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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 300 Documents
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Usability Evaluation of RAD-Based Production Monitoring Systems in Automotive Manufacturing Using System Usability Scale Method Tiaraningsih; Agung Nugroho; Karsito
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2353

Abstract

The rapid adoption of digital monitoring tools in the automotive sector often prioritizes technical deployment speed over user-centric design, leading to potential operational inefficiencies. This study evaluates the usability of a production monitoring system developed through Rapid Application Development (RAD) at an automotive manufacturing plant. Utilizing a purposive sampling of 20 active users comprising 15 shop-floor operators and 5 supervisors. This research employs the System Usability Scale (SUS) to quantify user experience post-implementation. Data integrity was rigorously verified through SPSS, yielding a Cronbach’s Alpha of 0.842, which confirms the high reliability of the evaluation instrument. The findings reveal an overall average SUS score of 71.25, categorized as Acceptable with a Good adjective rating and a Grade C classification. Despite this favorable total score, a notable disparity exists between user groups; supervisors rated the system at 74.0, while operators, who manage high-frequency data entry, provided a lower average of 68.5. This result underscores a usability friction inherent in rapidly developed systems that may hinder long-term industrial sustainability. The study concludes that while the RAD-based architecture meets professional functional standards, iterative refinements focusing on reducing interface complexity for operators are essential to minimize human error and optimize production performance.
Implementation of the MARCOS Method and Entropy Weighting for Selecting the Best Teacher at Madrasah Ibtidaiyah Siti Qayyimah; Indana Lazulfa
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2365

Abstract

Selecting the best teacher in a madrasah requires an assessment mechanism that is transparent, reproducible, and able to process several performance criteria simultaneously. This study evaluates 20 active teachers at Madrasah Ibtidaiyah Al-Huda using five benefit criteria: attendance, pedagogical competence, personality competence, social competence, and professional competence. The criterion weights were calculated objectively using Shannon Entropy, and the alternatives were ranked using the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The Entropy calculation produced weights of 0.011 for attendance, 0.266 for pedagogical competence, 0.266 for personality competence, 0.245 for social competence, and 0.212 for professional competence. The MARCOS results show that Wasilul Gaffar obtained the highest utility value of 0.783 and ranked first. Sensitivity testing and Simple Additive Weighting (SAW) comparison indicate that the leading alternative remains stable under several weighting scenarios. The findings show that the integration of Shannon Entropy and MARCOS can support evidence-based teacher performance evaluation in a small madrasah context without relying solely on subjective judgment.
Implementation of Ridge Regression and SHAP for Analyzing Anxiety Levels Based on the Digital Behavior of Social Media Users Yuri Yuliani; Kukuh Panggalih; Kudiantoro Widianto; Erni; M. Iqbal Alifudin; Irwan Herliawan
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2370

Abstract

Advances in technology have led to an increase in the frequency of smartphone, social media, and various digital app usage in daily life. These digital activities give rise to various digital behaviors—such as screen time, number of notifications, social media usage, and sleep patterns—which can be evaluated to understand users mental health. High digital device usage is suspected to be associated with increased anxiety levels among users. Therefore, this study aims to analyze the impact of digital behaviors on anxiety levels by utilizing explainable machine learning approaches and AI. This study uses the dataset, comprising 500 data points obtained from Kaggle. The research stages consist EDA, feature engineering, target leakage evaluation, comparison of several machine learning algorithms, cross-validation, and model interpretation using SHAP. Based on the EDA results, the variables social_media_time_min, notification_count, and digital_addiction_score showed a positive relationship with anxiety_level. During the model-building process, the variable digital_wellbeing_score was removed because it had a very high correlation with anxiety_level -0.84, which could lead to potential target leakage. The algorithm comparison revealed that the Ridge Regression model performed best compared to Random Forest, SVR, and XGBoost Regression, with an R² score of 0.221 and an RMSE of 1.627. Additionally, the results of 5-Fold Cross Validation showed an average R² Score of 0.158 with a standard deviation of 0.062, indicating that the model demonstrated fairly consistent performance. The SHAP interpretation reveals that notification_count and social_media_time_min are the variables that most strongly influence the prediction of anxiety_level. The results of this study indicate that digital behavior affects users anxiety levels, although the relationships among the variables remain quite complex. This study also emphasizes the importance of evaluating the target leakage and understanding the model in the development of machine learning-based mental health analysis to ensure that the prediction results are more objective and clear.
lmplementasi YOLOv11 untuk Penghitungan Kerumunan Real-Time dalam Arsitektur Microservices pada Video CCTV M.Sultonun Naim; Salamun Rohman Nudin
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2378

Abstract

The provision of accurate and easily accessible public information regarding the condition of urban parks remains a challenge in the management of public open spaces. People still do not have a system that allows them to monitor park crowd levels directly, making real-time observation of park conditions difficult. Previous studies on crowd counting have mainly focused on improving object detection performance, while the implementation of crowd monitoring systems as public information services remains limited. Therefore, this research integrates the YOLOv11 algorithm with a microservices architecture to provide real-time crowd information through a public park monitoring website. This research develops a park visitor counting information system based on computer vision using the YOLOv11 algorithm to detect and count crowds from CCTV video streams. The system is designed using a microservices architecture with the FastAPI framework to support real-time detection and data integration into the Surabaya park monitoring website. The research process involves several stages, including dataset preparation, data labeling using Roboflow, YOLOv11 model training, selection of the most optimal optimizer, and implementation of the system on the detection backend. The results show that the YOLOv11m model with the SGD optimizer achieved the best performance, obtaining an mAP@50 score of 92.76%, a recall value of 89.75%, and an F1-score of 90.07%. In addition, the system successfully performed real-time crowd detection and counting under various crowd density levels, lighting conditions, and CCTV camera angles.
Web-Based Attendance System Using Arcface, GPS Validation and Liveness Detection Diah Arifah Prastiningtiyas; Alvin Ricardo Laurence
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2382

Abstract

The development of digital attendance systems requires authentication mechanisms that are not only flexible but also secure against identity and location manipulation. Conventional fingerprint-based attendance systems have limitations in supporting employee mobility and still require dedicated hardware at each workplace location. Existing online attendance systems generally implement GPS validation or face recognition independently and often lack mechanisms to prevent identity spoofing attacks. In addition, computationally intensive models may reduce system efficiency in real-time web environments. Therefore, this study proposes a web-based attendance system that integrates lightweight facial authentication, GPS-based geofencing validation, and passive liveness detection to improve both security and computational efficiency.This study aims to develop a web-based attendance system integrating ArcFace-based facial authentication, GPS-based geofencing validation, and Passive Liveness Detection using MiniFASNet. Experimental evaluations were conducted to assess authentication accuracy, computational efficiency, and spoofing detection capability. The results showed that MobileFaceNet achieved an authentication accuracy of 95.00% with an average inference time of 0.0216 seconds per face. In addition, the implemented liveness detection mechanism effectively detected most spoofing attempts involving photographs and replay videos. The system also achieved a False Acceptance Rate (FAR) of 0% under the evaluated experimental conditions and successfully fulfilled all functional requirements. Therefore, the proposed system provides a secure, efficient, and centralized attendance solution suitable for organizations with high employee mobility.
Implementation of K-Means Clustering with Attribute Adjustment and Cluster Validation in a Web-Based TPQ Student Assessment Information System Aura Firdausiyah; Sri Widoyoningrum
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2386

Abstract

This study aims to implement K-Means Clustering with attribute-level adjustment in a web-based TPQ student assessment information system and evaluate the quality of the resulting clustering results. The study was conducted at TPQ Ashfiya' Kauman Ngoro Jombang using a dataset of 40 students at the Al-Qur'an level obtained from learning assessment data. The variables used include fashohah, tajwid, ghorib and musykilat, as well as voice and melody. The clustering process was carried out using the K-Means algorithm to group students based on similarity in scores, while attribute adjustment was applied in the system design to prevent unused assessment components from being interpreted as low ability scores. Evaluation of cluster quality was carried out using the Sum of Squared Error (SSE), Silhouette Score, and Davies-Bouldin Index (DBI). Based on the evaluation results, K=3 was selected because it formed an interpretable grouping structure and was supported by the Elbow Method. The results show that the application of the K-Means method in the information system can help group students' abilities in a more structured and objective manner and support assessment data management by TPQ teachers. However, the results of this study are a case study in the TPQ environment studied and have not been intended for generalization to a wider population.
Avocado Ripeness Classification Based on Digital Imagery Using an Artificial Neural Network Sriyanto; Febri Pratama; Zuriati
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2390

Abstract

Traditional methods for determining avocado ripeness rely primarily on subjective visual observation, which is highly prone to error. This study aims to develop an automated classification system for avocado ripeness using a sequential Artificial Neural Network (ANN) based on digital image data. The dataset consisted of 1,500 balanced avocado images distributed across three classes: Ripe (500 images), Rotten (500 images), and Unripe (500 images). A total of 1,200 images were used for training and 300 images for validation. Images were preprocessed through resizing to 128 × 128 pixels and pixel intensity normalization. The proposed ANN architecture consisted of a Flatten layer, two hidden Dense layers with ReLU activation, and an output layer with Softmax activation. Experimental results showed that the model achieved an overall accuracy of 89.00%, with macro-average Precision, Recall, and F1-Score values of 0.92, 0.89, and 0.89, respectively. The best classification performance was achieved for the Rotten class, with a Precision of 1.00 and a Recall of 0.96. Classification errors mainly occurred between the Unripe and Ripe classes, where visual similarities during the ripening transition stage led to cross-class predictions. Overall, the proposed ANN model demonstrated reliable performance for avocado ripeness classification using digital image data and showed its potential as a simple image-based decision-support tool for post-harvest quality assessment.
Rancang Bangun Sistem Informasi Jadwal Operasi Harian Berbasis Website Pada Rumah Sakit Al-Ihsan Qurrotul Aini; Mughits Aditya
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2225

Abstract

Penelitian ini berfokus pada pengembangan sebuah sistem informasi jadwal operasi harian berbasis website yang diterapkan di Rumah Sakit Umum Daerah Al-Ihsan. Sistem tersebut dirancang sebagai solusi untuk menggantikan mekanisme manual yang sebelumnya digunakan dan kerap menimbulkan permasalahan, seperti kesalahan data entry dan keterlambatan pembaruan informasi. Proses pengembangan sistem dilakukan dengan menerapkan metode Rapid Application Development (RAD), yang mencakup tahap analisis kebutuhan pengguna, perancangan sistem menggunakan Unified Modeling Language (UML), serta implementasi aplikasi dengan bahasa pemrograman PHP. Pengumpulan data dilakukan melalui observation, interview, dan literature study. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan efektivitas serta efisiensi proses penjadwalan operasi, memperbaiki kualitas user interface, serta meningkatkan keamanan sistem melalui penerapan mekanisme authentication dan pengelolaan access control. Penelitian ini juga menunjukkan penerapan konsep sistem informasi secara nyata dalam mendukung proses transformasi digital di lingkungan rumah sakit.
Performance Evaluation of Naive Bayes and SVM in Classifying Public Opinion toward Game-Based Learning Policy Septi Dwi Supriati; Hanifah Permatasari; Vihi Atina
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2319

Abstract

This study aims to analyze public sentiment toward the EDUBLOX program on Instagram comments using the Naive Bayes and Support Vector Machine (SVM) algorithms. The research process consisted of data collection, text preprocessing, manual sentiment labeling, feature extraction using TF-IDF, model training, and performance evaluation. The labeling process was conducted manually by two independent annotators based on predefined sentiment guidelines, and annotation reliability was evaluated using Cohen’s Kappa coefficient. The obtained Cohen’s Kappa value was (\kappa = 0.7907), indicating substantial agreement and good consistency between annotators. The dataset was divided into training and testing data using an 80:20 ratio. The evaluation process used confusion matrix metrics, including accuracy, precision, recall, and F1-score, while the McNemar test was applied to determine whether the performance difference between the two models was statistically significant. The results showed that the SVM model achieved a testing accuracy of 76.67%, marginally outperforming the Naive Bayes model with a testing accuracy of 75.29%. In addition, SVM demonstrated slightly better precision, recall, and F1-score values compared to Naive Bayes. However, the McNemar test produced a p-value of 0.1366, indicating that the performance difference between the two algorithms was not statistically significant. Therefore, both models can be considered to have relatively comparable classification capabilities, although SVM showed a slight numerical advantage in sentiment classification performance on Instagram comments related to the EDUBLOX program.
Improving the Performance of Convolutional Neural Networks (CNN) in Identification of Agricultural Plant Diseases Dedi Setiadi; Fido Rizki
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 02 (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.v16i02.2372

Abstract

This study aims to improve the performance of the Convolutional Neural Network (CNN) algorithm in detecting coffee leaf diseases using the Inception V3 architecture. The main challenge of this classification is the subtle visual similarity in color, texture, and symptom patterns between diseases. To overcome this, Inception V3 is implemented because of its superiority in multi-scale feature extraction through convolution factorization which reduces parameters while increasing accuracy. The dataset used consists of 1,120 images, evenly distributed into four classes (three types of diseases and one healthy class, each with 280 images), with a training, validation, and test data split ratio of 896:112:112. As a comparison, a conventional basic CNN architecture consisting of 3 convolution layers (3 X 3, stride 1), 3 max-pooling, and 1 dense layer, trained with the same hyperparameters (Adam optimizer, learning rate 0.001, batch size 32) is used. The experimental results show a significant performance improvement; Model accuracy increased from 74.4% on a standard CNN to 97.0% after integrating Inception V3. The scientific contribution of this research lies in mapping overlapping visual characteristics of coffee diseases through multi-scale feature optimization, which demonstrates that computational efficiency can go hand in hand with accuracy improvements on complex agricultural image datasets. These findings confirm that the Inception V3 architecture provides a robust and efficient solution for automating plant disease diagnosis in the field.

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