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JUITA : Jurnal Informatika
Published by Universitas Muhammadiyah Purwokerto
ISSN : 20869398     EISSN : 25798901     DOI : 10.30595/JUITA
Core Subject : Science,
Computer Science & IT
UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of Informatics. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology and Higher Education No. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 22 Documents
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Search results for , issue "juita vol. 14 issue 2, july 2026" : 22 Documents clear
Comparison of the Performance of the DBSCAN and ST-DBSCAN Text Mining Algorithms for the Distribution of Ornamental Fish Sales Atika Putri; Putri Yuli Utami; Rizki Surtiyan Surya
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.28207

Abstract

The ornamental fish trade in Indonesia is growing rapidly through e-commerce and social media. The large, diverse, and unstructured sales data complicates accurate market mapping. This study analyzes the distribution of ornamental fish sales using density-based clustering algorithms, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which identifies clusters based on data density, enabling the formation of global distribution patterns while detecting outliers, and ST-DBSCAN (Spatial-Temporal DBSCAN), an extension of DBSCAN, which incorporates spatial dimensions to provide more detailed mapping based on geographic proximity. Data were collected through e-commerce platforms and social media such as Twitter. The results revealed that ornamental fish distribution is primarily concentrated in Java and also distributed in Sumatra, Kalimantan, Bali, and Sulawesi. DBSCAN produced five clusters with two noise points and achieved a Silhouette Coefficient of 0.791. Meanwhile, ST-DBSCAN produced six clusters with three noise points, a Silhouette Coefficient of 0.656, and a Davies-Bouldin Index of 0.985. Overall, DBSCAN effectively represents global distribution patterns and detects anomalies, while ST-DBSCAN enhances the analysis with spatial insights that highlight geographic variations. Together, these two algorithms provide a more comprehensive understanding of the distribution of ornamental fish sales by species and location.
Evaluasi Pengaruh Sentimen Berita terhadap Pergerakan Harga Minyak Mentah dengan Pendekatan Klasifikasi Ahmad Muhariya; Indrawan Ady Saputro; Dziky Ridhwanullah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.28411

Abstract

Various factors, including market perception reflected in media information, Influence crude oil price fluctuations. This study aims to analyse the Influence of news sentiment on crude oil price movements using a deep learning–based sentiment analysis approach. The dataset consists of 108 news headlines and daily closing oil prices from January to May 2025. It is important to note that this dataset is relatively small for deep learning models like LSTM, GRU, and BiLSTM, which constitutes a major constraint for this study. The news text was processed with case folding, tokenisation, stopword removal, and lemmatisation (not stemming to preserve semantic integrity for BERT), then automatically labelled using the DistilBERT model. The BERT-based vector representations were used as input for three classification models: LSTM, GRU, and BiLSTM. The evaluation results showed that all three models achieved the same average validation accuracy of 85.27%. However, the GRU model is identified as the optimal performer, achieving the lowest validation loss (0.3324), indicating better generalisation performance than LSTM and BiLSTM. Further analysis reveals that news sentiment tends to align with oil price trends, particularly during significant market shifts.
Decision Tree Model for Maternal Risk Classification Based on Kartu Skor Poedji Rochjati Dyah Megawati Surip Solekhah; Annisa Maulida Ningtyas
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.28509

Abstract

Maternal mortality remains a serious public health issue. This can be prevented by taking preventive measures through early identification of pregnancy risks. This study aims to develop a classification model for screening maternal pregnancy risks using the “Kartu Skor Poedji Rochjati” (KSPR) as a clinical basis for data labelling. This research applies the Cross-Industry Standard Process for Medical Data Mining (CRISP-MED-DM) methodology to ensure a systematic and clinically relevant modelling process. A dataset containing 998 medical records from the Maternal Health and High-Risk Pregnancy Dataset was used, with rule-based labelling adapted from KSPR to classify maternal risk into three categories: Low-Risk Pregnancy, High-Risk Pregnancy, and Very High-Risk Pregnancy. Experiments were conducted with three Decision Tree models, namely the Baseline Model, Model with SMOTE, and Model with Class Weighting. Based on these experiments, it was found that the Decision Tree algorithm enhanced with the Synthetic Minority Oversampling Technique (SMOTE) to overcome class imbalance was the most optimal model. This model achieved balanced performance across all classes with an accuracy of 0.86 and a weighted average F1-Score of 0.87.
A Mamdani Fuzzy-C4.5 Hybrid Model for River Water Quality Assessment: A Case Study of the Beringin River Fianti Fianti; Risma Dwi Rahmawati; Sunarno Sunarno; Suharto Linuwih; Siti Wahyuni; Fifin Dewi Ratnasari
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.28583

Abstract

Urban and industrial activities along the Beringin River in Semarang City cause water pollution in the river. Therefore, assessing water quality is necessary an effort to preserve the environment, safeguard human well-being, and provide early warnings of potential decline. This study used a Mamdani fuzzy logic for development water quality assessment. Input variables consisted of TSS, TDS, Ph, DO, BOD, COD, Nitrate, and Nitrite with water quality as the output. System development was begun by determination of fuzzy set domains for fuzzification and defuzzification. Fuzzy rules were formulated using IF-THEN relationships integrated with C4.5 Algorithm, an algorithm was used to select the most relevant attributes and pruning redundant branches to simplify complex decision-making system, in accordance to the health and environmental standards. The validation of the water quality assessment system was carried out by comparing the Pollution Index (PI) calculation data of 27 data points over a five-year period. The validation results showed accuracy system of 88.9%, with the water quality was categorized as moderately polluted. These findings indicated that the water quality assessment system have high accuracy and can be specifically applied in the Beringin River.
Performance Evaluation of PHP Data Object and Native Database Connection for CRUD Optimization Across MySQL, PostgreSQL, and MySQLite Warto Warto; Anas Azhimi Qalban; Yusuf Heriyanto; Putra Aditya Priyono
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.28822

Abstract

This study presents an experimental performance evaluation of the PHP Data Object (PDO) compared to native database connections, specifically mysqli and pg_connect, in executing CRUD operations across three major relational database management systems: MySQL, PostgreSQL, and SQLite. The research aims to determine the extent to which PDO’s abstraction layer influences execution efficiency, memory utilization, and scalability in database-driven applications. Using datasets of varying sizes—1.000, 10.000, 100.000, and 1.000.000 records—each CRUD operation was benchmarked under identical system configurations. The aggregated results indicate that PDO exhibits a lower overall mean execution time of 82,54 ms with a standard deviation of 31.54, compared to Native implementations at 88,22 ms with a standard deviation of 34,28. PDO also demonstrates slightly lower average memory usage, 7,29 MB, whereas Native 7,96 MB and smaller dispersion, 0,61 MB, and Native 0,83 MB across configurations. Inferential statistical analysis using a paired t-test further indicates that the observed differences between PDO and Native implementations are statistically significant (p < 0,0001) under the evaluated experimental configurations. These findings suggest that PDO can provide comparable performance efficiency while maintaining the architectural advantages of abstraction and portability in PHP-based web systems.
Performance Comparison of Tree-Based Models for Heart Disease Prediction Using Feature Selection and SMOTE Santi Santi; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29098

Abstract

Heart disease remains the leading cause of mortality worldwide, highlighting the need for accurate early prediction models. This study proposes a machine learning framework for heart disease prediction using the BRFSS 2015 Heart Disease Health Indicators dataset, which contains 253,680 records and 22 attributes. The proposed approach integrates Synthetic Minority Oversampling Technique (SMOTE) for class imbalance handling, mutual information-based SelectKBest feature selection (k = 15), and three tree-based classifiers: Decision Tree, Random Forest, and XGBoost. A leakage-free preprocessing pipeline was implemented to ensure that SMOTE was applied only to the training data, and classification threshold optimization was performed to improve minority class detection. Model performance was evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC metrics. Experimental results show that XGBoost achieved the best performance with a cross-validation ROC-AUC of 0.9815 and a test ROC-AUC of 0.8444 at an optimized threshold of 0.20. The findings demonstrate that the proposed integration of oversampling, feature selection, and threshold optimization can improve predictive performance for imbalanced cardiovascular risk data, providing a practical foundation for machine learning–based decision support in early heart disease risk screening.  
Pengaruh Metode Imputasi terhadap Kelayakan Model LSTM dalam Peramalan Deret Waktu Klimatologi Dhia Rafifah Thifal; Aji Prasetya Wibawa; Adelia Desyana Eka Putri; Adelia Khansa Ristiaputri; Adhelia Wida Khaidir; Agung Bella Putra Utama
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29185

Abstract

Missing values substantially degrade the reliability of environmental time-series forecasting; however, prior studies largely evaluate imputation methods in isolation without systematically linking missingness mechanisms to deep learning forecasting performance. To address this gap, this study proposes a mechanism-aware comparative framework that evaluates deletion and six imputation methods (Mean, Median, Mode, LOCF, KNN, and MICE) across three environmental time-series datasets with naturally occurring missing values, using LSTM as the forecasting model. The novelty lies in jointly analyzing statistical error (MAPE, RMSE), goodness-of-fit (R²), and statistical significance to identify structurally aligned imputation strategies under different missingness patterns. Experimental results show that deletion as baseline consistently produces the worst performance (MAPE: 5.91429; 7.35000; 2.84881), whereas imputation reduces proportional error by more than 70% on average (p < 0.05). LOCF performs best under temporal dependency (MAPE 0.73959; R² 0.92757), KNN achieves the most balanced performance under MCAR-like behavior (R² 0.94086), and Mean imputation yields the lowest error in MAR-structured data (MAPE 0.41560; R² 0.97077). These findings demonstrate that imputation effectiveness depends on alignment with missingness structure rather than methodological complexity, providing evidence-based guidance for robust environmental.
Evaluating Hybrid GA-SVM Feature Selection for Indonesian Sentiment Classification Using LSTM Siti Mujilahwati; Noor Zuraidin bin Mohd Safar
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29602

Abstract

The high dimensionality and noisy characteristics of Indonesian social media text present significant challenges for sentiment classification models. Redundant and irrelevant features may reduce classification efficiency and negatively affect model generalization performance. This study evaluates a hybrid wrapper-based feature selection approach that integrates Genetic Algorithm (GA) and Support Vector Machine (SVM) to optimize TF-IDF feature representations before classification using Long Short-Term Memory (LSTM). The experiments were conducted on 1,918 Indonesian Twitter comments related to SARS-CoV-2 sentiment, consisting of 1,044 negative and 874 positive labels. The proposed GA-SVM mechanism reduced the feature space from 35,343 to 17,931 selected features. Two evaluation scenarios were employed in this study. Under the hold-out train-test split evaluation, the GA-SVM+LSTM model achieved the best accuracy of 91.41% using a learning rate of 0.0001. Meanwhile, the 10-fold cross-validation evaluation produced an average accuracy of 89.41%, indicating stable generalization performance across different data partitions. The experimental results also show that the proposed feature selection approach improved computational efficiency by reducing training time from 263.64 seconds to 173.54 seconds. Overall, the findings indicate that hybrid GA-SVM feature selection can effectively improve TF-IDF-based sentiment classification performance for Indonesian social media text.
Integrating User Acceptance Evaluation into District-Level Mobile Health System Design for Maternal Care Arif Setia Sandi Ariyanto; Purwono Purwono; Deny Nugroho Triwibowo
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29616

Abstract

Many mobile health information systems are developed based primarily on technical requirements, while user acceptance is often assessed only after deployment and rarely integrated into the design process. This study addresses that gap by incorporating user acceptance evaluation as a design-informed feedback mechanism in the development of a district-level mobile health information system for maternal care. An applied research approach was used through system development, pilot deployment across five sub-districts, and post-use evaluation involving 74 active users. User acceptance was assessed using structured questionnaires covering perceived usefulness, perceived ease of use, consultation feature acceptance, and automated conversational support acceptance. The results showed high overall acceptance, with direct consultation with local midwives receiving the highest score (mean = 4.41; SD = 0.52), followed by consultation routing effectiveness (mean = 4.35; SD = 0.55). Perceived ease of use was positively associated with consultation feature acceptance (ρ = 0.46, p < 0.01). Automated conversational support was positively perceived but obtained lower scores (mean = 3.92) than human-based consultation features, indicating its complementary role. These findings demonstrate that user acceptance evaluation can provide actionable evidence for iterative system refinement and support the development of context-aware mobile health systems for maternal care.
A Bilingual Academic Chatbot Based on Semantic Retrieval Using mBERT Leni Fitriani; Sahrudin Fiqri Muzahidar; Ade Sutedi; Fitri Nuraeni
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29667

Abstract

This study proposes a bilingual academic chatbot based on a semantic retrieval approach using the Multilingual BERT (mBERT) transformer architecture to support academic information services in higher education. The dataset was constructed from official academic information at Garut Institute of Technology, including new student admissions, academic calendars, institutional profiles, and lecturer and staff data. The data were organized in a bilingual question–and–answer format in Indonesian and English. The mBERT model was fine-tuned using a Sentence-BERT framework to generate sentence embeddings for semantic retrieval tasks, with MultipleNegativesRankingLoss applied during training. Model performance was evaluated using BERTScore to measure semantic similarity between chatbot responses and human reference answers. Experimental results show that the fine-tuned model outperformed the base model, achieving an average F1-score improvement from 0.7638 to 0.8152 for Indonesian and from 0.7556 to 0.8005 for English. The results also demonstrate more stable score distributions, indicating consistent semantic performance. The optimized model was subsequently integrated into a web-based prototype to enable real-time bilingual academic question answering. These findings confirm that combining mBERT with semantic retrieval effectively enhances the relevance and contextual accuracy of chatbot responses, thereby supporting digital transformation and improving the efficiency of academic services in higher education.

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