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Journal of Computer Science and Informatics Engineering
Published by Ali Institute of Research and Publication
ISSN : -     EISSN : 28278356     DOI : -
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Machine Learning Natural Language Processing Computer Vision Text Speech Text Mining Data mining Cryptography Data visualization Expert System Deep Learning Fuzzy Logic IoT and smart environments Neural Networks Pattern Recognition Image Processing Optimization Digital Signal Processing Networking Technology Web intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 125 Documents
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Comparative Analysis of MambaOut Tiny and Vision Transformer for Corn Leaf Disease Classification Adi Slamet Priyadi; Albert Yakobus Chandra
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1834

Abstract

Maize production remains vulnerable to foliar diseases such as Gray Leaf Spot, Common Rust, and Northern Leaf Blight, which can impair growth and reduce crop yields. Manual disease identification is time-consuming, labor-intensive, and reliant on the observer's expertise, necessitating automated detection systems based on deep learning. While Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are widely used for plant disease classification, a direct comparison between ViT-B/16 and MambaOut Tiny under identical training conditions remains rare. This study compares the two models using the PlantVillage dataset, which comprises 4,188 maize leaf images across four classes. Both models were fine-tuned using ImageNet-1K pre-trained weights, the AdamW optimizer, a learning rate of 1e-4, a batch size of 16, and 10 epochs. The results demonstrate that MambaOut Tiny achieved an accuracy of 97% and a macro-average F1-score of 0.96, outperforming ViT-B/16, which achieved 95% accuracy and an F1-score of 0.93. Additionally, MambaOut Tiny features a lower parameter count (30 million versus 86 million) and faster inference time (0.01262 seconds per image). These findings indicate that MambaOut Tiny is more efficient for precision agriculture systems with limited computational resources
Comparative Analysis of TF-IDF, TF-IDF+WordNet, and Sentence-BERT for News Document Retrieval Using Cosine Similarity Galib Haftha Zuhayir; Wiwik Suharso; Nanda Kurnia Wardati
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1835

Abstract

The rapid growth of digital news volume has produced information overload, while exact keyword-matching retrieval remains vulnerable to synonymy and polysemy, causing relevant documents to be missed. Prior studies generally compare only two document representation methods on small-scale datasets, leaving a gap in controlled evaluations that jointly compare statistical, lexical-hybrid, and neural approaches on a large-scale news domain. This study compares three document representation methods, namely Term Frequency-Inverse Document Frequency (TF-IDF), TF-IDF with WordNet-based query expansion, and Sentence-BERT (all-MiniLM-L6-v2), for news document retrieval using Cosine Similarity on the BBC News Dataset (14,305 documents with a hierarchical Ground Truth of 5 Topics and 51 Subtopics). Ten queries were evaluated using Precision@K, Recall@K, F1-Score@K (K=5, 10, 20), and execution time. The results show that Sentence-BERT consistently outperforms the other methods with a Precision@5 of 0.84, compared to TF-IDF (0.56) and TF-IDF+WordNet (0.52), while TF-IDF remains the fastest at online query time (23.86 ms per query). WordNet expansion actually reduces precision and increases execution time without a proportional accuracy gain. These findings confirm that transformer-based semantic representations are superior for news domains with high lexical variation, while TF-IDF remains relevant for computationally constrained real-time systems
Comparison of SVM and Random Forest with RFE for Diabetes Prediction Anggi Vandryan; Putry Wahyu Setyaningsih
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1838

Abstract

Individuals with a high Body Mass Index (BMI) are among the most vulnerable groups to developing Type 2 Diabetes Mellitus due to insulin resistance caused by visceral fat accumulation. However, most existing machine learning models have been developed using general population data without considering the specific characteristics of high-risk individuals. This study aims to analyze and compare the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms in predicting diabetes risk among individuals with a BMI ≥ 25, while also evaluating the impact of Recursive Feature Elimination (RFE) on improving model performance. The Pima Indians Diabetes Dataset from the UCI Machine Learning Repository was used as the data source. After filtering records based on BMI, a total of 662 instances were included in the analysis. The preprocessing stage consisted of median imputation for invalid values, feature normalization using StandardScaler, and feature selection using RFE to select four features for each model. The dataset was divided into training and testing sets using a 70:30 ratio. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the RF-RFE model achieved the best performance, with an accuracy of 78%, a recall of 68% for the diabetes class, and an F1-score of 72%, representing a significant improvement over the RF model without RFE (74% accuracy and 60% recall). The combination of Random Forest and Recursive Feature Elimination proved to be the most effective approach for reducing false negatives, which is particularly important in the context of early clinical detection of diabetes
Integrating Word Embeddings and IMDb Web Scraping for Keyword-Based Movie Recommendation Andani Chacha Cahya Dewi; Deni Arifianto; Nanda Kurnia Wardati
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1839

Abstract

The rapid growth of the film industry and streaming platforms has led to information overload and filter bubbles that make it difficult for users to find content matching their narrative preferences. Prior content-based filtering approaches relying on word-frequency methods (TFIDF) suffer from a semantic gap and commonly depend on a single public dataset and a reference title (seed movie) as input. This study combines a public dataset with IMDb web-scraping results (a maximum population of 5,000 titles) and applies a Skip-gram Word2Vec model to represent movie synopses as 200-dimensional semantic vectors, paired with Cosine similarity to measure the closeness between a user's free-text keyword and movie synopses without requiring a seed movie. Data were split using an 80:20 Holdout method, and algorithm performance was evaluated on a Top-3 Recommendation window using Precision@K, Recall@K, and Mean Reciprocal Rank (MRR), with ground truth validated by two experts through Inter-Annotator Agreement. Testing on 25 queries produced a Precision@3 of 0.5333, Recall@3 of 0.7800, and MRR of 0.7300. These results indicate that integrating word embeddings with web scraping yields semantically relevant movie recommendations from free keyword input, though comparisons with baseline methods are needed for more definitive performance claims
Web-Based Information System Design for BUMDes Sakinah in Sialang Panjang Village Hasan Arya Sandy; Ilyas Ilyas; Abdul Muni
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1843

Abstract

Village-Owned Enterprises (BUMDes) play an important role in improving the rural economy. However, administrative management, business data processing, document archiving, and information dissemination at BUMDes Sakinah in Sialang Panjang Village were still carried out manually, resulting in inefficiency, a high risk of data loss, and delays in providing accurate information. This condition indicates a gap in the utilization of information technology to support integrated BUMDes management. This study aimed to design a web-based information system for BUMDes Sakinah as a solution to these problems. A qualitative research method was employed, with data collected through observation, interviews, and documentation. The system was developed using the System Development Life Cycle (SDLC), consisting of planning, analysis, design, implementation, testing, and maintenance stages. The results show that the proposed system successfully integrates business data management, digital document archiving, and information services into a single web-based platform, thereby improving effectiveness, efficiency, data security, and accessibility of information. Practically, the system supports better administrative management and service quality, while academically it contributes to the implementation of web-based information systems for Village-Owned Enterprises. In conclusion, the developed system has proven to enhance the effectiveness, transparency, and accountability of BUMDes management

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