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Esther Irawati Setiawan
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insyst@istts.ac.id
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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 7 Documents
 1 
Search results for , issue "Vol 6 No 2 (2024): INSYST: Journal of Intelligent System and Computation" : 7 Documents clear
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.
Thesis Defense Scheduling Optimization Using Harris Hawk Optimization Setiono, Kevin; Setiawan, Mikhael; Dewi, Grace Levina; Dhaniswara, Erwin
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.v6i2.361

Abstract

This research discusses how the Harris Hawk Optimization (HHO) algorithm handles scheduling problems. The scheduling of thesis defenses at the Institut Sains dan Teknologi Terpadu Surabaya (ISTTS) is a complex issue because it involves the availability of lecturers, teaching/exam schedules, lecturer preferences, and limited room and time availability. The scheduling constraints in this research are divided into two categories: Hard Constraints and Soft Constraints. Hard constraints must not be violated, including each lecturer's unique availability, conflicts, and existing exam or teaching schedules. Soft constraints, on the other hand, include preferences for specific days or rooms for the defense. The complexity of scheduling due to these two types of constraints leads to longer scheduling times and an increased likelihood of human error. To automate and optimize this process, the author employs the HHO algorithm. HHO is inspired by the behavior of the Harris Hawk, known for its intelligence and ability to coordinate while hunting. The results of the HHO algorithm are translated into a slot meter, which helps to map the solution to available time slots. The HHO algorithm can generate schedules that comply with 90% of the hard constraints at ISTTS. Evolutionary algorithms typically have high complexity and computational time; in this case, the researcher experimented with multiprocessing. Multiprocessing improved the computational time by up to 39%.
Implementation of Hand Gesture Recognition as Smart Home Devices Controller Dewangga, Stanley; Subianto, Mochamad; Swastika, Windra
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.v6i2.372

Abstract

Some current virtual assistant products such as Alexa, Siri and Google Home facilitate features to control smart home devices through voice input, which has become increasingly popular in recent years. In addition to voice input, smart home devices can also be monitored and controlled through smartphones or computers using applications that provide users with flexibility. However, both control systems are less efficient, as they consume time and voice input utilization may sometimes not be recognized in crowded conditions. Therefore, this research introduces an application to recognize real-time hand gestures and utilize them for a new control system that provides time and energy efficiency. This application processes images using the Mediapipe framework, generating hand landmark outputs. These landmark outputs are utilized to determine the combination of raised or lowered fingers, which is then used to control smart home devices. The application is developed with ESP32 and ESP01s modules as data receivers from gesture recognition, and ESP32-CAM for image acquisition. Meanwhile, the gesture recognition computation process is executed on a Raspberry Pi 3 Model B. The gesture recognition application achieves good accuracy at 88%, but may experience occasional failures for certain gestures. However, the response time generated by the smart home control system is still relatively long, averaging 7.88 seconds.
Deep Learning Models Comparison for Emotion Classification With Image Pre-Processing Methods Anthony, Bryan; Lienardi, Nicholas; Sutanto, Richard; Dinata, Yuwono
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.v6i2.398

Abstract

This research investigates advancements in Facial Expression Recognition (FER) within the domain of affective computing, focusing on improving the accuracy and robustness of FER systems under diverse, real-world conditions. Facial expressions serve as critical non-verbal cues in human communication, yet existing FER systems often face challenges due to environmental variability such as changes in lighting, pose, and occlusions. This study evaluates the performance of three Convolutional Neural Network (CNN) architectures—ResNet50, VGG16, and MobileNetV3Large—integrated with preprocessing techniques like Contrast Limited Adaptive Histogram Equalization (CLAHE) and the Synthetic Minority Oversampling Technique (SMOTE). These methods address key challenges such as class imbalance and low contrast in datasets. Results demonstrate the pivotal role of tailored preprocessing strategies. For instance, the application of CLAHE and SMOTE improved the VGG16 model's test accuracy from 0.70 to 0.79, representing a 0.09 or 9% increase. This significant improvement underscores the effectiveness of combining advanced preprocessing methods with CNN architectures. Furthermore, the findings highlight the advantages of optimizing preprocessing to enhance the recognition of subtle emotions in uncontrolled settings, offering practical insights for deploying FER systems in real-time applications. Overall, this research demonstrates the potential of preprocessing techniques to enhance FER system performance significantly, particularly when paired with well-established deep learning models. These insights pave the way for the development of more accurate, robust, and adaptable FER systems capable of functioning reliably in dynamic, real-world environments.
Implementation of DenseNet Architecture With Transfer Learning to Classify Mango Leaf Diseases Likorawung, Marsha Alexis; Wonohadidjojo, Daniel Martomanggolo
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.v6i2.401

Abstract

Mango plants (Mangifera indica) are a significant export commodity in the horticultural industry, offering numerous nutritional and economic benefits. They are rich in essential micronutrients, vitamins, and phytochemicals, contributing to their high demand globally. However, mango plants are susceptible to various diseases that can severely impact their yield and quality. These diseases pose a challenge to mango farmers, many of whom struggle to identify and treat them effectively, leading to potential harvest failures. This study aims to address this challenge by implementing a Deep Learning approach to classify diseases in mango leaves. Specifically, the research utilizes a Convolutional Neural Network (CNN) with DenseNet architecture, known for its efficiency in image classification tasks. The study incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing to enhance detail and improve the model’s performance. Transfer Learning is utilized to optimize the DenseNet model, leveraging a pre-trained model to achieve high accuracy even with a relatively small dataset. The dataset used in this research comprises 4000 labeled images of mango leaves, covering seven disease categories and healthy leaves. These images include common diseases such as Anthracnose, Dieback, Powdery Mildew, Red Rust, Cutting Weevil, Bacterial Canker, and Sooty Mould. The DenseNet model achieved an overall accuracy of 99.5% in classifying mango leaf diseases.
A Cascading Evaluation of Digital Population Identity in Palembang: Insights from ILPE and IPA Fadly, Farid; Kholik, Abdul; Alie, Muhammad F.; Heryati, Agustina; Terttiaavini, Terttiaavini; Antoni, Darius
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.v6i2.406

Abstract

Since 2022, the Indonesian government has implemented the Digital Population Identity (IKD) application, introduced by the Palembang City Population and Civil Registration Office (Disdukcapil). However, user satisfaction with IKD remains low. This study evaluates IKD user satisfaction using a cascading method combining the Electronic Public Service Index (ILPE) and Importance Performance Analysis (IPA). The ILPE calculation yields a total score of 2.682. The Information Availability (I) dimension scores highest at 0.570, reflecting strong user satisfaction with data accuracy and relevance. In contrast, the Interaction (SI) dimension scores the lowest at 0.325, highlighting the need for better communication and interaction. The IPA analysis categorizes dimensions into quadrants: Quadrant 1 (Keep Up the Good Work) includes T1 (Password Security), T4 (Reputation Recognition), T5 (Clarity of Authentication Criteria), I1 (Data Accuracy), I2 (Timely Updates), R2 (Access Availability), and R3 (Response Speed), showing excellent performance. Quadrant 2 (Concentrate Here) includes E4 (Accuracy of Data Entry Instructions) and U3 (Intuitive Navigation), requiring significant improvement. Quadrant 3 (Low Priority) includes E1 (Intuitive Navigation), E3 (Personalized Experience), T2 (Authentication Clarity), U1 (Intuitive Interface), U2 (Instruction Clarity), SI1 (Social Interaction), and SI2 (Communication Ease), with lower improvement priorities. Quadrant 4 (Possibly Overrated) contains R1 (Form Download Speed), which may be overemphasized. These findings aim to guide policy refinement, enhance public service efficiency, and improve user satisfaction.
Comparison of Premium Rice Price Prediction in East Java with ARIMA and LSTM (Case Study: National Food Agency Data) Purwanto, Devi Dwi; Sitepu, Rasional; Honggara, Eric Sugiharto
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.v6i2.407

Abstract

Rice price prediction plays a crucial role in maintaining economic stability and food security, especially in East Java, one of Indonesia's major rice production centers. This study aims to forecast premium rice prices in East Java using the ARIMA (AutoRegressive Integrated Moving Average) method. The data utilized in this research comprises premium rice prices obtained from the National Food Agency over the period from March 15, 2021, to October 17, 2024. The analysis process begins with data exploration to identify trends and seasonal patterns in the rice price data. Subsequently, the data is analyzed using ARIMA and LSTM methods, both recognized for their effectiveness in time-series forecasting. The ARIMA(1,1,1) model was selected due to its capability to capture price dynamics through its autoregressive, integrated, and moving average components, making it well-suited for linear data with minimal seasonal variation. LSTM was employed as a comparative model because it is a subset of Machine Learning that integrates computational models and neural network algorithms, offering potential improvements in prediction accuracy. The LSTM model used for prediction consists of four layers, each with 50 neurons, dropout rates of 20% and 30%, and a single output layer representing the predicted price. The results indicate the ARIMA model provides highly accurate price estimates with a Mean Absolute Percentage Error (MAPE) of 0.485%, whereas the LSTM model achieves a MAPE of 1.95%. These findings serve as a reference for policymakers and food industry stakeholders in formulating strategic measures to stabilize rice prices in East Java.

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