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JSAI (Journal Scientific and Applied Informatics)
Published by Universitas Muhammadiyah Bengkulu
ISSN : 26143062     EISSN : 26143054     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal terbitan dibawah fakultas teknik universitas muhammadiyah bengkulu. Pada jurnal ini akan membahas tema tentag Mobile, Animasi, Computer Vision, dan Networking yang merupakan jurnal berbasis science pada informatika, beserta penelitian yang berkaitan dengan implementasi metode dan atau algoritma.
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Articles 507 Documents
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Prediksi Potensi Banjir Menggunakan Machine Learning Dengan Pendekatan XGBoost Dan Logistic Regression Nurita Evitarina; Fitriyanti, Fitriyanti; Utami, Tri Dewi Yuni
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9867

Abstract

Flooding is one of the most frequent natural disasters in Indonesia, causing significant material losses and casualties. This study aims to develop a flood potential prediction model based on weather data using machine learning approaches, namely XGBoost and Logistic Regression. The dataset consists of 1,513,505 weather records with 1,165 flood events (0.077%). The features include temperature, humidity, wind speed and direction, weather codes, and temporal features generated using a sliding window approach for H-1, H-2, and H-3. Data imbalance was addressed using a combination of stratified undersampling and SMOTE, changing the class ratio from 1:1,298 to 1:3.3. Experimental results show that XGBoost outperforms Logistic Regression, achieving an accuracy of 98.40%, precision of 97.93%, recall of 95.07%, and an ROC-AUC of 99.38%, while Logistic Regression achieved an accuracy of 62.77%. Feature importance analysis indicates that weather codes at H-3 and H-1 are the most influential predictors. With a low false negative rate of 4.9%, the proposed XGBoost model is considered reliable for implementation as a flood early warning system.
Model Kausal Pengaruh Fasilitas dan Konten Digital terhadap Kompetensi Teknologi Informasi Siswa dengan Mediasi Minat Belajar Aziz, Ezar; Yulianti; Probonegoro, Wishnu Aribowo
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9869

Abstract

This study aims to analyze the effect of facility availability and digital content on students’ information technology competencies with learning interest as a mediating variable. A quantitative approach with a survey method was employed involving 178 students from Information Technology and Visual Communication Design programs. Data were analyzed using multiple regression, simple regression, and path analysis with SPSS 25. The results indicate that facility availability and digital content have a positive and significant effect on students’ learning interest, accounting for 68.2% of the variance. Learning interest also has a significant positive effect on students’ information technology competencies, contributing 61.5% of the variance. Furthermore, digital content shows a significant indirect effect on students’ competencies through learning interest. These findings highlight the strategic role of learning interest as a mediating variable in the causal model of digital learning and imply that improving students’ competencies requires not only adequate facilities and high-quality digital content but also instructional strategies that foster students’ learning interest.
Evaluasi Keberhasilan Implementasi Sistem Informasi Perdagangan Daerah Berbasis HOT-Fit dan Technology Acceptance Model Dzalfa Tsalsabila Rhamadiyanti; Aditya Ahmad Fauzi; Fithriawan Nugroho
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9896

Abstract

The Pangkalpinang City Trade Information System (SIPGK) was developed as a digital instrument to support trade data management and data-driven public information services. This study aims to evaluate the implementation success of SIPGK using the Human–Organization–Technology Fit (HOT-Fit) model, with the Technology Acceptance Model (TAM) employed as a complementary interpretative lens. A qualitative evaluative approach was applied through observation, interviews, and system documentation. The results indicate that the technology aspect demonstrates a system availability rate of 95%, reflecting good system quality and service stability, while the organizational aspect is supported by formal policies and standard operating procedures. However, the human aspect remains a key limiting factor due to disparities in digital literacy and data input consistency, along with suboptimal cross-unit data integration. These findings reveal a gap between technological and organizational readiness and human resource capacity in achieving strategic system utilization. The novelty of this study lies in applying the HOT-Fit model to a regional trade information system context, which has been rarely examined, and in integrating TAM as an interpretative framework to explain user acceptance.
Evaluasi Metode Retrieval pada Chatbot Domain Khusus Berbasis Retrieval-Augmented Generation Asmaidin, Asmaidin; Budy Santoso, Cahyono
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9897

Abstract

This study evaluated retrieval methods in the implementation of a domain-specific chatbot based on Retrieval-Augmented Generation to improve information accuracy and relevance while reducing hallucination risks. The primary problem addressed was the incorrect selection and prioritization of contextual documents in chatbot systems built on large language models, particularly in technical domains. An experimental approach was applied by comparing three retrieval strategies: lexical retrieval based on term frequency–inverse document frequency, semantic retrieval using vector representations, and a hybrid retrieval method combining lexical and semantic signals. System performance was measured using Recall at different ranking thresholds and Mean Reciprocal Rank to assess both document discovery and ranking quality. The results demonstrated that lexical retrieval achieved the highest precision at the top-ranked position, while semantic retrieval showed reduced effectiveness due to semantic drift in technical documents. The hybrid approach improved mid-range recall performance but still exhibited ranking ambiguity for top-ranked results. These findings indicated that retrieval quality in Retrieval-Augmented Generation systems depended more on effective ranking and context prioritization than on document availability alone. The study concluded that systematic evaluation of retrieval methods was essential for developing reliable domain-specific chatbots.
Analisis Usability pada Perancangan Sistem Informasi Manajemen Aset Gudang Berbasis Web Menggunakan Metode Prototyping dan System Usability Scale (SUS) rubiyatno; Asri, Sri Dianing
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9856

Abstract

Operational asset management plays a strategic role in ensuring the smooth and continuous operation of warehouse activities within a company. PT. Indomarco Prismatama faces challenges in managing assets, particularly in the processes of asset borrowing, return, and maintenance, which are still conducted manually. This results in potential delays in data recording, reduced data accuracy, and limited information transparency. This study aims to design a web-based information system for managing asset borrowing, return, and maintenance to improve efficiency and accuracy. The system was developed using the Software Development Life Cycle (SDLC) methodology with a prototyping approach, which allows for user involvement through iterative stages. The system was built using the CodeIgniter 4 framework and a MySQL database. System testing was performed using the System Usability Scale (SUS) method, involving 20 respondents, including Asset Admins, VUM Admins, and Managers. The evaluation results show an average SUS score of 81.9, indicating a high level of usability (Excellent). However, these results are limited to the current group of respondents and may not represent a broader user base. Overall, the developed system supports more effective, efficient, and transparent operational asset management, although further testing is needed to confirm its scalability and usability across a wider audience.
Implementasi dan Evaluasi Kinerja Sistem IoT Multi-Sensor Berbasis ESP32 untuk Pemantauan dan Peringatan Dini Lingkungan secara Real-Time Arif Setia Sandi Ariyanto; Deny Nugroho Triwibowo; Imam Ahmad Ashari; Rito Cipta Sigitta Haryono
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9861

Abstract

Real-time environmental monitoring has become increasingly important due to growing urban and industrial activities that affect air quality, noise levels, and physical environmental stability. However, many existing monitoring systems remain relatively expensive, lack portability, and are limited to passive monitoring functions without clear performance evaluation. This study aims to implement and evaluate the performance of an Internet of Things (IoT)-based multi-sensor environmental monitoring system integrated with a mobile application and real-time early warning features. The system is developed using an ESP32 microcontroller connected to DHT22, MQ135, SW-420, and KY-037 sensors to monitor temperature, humidity, air quality, vibration, and noise levels. Sensor data are transmitted to a server via a RESTful API, stored in a MySQL database, and visualized in real time through a Flutter-based mobile application. The research adopts a Research and Development (R&D) approach, encompassing requirement analysis, system design, implementation, integration, and functional testing. The experimental results indicate that the system can transmit multi-sensor data reliably with low response time, present environmental information in real time, and consistently deliver early warning notifications when environmental parameters exceed the defined threshold values. This study contributes by providing a practical and replicable performance evaluation of an IoT-based multi-sensor system suitable for small-scale environmental monitoring.
Klasifikasi Sampah Multi-Kelas Berbasis Deep Learning Menggunakan Model VGG16 Ayumi, Vina
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9880

Abstract

The manual waste sorting process has faced various challenges, such as low efficiency and a high potential for classification errors. This study aimed to implement and analyze the performance of a deep learning–based VGG16 model for multi-class waste classification using digital images. The dataset used consisted of six waste classes, namely cardboard, glass, metal, paper, plastic, and residual waste, with an imbalanced initial number of images. To address this issue, data augmentation was performed so that each class contained 500 images. The dataset was then divided into 70% training data, 15% validation data, and 15% testing data. The experiments were conducted using a transfer learning approach by varying training parameters, including the RMSProp, Adam, and Stochastic Gradient Descent (SGD) optimizers, as well as batch sizes of 16, 32, and 64. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the selection of training parameters significantly affected model performance. The best configuration was achieved using the VGG16 model with the Adam optimizer and a batch size of 16, which produced the highest testing accuracy of 85.87%. This study was expected to serve as a foundation for the development of automated computer vision–based waste sorting systems
Evaluasi Sistem Pemesanan Jasa Make Up Artist Menggunakan Metode Rapid Application Development dan PIECES Jovin, Jhon Cavlin; Wawan Kurniawan
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9889

Abstract

This study aims to evaluate a Make Up Artist service booking system using the Rapid Application Development (RAD) method with PIECES evaluation. The evaluation was conducted using a pre-test and post-test approach to compare system conditions before and after implementation. The PIECES method was applied to measure system quality based on six dimensions: performance, information, economy, control, efficiency, and service. The results indicate that the average system quality score increased from 54% before implementation to 84% after implementation, showing an improvement of 30%. These findings demonstrate that the implemented system significantly improves service quality, operational efficiency, and customer satisfaction. Therefore, the combination of RAD and PIECES evaluation is effective in enhancing the quality of Make Up Artist service booking systems.
Analisis Regresi Linear dan Ensemble Learning Berbasis Kontribusi Fitur dalam Prediksi Harga Mobil Listrik Idris, Nur Oktavin; Pontoiyo, Fuad
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9891

Abstract

This study aims to analyze the performance of linear regression and ensemble learning methods in predicting electric vehicle prices based on technical specifications, as well as to examine the contribution of key features to the prediction results. The main challenge in electric vehicle price prediction lies in the high price variability driven by nonlinear relationships among technical attributes, which are difficult to capture using simple linear models. Linear regression was employed as a baseline model, while Random Forest and Gradient Boosting were used as ensemble learning approaches. The dataset was obtained from Kaggle and processed through data cleaning, categorical encoding, normalization, and an 80:20 train–test split. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R²). The results indicate that the Gradient Boosting model achieved the best performance, with an MSE of 8.63 and an R² of 0.891, outperforming both Random Forest and linear regression models. Feature contribution analysis reveals that vehicle acceleration time is the most influential factor in determining electric vehicle prices. These findings demonstrate that ensemble learning not only improves predictive accuracy but also provides analytical insights into the key technical factors shaping electric vehicle pricing.
Model Rekomendasi Musik Berbasis Representasi Semantik Lirik Lagu Menggunakan BERT zia, Dziaul Hululiah; Fersellia, Fersellia
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9919

Abstract

The rapid growth of digital music platforms has resulted in an information overload problem, making it difficult for users to discover songs that match their preferences. This study proposes a content-based music recommendation model through semantic analysis of song lyrics using a Natural Language Processing approach with Bidirectional Encoder Representations from Transformers. The research stages include Indonesian song lyric data collection, data cleaning, text preprocessing, contextual lyric embedding generation, and lyric similarity computation using cosine similarity. Model performance is evaluated using Mean Squared Error and accuracy. Experimental results show that the proposed model achieves an accuracy of 83.69% with a Mean Squared Error value of 1.4066, indicating that lyric representations generated by Bidirectional Encoder Representations from Transformers effectively capture semantic meaning and quantitatively improve the relevance of music recommendations. Therefore, the proposed approach enhances the accuracy and personalization of content-based music recommendation systems.

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