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Esther Irawati Setiawan
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Kampus Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya) Ngagel Jaya Tengah 73-77, Surabaya, Indonesia
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Insyst : Journal of Intelligent System and Computation
Published by Sekolah Tinggi Teknik Surabaya
ISSN : 26219220     EISSN : 27221962     DOI : https://doi.org/10.52985/insyst
Core Subject : Science,
Computer Science & IT
The Intelligent System and Computation Journal will be published for 2 editions in a year, every April and October. The Intelligent System and Computation Journal is an open access journal where full articles in this journal can be accessed openly. Review in this journal will be conducted with a blind review system. All articles in this journal will be indexed by Google Scholar. The topics contained in this journal consist of several fields (but not limited to): Algorithms and complexity Artificial Intelligence Big Data Analytics Biomedical Instrumentation Computational logic Computer Vision and Biometric Data and Web Mining Digital Signal Processing Image Processing Information Retrieval & Information Extraction Intelligence Embedded Systems Machine Learning Mathematics and models of computation Natural Language Processing Parallel & Distributed Computing Pattern Recognition Programming languages and semantics Speech Processing Virtual Reality & Augmented Reality
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 91 Documents
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Identifying Types of Corn Leaf Diseases with Deep Learning Firmansyah, Rahul; Nafi'iyah, Nur
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.347

Abstract

The government is trying to increase corn yields to meet the Indonesian population's food needs and for export abroad. Some farmers have yet to gain experience with the types of diseases in corn, so they need tools or systems to guide and provide information to new farmers. Many previous studies have developed automatic systems to identify corn leaf diseases, with the goal of increasing corn crop production by early recognition and control. We propose a system for identifying types of corn leaf diseases using the CNN (Convolutional Neural Network) method to be more precise in recognizing corn diseases early on. The methods used in previous research mostly used deep learning with high accuracy results above 90%. CNN is one of the deep learning methods, so we use it to identify types of leaf diseases. Our data comes from Kaggle; we process it first. The Kaggle dataset has corn plants similar to those in Indonesia, so we use this data with identification classes (Blight, Common rust, Gray leaf spot, and Healthy). The training data is 2000 images with 500 images for each class, and the testing data is 120 images with 30 images for each class. The evaluation results show that the classification process using the CNN method has an accuracy of 84.5%. The results we produced for identifying types of corn leaf disease still lack accuracy in their prediction, indicating the need to improve the CNN architecture model.
Chi-Square Histogram Analysis of Woven Fabric Images Made from Natural Dyes Due to Exposure to Sunlight Batarius, Patrisius; Alfry Aristo Jansen Sinlae
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.348

Abstract

This research aims to conduct a Chi-square analysis on the histogram of woven fabric images dyed with natural dyes following exposure to sunlight. Woven fabrics dyed with natural dyes have attracted attention in the textile industry due to their sustainability and environmental safety. Continuous sunlight is a significant factor influencing color changes in woven fabric dyed with natural dyes. The methodology involves capturing images of woven fabric pre- and post-sunlight exposure, followed by histogram analysis using Chi-Square testing, mean, mode, and standard deviation. We utilize pre-cropped and resized grayscale images. Research findings demonstrate that sunlight significantly impacts the histogram of woven fabric images dyed with natural dyes, causing shifts in color distribution, standard deviation, and mode. These findings hold critical implications for the textile industry, particularly for manufacturers of woven fabrics dyed with natural dyes. The application of Chi-Square analysis and standard deviation provides guidelines for product design, maintenance procedures, and consumer education regarding the preservation of color quality in fabrics exposed to sunlight. Changes in the quality of woven fabric images under sunlight exposure can offer essential guidance in the care and maintenance of textile products dyed with natural dyes. This research contributes to a deeper understanding of the interplay between natural dyes, sunlight, and woven fabrics, supporting the development of sun-resistant natural dyes.
Procedural Map Generation for 'Splatted': Enhancing Player Experience through Genetic Algorithms and AI Finite State Machines in a Snowball Throwing Game Hariyanto, Lukky; Armanto, Hendrawan
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.353

Abstract

Games, a now extremely prevalent form of global entertainment, have emerged as a leading industry in the entertainment media, surpassing other entertainment media such as books, films, and music. However, game development is a complex endeavor, requiring a diverse set of talents to create a decent game for people to enjoy. Some of the talents needed to create a good game is a game designer, which dictates how a player can interact with the world, a writer, which pours a meaningful story inside said world, and a composer, which uses music to elevate the emotions evoked by the game and its events. With that being said, this research aims to streamline the creation process of the game designers, specifically the level designers by focusing on procedural map generation and artificial intelligence to create a map that is in a playable state for the players to play in. Procedural map generation, facilitated by a genetic algorithm inspired by Darwin's evolutionary theory, expedites the level design process. The research explores two types of map generation—tile-based and template-based, each with distinct advantages and disadvantages. Through user acceptance tests and expert-level analysis, it is evident that the genetic algorithm performs effectively, achieving a noteworthy level of player satisfaction.
Prediction of Physico-Chemical Characteristics in Batu Tangerine 55 Based on Reflectance-Fluorescence Computer Vision Ariani, Safitri Diah Ayu; Maharsih, Inggit Kresna; Al Riza, Dimas Firmanda
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.363

Abstract

Oranges (Citrus sp.) are one of the most abundant agricultural commodities in Indonesia. One of the popular local citruses is Batu Tangerine 55. Harvesting tangerines begins 252 days after the flowers bloom. Conventionally, we still determine the level of maturity by observing the color, shape, and hardness. The results of manual grouping tend to be subjective and less accurate. Destructive testing could be carried out and provide objective results; however, it would require sampling and damaging the fruits. Computer vision could be used to evaluate the maturity level of the fruit non-destructively. Dual imaging computer vision, i.e., reflectance-fluorescence mode, could be used to enhance the accuracy of the prediction. This study aims to develop a classification model and predict the physico-chemical characteristics of Batu Tangerine 55. Destructive testing is still being carried out to determine the value of TPT, the degree of acidity, and the firmness of the fruit. Non-destructive testing was carried out to obtain reflectance and fluorescence images. Once we obtain the destructive and non-destructive data, we will incorporate them into the classification and prediction models. The machine learning method for maturity classification uses three models, namely KNN, SVM, and Random Forest. The best results on the reflectance data (RGB) SVM model resulted in an accuracy of 1 for training data and 0.97 for testing data. The maturity parameter prediction method uses the PLS method. The best results for the predicted Brix/Acidity ratio R2 parameter are 0.81 and RMSE 3.4.
Predictive Buyer Behavior Model as Customer Retention Optimization Strategy in E-commerce Muhammad A. A. Hakim; Terttiaavini, Terttiaavini
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.379

Abstract

Lazada is one of the rapidly growing E-commerce platforms in this digital era. One of the main challenges faced by Lazada is customer retention, where customers make purchases once or a few times before switching to other platforms. Therefore, it is important to understand buyer behavior in E-commerce through customer prediction to identify factors influencing retention. This study employs the Random Forest (RF) method to analyze Lazada customer data and formulate more effective marketing strategies. The analysis is conducted by loading preprocessed datasets into the KNIME workflow and utilizing various nodes and algorithms available in KNIME to build and evaluate predictive models. The Random Forest model is trained multiple times to achieve the highest Accuracy rate, which is 72.472%, with a fairly high level of agreement and a balanced trade-off between recall and precision. Additionally, this model successfully predicts that customers purchasing electronic equipment are potentially churning at a rate of 3.85%. Subsequently, customer strategy analysis for customer retention optimization in the E-commerce industry is conducted through data visualization using Tableau. Predictive analysis of customer behavior serves as a strong foundation for formulating effective retention strategies in the E-commerce industry. With this approach, Lazada can enhance customer experience and ensure sustainability in facing the increasingly fierce competition in the digital market.
Comparison of CNN Transfer Learning in Detecting Superior Local Fruit Types in Bali Purnama, Nyoman
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.389

Abstract

Bali Province is an island that has unique geographical conditions, as well as the diversity of fruit it has. The specialty of local fruit is not only of economic value for food needs but also for religious ceremonial needs. Bali provincial government is currently actively promoting local fruit so that it can be used as consumption for Bali's increasingly rapid tourism. Several superior fruits were developed as an effort to raise the potential of local fruit in the tourism sector. Some of the superior fruits are Balinese snake fruit and sapodilla. However, snake fruit is one of the superior local fruits in Bali which has not experienced degradation over time. This research aims to detect the types of snake fruit in Indonesia. This fruit is not popular compared to imported fruit. Therefore, an application is needed to recognize this type of snake fruit automatically. This research uses a deep learning method with the CNN (Convolutional Neural Network) algorithm. This algorithm is able to recognize and classify an image well. The fruit images used were 400 fruits for 4 types of snake fruit. Where the training data for snake fruit is special because it has different skin and fruit contents. In this research, 2 transfer learning models from the CNN algorithm were also compared, namely mobilenetv2 and ResNet152. Based on the test results, it was found that the best level of accuracy was obtained using the ResNet152 model with an accuracy value of 92% in identifying images of Balinese snake fruit.
A Hybrid Approach Using K-Means Clustering and the SAW Method for Evaluating and Determining the Priority of SMEs in Palembang City Terttiaavini, Terttiaavini
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.392

Abstract

The current efforts to develop Small and Medium Enterprises (SMEs) are still facing challenges in setting appropriate targets. Although the Palembang City Cooperative and SME Agency has launched various programs and initiatives to support SME development, they have not yet successfully identified the SMEs that should be given priority for development. This study aims to apply a hybrid approach that combines the K-Means Clustering method and Simple Additive Weighting (SAW) to evaluate and prioritize SME development in Palembang City. The K-Means Clustering method is used to group SMEs based on their characteristics, while SAW provides preference values ( ). The SME data was obtained from the Palembang City Cooperative and SME Agency, covering 362 SME units. The K-Means Clustering results yielded two clusters: Cluster 0 as the High Growth Cluster and Cluster 1 as the Stability and Improvement Cluster. Validation using cross-validation showed that this model achieved an accuracy of 99.72%. The SAW analysis on Cluster 0 indicated that the Kopi Kaljo SME received the highest priority with a preference value of 45.71. This study confirms that this hybrid approach is effective in grouping SMEs based on their characteristics and prioritizing them based on data-driven evaluation. The research results are expected to help the Palembang City Cooperative and SME Agency design more effective and targeted assistance programs to optimize the contribution of SMEs to local economic growth to the maximum extent.
Hand Sign Virtual Reality Data Processing Using Padding Technique Tju, Teja Endra Eng; Anggraini, Julaiha Probo; Shalih, Muhammad Umar
Intelligent System and Computation Vol 6 No 2 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i2.395

Abstract

This study focuses on addressing the challenges of processing hand sign data in Virtual Reality environments, particularly the variability in data length during gesture recording. To optimize machine learning models for gesture recognition, various padding techniques were implemented. The data was gathered using the Meta Quest 2 device, consisting of 1,000 samples representing 10 American Sign Language hand sign movements. The research applied different padding techniques, including pre- and post-zero padding as well as replication padding, to standardize sequence lengths. Long Short-Term Memory networks were utilized for modeling, with the data split into 80% for training and 20% for validation. An additional 100 unseen samples were used for testing. Among the techniques, pre-replication padding produced the best results in terms of accuracy, precision, recall, and F1 score on the test dataset. Both pre- and post-zero padding also demonstrated strong performance but were outperformed by replication padding. This study highlights the importance of padding techniques in optimizing the accuracy and generalizability of machine learning models for hand sign recognition in Virtual Reality. The findings offer valuable insights for developing more robust and efficient gesture recognition systems in interactive Virtual Reality environments, enhancing user experiences and system reliability. Future work could explore extending these techniques to other Virtual Reality interactions.
Identifying Types of Peanuts Diseases with Naive Bayes Method Buntoro, Ghulam Asrofi; Fahmi, Anas; Astuti, Indah Puji
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.390

Abstract

Peanut plants are susceptible to various constraints that significantly hinder their productivity, with eight prevalent diseases posing serious threats to their health. The peanut plant is one of the important commodities in Indonesia; peanuts play a strategic role in supporting the country's economy and food, where peanuts are a source of protein and a source of vegetable oil. Many farmers, especially those new to peanut cultivation, often lack the necessary knowledge to identify and manage these diseases effectively. To address this gap, this study developed an expert system that employs the Naive Bayes method to facilitate the identification of peanut plant diseases. This system aims to provide farmers with accessible and accurate information regarding symptoms, disease types, and management strategies. The knowledge base for the expert system was constructed from data gathered from peanut farming experts, ensuring the reliability of the information provided. Testing of the system revealed consistent results with manual calculations, particularly in identifying Sclerotium stem rot disease with a probability value of 0.44507. Additionally, the system successfully recognized leaf rust disease during its evaluation. By equipping farmers with a user-friendly tool for disease identification and management, this expert system seeks to enhance their understanding and response to peanut plant diseases, ultimately improving productivity and sustainability in peanut farming. The findings underscore the potential of integrating technology into agriculture to support farmers in overcoming challenges related to crop health.
Classification of Skin Diseases Using Transfer Learning with ResNet-50 Architecture and Data Preprocessing Using Real-ESRGAN and Wiener Filter Jiemesha, Micheila; Wonohadidjojo, Daniel Martomanggolo
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.399

Abstract

The skin is a vital organ which serves as a barrier against external factors, yet it’s highly susceptible to diseases. These diseases are often presented as lesions with similar appearances, making it difficult to be diagnosed and prone to human errors. To address this challenge, this study uses Deep Learning, particularly the ResNet-50 architecture using Transfer Learning, to classify skin diseases from lesion. In this study, data augmentation is implemented to increase dataset size, thus improving model performance and preventing overfitting. Data is then preprocessed using Real-ESRGAN to enhance resolution and the Wiener Filter to sharpen the features. Adam optimizer is used to further enhance the model’s performance. Hyperparameter tuning is also implemented to optimize the model parameters, and dropout regularization is applied to enhance the model's ability to be able to accurately classify unseen data. The model managed to achieve a high accuracy of 99.09%, with 0.96 precision, 0.95 recall, and 0.95 F1-score. These results demonstrate the effectiveness of combining Real-ESRGAN and Wiener Filter with the ResNet-50 architecture and the Adam optimizer in developing a robust model for skin disease classification. This approach offers a promising tool for healthcare professionals which may help reduce human error in dermatological diagnosis.

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