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Smatika Jurnal : STIKI Informatika Jurnal
Published by Universitas Bhinneka Nusantara
ISSN : 20870256     EISSN : 25806939     DOI : https://doi.org/10.32664/smatika
Core Subject : Science,
Computer Science & IT
SMATIKA: STIKI Informatika Jurnal is a journal published by Lembaga Penelitian & Pengabdian kepada Masyarakat (LPPM) of Universitas Bhinneka Nusantara Malang. The scope of this journal in the field of Computer Science, Information Systems, and Information Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 277 Documents
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The Cyber Threat Landscape in Indonesia: Attacks and Security System Analysis Sama, Hendi; Stefanie; Prasetyo, Stefanus Eko
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2200

Abstract

Cybersecurity in learning systems has become increasingly important as educational institutions rely more heavily on digital platforms such as learning management systems, online assessments, and cloud-based academic services. This rapid digital transformation exposes learning environments to sophisticated cyber threats that may disrupt academic activities and compromise sensitive information. However, many institutions still lack a clear understanding of how users perceive cyber risks and how these perceptions influence the effectiveness of cybersecurity systems. Currently, there is a significant research gap regarding empirical evidence that links user behavioral psychology with technical security outcomes in the Indonesian educational context. This study aims to empirically analyze the relationship between cyber awareness, perceived impact of cyber attacks, and perceived effectiveness of cybersecurity systems in digital learning environments. A quantitative research approach was applied using data collected from 402 respondents. The data were analyzed through descriptive statistics, correlation analysis, regression analysis, and group comparison tests to examine variable relationships and demographic differences. The findings indicate that cyber awareness significantly and positively predicts perceived system effectiveness (β = 0.501, p < 0.001), demonstrating that higher awareness levels enhance overall cybersecurity performance. Conversely, the perceived impact of cyber attacks does not show a significant effect on system effectiveness, suggesting that awareness is more influential than threat perception alone. Additional results reveal gender-based differences in cyber incident experiences, while awareness levels remain similar. The practical implications emphasize the importance of cybersecurity awareness programs, digital safety education, and proactive defense strategies to strengthen protection in learning systems and improve institutional cybersecurity readiness.
A SMIL-Based Approach for Designing Interactive Multimedia Film Players Tanubrata, Markus; Wijaya, Marvin Chandra; Maksom, Zulisman; Lehman, Andrew Sebastian; Tjiharjadi, Semuil; Siahaan, Agnes Hardiani
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2206

Abstract

Typically, an HTML-style application requires extensive scripting to coordinate user interactions, manage display layout, and synchronize the timing of all interaction events. With Synchronized Multimedia Integration Language (SMIL), an alternative approach to developing an interactive multimedia film application has been defined, supporting time-based multimedia cooperation and user interactivity without the need for complex scripts. This study presents the development and evaluation of an SMIL-based interactive multimedia film application. The application allows for user-selected films to be automatically selected, played, and managed (synchronized) through an interactive GUI. The two measures, the Layout Consistency Score (LCS) and Screen Utilization Efficiency (SUE), were evaluated. Experimental evaluations have shown that the average LCS for the application = 0.92, indicating that the application maintains a consistent and predictable layout across multiple instances of the interface. In addition, the average SUE score for the application was calculated to be 0.79, indicating that it uses the available screen space efficiently and does not create a visually competing cluster. These results reinforce the concept that the region-based approach of SMIL and its declarative style for creating applications enable the development of multimedia that is consistently and efficiently presented across multiple contexts. As a result, the findings of this study indicate that the SMIL approach will simplify and allow for a practical methodology to build interactive multimedia film player applications, while preserving layout quality and interactivity.
Mountain Climbing Safety Induction for Beginners Using Augmented Reality setiawan, aldi; Aldi Setiawan, M; Dijaya, Rohman; Taurusta, Cindy; Senja Fitrani, Arif
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2215

Abstract

The growing popularity of mountain climbing among novice participants has been accompanied by an increased risk of accidents, largely attributable to inadequate understanding of safety procedures. Conventional safety induction methods—primarily verbal briefings and printed materials—lack interactivity and fail to adequately simulate real climbing conditions. While prior research has examined general safety education and conventional multimedia learning approaches, the application of markerless augmented reality (AR) as an interactive, mobile-based safety induction tool specifically designed for beginner mountain climbers remains underexplored . Accordingly, this study aims to develop and evaluate an Android-based markerless AR application to enhance pre-climbing safety induction for novice climbers.  The application was developed following the Multimedia Development Life Cycle (MDLC), encompassing the stages of concept development, design, material collection, assembly, testing, and distribution. Functional performance was assessed using black-box testing, while feasibility was evaluated through a Likert-scale questionnaire administered to 20 beginner climbers.  Black-box testing confirmed that all system functionalities operated as intended. The feasibility evaluation yielded a score of 87%, indicating a good to very good level of acceptance among users. By integrating markerless AR technology into mobile-based safety induction, this study addresses a critical gap in outdoor safety training research. The findings demonstrate the feasibility and practical potential of immersive AR applications to enhance safety awareness and improve the effectiveness of preparatory training for beginner mountain climbers.
Large Language Models for JSON-Based Function Call Planning from Indonesian Natural Language: A Restaurant Search Chatbot Case Study Mohammad Mauludin; Santoso, Joan; Junaedi, Hartarto
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2216

Abstract

Large Language Models are increasingly adopted as planning components that translate natural language into structured representations for tool invocation, enabling executable interaction with backend systems through JSON based function calling. However, empirical studies focusing on Indonesian natural language remain limited. This paper presents a restaurant search chatbot case study that investigates JSON based function call planning from Indonesian user queries, with emphasis on the upstream planning task rather than conversational response generation. A synthetic dataset of 33,470 Indonesian restaurant search queries paired with ground truth JSON plans was constructed based on a predefined tool set and database schema. Supervised fine tuning with parameter efficient adaptation was applied to a pretrained language model. The fine tuned Mistral 7B model was evaluated using multiple metrics measuring JSON structural validity, tool sequence correctness, and parameter accuracy at different granularities. The results show strong performance, achieving a JSON structure validity rate of 0.97, tool sequence exact match accuracy of 0.92, column level accuracy of 0.97, and value level accuracy of 0.94. More stringent evaluation at the session level reveals remaining challenges in composing all parameters correctly within a single planning instance. Overall, the findings demonstrate that with carefully designed datasets and strict supervision, Large Language Models can reliably perform structured JSON based function call planning from Indonesian natural language, providing a practical foundation for extending this approach to other structured application domains where execution correctness is critical.
Klasifikasi Status Gizi Anak dengan Metode Decision Tree (Studi Kasus di RS AN-NISA) Berbasis Web Michael Julius Hutabarat; Dwi Sartika Simatupang; Masmur Tarigan; Y Yulhendri
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2238

Abstract

Penelitian ini mengembangkan model klasifikasi status gizi anak usia 0–5 tahun berbasis web menggunakan algoritma Decision Tree (C4.5) pada data antropometri RS AN-NISA Tangerang periode Januari–Desember 2024. Atribut yang digunakan meliputi umur (bulan), jenis kelamin, berat badan, dan tinggi badan, dengan label status gizi yang diklasifikasikan ke dalam kategori Gizi Kurang, Gizi Baik, dan Gizi Lebih. Data diproses melalui tahapan preprocessing yang mencakup pembersihan data kosong dan nilai ekstrem, seleksi atribut, encoding data kategorikal, normalisasi, serta pembagian data sebesar 80% data latih dan 20% data uji. Model dibangun menggunakan kriteria entropy dengan pengaturan hyperparameter untuk mengurangi risiko overfitting, kemudian dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score, serta dibandingkan dengan model baseline Logistic Regression. Hasil evaluasi menunjukkan bahwa Decision Tree mencapai nilai akurasi sebesar 96,12% dan recall macro 89,49%, yang lebih unggul dibandingkan Logistic Regression. Selanjutnya, model diserialisasi dan diintegrasikan ke dalam aplikasi berbasis Flask untuk memfasilitasi input data dan menghasilkan prediksi status gizi secara langsung. Hasil pengujian black box dan User Acceptance Test (UAT) menunjukkan tingkat kepuasan pengguna sebesar 88%, sehingga sistem dinilai layak digunakan sebagai alat bantu deteksi dini status gizi anak pada layanan kesehatan sehari-hari.
Development and Evaluation of an E-learning System using the CodeIgniter framework to Support Informatics Learning at SMA Negeri 1 Tanjung Raja Safitri, Rosdiyanna; Firdaus, Rangga; Suryadinata, Nurain
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2247

Abstract

This study aims to develop and evaluate a web-based e-learning system to support Informatics learning at SMA Negeri 1 Tanjung Raja. The research employed a Research and Development (R&D) approach using the Waterfall model, consisting of requirements analysis, system design, implementation, testing, and maintenance. The system was developed using the CodeIgniter 4 framework with the Model–View–Controller (MVC) concept. Functional testing was conducted using the Black Box Testing method, executed on 13 different test scenarios by three expert validators with information technology education lecturers with backgrounds in software engineering. All 13 test items were evaluated independently by each validator, resulting in a 100% pass rate (13 out of 13 items were functional in the third validator). Usability testing using the System Usability Scale (SUS) instrument with 30 respondents comprising 29 Grade X students and 1 Informatics teacher, resulted in an average SUS score of 89, which is classified as Very Good. These findings indicate that the system has robust functionality and is considered very easy to use in this particular school context. Limitations include the single-school setting and relatively small sample size, and further validation across different educational contexts is recommended.
ANALISIS SENTIMEN PADA TREN OPINI PUBLIK TERHADAP PROGRAM #MAKANBERGIZIGRATIS DI PLATFORM X MENGGUNAKAN JARINGAN LONG SHORT-TERM MEMORY (LSTM) Wahyuningsih, Veny Dwi; Sari, Yayak Kartika; Prasetya, Agung
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2260

Abstract

The implementation of the Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) as a strategic government initiative has generated diverse responses from the public, widely discussed on social media, particularly on the X (Twitter) platform. Differences in perceptions regarding the objectives, implementation, and impacts of the policy have encouraged intensive public discussions. However, the tendency of public sentiment toward this program has not been widely analyzed systematically using machine learning approaches based on contextual representations. Therefore, this study analyzes public sentiment toward the hashtag #MakanBergiziGratis using the Long Short-Term Memory (LSTM) method. A total of 5,516 Indonesian-language tweets were collected through a web scraping process within the period of January 1 to November 30, 2025. Sentiment labeling employed a lexicon-based approach to classify the data into three categories: positive, neutral, and negative. The analysis stages included text preprocessing, BERT tokenization and embedding, handling imbalanced data using the Synthetic Minority Over-sampling Technique (SMOTE), and sentiment classification using LSTM. The results reveal that neutral sentiment dominates with 60.80%, followed by positive sentiment at 34.34% and negative sentiment at 4.86%. The developed model achieved an accuracy of 82.50% with a weighted F1-score of 82.66%. Furthermore, evaluation using 5-fold cross-validation produced an average accuracy of 82.8%, indicating stable model performance and good generalization capability in identifying public opinion trends toward the MBG policy.

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