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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : -
Articles 1,172 Documents
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Neural Machine Translation of Spanish-English Food Recipes Using LSTM Dedes, Khen; Putra Utama, Agung Bella; Wibawa, Aji Prasetya; Afandi, Arif Nur; Handayani, Anik Nur; Hernandez, Leonel
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.804

Abstract

Nowadays, food is one of the things that has been globalized, and everyone from different parts of the world has been able to cook food from other countries through existing online recipes. Based on that, this study developed a translation formula using a neural machine translation (NMT). NMT is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders. Our experiment led to novel insights and practical advice for building and extending NMT with the applied long short-term memory (LSTM) method to 47 bilingual food recipes between Spanish-English and English-Spanish. LSTM is one of the best machine learning methods for translating languages because it can retain memories for an extended period concurrently, grasp complicated connections between data, and provides highly useful information in deciding translation outcomes. The evaluation for this neural machine translation is to use BLEU. The comparing results show that the translation of recipes from Spanish-English has a better BLEU value of 0.998426 than English-Spanish with a data-sharing of 70%:30% during epoch 1000. Researchers can convert the country's popular cuisine recipes into another language for further research, allowing it to become more widely recognized abroad.
Intelligent Warehouse Picking Improvement Model for e-Logistics Warehouse Using Single Picker Routing Problem and Wave Picking Diah Damayanti, Dida; Novitasari, Nia; Bayu Setyawan, Erlangga; Suksessanno Muttaqin, Prafajar
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.1006

Abstract

Abstract— The development and use of technological innovations have changed people's behavior from an industrial society to an information society. It can be seen in the increase in people's consumption patterns from trading through physical stores (offline) to trading through electronic systems, often referred to as e-commerce. Logistics services are distribution actors in the downstream line which are tasked with delivering products from the fulfillment center from e-commerce to the end customer. The uncertainty of the number of requests is the biggest challenge for logistics service players. The growth of e-commerce has also led to an increase in sales volume in e-commerce which has given rise to a new generation of warehouses that are specifically tailored to the special needs of online retailers who directly serve the demands of end-customers in the business-to-consumer (B2C) segment. Traditional warehousing systems cannot handle orders with the characteristics of many transactions but smaller sizes. In addition, warehouses that handle e-commerce are also required to have a fast process in the warehouse because shipments must be made on the same day. In this study, the author aims to perform calculations to find the optimal order picking time in the warehouse, so orders in e-commerce can be processed faster by comparing the picking process time using ordinary Single Picker Routing Problem (SPRP) and combined with the concept of wave picking using Genetic Algorithm (GA). Based on a theoretical study in this paper, the combination between SPRP and wave picking can reduce 42.28% picking time. 
High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease Fajrianti, Evianita Dewi; Pratama, Afis Asryullah; Nasyir, Jamal Abdul; Rasyid, Alfandino; Winarno, Idris; Sukaridhoto, Sritrusta
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.793

Abstract

In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system.
Avoiding Overfitting dan Overlapping in Handling Class Imbalanced Using Hybrid Approach with Smoothed Bootstrap Resampling and Feature Selection Hartono, Hartono; Ongko, Erianto
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.985

Abstract

The dataset tends to have the possibility to experience imbalance as indicated by the presence of a class with a much larger number (majority) compared to other classes(minority). This condition results in the possibility of failing to obtain a minority class even though the accuracy obtained is high. In handling class imbalance, the problems of diversity and classifier performance must be considered. Hence, the Hybrid Approach method that combines the sampling method and classifier ensembles presents satisfactory results. The Hybrid Approach generally uses the oversampling method, which is prone to overfitting problems. The overfitting condition is indicated by high accuracy in the training data, but the testing data can show differences in accuracy. Therefore, in this study, Smoothed Bootstrap Resampling is the oversampling method used in the Hybrid Approach, which can prevent overfitting. However, it is not only the class imbalance that contributes to the decline in classifier performance. There are also overlapping issues that need to be considered. The approach that can be used to overcome overlapping is Feature Selection. Feature selection can reduce overlap by minimizing the overlap degree. This research combined the application of Feature Selection with Hybrid Approach Redefinition, which modifies the use of Smoothed Bootstrap Resampling in handling class imbalance in medical datasets. The preprocessing stage in the proposed method was carried out using Smoothed Bootstrap Resampling and Feature Selection. The Feature Selection method used is Feature Assessment by Sliding Thresholds (FAST). While the processing is done using Random Under Sampling and SMOTE. The overlapping measurement parameters use Augmented R-Value, and Classifier Performance uses the Balanced Error Rate, Precision, Recall, and F-Value parameters. The Balanced Error Rate states the combined error of the majority and minority classes in the 10-Fold Validation test, allowing each subset to become training data. The results showed that the proposed method provides better performance when compared to the comparison method
A Design and Application of Software Liberal Arts Course based on CT-CPS Model for Developing Creative Problem-Solving Ability and Learning Motivation of Non-software Majors Hee Jung Park; Yong Ju Jeon
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.996

Abstract

As the importance of computing education for nurturing computational thinking skills is emphasized in preparation for the era of the 4th industrial revolution, computing education for non-majors is also expanding in liberal arts education at universities. In this study, a software liberal arts course based on the CT-CPS model was designed and applied to non-software majored university students, and the effect on creative problem-solving ability and learning motivation were analyzed. The CT-CPS (computational thinking-based creative problem solving) model is an instructional model devised by fusing each element of computational thinking ability to the creative problem-solving stages. Creative problem-solving ability test paper and learning motivation test paper were used as test tools. Moreover, quantitative analysis through independent sample t-test and paired sample t-test and qualitative analysis through subjective responses were conducted. As a result of the study, it was verified that the software class applied with the CT-CPS model had a statistically significant effect on the creative problem-solving ability and learning motivation of non-software majors. In particular, compared to the control group, the experimental group showed significant changes in the motivational elements among the sub-factors of creative problem-solving ability and the self-efficacy factor among the sub-factors of learning motivation. In addition, it was confirmed through qualitative analysis that the software class to which the CT-CPS model was applied helped develop the problem-solving ability and learning motivation based on computational thinking through the process of discovering and solving problems on their own in real life.
An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning Hanafi, Hanafi; Muhammad, Alva Hendi; Verawati, Ike; Hardi, Richki
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.990

Abstract

In the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection
The Effect of Layer Batch Normalization and Droupout of CNN model Performance on Facial Expression Classification Norhikmah, -; Lutfhi, Afdhal; Rumini, -
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.921

Abstract

One of the implementations of face recognition is facial expression recognition in which a machine can recognize facial expression patterns from the observed data. This study used two models of convolutional neural network, model A and model B. The first model A was without batch normalization and dropout layers, while the second model B used batch normalization and dropout layers. It used an arrangement of 4 layer models with activation of ReLU and Softmax layers as well as 2 fully connected layers for 5 different classes of facial expressions of angry, happy, normal, sad, and shock faces. Research Metodology are 1). Data Analysis, 2). Preprocessing grayscaling, 3). Convolutional Neural Network (CNN), 4). Model validation Testing, Obtained an accuracy of 64.8% for training data and accuracy of 63.3% for validation data. The use of dropout layers and batch normalization could maintain the stability of both training data and validation data so that there was no overfitting. By dividing the batch size on the training data into 50% with 200 iterations, aiming to make the load on each training model lighter, by using the learning rate to be 0.001 which works to improve the weight value, thus making the training model work to be fast without crossing the minimum error limit. Accuracy results in the classification of ekp facial receipts from the distance of the camera to the face object about 30 cm in the room with the use of bright enough lighting by 78%.
University Examination Timetabling Using a Hybrid Black Hole Algorithm Cheng Weng Fong; Pui Huang Leong; Hishammuddin Asmuni; Yee Yong Pang; Hiew Moi Sim; Radziah Mohamad; Jun Kit Chaw
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1092

Abstract

University timetabling construction is a complicated task that is encountered by universities in the world. In this study, a hybrid approach has been developed to produce timetable solution for the university examination timetabling problem. Black Hole Algorithm (BHA), a population-based approach that mimics the black hole phenomenon has been introduced in the literature recently and successfully applied in addressing various optimization problems. Although its effectiveness has been proven, there still exists inefficiency regarding the exploitation ability where BHA is poor in fine tuning search region in reaching for good quality of solution. Hence, a hybrid framework for university examination timetabling problem that is based on BHA and Hill Climbing local search is proposed (hybrid BHA). The aim of this hybridization is to improve the exploitation ability of BHA in fine tuning the promising search regions and convergence speed of the search process. A real-world university examination benchmark dataset has been used to evaluate the performance of hybrid BHA. The computational results demonstrate that hybrid BHA capable of generating competitive results and recording best results for three instances, compared to the reference approaches and current best-known recorded in the literature. Other than that, findings from the Friedman tests show that the hybrid BHA ranked second and third in comparison with hybrid and meta-heuristic approaches (total of 27 approaches) reported in the literature, respectively.
A Visual-based Project Production Package for Design & Technology Subject, Based on Computational Thinking Skills Across-STEM Ismail, Rahimah; Zaman, Halimah Badioze; Mohammad, Ummul Hanan
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1029

Abstract

Computational Thinking (CT) is a concept introduced in the problem-solving process and is a systematic way of thinking not only in computer science but also in various other disciplines. Awareness to apply CT into education at different curricular levels has started from the beginning of schooling in various contexts and directions. Development of a Visual-based Project Production Package Model (KHP4) across –STEM, using computational thinking skills in the Reservoir Crop System (ST2) project production process for the Design and Technology (RBT) subject in primary schools,  aims to improve students' problem solving and thinking skills, to be more critical, creative and innovative, which includes the development of RBT learning model and modules. This life cycle model is adapted form the ADDIE modeL, which integrates the concept of 'prototyping' based on five (5) main phases, namely analysis, design, development, implementation, and evaluation. Assessment with aprropriate iteration. This development model is adapted based on the COMEL learning Model with the practice of interactive, fun, interesting and motivating learning for students with the addition of a new component and elements. Thus, this paper highlights the evaluation of the Visual-based Project Production Package Development (KHP4) model in Project Production which is able to improve thinkingand problem solving skills,  based on CT to prepare students towards 21st century learning and to instill sustainable development practices in students in facing Energy Transition that is experienced nationally and globally.
Dynamic Ransomware Detection for Windows Platform Using Machine Learning Classifiers Jaya, M. Izham; Razak, Mohd Faizal Ab.
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1093

Abstract

In this world of growing technological advancements, ransomware attacks are also on the rise. This threat often affects the finance of individuals, organizations, and financial sectors. In order to effectively detect and block these ransomware threats, the dynamic analysis strategy was proposed and carried out as the approach of this research. This paper aims to detect ransomware attacks with dynamic analysis and classify the attacks using various machine learning classifiers namely: Random Forest, Naïve Bayes, J48, Decision Table and Hoeffding Tree. The TON IoT Datasets from the University of New South Wales' (UNSW) were used to capture ransomware attack features on Windows 7. During the experiment, a testbed was configured with numerous virtual Windows 7 machines and a single attacker host to carry out the ransomware attack. A total of 77 classification features are selected based on the changes before and after the attack. Random Forest and J48 classifiers outperformed other classifiers with the highest accuracy results of 99.74%. The confusion matrix highlights that both Random Forest and J48 classifiers are able to accurately classify the ransomware attacks with the AUC value of 0.997 respectively.  Our experimental result also suggests that dynamic analysis with machine learning classifier is an effective solution to detect ransomware with the accuracy percentage exceeds 98%.

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