Edumatic: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) is a scientific journal in the field of informatics education published by Universitas Hamzanwadi three times a year, namely in April, August, and December. Since 7 April 2022, the Edumatic: Jurnal Pendidikan Informatika has been accredited with a rank of 3 (SINTA 3) based on the Decree of the Director General of Higher Education Tinggi (SK No. 105/E/KPT/2022). This means that articles published in Vol. 5 No. 2, December 2021 are included in SINTA 3. The focus and scope of this journal are (1) Computers and Informatics in Education, (2) Learning Models and ICT Models, (3) Development of Learning Media Based on Information Technology, (4) Human-Computer Interaction, (5) Information Systems and Information Technology, (6) Data Mining, (7) Internet of Things (IoT), (8) Artificial Intelligence, (9) Algorithms and Numerical Computation, (10) Computers and Networks, and other focuses related to computers and education. All articles in Edumatic: Jurnal Pendidikan Informatika have a Digital Object Identifier (DOI) with the DOI Prefix: 10.29408 by Crossref. In addition, Edumatic: Jurnal Pendidikan Informatika has been indexed by a number of indexing agencies, including GARUDA, Dimensions, Google Scholar, and others. Journal Name Edumatic: Jurnal Pendidikan Informatika Inisial Edumatic Abbreviation Edumatic Publication Schedule The journal will publish three issues per year (April, August, and December) effective from 2025. DOI Prefix 10.29408 e-ISSN 2549-7472 Editor-in-Chief Muhammad Zamroni Uska Publisher Universitas Hamzanwadi Quotation Analysis Google Scholar | SINTA
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Integrasi CNN VGG19 dalam Aplikasi Real-Time untuk Mendeteksi Ekspresi Konsumen sebagai Indikator Kepuasan
Facial expressions are an important nonverbal indicator in measuring customer satisfaction levels. The objective of our research is to develop a real-time desktop application for detecting facial expressions as indicators of satisfaction using the Convolutional Neural Network (CNN) VGG19 architecture. The application development process employs the waterfall method, encompassing: needs analysis using the Japanese Female Facial Expression (JAFFE) dataset, comprising 213 grayscale images across seven emotional categories sourced from zenodo.org; followed by system design through format conversion, augmentation (rotation, flip), and mapping expressions into satisfaction categories, as well as Python-based application development with VGG19 model integration. The final stage involved black-box testing to verify functionality. Our findings resulted in a GUI-based desktop application using PySide capable of automatically detecting and displaying satisfaction levels in real-time without an internet connection. Validation results showed an accuracy of 89.39%, with the highest performance in the disgust and happiness classes and the lowest in the fear and sadness classes. The system successfully passed functional testing with expression detection functioning as designed, consistently displaying satisfaction mapping results. Our research demonstrates the potential of integrating CNN into efficient, objective, and flexible consumer satisfaction measurement across various operational conditions, making this application usable by businesses to monitor.
Copyright (c) 2025 Arnold Robert, Ida Nurhaida https://creativecommons.org/licenses/by-sa/4.0/
Integrasi Algoritma Apriori dan K-Means untuk Optimalisasi serta Analisis Pola Pemasaran Suku Cadang Otomotif
The automotive parts industry in Indonesia faces challenges of inefficient inventory management and a lack of understanding of customer purchasing patterns, resulting in overstocking or stockouts that cause financial losses. This study aims to apply association rule mining and K-Means clustering to automotive parts retail transaction data to uncover purchasing patterns and product segmentation to support effective inventory management and marketing strategies. Our research is quantitative in nature with a dataset of 14,165 transactions analyzed using Google Colab-Python. The Apriori algorithm was applied with a minimum support of 1% and confidence of 50%, while K-Means clustering was used for product segmentation with normalized numerical attributes. Association rule mining identified 15 significant rules with the strongest pattern between differential oil and brake fluid (confidence 73.5%, lift ratio 5.515). K-Means produced seven optimal clusters (silhouette score 0.68) that categorized products into premium, fast-moving, slow-moving, and other specific characteristics. The main contribution is an integrated framework that combines clustering to enrich the interpretation of association rules, enabling effective bundling strategies. Practical implications include a cross-selling recommendation system (15-20% revenue increase), differential pricing per cluster, and stock predictions that reduce overstocking by 25% and avoid stock-outs, supporting the digital transformation of data-driven retail management.
Copyright (c) 2025 Benny Martha Dinata, Eri Zuliarso https://creativecommons.org/licenses/by-sa/4.0/
Smart Oven berbasis IoT menggunakan Fuzzy Mamdani untuk Pengeringan Cepat Kerupuk Kemplang
Palembang kemplang crackers are traditionally dried in the sun for 8-16 hours, even up to three days in cloudy conditions. This process is inefficient and highly dependent on the weather. This research aims to develop a Smart Oven dryer for kemplang crackers based on the Fuzzy Mamdani method as an automated solution to speed up the drying process while maintaining product quality. Our type of research is development using protoype method with stages of analysis, design, prototyping and testing. The system is designed using thermocouple sensor for temperature measurement, HX711 weight sensor, heater as heater, and ESP32 module connected with Blynk application to allow setting and monitoring through smartphone. Fuzzy Mamdani method is used to control temperature, weight, and drying time adaptively to dynamic conditions. The test results show that the Smart Oven is able to reduce the drying time from an average of three days to only 2-5 hours at an optimal temperature of around 150-160°C, with a weight loss rate below 3%. The resulting product is crispier, evenly coloured, and has a longer shelf life compared to traditional methods and steam engines. These findings show that the application of Fuzzy Mamdani logic in the Smart Oven not only improves energy and time efficiency, but also ensures consistent quality of kemplang crackers.
Copyright (c) 2025 Putri Salsabila Utami, Aryanti Aryanti, Lindawati Lindawati, Ibnu Asrafi https://creativecommons.org/licenses/by-sa/4.0/
Sistem Pakar Inklusif: Diagnosis Kesulitan Belajar Siswa dengan Teori Dempster-Shafer
Learning difficulties are complex issues in education that require early diagnosis to support effective and inclusive learning. This study aims to develop a web-based expert system using the Dempster–Shafer theory to diagnose students’ learning difficulties more comprehensively than conventional approaches such as Certainty Factor or Fuzzy Logic. The Dempster–Shafer method was selected for its ability to manage data uncertainty and overlapping symptoms. The research employed a Research and Development (R&D) approach with the Waterfall model, involving the stages of requirement analysis, system design, implementation, and testing. Data were collected from 50 students at MAS Teladan Ujung Kubu through teacher observations, exam results, and questionnaires from both students and parents. The findings indicate that the developed system can identify various types of learning difficulties such as dyslexia, dyscalculia, dysgraphia, and ADHD as well as categorize them into mild, moderate, and severe levels. The system functioned effectively without errors based on black box testing results. These outcomes demonstrate that the Dempster–Shafer theory is effective in handling diagnostic uncertainty and producing multi-level, comprehensive evaluations. Furthermore, the system shows potential for integration with artificial intelligence to support adaptive learning and personalized interventions within inclusive e-learning environments.
Copyright (c) 2025 Sri Rahayu Ningsih, Rohminatin Rohminatin, Rizaldi Rizaldi https://creativecommons.org/licenses/by-sa/4.0/
Pendekatan Machine Learning untuk Klasifikasi Kepribadian: Studi Logistic Regression dan Naive Bayes
Personality classification plays an important role in education, counseling, and recruitment. This study applies a quantitative approach in the form of computational experiments using machine learning algorithms, namely Logistic Regression and Naive Bayes. The data were obtained from a public Kaggle dataset consisting of 2,900 records, with 1,491 labeled as Extrovert and 1,409 as Introvert. The preprocessing stage involved converting categorical variables with LabelEncoder, normalizing numerical features using StandardScaler, and splitting the dataset into 80% for training and 20% for testing with 5-fold cross-validation. Model performance was evaluated using four metrics: Accuracy, Precision, Recall, and F1-Score, providing a more comprehensive assessment compared to previous studies that focused only on accuracy. Our findings show that Logistic Regression achieved an accuracy of 91%, while Naive Bayes yielded a higher accuracy of 92% and performed better in detecting the Introvert category. These results imply that simple algorithms remain relevant and effective in personality classification. The application of this research can assist counselors in identifying students’ personality types, support the development of learning strategies tailored to individual characteristics, and enhance the effectiveness of recruitment processes by aligning candidates’ personalities with organizational needs.
Copyright (c) 2025 Laurensius Marcellano Adi Pradana, Fikri Budiman, Defri Kurniawan https://creativecommons.org/licenses/by-sa/4.0/
Forecasting Penjualan Sembako berbasis Model Prophet: Strategi Efisiensi Stok pada pada Ritel Tradisional
Maintaining a stable supply of staple commodities remains a major challenge for retail stores, as inventory management often relies on intuition rather than data-driven insights, leading to potential losses from overstocking or missed sales when supplies run out. This study implements the Prophet model to forecast staple-food sales and to support data-driven inventory optimization. In contrast to previous research that has predominantly focused on large-scale industries or e-commerce, this study examines small-scale retail settings characterized by pronounced seasonal demand fluctuations. Monthly sales data from August 2023 to May 2025 were analyzed using the Prophet model, with performance evaluated through MAE, RMSE, and MAPE metrics. The Prophet model was selected for its capability to capture nonlinear trends and seasonal effects without the complex parameterization required by models such as ARIMA. The results show an average forecasting accuracy of 94.09%, demonstrating the model’s adaptability in identifying sales fluctuations during seasonal periods such as Ramadan. Practically, the findings assist store owners in optimizing stock levels and promotional strategies, while academically the study extends the forecasting literature within small-scale retail contexts with limited data. The novelty of this research lies in applying the Prophet model to enhance data-driven inventory-management decisions in micro and small enterprises exhibiting irregular seasonal patterns.
Copyright (c) 2025 Haikal Tirta Albanna, Diana Diana https://creativecommons.org/licenses/by-sa/4.0/
Mentransformasi Pemilihan Sekolah: Integrasi Flask dan TOPSIS untuk Rekomendasi SMA yang berbasis Preferensi Individu
The selection of Senior High Schools (SMA) is often conducted subjectively without objective analysis, while previous studies on decision support systems (DSS) mostly used SAW or AHP methods with fixed weights, resulting in less adaptive recommendations. This study aims to develop and evaluate a web-based DSS integrating the TOPSIS method and Flask framework to provide SMA recommendations through customizable criteria weighting. The system was developed using the Waterfall model consisting of requirement analysis, design, implementation, and testing stages. Six main criteria were used: distance, location, academic achievement, non-academic achievement, accreditation, and extracurricular activities. The developed system successfully generates automated and responsive school recommendations based on user preferences. The black-box testing confirmed that all main functions worked properly, while the Spearman correlation test yielded a value of 0.9758, indicating very high accuracy and strong consistency between system results and manual calculations. These findings demonstrate that applying adaptive weighting within the TOPSIS method enriches the multi-criteria decision-making (MCDM) framework by introducing a personalized decision-making approach. Practically, the system assists users in making educational choices that are more objective, transparent, and aligned with individual needs.
Copyright (c) 2025 Nur Khalidzah, Heliawati Hamrul, Farid Wajidi https://creativecommons.org/licenses/by-sa/4.0/
Algoritma Simple Additive Weighting pada SPK Penerima BPNT: Validasi Lapangan dengan Kriteria Nasional
The selection of Non-Cash Food Assistance (BPNT) recipients at the village level often suffers from inaccuracies and limited transparency due to reliance on manual, locally defined indicators. This study develops and validates a decision support system (DSS) based on the Simple Additive Weighting (SAW) method, integrating 14 nationally standardized criteria and a Live Ranking feature to enhance public accountability. The system was implemented using the Personal Extreme Programming (PXP) approach, encompassing planning, design, coding, testing, and deployment. Validation involved comparison with manual SAW calculations and black-box and white-box unit testing. Empirical testing with 30 real cases from Lagi-Agi Village demonstrated 100% accuracy, with system rankings fully consistent with manual results. The DSS ensures objective prioritization of recipients, offering a reliable tool to strengthen fairness, accountability, and transparency in BPNT distribution. Scientifically, this work extends the application of Multiple Attribute Decision Making (MADM) methods to social assistance programs, while practically supporting measurable and equitable aid allocation through the Live Ranking mechanism.
Copyright (c) 2025 Wajida Wajida, Muh Rafli Rasyid, Heliawati Hamrul https://creativecommons.org/licenses/by-sa/4.0/
Pendekatan Denoising Hibrid untuk Pengenalan Aksara Jawa: Upaya Integrasi Teknologi dan Pelestarian Budaya Digital
The digitization of Javanese manuscripts is hindered by image degradation such as bleed-through, stains, blur, and uneven illumination that obscures fine graphemic details and reduces the reliability of optical character recognition (OCR). This challenge is significant for computational heritage preservation, as degraded visual inputs threaten the accuracy and longevity of digital cultural archives. This study applies and evaluates a Structural-Preserving Denoising (SPD) approach designed to retain graphemic contours during noise reduction. Using a comparative-experimental design, six denoising methods (Wavelet, Bilateral, Non-Local Means/NLM, and three hybrids) were tested as independent variables, with PSNR, SSIM, and OCR accuracy as dependent variables. A dataset of 4,632 degraded Javanese character images (64×64 pixels) was processed using Histogram of Oriented Gradients and classified via a cosine-kernel Support Vector Machine. Analyses included comparative evaluation, error analysis, and Pearson correlation. Results show that Bilateral Filtering achieved the best balance of structural preservation and recognition performance (SSIM 0.9108; PSNR 29.85 dB, accuracy 96.97%). The strong correlation between visual quality and OCR accuracy indicates that maintaining graphemic structure is crucial for robust recognition. The study contributes a theoretical SPD framework and a practical preprocessing pipeline applicable to the digital preservation of traditional scripts.
Copyright (c) 2025 Aji Priyambodo, Prihati Prihati https://creativecommons.org/licenses/by-sa/4.0/
Optimasi Decision Tree menggunakan Pendekatan Ensemble Bagging untuk Klasifikasi Klinis Hipertensi
Hypertension is a common health problem with a high risk of serious complications, so it is important to detect it early. This study is an experimental quantitative study that aims to improve the accuracy of the hypertension classification model by optimizing the Decision tree algorithm using the Bagging ensemble method. The aspects studied include the effect of data balancing techniques on the performance of classification models in detecting the risk of hypertension. The dataset used consisted of 2,655 medical records of primary health care patients. The research stages included data pre-processing with imputation, normalization, and balancing with SMOTE, Decision Tree and Bagging modeling, and model evaluation using 5-fold cross-validation, Confusion Matrix, ROC-AUC, and paired t-test. The results showed that the application of Bagging significantly improved the performance of the Decision Tree (p < 0.05) with an accuracy of 96.88%, precision of 95.56%, recall of 98.32%, F1-score of 96.93%, and AUC of 0.996. This improvement indicates that the Bagging method is able to reduce model variance and produce more stable predictions on imbalanced clinical data. These findings contribute to the development of a classification model based on real clinical data, which also opens up opportunities for the development of machine learning-based Clinical Decision Support System (CDSS) for early detection of hypertension in primary health facilities.
Copyright (c) 2025 Agustina Diah Kusuma Dewi, Danang Wahyu Utomo https://creativecommons.org/licenses/by-sa/4.0/
Penerapan LSTM pada Prediksi Harga Saham BBCA: Akurasi dan Interpretabilitas Model
Financial market instability driven by volatility and stock–bond correlation increases the need for accurate stock price prediction for investment and risk management. Previous deep learning approaches act as black box models with low interpretability, hindering audit and model understanding. The purpose of this study is to analyze the application of the LSTM algorithm to predict the daily closing price of BBCA.JK shares and explain its internal gate mechanism in generating these predictions. The quantitative experimental method uses 1,390 samples (09/19/2019–06/26/2025) from investing.com with 30-, 60-, and 90-day windows. Results show that the 60-day window achieves the lowest RMSE 191.94 and MAE 151.99, while the 30-day window is overly sensitive to short-term fluctuations and the 90-day window retains irrelevant information. The novelty lies in gate-level interpretability analysis, which maps how the forget, input, and output gates manage memory and filter information, addressing black box limitations in prior models. Gate activation analysis shows a positive correlation between the input and output gates and price features, and a negative correlation with volume, representing adaptive information filtering under high market activity. This research expands explainable deep learning in finance and strengthens transparency and model trustworthiness for data-driven investment and risk management.
Copyright (c) 2025 Rara Fazira, Sriani Sriani https://creativecommons.org/licenses/by-sa/4.0/
Integrasi Sistem IoT untuk Pemantauan Cuaca Real-Time dan Prediksi Curah Hujan berbasis LSTM
The inaccuracy of non-real-time macro-regional weather forecasts creates an information gap for MSME actors in the agricultural sector who need updates on actual weather conditions at their business locations. This study aims to develop a real-time weather monitoring system based on ESP32 and an LSTM rainfall prediction model, the results of which can be accessed via a web interface by MSME actors such as farmers. The system was developed using an ESP32 microcontroller, DHT22 sensor, and Hall-effect 3144, with an LSTM model trained using historical BMKG data and field data. Our findings show that the system is capable of monitoring weather conditions in real time while generating microspatial rainfall predictions through full integration between IoT devices and the LSTM model. Test results show stable system performance with a transmission delay of 5 seconds, an initial fetch latency of 48 ms, and prediction performance RMSE of 17.05 mm, MAE of 12.40 mm, and F1-score of 0.175, which is adequate for detecting extreme rainfall intensity. These findings demonstrate the potential for developing an affordable, easily replicable, and adaptable microspatial weather forecasting system to support early warning systems or smart irrigation in agricultural areas.
Copyright (c) 2025 Dimas Yudistira, Muhammad Ikhsan https://creativecommons.org/licenses/by-sa/4.0/
Model Pembelajaran Mesin untuk Deteksi Gangguan Tidur: Perbandingan Logistic Regression dan Random Forest
Sleep disorders must be classified accurately to enable early detection and proper treatment. However, previous studies remain limited because they often rely on a single method or suffer from imbalanced datasets. This study aims to compare the performance of Logistic Regression and Random Forest in classifying sleep disorders using the Sleep Health dataset from Kaggle. The dataset consists of 374 samples and 12 variables categorized into three classes: Insomnia, No Disorder, and Sleep Apnea. The data processing steps include cleaning, splitting the dataset, and balancing the classes using the SMOTE technique to address distribution imbalance. Model evaluation was conducted using Accuracy, precision, recall, F1-score, and confusion matrix. Our findings show that Logistic Regression achieved an Accuracy of 96%, while Random Forest obtained 92%. Scientifically, Logistic Regression demonstrated greater stability on balanced data, whereas Random Forest was more sensitive to variations introduced by oversampling. Model analysis revealed that frequency of nighttime awakenings, total sleep duration, perceived sleep quality, and symptoms such as snoring or breathing pauses were the most influential factors in detecting sleep disorders. These results indicate that simpler models can outperform more complex ones after class balancing, a finding that has not been widely reported in the existing sleep health literature.
Copyright (c) 2025 Berliano Kurniawan, Fikri Budiman https://creativecommons.org/licenses/by-sa/4.0/
Augment During Training (ADT) pada DenseNet-121: Klasifikasi Tingkat Keparahan Retinopati Diabetik pada Citra Fundus
Diabetic Retinopathy (DR) is a severe complication of diabetes mellitus and a leading cause of blindness among the working-age population. Manual diagnosis using fundus images is often subjective and inefficient for mass screening, while automated diagnosis faces challenges due to class imbalance, limiting model performance and generalization. This study aims to develop a DR severity classification model based on the DenseNet-121 architecture by introducing a dynamic augmentation strategy called Augment During Training (ADT). This strategy was evaluated and compared against three other approaches: Baseline, Oversampling Augmentation, and SMOTE. We used the APTOS 2019 Blindness Detection dataset, comprising 3,662 fundus images across five severity levels. The results showed that the model with ADT achieved an accuracy of 83.65% (95% CI: 81.53–85.77%), an F1-score of 83.12%, precision of 83.80%, and recall of 83.65%, outperforming the other three approaches by a margin of 3.27%. Class-wise analysis demonstrated that ADT effectively addressed data imbalance, yielding high performance in the No DR (0.98) and Moderate (0.77) classes, though improvements are still needed for minority classes such as Severe and Proliferative. This study provides a solid computational foundation for developing future early detection tools for Diabetic Retinopathy.
Copyright (c) 2025 Willy Cahyadi, Daniel Martomanggolo Wonohadidjojo https://creativecommons.org/licenses/by-sa/4.0/
Deteksi dan Klasifikasi Kematangan Pisang menggunakan YOLOv8 dan Model Hibrida MobileNetV2–KNN
Banana ripeness is a key indicator of fruit quality, shelf life, and commercial value; however, manual assessment remains subjective and inconsistent, highlighting the need for an automated and objective classification system. This study aims to develop a high-accuracy and stable hybrid model for classifying banana ripeness levels and to implement it in a real-time image-based quality inspection system. This research is an experimental study combined with software development using the Waterfall model. The proposed method integrates YOLOv8 for object pre-filtering and banana detection, MobileNetV2 as a lightweight yet representative deep feature extractor, and K-Nearest Neighbor (KNN) as a Euclidean-distance-based classifier. A dataset of 13,478 images representing four ripeness categories was processed through detection, feature extraction, normalization, and classification stages. Performance evaluations using accuracy, precision, recall, F1-score, confusion matrix, and 5-fold cross-validation show that the model achieves an accuracy of 96.57% with balanced precision–recall values and low cross-validation variance, indicating strong stability and minimal overfitting. The main contributions of this study include the design of the YOLOv8–MobileNetV2–KNN hybrid architecture that overcomes the limitations of manually engineered features and pure CNN models, as well as the development of a GUI-based banana ripeness classification system suitable for real-time smart farming applications.
Copyright (c) 2025 M. Rinaldi Pratama, Joko Aryanto https://creativecommons.org/licenses/by-sa/4.0/
Platform Donasi Barang Bekas berbasis Pengguna dengan Arsitektur Cloud-Based Mobile Berkelanjutan
The management of used goods in urban areas remains a social and environmental challenge due to the low utilisation of digital technology in transparent and structured donation activities. This study aims to produce a mobile-based used goods donation application that utilises cloud architecture for real-time media and data management. The system was developed using Flutter, Firebase, and Cloudinary through a Research and Development (R&D) approach with a waterfall model, including needs analysis, design, implementation, and black box testing of eight main functions. The results are an application with an intuitive interface that displays the main page, item upload, item details, and user profiles, where each item has its current status, photos, description, and donation history notes. Testing results show that all application features run according to specifications with a 100% success rate, an average media loading time of 2.5–3.5 seconds, and direct interaction between users without any obstacles. This application supports the rapid, efficient, and transparent redistribution of goods and encourages community participation in sustainable donation practices. The research findings contribute to the development of a cloud-based digital donation platform that is oriented towards user experience and circular economy principles.
Copyright (c) 2025 Dwi Yulita Anggraeni, Suyud Widiono https://creativecommons.org/licenses/by-sa/4.0/
Inovasi Aplikasi Smart Tourism berbasis Mobile untuk Optimalisasi Informasi Destinasi Wisata
Tourism in Yogyakarta has shown significant growth in the post-pandemic period, yet most existing tourism information applications remain static and lack interactivity. This study aims to develop a mobile smart tourism application based on spatial data to enhance the accessibility, interactivity, and relevance of destination information. The research employs the Research and Development (R&D) method using the Waterfall model, covering needs analysis through interviews with five tourists, interface and system architecture design, implementation using Flutter and PostgreSQL, and functional and usability testing. The findings show that the developed application is capable of presenting destination lists, structured detail information, interactive map visualization, and a favorites feature that supports user personalization. The testing results indicate that all core features operated with a 100% success rate and an average response time of 1.2 seconds. Usability evaluation shows that navigation is easy to understand, the interaction flow is intuitive, and the presented information is perceived as more dynamic compared to similar applications that remain static. These findings provide practical contributions to the development of smart tourism services in Yogyakarta and strengthen the literature on mobile-first approaches and spatial database utilization for digital tourism information. Future research may increase the number of respondents and conduct large-scale performance testing.
Copyright (c) 2025 Indah Setianingsih, Suyud Widiono https://creativecommons.org/licenses/by-sa/4.0/
Transformasi Digital Pengelolaan Metadata Jurnal: Studi Eksperimental Otomasi Entri Data berbasis OCR
Manual entry of journal metadata often requires substantial time and is prone to errors, potentially hindering indexing processes and reducing editorial workflow efficiency. Optical character recognition (OCR) offers an automated approach that may accelerate this process, yet its performance on densely structured journal metadata has not been extensively examined. This study aims to evaluate the time efficiency and accuracy of OCR-based metadata entry and compare it with manual methods. Metadata from the arxiv-metadata-oai-snapshot were rendered into visual documents and processed under three workload scenarios consisting of 100, 500, and 1,000 entries. Two metrics were analyzed: processing time and record accuracy. The results reveal a substantial time difference between the two workflows; in the 1,000-entry scenario, manual entry required approximately 50,000 seconds, whereas OCR completed the task in only 0.075 seconds. Manual accuracy remained stable at 88%, while the automated approach achieved 97%, although character-level accuracy on visual documents ranged only from 1–3%, reflecting the complexity of journal metadata structures. These findings indicate that OCR can serve effectively as an initial stage of metadata automation but still requires human verification through a Human-in-the-Loop approach to maintain data integrity.
Copyright (c) 2025 Amanda Laurensia, Erni Seniwati, Yoga Pristyanto https://creativecommons.org/licenses/by-sa/4.0/
Integrasi Geographic Information System dalam Smart-PPL: Sistem Informasi Monitoring dan Absensi Mahasiswa Praktik Lapangan
Geographic Information System (GIS) integration for real-time spatial monitoring of Field Experience Practices (PPL) in higher education is still rarely applied, so data collection, attendance, and supervision are still mostly manual and less efficient. This research aims to develop Smart-PPL, a GIS-based system with the Laravel framework, which maps the location of partner schools and location-based attendance to support real-time monitoring. The system was developed using the Research and Development (R&D) method with an Agile approach, including analysis, design, implementation, and testing. Analysis was conducted through interviews and observations; design included Use Case Diagram, ERD, and UI/UX; implementation was integrated with Google Maps API and LeafletJS; testing included black box testing, validity testing by three experts, and practicality and effectiveness by 22 users (lecturers, supervisors, students). Results show the system is valid (Aiken's V = 0.89), highly practical (moment Kappa = 0.9377), and highly effective (N-Gain = 0.94). GIS integration improves the efficiency, accuracy, and transparency of PPL supervision. Scientifically, this research contributes to the application of usability and information efficiency theories in spatial-based educational information systems. Scientifically, Smart-PPL can be used as a replication model for other universities to support digitization and optimization of integrated PPL activity supervision.
Copyright (c) 2025 Wahyudi Primadana , Firdaus Annas, Gusnita Darmawati, Yulifda Elin Yuspita https://creativecommons.org/licenses/by-sa/4.0/
Integrasi Sistem Informasi Web–Mobile untuk Mendukung Transformasi Digital Layanan Bengkel Las Global Sufik
Small and medium-sized enterprises (SMEs) in the service sector, such as welding workshops, still face challenges in order efficiency and service transparency due to manual processes. Most previous studies have only implemented web-based systems without real-time mobile integration. This study aims to develop and test an integrated web-mobile information system application to improve efficiency and clarity of service information. The study uses a Research and Development (R&D) approach with a Waterfall model, through the stages of needs analysis, design, implementation, and testing. The research subjects consisted of 1 workshop owner, 2 employees, and 10 active customers selected purposively. Our findings are in the form of an integrated web-mobile application that supports service booking, real-time job status tracking, catalogue management, and customer reviews. The test results show that all features are 100% functional, booking time is 32% faster, recording errors have dropped to 0%, and user satisfaction has reached 89%. These findings confirm that cross-platform integration can simplify service flows, improve data accuracy, and provide transparency of information for welding workshop customers. The results show that the developed system can serve as a relevant digital service model for similar SMEs and is effective in improving operational quality and user experience.
Copyright (c) 2025 Jefri Bayu Saputra, Ahmad Tri Hidayat https://creativecommons.org/licenses/by-sa/4.0
Model Integrasi Design Thinking dan AIDA untuk Pengembangan Strategi Pemasaran Digital pada Industri Properti
Digital transformation in the property industry continues to face a gap between user-centered design approaches and effective marketing communication strategies. Telkom Property Area Cirebon encounters similar challenges, as its marketing activities still rely heavily on inefficient offline promotions that fail to reach wider markets. This study aims to apply the Design Thinking method integrated with the AIDA model in designing a marketing website as part of a digital business strategy. The research employs a Research and Development (R&D) approach consisting of the stages: understand, observe, define, ideate, prototype, and test. The evaluation involved eight internal and external participants of Telkom Property Area Cirebon using a Likert-scale questionnaire based on the ISO 9241-11:2018 usability standard and the AIDA framework. Our findings resulted in a website designed to support the marketing of leveraging assets for Telkom Property Area Cirebon, with testing results showing a usability score of 4,75 and a content effectiveness score of 4,68, indicating that the website is easy to use, attracts attention, generates interest, builds desire, and encourages user action. These findings expand the theoretical understanding of Design Thinking by empirically integrating it with persuasive digital communication frameworks and provide a practical contribution in the form of an adaptive website model that can be replicated by other property companies in their ongoing digital transformation efforts.
Copyright (c) 2025 Sifa Mutiaratulhikmah, Syti Sarah Maesaroh, Rangga Gelar Guntara https://creativecommons.org/licenses/by-sa/4.0/
Model Graf Spasial Adaptif untuk Optimasi Jalur UAV pada Sistem Mitigasi Kebakaran Hutan
Dynamic wildfires require adaptive mitigation systems capable of responding in real time. Optimizing Unmanned Aerial Vehicle (UAV) routes is critical for effective fire mitigation. This study compares the efficiency of the Dijkstra, A*(star), and Bidirectional Dijkstra algorithms on spatial hotspots graph. The quantitative experimental design used data-driven simulation. Data were obtained from the Forest Fire Area dataset (Kaggle), containing 517 wildfires with (X, Y) coordinates in the Montesinho Natural Park, Portugal. Preprocessing involved analyzing spatial patterns using scatter plots and de-duplication of coordinates. These coordinates were then utilized as nodes to construct a graph using Triangulasi Delaunay. The algorithms were implemented using custom-coded. Performance was evaluated through repeated tests based on the metrics of execution time, memory usage, nodes explored, and path length. The results show that all three algorithms achieved identical optimal paths, however A* achieved the highest computational efficiency by balancing time and memory usage. The combination of the Delaunay spatial graph and the A* algorithm effectively reduces search complexity, it suitable for adaptive UAV navigation. This study provides an empirical comparison of algorithms on a spatial hot-spots graph using Triangulation Delaunay, a method rarely used to evaluate the efficiency of UAV path tracing in real spatial conditions.
Copyright (c) 2025 Adinda Salsa Leviona, Triyanna Widiyaningtyas https://creativecommons.org/licenses/by-sa/4.0/
Model Reservasi Hotel Desentralisasi menggunakan Smart Contract ERC-1155 dan Escrow di Optimistic Rollups
Digital hotel reservation systems remain vulnerable to fraud due to reliance on third parties and centralized authorities. This study aims to assess transaction cost efficiency through the execution behavior of smart contract functions that integrate ERC-1155, escrow, and Optimistic Rollups in the context of hotel reservations. This research employs a descriptive-quantitative approach using an experimental method. Four smart contract functions were tested five times on the Sepolia Testnet and the Optimism Sepolia Testnet. Gas usage and transaction fee data were collected through Blockscout, analyzed using mean values, and tested for significance using a t-test with α = 0.05. The results show that Optimistic Rollups provide a cost efficiency of 99.97–99.98%, with all functions producing p-values < 0.05, indicating that Layer 2 is more efficient and exhibits more stable cost patterns. The integration of ERC-1155 and escrow also does not significantly increase cost variability. These findings enrich the discussion on hybrid tokens and trustless settlement within tokenomics and demonstrate strong commercial potential for OTAs and hotels due to the extremely low operational costs. However, the seven-day challenge period remains a major limitation. Further research is required on mainnet environments, high-load scenarios, and the development of reservation cancellation and modification features.
Copyright (c) 2025 Yobel Nathaniel Filipus, Mychael Maoeretz Engel, Elizabeth Nathania Witanto https://creativecommons.org/licenses/by-sa/4.0/
Integrasi Naive Bayes dalam Sistem Pendukung Keputusan berbasis Web untuk Kelayakan Kredit Koperasi Simpan Pinjam
assessment, which is still manual and subjective. This study aims to develop a web-based Decision Support System (DSS) using the Naive Bayes algorithm that not only validates the model but also integrates it into an operational system that can be used immediately. The research method used is development using the waterfall model with analysis, design, implementation and testing. The analysis was conducted by applying the Naive Bayes algorithm to generate confidence scores to improve prediction transparency and estimate monthly instalments to assess the repayment ability of prospective borrowers. The dataset included 6,883 historical loan data from 714 members with nine classification features, analysed using the Naive Bayes algorithm and validated using the k-fold cross-validation technique. Results The Naive Bayes classification model achieved 93% accuracy, 0.96 AUC, 94% precision, 96% recall, and a cross-validation score of 0.921 (±0.035). System testing showed that this web-based system functioned well without any errors. In addition, the average response time of this system was 1.95 seconds per input. The implementation of this web-based system successfully overcomes subjective assessment problems, facilitates real-time objective decision-making, and improves transparency and accountability in credit risk management at KSP Tepian Taduh.
Copyright (c) 2025 Veronika Yusiana Yusefin, Suhirman Suhirman https://creativecommons.org/licenses/by-sa/4.0/
Transformasi Digital Layanan Laundry melalui Mobile Tracking untuk Meningkatkan Transparansi dan Efisiensi Operasional UMKM
Digital transformation in laundry services for Micro, Small, and Medium Enterprises (MSMEs) still faces challenges in information transparency, record-keeping accuracy, and operational efficiency. This research aims to create a mobile-cloud-based laundry service application that supports the ordering process, laundry status tracking, and transaction recording in an integrated manner. Development was carried out through needs analysis, interface design, implementation using Flutter and Firebase, and evaluation through Black Box Testing and System Usability Scale (SUS). Our findings are a mobile application that provides real-time laundry tracking, payment receipt uploads, and automated transaction data management. Test results showed that all 16 Black Box scenarios across eight feature groups were 100% successful with a stable response time of 1–3 seconds. User assessments resulted in a SUS score of 82.5 (Excellent category) and a 92% satisfaction rate, while also demonstrating an increase in order recording efficiency from approximately 10 minutes to 3 minutes per transaction. Although application performance is affected by network quality and testing is limited to one MSME, the research results confirm that the mobile-cloud approach is effective in improving information transparency, recording accuracy, and operational quality for small laundry services.
Copyright (c) 2025 Kurnia Diah Nurmayangsari, Suyud Widiono https://creativecommons.org/licenses/by-sa/4.0/
Inovasi Teknologi Kesehatan Inklusif: Kotak Obat Pintar berbasis RFID dan Panduan Suara bagi Tunanetra
Indonesia ranks third as the country with the highest number of visually impaired people after India and China. However, to date, there is no inclusive and integrated medication management system available for them. Visually impaired people face a high risk of taking the wrong type or dose of medication, which cannot be overcome with simple reminders alone. The aim of our research is to develop an RFID-based smart medicine box and integrated voice guidance using research and development. Families manage medication and schedules through an application, while visually impaired individuals can independently verify and use medication through the device. The system uses RFID tags to identify and verify medication types and is equipped with voice command features for user interaction. The medicine box is an IoT-based system with integrated RFID, voice modules, and an Android application. The testing method was carried out by measuring the accuracy of the alarm time, voice response, and medicine verification validation. Our findings show that the system can detect medication errors and provide voice warnings with high accuracy, with an alarm time of approximately 3 seconds. Testing results show a significant improvement in safety compared to conventional medication reminders. The contribution of this research is the integration of RFID verification, voice guidance, and caregiver applications into a comprehensive system that has not been widely developed, thereby supporting the independence and safety of the visually impaired in medication management.
Copyright (c) 2025 Putri Elfa Mas'udia, Fatonatul Mubarokah, Mila Kusumawardani, Nurul Hidayati, Lis Diana Mustafa https://creativecommons.org/licenses/by-sa/4.0/
BookCamp: Model Aplikasi Mobile untuk Digitalisasi Reservasi Camping dengan Integrasi Fitur Komunikasi Real-Time
Manual reservation systems based on asynchronous communication such as WhatsApp have become a critical bottleneck in managing camping tourism destinations in Indonesia, resulting in increased response times, data entry errors, and user dissatisfaction. This research aims to design an integrated architecture model that combines real-time reservation systems with live chat functionality to optimize camping tourism service transactions. Using design science research methodology with a combination of development and empirical case study approaches, we implemented a mobile application based on Flutter and Firebase at Pantai Menganti, Kebumen Regency. Testing results demonstrate that the integration of live chat with real-time reservation significantly reduces booking processing time by 281 times (from 4.2 hours to 890 milliseconds), decreases data entry error rates by 100%, and increases user satisfaction scores from 2.8/5.0 to 4.3/5.0 based on the System Usability Scale. Theoretical implications include proposing an eco-tourism service digitalization framework applicable to various destinations, while practical implications encompass improvements in operational efficiency, customer experience, and sustainable revenue models. Research limitations include single-location implementation and limited sample size (n=20 users), however this opens opportunities for exploring scalability across multiple locations, comprehensive evaluation of cybersecurity and data privacy as strategic priorities, and analysis of long-term user retention to inform digital transformation of the eco-tourism industry in Indonesia.
Copyright (c) 2025 Rafi Ikko Rastyo, Yuli Asriningtias https://creativecommons.org/licenses/by-sa/4.0/
Integrasi Naïve Bayes dalam Sistem Klasifikasi Permintaan Pakan Ayam: Strategi Data-Driven untuk Efisiensi Stok
Frequent fluctuations in chicken feed demand cause stock imbalances and operational cost wastage. This study aims to develop an end-to-end decision support system (DSS) based on the Naïve Bayes algorithm to classify feed demand patterns and provide distribution recommendations through an interactive dashboard. The integration of this system is an innovation that is rarely applied in the chicken feed agribusiness in Indonesia. The type of research used is System Development Research. The company's historical sales data for the past year (covering the variables of date/month, product name, and sales volume) was used as the main input. The system was developed using the Waterfall development model, which includes the stages of data collection, system design, implementation, and testing. Naïve Bayes modelling was carried out through the stages of data preprocessing, model training (80%), and testing (20%). Our findings are in the form of an interactive dashboard-based DSS that is ready to be used to improve stock management efficiency. System testing shows that the developed end-to-end architecture has been successfully implemented, with a classification accuracy rate of 99%. Analysis proves that the Naïve Bayes model shows high reliability in classifying three demand categories ('Low', 'Medium', 'High'), as evidenced by precision, recall, and F1-score values above 97% for all categories. This system can be used as an effective and practical decision-making tool.
Copyright (c) 2025 Anggi Malinda, Suhirman Suhirman https://creativecommons.org/licenses/by-sa/4.0/
Sistem Rekomendasi Program Studi berbasis K-Means Clustering menggunakan Asesmen Komprehensif dan Nilai Akademik
The high level of inaccuracy in choosing study programmes in Indonesia demonstrates the need for an assessment tool that can map students' potential holistically. This study aims to develop a Dream Major Recommendation System (SRJI) based on K-Means Clustering that integrates six semesters of academic grades and four non-academic assessments to help students at SMAN 3 Batang Hari choose study programmes according to their multidimensional potential. This study uses an applied quantitative design with a Prototype Model through the stages of needs identification, K-Means prototype development, and iterative testing. Data from 152 students were processed using median imputation, Isolation Forest, normalization, and Principal Component Analysis (PCA), resulting in 123 valid datasets clustered using Euclidean distance. The study resulted in a study program recommendation system featuring three optimal clusters with a Silhouette Score of 0.6164 and a Davies–Bouldin Index (DBI) of 0.5684. The analysis identified Cluster 0 (n=42, Social Science 64%, mean score 75.6) with a social-enterprising pattern, Cluster 1 (n=58, Natural Science 72%, mean score 87.5) as an investigative-realistic type, and Cluster 2 (n=35, balanced distribution, mean score 81.2) with realistic-conventional characteristics. Testing results demonstrated an 80% recommendation suitability rate based on validation by Guidance and Counseling teachers. This study contributes an Integrated Multidimensional Student Profiling model that expands the educational data mining literature and supports objective, data-driven decision-making in study program selection.
Copyright (c) 2025 Arvelynia Deriska, Suhirman Suhirman https://creativecommons.org/licenses/by-sa/4.0/
Platform Cerdas dengan Model Pendukung Keputusan Keuangan dan Spasial Terintegrasi untuk Investasi Tambak Ikan
The fish farming sector has high economic potential, but still faces several obstacles, particularly limited capital for fish farmers and limited access to information for investors to assess the feasibility of pond businesses. This research is a developmental study using the Waterfall model with stages of needs analysis, design, implementation, and testing. In the needs analysis stage, user needs are identified. The design stage includes the development of system architecture, database design, and interface design. The implementation stage produces a smart pond web platform that displays pond data through an interactive dashboard, provides data input features, ROI calculations, and investment rankings using TOPSIS. In the testing stage, the system is tested using functional tests with the black box method to ensure that all functions, from data input and calculation processes to the presentation of recommendations, run as expected. This research produces a platform capable of providing accurate information and supporting the investment decision-making process in the fish farming sector. The test results show that all features function well. Overall, this platform is able to improve connectivity between farmers and investors, accelerate access to investment information, and strengthen the basis for analyzing the feasibility of fish pond businesses.
Copyright (c) 2025 Dian Pramadhana, Dita Rizki Amalia, Sonty Lena, Ahmad Lubis Ghozali https://creativecommons.org/licenses/by-sa/4.0/
Benchmarking Model Transformer Modern untuk Analisis Sentimen dan Tren Konsumen dalam Industri Fashion
The dynamic trends and consumer preferences in the fashion industry demand accurate sentiment analysis to support decision-making. This study aims to compare the performance of four Transformer models BERT, DistilBERT, RoBERTa, and DeBERTa, in sentiment classification of fashion product reviews to identify which model best captures the semantic nuances specific to this domain. Using an experimental quantitative approach, we analyzed 278,098 TeePublic reviews that underwent text cleaning, class balancing, and tokenization, and were then split into training, validation, and test sets. The four models were fine-tuned using Optuna-optimized hyperparameters and evaluated with accuracy and F1-score. Our results show that all models achieved nearly identical performance, with F1-scores and accuracy ranging from 0.824 to 0.826. ANOVA testing confirmed no significant differences among models, indicating that increased architectural complexity does not yield substantial benefits for a task of this scale. These findings suggest that performance consistency is driven more by preprocessing quality, class-balancing strategies, and pipeline design. The study offers a comparative basis for model selection in fashion-domain sentiment analysis and highlights that optimizing the processing pipeline often brings more meaningful improvements than adopting larger architectures.
Copyright (c) 2025 Daniel Fernando Herawan, Theresia Ratih Dewi Saputri https://creativecommons.org/licenses/by-sa/4.0/
Model Convolutional Neural Network berbasis Data Lapangan untuk Deteksi Penyakit Daun Padi
Early detection of rice leaf diseases is an important component in maintaining agricultural productivity, especially since rice is a major commodity in Indonesia. However, research on Convolutional Neural Networks (CNN) still uses homogeneous laboratory datasets, which are less than optimal when applied to varying field conditions. This study aims to develop a CNN-based system for classifying rice leaf diseases using field images. This type of research is development using a waterfall model. These stages consist of needs analysis with pre-processing in the form of resizing and augmentation, design, and implementation in a web application. Testing was conducted using a black box to examine the functionality of the system. Our findings show that the system is capable of classifying leaf diseases well based on the field data and functions properly. The training results show an accuracy of 94.64%, precision of 0.93, recall of 0.90, and F1-score of 0.91, with the best performance in the blast and blight classes. Classification errors mainly occurred in the tungro class due to its visual similarity to blast in the early stages of infection. The developed system is capable of performing automatic diagnosis through image uploads and demonstrates learning stability based on the accuracy and loss curves. This research contributes by providing an end-to-end rice leaf disease detection system based on accurate, adaptive, and ready-to-use field data to support direct plant health monitoring in agricultural environments.
Copyright (c) 2025 Adi Suryadi, Suhirman Suhirman https://creativecommons.org/licenses/by-sa/4.0/
SITeMu: Sistem Informasi Temukung sebagai Inovasi Digitalisasi Pendataan Masyarakat Desa
Data collection on underprivileged communities in Sukamulia Village is still carried out manually on paper, resulting in data that is prone to loss, incomplete, slow to process, and difficult to access. This situation hinders decision-making and the distribution of targeted social assistance. This study aims to develop a web-based Information System (SITeMu) to support the digitisation of data on underprivileged residents, while also testing the quality of the system using the ISO/IEC 25010 standard. SITeMu was developed using a waterfall approach through the stages of needs analysis, design, implementation, testing, and maintenance. Software quality testing was conducted in accordance with ISO 25010, covering functional suitability, reliability, usability, performance efficiency, and security. The test results showed a functional suitability score of 4.15 (excellent category), 100% reliability with no failures, 82.7% usability based on an evaluation of 10 village staff, and 80% performance efficiency (category B). Security testing using SiteCheck showed that the system was free of malware and not blacklisted, with a security score of 91/100. These findings confirm that SITeMu is suitable for implementation as a village data digitisation solution. The application of the system enables more accurate, efficient, and accessible data collection on underprivileged communities, thereby supporting the accuracy of social assistance programmes and strengthening data-based governance at the village level.
Copyright (c) 2025 Yosi Nur Kholisho, Rani Arianti, Kholida Ismatulloh, Baiq Desi Dwi Arianti https://creativecommons.org/licenses/by-sa/4.0/
Literanu: Media Animasi Interaktif pada Pembelajaran Literasi dan Numerisasi untuk Anak Usia Dini
Disparities in early literacy and numeracy achievement remain a challenge in early childhood education, highlighting the need for learning media that align with young children's information-processing characteristics. This study aims to develop Literanu, an interactive animated media designed to support early literacy and numeracy learning. The research employed a Research and Development (R&D) approach using the ADDIE model, which includes needs analysis, storyboard and character design, prototype development, classroom implementation, and formative evaluation. The media was tested with 50 kindergarten B children at TK Aisyiyah Bustanul Athfal Pandansurat. Data were collected through observations, teacher interviews, expert validation, and practicality questionnaires, and analyzed descriptively. The findings indicate that Literanu achieved a high level of feasibility based on expert validation (94% from the material expert and 96% from the media expert) and was considered highly practical by teachers. Classroom implementation further demonstrated that children were actively engaged and able to follow instructions delivered through characters and narration. Overall, Literanu serves as a relevant digital learning media for early childhood education settings, particularly those with limited technological resources, and holds potential for further development to cover broader learning content.
Copyright (c) 2025 Aviv Fitria, Hafsah Mukaromah, Dita Septasari https://creativecommons.org/licenses/by-sa/4.0/