JOIV : International Journal on Informatics Visualization
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Exploring The Determinants of Supply Chain Social Sustainability Disclosure for Indonesian Banking
Get PDFSupply chain transformation that focuses on sustainability encourages companies not only for business growth but also to improve the welfare of the people along the supply chain. The company considers business decisions to reduce environmental impacts arising from the supply chain and positively impact society. Banking has a close relationship with technological developments that strongly support the banking business processes in producing customer services. This study aimed to identify and analyze the factors that influence a banking sector's disclosure of a sustainability report. This quantitative research uses data processing with multiple regression analysis by using SPSS. The research object is a company engaged in the financial services sector listed on the Indonesia Stock Exchange in 2017-2019 with a purposive sampling method. This study indicates that only foreign ownership has influenced sustainability report disclosure. In contrast, profitability, leverage, the board of directors, and the audit committee have no effect. Characteristics and culture of foreign parties who are very concerned about the future risk of the business activities believed able to encourage Indonesian Banking to make changes to business processes and tend to pay attention to the resulting business impact and weigh the business risks that arise in the future. This will lead to sustainable changes in the company and the stakeholders in the utilization of resources, processes, and business outputs generated. Banking in Indonesia should transform into a supply chain strategy concerned with the quality of disclosure of the company's business sustainability on social, environmental, and economic aspect
Design of Livestream Video System and Classification of Rice Disease
Get PDFOne of the agricultural products which is an important aspect of the life of Indonesian people is rice. Rice disease has a devastating effect on rice production, while detecting rice diseases in real-time is still difficult. Therefore, this study designed a Livestream video system that is equipped with a rice disease Classification system. The Livestream system utilizes 4G network communication and is assisted by the WebSocket protocol to communicate in real-time and for the rice disease Classification system using YOLO algorithm. In addition, Livestream uses the raspberry pi camera V2 to take video stream data. In analyzing the performance of the Livestream system, four tests were carried out, namely: functionality test, connectivity test, classification performance test, and implementation performance test. The test was carried out using the wireshark and conky tools, while the classification training used 5447 images from the Huy Minh do dataset that he provided on the Kaggle website. The results show that all programs run well and get a good QoS value according to the index of the parameter results, it is also found that sending non-base64 can reduce the size of the data to approximately 200,000 bytes/s and the performance of the classification system is good because it has an average accuracy of 80% even though it is quite burdening the raspberry pi. This system can still be optimized and developed further to support research in the field of data transmission and the performance of machine learning in a microcontroller
Land Suitability for Mustard Plants Using Multi-Objective Optimization by Ratio Analysis Method
Get PDFSawi dapat dikembangkan atau dikembangkan dari sudut pandang finansial dan bisnis untuk memenuhi permintaan pembeli dan menangkap peluang pasar yang signifikan. Sawi merupakan tanaman hortikultura yang mempunyai daya adaptasi tinggi dan waktu panen yang relatif singkat. Sawi ini menawarkan banyak keuntungan bagi petani. Misalnya saja banyak petani yang menanam sawi di Samarinda, Kalimantan Timur, Indonesia. Meskipun sangat mudah beradaptasi, beberapa spesies sawi tidak tumbuh subur di tanah tertentu. Tanah yang baik sangat penting untuk hasil optimal saat menanam sawi. Sawi yang ditanam dapat diseleksi dengan menggunakan pendukung keputusan berdasarkan kriteria lahan untuk mendapatkan hasil terbaik. Tujuan dari penelitian ini adalah untuk merekomendasikan tanaman sawi yang cocok berdasarkan kebutuhan luas dengan menggunakan pendekatan multi-objective optimize by ratio analysis (MOORA). MOORA merupakan suatu metode pengambilan keputusan yang membantu dalam memilih alternatif terbaik dari beberapa pilihan atau alternatif berdasarkan beberapa kriteria atau tujuan. Pengamatan ini menggunakan lima kriteria yaitu jenis tanah, pH tanah, curah hujan, suhu, ketinggian lokasi, dan enam alternatif sawi. Berdasarkan uji lahan, sawi yang direkomendasikan metode MOORA adalah Sawi Sendok atau Pak Choy dengan nilai Yi sebesar 7,6698. Jadi yang dipilih sebagai sawi yang ditanam di lahan tersebut adalah Sawi Sendok atau Pak Choy. Untuk penelitian selanjutnya perlu dilakukan penambahan atau penyesuaian kriteria dan sensor baru secara real-time yang dapat diterapkan untuk meningkatkan efisiensi sawi menuju smart farming yang fokus pada hasil yang lebih baik dengan tetap menjaga keseimbangan alam
Transformer in mRNA Degradation Prediction
Get PDFThe unstable properties and the advantages of the mRNA vaccine have encouraged many experts worldwide in tackling the degradation problem. Machine learning models have been highly implemented in bioinformatics and the healthcare fieldstone insights from biological data. Thus, machine learning plays an important role in predicting the degradation rate of mRNA vaccine candidates. Stanford University has held an OpenVaccine Challenge competition on Kaggle to gather top solutions in solving the mentioned problems, and a multi-column root means square error (MCRMSE) has been used as a main performance metric. The Nucleic Transformer has been proposed by different researchers as a deep learning solution that is able to utilize a self-attention mechanism and Convolutional Neural Network (CNN). Hence, this paper would like to enhance the existing Nucleic Transformer performance by utilizing the AdaBelief or RangerAdaBelief optimizer with a proposed decoder that consists of a normalization layer between two linear layers. Based on the experimental result, the performance of the enhanced Nucleic Transformer outperforms the existing solution. In this study, the AdaBelief optimizer performs better than the RangerAdaBelief optimizer, even though it possesses Ranger’s advantages. The advantages of the proposed decoder can only be shown when there is limited data. When the data is sufficient, the performance might be similar but still better than the linear decoder if and only if the AdaBelief optimizer is used. As a result, the combination of the AdaBelief optimizer with the proposed decoder performs the best with 2.79% and 1.38% performance boost in public and private MCRMSE, respectively
An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling
Get PDFMaritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved
Small Scale Aerial Monitoring for Human Body Temperature Measurement Using Rotary Wing Drone
Get PDFIn Indonesia, the COVID-19 pandemic has had an impact on a variety of sectors. Using all available technology for disaster mitigation is critical for pandemic prevention and control. Recent studies have uncovered the advantage of Unmanned Aerial Vehicle (UAV) or drones, particularly those with rotary wings, in dealing with the pandemic. Much effort has been devoted to developing a rotary-wing drone system as a flying platform for aerial monitoring. However, several factors must be considered when visually observing a specific region, i.e., the area's size, topographic contours, locations of special interest inside the area, approach points to the area, and the observation timeframe. Since fever is a common symptom of COVID-19, human body temperature monitoring is highlighted for fever screening, with the objective of minimizing people with high body temperatures going to the crowd. A major challenge is creating a system that can provide accurate body temperature data, which is critical for fighting the pandemic. The purpose of this paper is to present a rotary wing drone application for aerial human body temperature measurements. The paper also proposed an alternative solution based on using a portable, low-cost, Forward-looking Infrared (FLIR) thermal imaging camera. The FLIR thermal camera is incorporated into the drone's electronic system. Furthermore, thermal image data are transmitted into the ground station via a radio telemetry transceiver to allow flexible surveillance by the operator. Indoor and outdoor experiments reveal that the system has been effectively installed and provide data collection for further research. The results show that the system can be used for small-scale area aerial monitoring
Feature Selection Technique to Improve the Instances Classification Framework Performance for Quran Ontology
Get PDFThe Al-Quran is the sacred book of Muslims, and it provides God's word in the form of orders, instructions, and guidelines for people to follow to have happy lives both here and in the afterlife. Several earlier research has used ontologies to store the knowledge found in the Quran. The previous study focused on extracting the relationship between classes and instances or the "is-a relation" by classifying instances based on the referenced class. Based on the performance testing of the instances classification framework, the test results show that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation had dropped the accuracy value to 65.41% when the test data size was increased to 30%. Likewise, with BPNN with TF-IDF and stemming operations. In the Indonesian Quran translation dataset with a test data size of 30%, the accuracy value drops to 57.86%. Instances classification based on the thematic topics of the Qur'an aims to connect verses (instances) to topics (classes) to get an overall picture of the topic and provide a better understanding to users. This study aims to apply the feature selection technique to the instances classification framework for the Al-Quran ontology and to analyze the impact of applying the feature selection technique to the framework with a small dataset and training data. The instances classification framework in this study consists of several stages: text-preprocessing, feature extraction, feature selection, and instances classification. We applied Chiq-Square as a technique to perform feature selection. SVM and BPNN as a classifier. Based on the experiment results, it can be concluded that the feature selection implementation using Chi-Square increases the value of precision, f-measure, and accuracy on the test data size from 40% to 60% in all datasets. The feature selection using Chi-Square and SVM classifier provides the highest precision value with a test data size of 60% on the Tafsir Quran dataset from the Ministry of Religious Affairs Indonesia: 64.36%. Furthermore, the feature selection implementation and BPNN classifier also increase the highest accuracy value with a test data size of 60% in the Quranic Tafsir dataset from the Ministry of Religion of the Republic of Indonesia: 63.09%
Managing Information Technology Risks to Achieve Business Goals: A Case of Pharmaceutical Company
Get PDFExtant literature has shown that sectoral characteristics play a critical role in business value creation through information technology (IT). Therefore, managing IT and its associated risks needs to consider specific industrial traits to understand the distinct business nature and regulations that shape IT-enabled business value creation. This study presents an in-depth analysis of business goals, IT processes, and IT risks in the case of a pharmaceutical company through which appropriate controls are designed to ensure business value creation through IT. Drawing on a case study of a pharmaceutical company in Indonesia, we found that managing IT risks in the pharmaceutical industry entails two main objectives: 1) ensuring compliance with external laws and regulations as well as internal policies, 2) supporting the optimization of business functions, processes, and costs. Throughout one year of engagement during the project, this study identified ten risks associated with the operation of business processes. Risks are dominated by moderate levels given the current state of controls and appetite, most of which emerge from the company’s existing internal processes. Internal actors are involved in all risks, with most events occurring due to laws and regulations. Further, the study designs and elaborates IT risk controls by drawing from COBIT 5 Seven Enablers. Overall, IT risk management through cascading processes of analysis ensures the alignment of IT risk controls with achieving business goals in the pharmaceutical industry
Measurement on University Websites: A Perspective of Effectiveness
Get PDFThis paper highlights the importance of evaluating the performance of university websites and how this can affect the reputation of universities. Different quality evaluation models are analyzed and emphasized in the context of efficiency and how factors such as response time, processing capacity, efficient use of resources, scalability, data transfer rate, concurrency capacity, and fault tolerance can positively or negatively affect websites. In addition, the importance of applying specific techniques to increase efficiency in loading speed is pointed out, such as image optimization, responsiveness on desktop and mobile devices, and content caching, among others, which allow to improve the website's efficiency. To conduct this process, a case study was applied where the university websites were selected, efficiency metrics were defined, and the data provided by the performance measurement tools that provide metrics and quantitative data for the evaluation were collected and analyzed. from the website. The results of the study revealed that there is room for improvement in page load time and page size optimization. In addition, the need to upgrade the performance of mobile devices was identified, given the increasing use of smartphones and tablets to access websites. As a final recommendation, it is advised to implement a comprehensive strategy to improve website performance. This strategy should include optimization of page load time and page size as well as user experience considerations. By achieving optimal performance, universities can offer their users a more satisfying online experience, thus strengthening their reputation and their ability to attract new users
TPPSO: A Novel Two-Phase Particle Swarm Optimization
Get PDFParticle swarm optimization (PSO) is a stout and rapid searching algorithm that has been used in various applications. Nevertheless, its major drawback is the stagnation problem that arises in the later phases of the search process. To solve this problem, a proper balance between investigation and manipulation throughout the search process should be maintained. This article proposes a new PSO variant named two-phases PSO (TPPSO). The concept of TPPSO is to split the search process into two phases. The first phase performs the original PSO operations with linearly decreasing inertia weight, and its objective is to focus on exploration. The second phase focuses on exploitation by generating two random positions in each iteration that are close to the global best position. The two generated positions are compared with the global best position sequentially. If a generated position performs better than the global best position, then it replaces the global best position. To prove the effectiveness of the proposed algorithm, sixteen popular unimodal, multimodal, shifted, and rotated benchmarking functions have been used to compare its performance with other existing well-known PSO variants and non-PSO algorithms. Simulation results show that TPPSO outperforms the other modified and hybrid PSO variants regarding solution quality, convergence speed, and robustness. The convergence speed of TPPSO is extremely fast, making it a suitable optimizer for real-world optimization problems