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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Pendidikan (Teori dan Praktik) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Ilmiah Flash Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) JTAM (Jurnal Teori dan Aplikasi Matematika) ILKOM Jurnal Ilmiah SENTIA 2016 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Community Development Journal: Jurnal Pengabdian Masyarakat Letters in Information Technology Education (LITE) Indonesian Journal of Instructional Media and Model Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Bulletin of Community Engagement Indonesian Journal of Innovation Studies Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) JAPI: Jurnal Akses Pengabdian Indonesia JP (Jurnal Pendidikan) : Teori dan Praktik JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Jurnal Riset Rumpun Agama dan Filsafat Journal of Artificial Intelligence and Digital Business Information Technology Education Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal ilmiah teknologi informasi Asia Advance Sustainable Science, Engineering and Technology (ASSET) Systematic Literature Review Journal Jurnal Elektronika dan Telekomunikasi
Triyanna Widiyaningtyas
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Pengembangan SIBAKOS dengan Teknologi Live Preview Berbasis MyWebAR pada UMKM Barbershop di Lowokwaru Kota Malang Muhammad Ilham Ramdhani; Nabila Salsabila; Triyanna Widiyaningtyas; Heni Vidia Sari
Jurnal Pengabdian UNDIKMA Vol. 7 No. 2 (2026): May
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i2.18288

Abstract

This community service program aims to develop a website-based information system called SIBAKOS (Koko Salon Barbershop Information System) to address the operational problems of a barbershop MSME through online reservation and live preview features. The implementation methods included participatory system design, development of live preview and online reservation features, system implementation, functional testing using the black-box method, and direct training. Evaluation instruments consisted of field observations, interviews, and daily reservation data records, which were analyzed descriptively using quantitative methods. The results showed that SIBAKOS successfully facilitated the management of daily reservations through a structured system, reduced overlapping schedule cases from 5–7 cases per week to 1–2 cases per week, and improved operational efficiency by 78%. The live preview feature using MyWebAR provided visual references that facilitated communication between customers and salon staff, reducing consultation time by an average of 3–7 minutes per customer. System implementation demonstrated that SIBAKOS had a positive impact on Koko Salon by improving service quality and operational efficiency in the digital era.
Comparative Evaluation of BM25–FAISS and Small-LLM–GPT in Retrieval-Augmented Generation Concept Map Assessment Maskur Maskur; Didik Dwi Prasetya; Triyanna Widiyaningtyas; Azlan Mohd Zain
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2594

Abstract

Concept map-based assessment is a practical approach to measure students’ conceptual understanding, but manual assessment still faces challenges such as subjectivity, inconsistency, and limited scalability. This study proposes the application of Retrieval-Augmented Generation (RAG) as an artificial intelligence-based automated assessment solution in an educational context. The objectives of this study are to compare the effectiveness of two retrieval methods, BM25 and FAISS, and to analyse the trade-off between large-scale generative models (GPT) and Small-LLM in assessing concept map propositions. This study uses a quantitative experimental approach by combining a retriever and a generator in the RAG system. Performance evaluation is carried out using the Macro-F1 and QWK metrics to measure agreement with expert judgment, and the Explanation Relevance Score (ERS) to assess explanation quality. The experimental results show that the FAISS–GPT combination achieves the best performance, with a Macro-F1 of 0.338 and a QWK of 0.146, slightly superior to the BM25–GPT combination. In contrast, the use of Small-LLM, both with BM25 and FAISS, showed lower performance with Macro-F1 values in the range of 0.167–0.221 and QWK close to zero. This finding confirms that semantic-based retrieval plays a vital role in improving the accuracy of automated assessment, while large-scale generative models are more effective in representing conceptual relationships in depth. This study contributes through a comparative analysis of retrievers and generators, and by introducing ERS as an additional metric for RAG-based automated assessment in the field of education.
SQL Logic Error Detection using Start End Mid Algorithm Ardiansyah, Jevri Tri; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Yasuhisa, Okazaki
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Database is an important part of a system and it stores data to be manipulated. SQL (Structured Query Language) is used for manipulating those data to extract information and make decision. There are two types of error which make SQL is challenging to learn, namely syntax error and logic error. Compiler can detect syntax error, but it does not show error warning while logical error occurred. It makes logic error more difficult to understand than syntax error. A web based SQL compiler with errors detection ability by using Start End Mid algorithm is then developed, To help database's user to learn SQL in practical implementation.
Digitalisasi Layanan Laundry: Implementasi Sistem Pemesanan dan Kasir Berbasis Web pada UMKM LBC Laundry Malang Alvin Bintang Abitara; Triyanna Widiyaningtyas; Heni Vidia Sari; Ahmad Reza; Adrian Juhari
Jurnal Pengabdian UNDIKMA Vol. 7 No. 3 (2026): August
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i3.21297

Abstract

This community service program aims to design and implement a web-based ordering and cashier system for LBC Laundry, an MSME in Malang, to improve operational efficiency, financial reporting accuracy, and information transparency for customers. The program was carried out through field observation, needs assessment using semi-structured interviews and Focus Group Discussions (FGDs) with the business owner, system design based on a user-centered design (UCD) approach, iterative system development using the MERN Stack framework with CRUD (Create, Read, Update, Delete) functionality, followed by training, mentoring, and evaluation. Quantitative data were analyzed using descriptive statistics by comparing key performance indicators before and after the program implementation. The results demonstrated an 80% improvement in transaction efficiency, with the average transaction time decreasing from 5 minutes to 1 minute per customer. In addition, data entry errors were reduced to zero throughout the one-month evaluation period, while the accuracy of financial reporting reached 100% through real-time reporting capabilities. The MSME owner provided positive feedback, indicating that the system simplified business operations and enhanced information transparency for customers. Furthermore, the system has the potential to be further developed into a mobile application to improve accessibility and support the long-term digital transformation of MSMEs.
Representation over Kernel: A Cross-Category Analysis of Semantic Review Embedding versus Rating-Overlap Collaborative Filtering for User Cold-Start Moh Rahmat Irjii Matdoan; Triyanna Widiyaningtyas; Firmansyah Ibrahim; Rasna
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.13484

Abstract

Purpose – The user cold-start problem cripples recommenders when a new user has almost no interaction history. This study asks two questions prior work conflates: does semantic review embedding similarity outperform conventional rating-overlap collaborative filtering (CF) in cold-start, and does a magnitude-sensitive kernel over the embedding add anything beyond ordinary cosine similarity? Design/methods/approach – Under one identical protocol on two Amazon review domains of differing sparsity, RBF-SBERT (an RBF kernel over SBERT embeddings) is compared with cosine, Pearson, an XGBoost baseline, rating-overlap CF, and two popularity baselines. Cold-start users (one to three interactions) use a per-user sampled protocol; significance uses Wilcoxon and Cohen's d. Findings – In the sparse domain the embedding beats even the strongest simple baseline, item-count popularity, on precision (P@5 0.567 vs 0.252; d=0.65) and ranking (AUC 0.869 vs 0.821); in the denser domain that edge vanishes as popularity matches AUC and exceeds precision, so the advantage is governed by sparsity. Rating-overlap CF is weakest, collapsing to its mean-rating fallback. The RBF kernel is inert (≈ cosine, d=0.007–0.107) because the pooled magnitude it uses is nearly constant (CV 0.07–0.09) and tracks review length, not preference. Research implications/limitations – Cold-start gains arise from the representation, not the kernel; methods must be benchmarked against popularity under sparsity-aware protocols. Evaluation covers two Amazon categories under one sampled protocol. Originality/value – The study separates representation from kernel and precision from ranking quality, giving a reproducible account of when review-based embedding helps cold-start and why.
Explainable IoT Intrusion Detection Using Random Forest, SMOTE, and SHAP Julfikar Mawansyah; Anik Nur Handayani; Aji Prasetya Wibawa; Triyanna Widiyaningtyas; Mokh. Sholihul Hadi; Fidyah Ajeng Wulandari
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.823

Abstract

Class imbalance in Internet of Things (IoT) Intrusion Detection System (IDS) datasets is a major challenge that degrades the detection performance on minority attacks and complicates model interpretability. This study investigates the performance of an IoT IDS based on Random Forest (RF) combined with the Synthetic Minority Over-sampling Technique (SMOTE) and explainable AI analysis using SHapley Additive exPlanations (SHAP) on the public IoTID20 dataset. The research pipeline consists of data preprocessing (cleaning, numerical and categorical feature encoding, normalization), stratified train–test split into 7,000 training and 3,000 test samples, training an RF baseline on the imbalanced training set, applying SMOTE to balance the DoS, Scan, Normal, and MITM ARP Spoofing classes, and training an RF–SMOTE model. The models are compared using accuracy, precision, recall, and F1-score per class as well as macro averages. Afterwards, SHAP is employed to analyse global feature importance for both models. Experimental results show that the RF baseline already achieves very high performance with an accuracy of about 0.99 and a macro F1-score of approximately 0.984, while the RF–SMOTE model maintains the same accuracy with a macro F1-score of around 0.983. SMOTE substantially improves the class distribution in the training set but yields only minor differences in aggregate performance, RF–SMOTE slightly enhances sensitivity for some minority classes, whereas the F1-score for the MITM ARP Spoofing class decreases marginally compared to the baseline. SHAP analysis indicates that flow-related traffic features such as connection duration, packet counts, byte volume, and packet direction ratios are consistently the most influential features in both models. Changes in SHAP values for the RF–SMOTE model highlight an increased relative contribution of features representing rare attack patterns, making explanations for minority classes more prominent. Overall, the proposed RF–SMOTE–SHAP framework delivers a high-performing IoT IDS while providing improved transparency in explaining detection decisions, thereby supporting the development of trustworthy and interpretable IDS solutions for IoT environments.
Performance Evaluation of Relational, JSON, and Hybrid Models in MariaDB for Dynamic Form Builders Muhammad Salmansyah Mousavi Akhyar; Triyanna Widiyaningtyas
SISTEMASI Vol 15, No 10 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i10.6988

Abstract

Dynamic form builders require database structures that can accommodate variable attributes while maintaining efficient query performance. However, the performance implications of different storage representations within the same database management system remain insufficiently characterized, particularly for relational, raw JSON, and hybrid relational-JSON models. This study evaluates the performance of these three storage representations for dynamic-form data using MariaDB 10.6.19. A controlled quantitative experiment was conducted using datasets of 10,000, 50,000, and 100,000 records and six read and aggregation workloads covering point retrieval, dynamic-field search, multi-field filtering, counting, averaging, and grouping. Each workload was executed using 10 warm-up iterations followed by 30 measured repetitions. Query latency, storage consumption, execution plans, and statistical differences among representations were analyzed. The results show that performance varied according to workload and dataset scale. Statistically significant differences were observed in 13 of 18 workload-dataset combinations. The representation with the lowest median latency also varied across conditions, indicating that no single storage representation consistently provided the lowest query latency. Execution-plan analysis for DYNAMIC_FIELD_SEARCH at 100,000 records showed indexed access for relational storage and full-table scans for the evaluated raw JSON and hybrid configurations. Raw JSON required the lowest storage consumption, followed by the hybrid representation. These findings indicate that storage representation should be selected according to workload characteristics, dataset scale, and structural requirements rather than based on a single overall performance criterion.
Ontologi, Aksiologi, dan Epistemologi Energi Baru Terbarukan Terhadap Smart City: Tinjauan Literatur Sistematis Alfarid Hendro Yuwono; Syaad Patmanthara; Aripriharta Aripriharta; Triyanna Widiyaningtyas; Nafi Isbadrianingtyas; M Ibrahim Ashari; Ni Putu Agustini
Jurnal Riset Rumpun Agama dan Filsafat Vol. 4 No. 3 (2025): Desember: Jurnal Riset Rumpun Agama dan Filsafat
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrafi.v4i3.7273

Abstract

The development of digital technology and the Internet of Things (IoT) has transformed the paradigm of urban development towards the concept of a smart city based on data and connectivity. This study analyzes the nature of smart cities through three dimensions of the philosophy of science: ontology, epistemology, and axiology. From an ontological perspective, a smart city is understood as a complex entity consisting of physical, digital, social, and ecological systems that interact with each other to create efficient and adaptive urban governance. Epistemologically, knowledge in a smart city is obtained through the process of collecting, processing, and analyzing data from various IoT devices, sensors, and citizen participation, thereby producing new insights that support evidence-based decision-making (data-driven governance). From an axiological perspective, a smart city has ethical values and goals to improve the quality of life of its citizens, strengthen government transparency, maintain environmental sustainability, and promote inclusive social participation. Thus, this study asserts that the development of a smart city is not only technological but also has a philosophical foundation oriented towards a balance between efficiency, humanity, and sustainability.
Co-Authors - Ardiansyah, - Abdul Hadi, Afif Abdul Karim Abdullah Iskandar Syah Adam Ramadhani P Adiba Qonita Adrian Juhari Ahmad Farobi Ahmad Fuadi Ahmad Reza Aji P Wibawa Aji Prasetya Wibawa Alfarid Hendro Yuwono Ali, Waleed Alvin Bintang Abitara Anik Nur Handayani Annas Gading Pertiwi Ardiansyah, Jevri Tri Arif Mudi Priyatno Aripriharta - Aya Shofia Mufti Azlan Mohd Zain Bambang Nurdewanto Bintang Romadhon Binti Afifah Brilliant, Muhammad Zidan Budi Wibowotomo Dasuki, Moh. Didik Dwi Prasetya Ega Gefrie Febriawan Elta Sonalitha Fadhlullah, Aufar Faiq Falah, Moh Zainul Febrianto Alqodri Fidyah Ajeng Wulandari Fitriyah Fitriyah Fitriyah Fitriyah Frendy Rumambi Gading Pertiwi, Annas Gamma Fitrian Permadi Hairani Hairani Hakkun Elmunsyah Haviluddin Haviluddin Hazizah, Chalista Yulia Heni Vidia Sari Heru Wahyu Herwanto Hirashima, Tsukasa I Made Wirawan Ibrahim, Firmansyah Imansyah, Pranadya Bagus Indriana, Poppy Julfikar Mawansyah Kornelius Kamargo/Irawan Dwi Wahyono Kornelius Kamargo Kurniawan, Rizky Rizaldi M Ibrahim Ashari M. Ardhika Mulya Pratama M. Habibullah Arief M. Noer Fadli Hidayat M. Zainal Arifin Martin Indra Wisnu Prabowo Maryani, Sri Maskur Maskur Moh Rahmat Irjii Matdoan Moh Zainul Falah Moh. Robieth Alfan Alhamid Mohamad Yusuf Kurniawan Mokh Sholihul Hadi Muhammad Afnan Habibi Muhammad Firman Aji Saputra Muhammad Ilham Ramdhani Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Rizki Irwanto Muhammad Salmansyah Mousavi Akhyar Mulki Indana Zulfa, Mulki Indana Mulya Pratama, M. Ardhika Nabila Salsabila Nafalski, Andrew Nafi Isbadrianingtyas Natasya Titania Ramadhanti Nazhiroh Tahta Arsyillah Ni Putu Agustini Nurhidayati Nurul Rismayanti Okazaki Yasuhisa, Okazaki Pindo Tutuko Poppy Indriana Purnawansyah Purnawansyah Qonita, Adiba Raja, Roesman Ridwan Rasna Rendy Yani Susanto Rhomdani, Rohmad Wahid Rizal, Muhammad Fatkhur Rosydah, Lucyta Qutsyaning Saifudin, Ilham Sari, Heni Vidia Satria Putra Pratama Setiadi Cahyono Putro Shandy Krisnawan Sihombing, Wesly M Siti Sendari Soenar Soekopitojo Soraya Norma Mustika Sri Farida Utami Suastika Yulia Riska Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sujito Sujito Syaad Patmanthara Syamsul Arifin Utomo Pujianto Wahyu Caesarendra Wahyu Sakti Gunawan Wahyu Sakti Gunawan Irianto Wibawa, Aji P Wisnu Prabowo, Martin Indra Yogi Dwi Mahandi Yuniardini, Fatma