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Journal of Soft Computing Exploration
Published by shm publisher
ISSN : 27467686     EISSN : 27460991     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Soft Computing Exploration is a journal that publishes manuscripts of scientific research papers related to soft computing. The scope of research can be from the theory and scientific applications as well as the novelty of related knowledge insights. Soft Computing: Artificial Intelligence Applied Algebra Neuro Computing Fuzzy Logic Rough Sets Probabilistic Techniques Machine Learning Metaheuristics And Many Other Soft-Computing Approaches Area Of Applications: Data Mining Text Mining Pattern Recognition Image Processing Medical Science Mechanical Engineering Electronic And Electrical Engineering Supply Chain Management, Resource Management, Strategic Planning Scheduling Transportation Operational Research Robotics
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 146 Documents
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Design of ANFIS system to detect the condition of generator set model P22-6 based on Omron CJ1M PLC Rahmawati, Nanda Putri; Adhitya, Ryan Yudha; Widodo, Hendro Agus; Afianto, Afif Zuhri; Khumaidi, Agus; Budiawati, Ratna; Hardiyanti, Fitri; Santoso, Mochamad Yusuf
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.435

Abstract

The application of machine monitoring systems is currently increasingly needed, one of which is on generators. Generator sets are one of the important elements in providing energy needed in company operations. However, to ensure optimal performance and prevent unexpected engine damage, careful monitoring of the generator set's operational conditions is required, especially of key variables such as temperature, rotation speed, and engine vibration. The purpose of this study is to identify the condition of the generator set using three parameters. In this research, adaptive neuro fuzzy inference system (ANFIS) is used as a tool to model the relationship between inputs (temperature, speed, and vibration) and outputs (engine condition). The dataset for normal conditions amounted to 25 data and for abnormal conditions amounted to 25 data. From this data, an RMSE of 0.000032 was obtained in the 3-3-5 membership function structure with a trapezoidal type membership function. And at the stage of applying fuzzy to the Omron PLC, the RMSE is 0. Simulations are carried out to test the effectiveness of ANFIS in predicting machine conditions based on monitored parameters.
Adaptive deep learning based on FaceNet convolutional neural network for facial expression recognition Al-Ghiffary, Maulana Malik Ibrahim; Cahyo, Nur Ryan Dwi; Rachmawanto, Eko Hari; Irawan, Candra; Hendriyanto, Novi
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.450

Abstract

Facial recognition technology has become increasingly crucial in various applications, from personal identification, security, and human-care. Facial recognition has numerous practical applications, ranging from assessing mental health and well-being through facial expressions to evaluating customer satisfaction in service quality ratings. This study aims to develop a facial recognition model using a Convolutional Neural Network (CNN) with FaceNet architecture. The proposed method utilizes an advanced deep learning approach to generate high-quality facial embeddings, enhancing the model's ability to accurately identify and verify individuals. Our methodology includes training the CNN with FaceNet architecture, achieving an impressive average accuracy of 99.93%, with precision, recall, and F1-score all reaching 100%. The model demonstrated both high accuracy and efficiency, with an average training time of 13 minutes and 51 seconds. Future research should explore incorporating data augmentation, K-fold cross-validation, and additional transfer learning techniques to further enhance model performance and generalization. These advancements could lead to more resilient and flexible facial recognition systems capable of functioning effectively in diverse and challenging real-world conditions.
Real-time detection of indonesian sign language (ISL) gestures based on long short-term memory Sari, Christy Atika; Rachmawanto, Eko Hari; Saifullah, Zidan; Jatmoko, Cahaya; Sinaga, Daurat
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.452

Abstract

eaf people often encounter communication challenges, and sign language serves as a crucial tool for those who cannot speak. In Indonesia, Indonesian Sign Language (ISL) or Sistem Isyarat Bahasa Indonesia (SIBI) is officially recognized by the government and is taught in Special Schools (Sekolah Luar Biasa - SLB). The sign language dictionary comprises 3483 words, facilitating communication and participation in daily life for the deaf community. This research aims to convert ISL gestures within SIBI into understandable text, employing the Long-Short-Term Memory (LSTM) method as the primary approach. The study conducted experiments with two models: Model 1, using a smaller dataset, and Model 2, employing a larger dataset and implementing the k-fold method. The results indicate that Model 2 with k-fold accuracy achieved an accuracy of 98%, while Model 1 reached an accuracy of 85%. Nevertheless, challenges persist in these models, particularly in detecting words with similar gestures, such as’maaf’ (sorry) and 'cinta' (love), which may still be misidentified. Despite these challenges, this research contributes positively to the development of assistive technology for the deaf community, enabling more effective communication through sign language.
Sentiment based-emotion classification using bidirectional long short term-memory (Bi-LSTM) Utami, Putri; Ningsih, Maylinna Rahayu; Pertiwi, Dwika Ananda Agustina; Unjung, Jumanto
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.461

Abstract

Social media is now an important platform for sharing information, expressing opinions, and daily feelings or emotions. The expression of emotions such as anger, sadness, fear, happiness, disappointment, and so on social networks can be further analyzed either for business purposes or just analyzing the habits of a community or someone's posts.  However, analyzing manually will be a time-consuming process, and the use of conventional methods can affect the results of less accurate accuracy. This research aims to improve the accuracy of recognizing emotions in text by using the Bidirectional Long Short Term Memory (Bi-LSTM) method, which is a subset of RNNs that tend to be more stable during training and show better performance on various NLP and other processing tasks. The method used includes several stages, namely preprocessing, tokenization, sequence padding, and modeling. The results of this study show that the Bi-LSTM model is capable of predicting emotions in text with an accuracy of 94.45% because it excels in handling the temporal context and can avoid vanishing gradients.
Improving car price prediction performance using stacking ensemble learning based on ann and random forest Tanga, Yulizchia Malica Pinkan; Simanjuntak, Robert Panca R.; Rofik, Rofik; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.462

Abstract

Determining the right selling price for a car can be a challenge for car sales companies. The selling price of a car is highly influenced by car characteristics such as brand, type, year of production, fuel type, and mileage. Therefore, the research aims to develop a more accurate model of car price prediction model by using a stacking ensemble technique that combines Random Forest and ANN. Random Forest is effective in handling outliers and reducing the risk of overfitting, while ANN has the advantage of capturing complex nonlinear patterns. The results show that the stacking ensemble model combining ANN and Random Forest can predict car sales prices by achieving an R2 value of 0.97. The results of this study can help distributors in selling cars make the right decisions regarding the sales price of cars. To improve the generalization of the model, future research is recommended to try a combination of different ensemble methods and the use of larger and more diverse datasets.
Analysis of ODP point placement using algorithm K-means in RW. 01 Gendongan village (case study: PT. Indomedia) Setyadi, Candra; Chandra, Dian W.
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.473

Abstract

This study discusses the placement of ODP points for designing Fiber To The Home (FTTH) networks in RW. 01 Gendongan Village using the K-Means algorithm. The purpose of this study is to facilitate the determination of the optimal location of the Optical Distribution Point (ODP) without the need for manual determination by the network designer. The initial stage of the study began with five stages, namely Determining the location of the study, Conducting surveys and data collection, Determining the location of the ODP placement using K-Means, Network design, Finished. The K-Means algorithm is used to determine the best ODP placement point after the study was conducted. The results of this study are divided into two stages, namely determining the location of the ODP placement and creating an FTTH access network scheme using Google Earth Pro software. The results obtained from using the K-Means algorithm with a value of K = 8 need to be adjusted. ODP adjustments are made to ODPs located in houses or in the middle of the road which will later be shifted to the shoulder of the road. Distribution cable design is carried out at the location of the ODP point that has been adjusted. The design of this distribution cable has 4 paths, each path has 2 ODPs. Previous research has focused on residential areas with relatively small coverage, while the current research covers a wider area, namely RW. This significant difference shows a shift in focus from a small scale to a larger scale so that it can optimize the deployment of FTTH networks in wider areas and improve more services.
Gabor wavelet and multiclass support vector machine for braille image classification Agustina, Feri; Rachmawanto, Eko Hari; Putri, Ni Kadek Devi Adnyaswari; Saputro, Fakhri Rasyid; Lestiawan, Heru; Suprayogi, Suprayogi; Huda, Solichul
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.474

Abstract

Braille is a letter designed for the visually impaired. As a family with normal vision who have a visually impaired child find it difficult to Teach their child how to learn and understand the process of learning from home. Learning braille requires good finger sensitivity and memory to memorize each letterform, making it difficult to learn.  With this study, braille letters can be detected from the image using the Gabor Wavelet method to extract braille images and combined with the Multiclass Support Vector Machine (Multiclass SVM) algorithm as a classification method for extracted braille images. Data testing was performed using a confusion matrix to determine the level of precision, accuracy, and recall. According to the results of tests performed on 910 braille data using confusion matrix, the highest recognition accuracy was 98,02%. The accuracy of these results is impacted by the parameters of the training process, the training data, and the test data used. This research has the opportunity to be developed in voice-based card recognition to help the visually impaired in the future research.
Implementation of text summarization on indonesian scientific articles using textrank algorithm with TF-IDF web-based Sihombing, Jeremia Jordan; Arnita, Arnita; Al Idrus, Said Iskandar; Niska, Debi Yandra
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.475

Abstract

The development of information technology has significantly changed how information is accessed, necessitating readers to absorb content efficiently and make quick decisions. To address this challenge, this research developed a text summarization system specifically for Indonesian scientific articles using a web-based implementation of the TextRank and TF-IDF algorithms. TextRank was selected for its capability to identify key sentences without requiring training data, while TF-IDF was employed to weight words based on their frequency within the document. The dataset comprised 100 scientific articles in Indonesian from the Unimed Kode Journal, covering the years 2022-2024. The summarization process included several critical stages: text preprocessing, TF-IDF weighting, cosine similarity calculation, and sentence ranking. The resulting summaries were rigorously evaluated by language experts and website specialists using a Likert scale to assess both the quality of the summaries and the usability of the system. The findings demonstrated that the system effectively generated summaries that retained essential information from the original articles, with the highest accuracy observed at a 50% compression rate (88.533%). Additionally, the system achieved good performance at 40% compression (85.133%) and 30% compression (81.26%). The web-based system allows users to input article text and quickly obtain a summary, offering a practical tool for researchers and readers to efficiently comprehend academic content.
Implementation of integrated temperature, humidity, and dust monitoring system on building electrical panel Khumaidi, Agus; Hasin, Muhammad Khoirul; Pujiputra, Anggarjuna Puncak; Irsyad, Sholahuddin Muhammad; Rinanto, Noorman; Rachman, Isa; Budi, Perdinan Setia; Malik, Alfianto Taufiqul; Bayu, Nurissabiqoh Binta
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.483

Abstract

This research aims to develop and implement an electrical power monitoring system at the Sub Sub Distribution Panel (SSDP) in the Building. The system is designed to monitor power usage in real-time, provide accurate information on energy consumption, and detect potential energy waste. The methodology used includes hardware and software design. The hardware consists of current and voltage sensors connected to a microcontroller. The data collected by the sensors is then transmitted via Wi-Fi network to the server for analysis. The software uses an Internet of Things (IoT) platform that displays the data in the form of graphs and tables. The implementation shows that the system is capable of monitoring power usage with a high degree of accuracy. The sensors used, namely PM2100 for voltage, SHT20 for temperature and humidity, and GP2Y101AU0F for dust concentration, proved effective in generating accurate real-time data. Based on the test results, the voltage measurement error with the PM2100 was only 0.035%, while the current measurement resulted in an error of 0.48%. The SHT20 sensor recorded an error of 2.4% for temperature and 1.0% for humidity. Dust measurements with the GP2Y101AU0F sensor had a very small error of 0.02%. These results indicate that the tested device has a sufficient level of precision for electrical power and environmental monitoring applications.
Squeeze-and-Excitation networks and attention mechanism in automatic detection of coffee leaf diseases based on images Iqbal, Muhammad Izza; Avianto, Donny
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.490

Abstract

This research examines the effectiveness of Squeeze-and-Excitation Networks (SENet) combined with Attention Mechanism for automated detection of coffee leaf diseases. The integration of SENet and Attention Mechanism presents a promising technological opportunity as SENet has proven effective in improving CNN performance by modeling channel interdependencies, while Attention Mechanism enables focused feature extraction on crucial leaf areas - a combination that remains underexplored in coffee leaf disease detection. Using a combination of three datasets: Coffee Leaf Diseases, Disease and Pest in Coffee Leaves, and RoCoLe.Original, comprising 3,177 coffee leaf images divided into four classes (Healthy, Miner, Phoma, and Rust), this study compares the performance of SENet against other deep learning architectures such as InceptionV3, ResNet101V2, and MobileNet. Experiments were conducted with variations in epochs (15 and 30), three data split ratios, and three optimizer types. Results demonstrate that SENet with Attention mechanism performs, achieving a peak accuracy of 96% at 30 epochs with an 80:20 data ratio and RMSprop optimizer. InceptionV3 and MobileNet showed competitive performance with 93% accuracy, while ResNet101V2 achieved 81%. Class-wise analysis reveals SENet's proficiency in detecting various coffee leaf diseases, with F1-scores 91% for all classes.

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