi didasarkan pada dua metrik utama, yaitu total waktu eksekusi dan total panjang rute yang dihasilkan. Hasil penelitian menunjukkan bahwa penggunaan Min-Heap Priority Queue membuat waktu eksekusi mengalami penurunan rata – rata 46,5% dibandingkan implementasi default dengan efektivitas tertinggi sebesar 73,3% pada skenario ukuran 256 studs x 256 studs. Waktu eksekusi dan hasil rute untuk setiap fungsi heuristik memiliki perbedaan yang tidak signifikan kecuali Euclidean Distance. Fungsi heuristik Euclidean Distance mencatatkan waktu eksekusi tercepat di antara fungsi lain sebesar 2,03ms di 64x64, 5,8ms di 128x128, dan 42,35ms di 256x256. Selain itu, fungsi Euclidean Distance menghasilkan rute yang kurang optimal dengan jarak terjauh sebesar 134 studs di 64x64, 427 studs di 128x128, dan 1062 studs di 256x256 dibandingkan fungsi heuristik lainnya. Penelitian ini membuktikan bahwa dalam pengembangan Roblox, pemilihan konfigurasi dan optimisasi algoritma yang tepat sangat krusial bagi pengembang untuk menyeimbangkan antara kecepatan proses dan akurasi rute sesuai kebutuhan.
Ethical Challenges in Primary vs. Secondary Datasets: A Systematic Review of Manipulation and Transparency Riska, Suastika Yulia; Widiyaningtyas, Triyanna; Elmunsyah, Hakkun; Sendari, Siti
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.1227

Abstract

The swift advancements in Artificial Intelligence and Machine Learning have rendered datasets essential; nonetheless, their heightened utilization has engendered intricate ethical dilemmas that are frequently neglected. This study seeks to delineate and highlight ethical concerns associated with the collection of primary data and the reutilization of secondary datasets in computer science research. We employed a Systematic Literature Review (SLR) methodology in accordance with the PRISMA 2020 guidelines, examining 72 publications sourced from five esteemed academic databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Google Scholar) published from 2021 to 2025. The study results indicate that ethical difficulties emerge uniformly in both primary and secondary datasets. Primary datasets primarily face challenges related to privacy threats, anonymization, and Informed Consent, whereas secondary datasets are more susceptible to licensing infringements, dataset repurposing, and insufficient preparation transparency. The three domains that predominantly encountered these challenges were Machine Learning, Computer Vision, and Natural Language Processing. Moreover, practices of data manipulation, including cherry-picking and concealed preparation, were identified as detrimental to scientific integrity. This study's findings underscore the need for enhanced ethical standards for datasets and greater transparency in preparation documentation to ensure the repeatability of data-driven research.
Enhancing UI/UX Design Competency Using Figma at SMK NU Donomulyo Adriani Kala'lembang; Suastika Yulia Riska; Azwar Riza Habibi; Widya Adhariyanty Rahayu; Yudistira Arya Sapoetra
Jurnal Pengabdian Masyarakat Vol. 7 No. 1 (2026): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v7i1.2855

Abstract

Purpose: This study addresses the competency gap between vocational graduates and industry demands for UI/UX design skills through a Figma-based training program at SMK NU Donomulyo. Method: A hands-on, project-based workshop was delivered to 30 students. Effectiveness was measured via pre-test/post-test assessments, final project rubrics, and participant satisfaction surveys. Practical Applications: The program provides students with industry-standard prototyping capabilities and ready-to-use digital portfolios, directly improving their employability in the growing digital design sector. Conclusion: Findings revealed a 40.6% knowledge improvement, 100% project completion, and high satisfaction (4.5/5.0). The initiative successfully bridges educational and industrial requirements, confirming hands-on training as a vital strategy for vocational digital readiness.
Perbandingan K-Means dan K-Medoids Untuk Clustering Lagu Setipe di Spotify Berdasarkan Karakteristik Audio Syalomiele Pratama Agustinus Susanto; Suastika Yulia Riska
INTEGER: Journal of Information Technology Vol 11, No 1 (2026): Maret
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2026.v11i1.8394

Abstract

Pertumbuhan layanan streaming musik seperti Spotify menghadirkan kebutuhan akan sistem pengelompokan lagu yang mampu meningkatkan pengalaman pengguna melalui rekomendasi yang lebih akurat. Untuk meningkatkan pengalaman pengguna, diperlukan sistem pengelompokan lagu berdasarkan kemiripan fitur audio seperti danceability, energy, acousticness, instrumentalness, liveness, speechiness, dan valence. Penelitian ini membandingkan dua algoritma clustering, yaitu K-Means dan K-Medoids, dalam mengelompokkan lagu-lagu Spotify berdasarkan fitur audio tersebut. Algoritma K-Means dikenal efisien dalam komputasi, sementara K-Medoids lebih robust terhadap outlier. Evaluasi dilakukan menggunakan Davies-Bouldin Index (DBI) untuk mengukur kualitas pemisahan antar-kluster. Hasil penelitian menunjukkan bahwa K-Means memberikan hasil terbaik pada k = 3 dengan DBI 0,857, sedangkan K-Medoids memberikan hasil terbaik pada k = 9 dengan DBI 0,844. Meskipun K-Medoids sedikit lebih baik dalam hal kualitas klaster, K-Means lebih unggul dalam efisiensi waktu komputasi. Penelitian ini memberikan wawasan penting mengenai efektivitas kedua algoritma dalam sistem rekomendasi musik berbasis clustering dan dapat memperkaya literatur tentang pengelompokan lagu di platform streaming
Outcome-Based Education Curriculum Development: Conceptual Foundations and Implementation Challenges in Indonesian Higher Education Fadhli Almu'iini Ahda; Suastika Yulia Riska; Danang Arbian Sulistyo
Cendekia: Jurnal Pengembangan Kurikulum dan Pendidikan Vol. 3 No. 1 (2026): Transforming Education in the Digital Era
Publisher : CV Faliha Cendekia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64683/cendekia.v3i1.57

Abstract

The adoption of Outcome-Based Education (OBE) has become the mainstream of higher education curriculum reform in Indonesia, driven by the Indonesian National Qualifications Framework (KKNI), the National Standards for Higher Education, the Merdeka Belajar–Kampus Merdeka (MBKM) policy, and national as well as international accreditation requirements. Its implementation, however, often stops at the documentary level and has yet to reach the transformation of learning processes and assessment. This article is an integrative literature review that aims to (1) examine the conceptual foundations of OBE, (2) synthesise models of outcome-based curriculum development, (3) analyse implementation challenges in the Indonesian context, and (4) formulate a coherent development framework. A search was conducted across 64 sources from the Scopus, ERIC, DOAJ, and Garuda databases for the period 1994-2025, then analysed thematically using Spady’s OBE principles and Biggs’ constructive alignment. The review shows that effective OBE curriculum development requires vertical alignment from the Graduate Profile down to lesson-level learning outcomes, constructive alignment among outcomes, learning experiences, and assessment, and a continuous quality improvement mechanism that closes the loop. The main challenges include shifting lecturers’ mindsets, assessment workload, learning-outcome inflation, limited information systems, and the gap between the written and the enacted curriculum. The article proposes a capacity-building framework and an agenda for further research.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Aziz Muslim Abdul Aziz Muslim Achmad Noercholis Adriani Kala'lembang Adriani Kala’lembang Adriani Kala’lembang Ahda, Fadli Almu'ini Amin, Angger Nur Aqromi , Nur Lailatul Azwar Riza Habibi, Azwar Riza BUKHORI, MOHAMMAD Fadhli Almu'iini Ahda Fadhli Almu'ini Ahda Fadhli Almu’iini Ahda Fadilla Cahyaningtyas Faqih, Luthfi Ramdhan Farokah, Lia Farokhah, Lia Ginardi, R.V. Hari Hakkun Elmunsyah Hanif, Rifki Jaenal Arifin Justita Dura Kala'lembang, Adriani Laili Cahyani Lilis Widayanti Mufidatul Islamiyah, Mufidatul Muhammad Imron Rosadi Muhammad Imron Rosadi Nanik Suciati Nanik Suciati Nira Rusanti Prasetya, Hafid Arjul Prasetyo, Bima Puji Subekti R. V. Hari Ginardi Rahmawati, Nisrina Salsabil Reda Maulidina Restu Andra Ahmad Saeroji Rina Dewi Indah Sari Rina Dewi Indahsari Samsul Arifin Setyorini Setyorini Siti Maharani, Farah Shafiyah Siti Nurul Afiyah Siti Sendari Suci Imani Putri Sulistyo, Danang Arbian Sunu Jatmika, Sunu Syaad Patmanthara Syalomiele Pratama Agustinus Susanto Triyanna Widiyaningtyas Vivi Aida Fitria Widya Adhariyanty Rahayu Widya Adhariyanty Rahayu Yogatama, Ahmad Nizar Yogie Susdyastama Putra Yudistira Arya Sapoetra