System Development Process Research Articles

Rancang Bangun Sistem Informasi Keuangan Sekolah di Pusdiklatkom Tanjungsari Berbasis Desktop

Based on observations, that at the Tanjungsari Pusdiklatkom the process of processing financial data such as tuition payments, payment of education fees, exam payments, and recaps of money expenditures still uses a manual system. The design method used is the SDLC (System Development Life Cycle) method. Starting from the system development process to the system is applied with the Waterfall model which consists of several basic work stages, : Requirement, Analysis, Design, Coding, Testing and Maintenance. The results showed that the design of a desktop-based school financial information system at Pusdiklatkom Tanjungsari made it easier for Pusdiklatkom Tanjungsari Desktop-based makes it easier for Pusdiklatkom Tanjungsari to process its financial data.

Analisis Data Perencanaan untuk Sistem Pengolahan Sampah Kampus Universitas Andalas Limau Manis Padang

The waste processing performance at the Integrated Waste Processing Center of Andalas University has experienced a decline; therefore, an evaluation and development of the waste processing system are needed. This research aims to analyze planning data for the waste processing system, including waste generation analysis, composition, recycling potential, and waste characteristics. Waste samples were taken from 48 campus facilities representing office activities, academic activities, student activities, and other activities. Waste generation, composition, and recycling potential analyses were conducted in the field using gravimetric methods, while characteristic analyses were performed in the laboratory. From the research results, planning data for the campus waste generation unit was obtained at 0.0592 kg/person/hour or 0.00163 kg/m²/hour, and in volume units, it was 0.5245 liters/person/hour or 0.02945 liters/m²/hour. The waste composition consisted of 49.61% easily decomposable waste, 38.22% recyclable waste, 1% hazardous waste (B3), and 16.21% residual waste. The potentially recyclable waste includes food waste, yard/wood waste, plastic, paper, glass, and metal, with an average potential of 89%. Waste characteristics include a specific gravity of 0.058, moisture content of 22.18%, volatile matter content of 59.95%, fixed carbon content of 12.09%, ash content of 5.79%, C/N ratio of 30.28, biodegradability of 54.06%, and a fly population of 2 flies/m². Campus waste has the potential to be processed through composting, Black Soldier Fly (BSF) bioconversion and biodrying for easily decomposable waste, and it can be sold to the informal sector for recycling.

A Three-State Space Modeling Method for Aircraft System Reliability Design

Reliability is an inherent attribute of a system through optimal system design. However, during the aircraft system development process, the reliability evaluation and system function design efforts are often disconnected, leading to a divide between reliability experts and system designers in their work schedule. This disconnect results in an inefficient aircraft system reliability optimization process, known as the “two-skin” phenomenon. To address this issue, a three-state space model is proposed. Firstly, an analysis was conducted on the relationship between the system function architecture developed by the system designers and the reliability evaluation performed by the reliability experts. Secondly, based on the principle of function flow, the state of failure was categorized into “physical failure” and “non-physical failure”. Additionally, a new state of “function loss” was introduced as the third state for the system, in addition to the traditional states of “normal” and “faulty”. Thirdly, through the state of “Function loss”, an effective integration of system fault modes and function modes was achieved, leading to an optimized system reliability model. A three-state space modeling method was then developed by transforming the system function architecture into a system reliability model. Finally, this new model was applied to an aircraft’s rudder and fly-by-wire control system. The results demonstrate that the function architecture at the design stage of the system can be accurately transformed into the new three-state space model. The structure aligns closely with the function architecture and can be effectively utilized in quantitative system reliability calculations. In this way, the process of ensuring system reliability can be seamlessly integrated into the system optimization design process. This integration alleviates the issue of disjointed work between reliability experts and system designers, leading to a more streamlined and efficient aircraft system optimization process.

A Layered Architecture and Taxonomy for Blockchain-empowered Reputation-based Reward Systems

Blockchain based rating and review systems have changed the operational structure of the traditional market by introducing characteristics like immutability, security, anonymity etc. to liberate users from potential malicious acts of sellers such as altering and hiding ratings or reviews, collusion with users or service providers. The lack of standardization for developing decentralized applications does not depict flow of information and cataloguing of specific functions and roles for a particular set of tasks. The development of decentralized applications for e-commerce systems is in its immature age of progress and has lack of interoperable sharing of data and workflows for new innate systems. Thus, it is significant to catalogue blockchain-based rating and review systems by identifying key parameters to generate a taxonomy and develop a conceptual layered framework for identifying core components and their interaction. This manuscript presents a substantial analysis of existing blockchain-empowered reputation-based reward systems. It uses an iterative approach following observed to rational and rational to observed for taxonomy development. The analysis results identify 11 key parameters for categorizing systems and propose a 4 layered architecture to signify IPFS, P2P network, Blockchain and DApps. The proposed model identifies underlying subsystems, their services, and their interaction. The new taxonomy identifies natural roadmaps in system development process. This study is key because it allows developers to design new reputation-based reward framework in different dimensions by following an open workflow with a common understanding of underlying core entities.

Single-Cell Sequencing Technology and Its Application in the Study of Central Nervous System Diseases.

The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.

Rancang Bangun Sistem Financial Accounting Standars Berbasis Website Pada CV. Hartop Jaya

Abstract
 The financial accounting standard system is the system used to manage financial data at CV Hatop Jaya. In today's digital era, the use of web technology and scrum methods can be used to develop a more efficient and accurate financial accounting standard system. This study aims to design and develop a web-based financial accounting standard system using the Scrum method. The Scrum method is used throughout the system development process, from requirements analysis to testing. By using the Scrum method, this research enables the development of a more structured and adaptive web-based financial accounting standard system. Researchers can leverage the principles of Scrum to ensure needs are met efficiently and set the right priorities. This research is expected to contribute to the development of a more efficient financial accounting standard system. The Scrum method still provides a framework that is focused, organized, and responsive to change, thereby helping researchers manage financial data and create financial reports more effectively within the scope of work performed. The system developed in this study can be used by CV Hartop Jaya to manage their financial data and make financial reports easily and quickly. In addition, this research can also be a reference for further research in developing a web-based financial accounting standards system.programming language and uses the MySql Database because it is more accessible to users.
 Keywords: financial, accounting, Scrum

Use of Transcriptomics to Reveal the Joint Immunotoxicity Mechanism Initiated by Difenoconazole and Chlorothalonil in the Human Jurkat T-Cell Line.

It is very important to evaluate the immunotoxicity and molecular mechanisms of pesticides. In this study, difenoconazole and chlorothalonil were evaluated for immunotoxicity by using the human Jurkat T-cell line, and the EC50 were 24.66 and 1.17 mg/L, respectively. The joint exposure of difenoconazole and chlorothalonil showed a synergistic effect at low concentrations (lower than 10.58 mg/L) but an antagonistic effect at high concentrations (higher than 10.58 mg/L). With joint exposure at a concentration of EC10, the proportion of late apoptotic cells was 2.26- and 2.91-fold higher than that with exposure to difenoconazole or chlorothalonil alone, respectively. A transcriptomics analysis indicated that the DEGs for single exposure are associated with immunodeficiency disease. Single exposure to chlorothalonil was mainly involved in cation transportation, extracellular matrix organization, and leukocyte cell adhesion. Single exposure to difenoconazole was mainly involved in nervous system development, muscle contraction, and immune system processes. However, when the joint exposure dose was EC10, the DEGs were mainly involved in the formation of cell structures, but the DEGs were mainly involved in cellular processes and metabolism when the joint exposure dose was EC25. The results indicated that the immunotoxicological mechanisms underlying joint exposure to difenoconazole and chlorothalonil are different under low and high doses.

Spiral model development of retrofitted robot for tele-operation of conventional hydraulic excavator

This paper describes the effect of applying spiral model to the development process of robot system for a new entrant company. The robot system was developed to remotely control a conventional hydraulic excavator in order to improve the safety of operators in disaster emergency restoration. The issues of development are the definition of requirements and integration for a practical system in a real environment by a new entrant company. The constraints to the new entry of smaller companies are the following three points. (1) Lack of industry knowledge and data to define requirements (2) Lack of on-site environment and machinery for investigation and testing (3) Lack of experience in robot development To solve the problems under these constraints, the spiral model divides the development based on the prototype into 4-steps, and repeats this series of processes. This method was applied to clarify the necessary functions and performance of the robot step by step, and to construct a system with robustness in a real environment. As a result, this robot system has been successfully utilized in emergency disaster recovery tasks due to landslides, and removing debris in the Fukushima Daiichi Nuclear Power Plant, reducing the mental and physical burden on the operators.

Enhancing Support For Senior Citizens: Development And Evaluation of The OSCA Information Management System With Agile Methodology and ISO/IEC 25010 Compliance

The elderly were a member of the vulnerable group which the government supported and assisted in every way possible. To contribute to the support and assistance needed by the elderly who are called senior citizens, this study was conducted. This aims to develop and design a usable, performance-efficient, and functional suitable to the operation and service of the Office of the Senior Citizen Affairs (OSCA), Cabanatuan City office. The System Application Office of the Senior Citizen Affairs Information Management System with Analytics (OSCA-IMSA) was developed using the Agile System Development Life Cycle (SDLC) Model. The agile model has different phases and sub-phases that guarantee the quality and efficient development of the system. Every phase and sub-phases lead to a well-organized, and systematic process of system development. The proponents adopted the ISO/IEC 25010 criteria as an evaluation tool to assess the system's usability, reliability, performance efficiency, functional suitability, security, portability, maintainability, and compatibility. The self-made survey questionnaire was used as the main tool to collect the data from the respondents. Purposive sampling was used to determine the right respondents for the study. The study was composed of two different sets of respondents, the first set was the System Users and the second set was composed of IT Experts. The Office of the Senior Citizen Affairs Information Management System with Analytics (OSCA-IMSA) was evaluated and assessed by the respondents with a result of being highly functional, highly efficient, highly portable, highly maintainable, highly compatible, highly secured, highly reliable, and highly usable. This result indicates that the system passed and conformed to the ISO/IEC 25010 Software Product Quality Standard, hence, is recommended to be deployed at the research locale.

Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system.

The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks' data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system's development and validation processes. Rigorous validation confirms the system's accuracy, performance, and security through an experimental testbed. The results demonstrate the system's dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system's robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.

Development and validation of a natural language processing system that extracts cognitive test results from clinical notes

AbstractBackgroundCognitive test results from electronic health records (EHRs) are key information for assessing the severity and progression of patients with mild cognitive impairment (MCI) and Alzheimer’s’ disease (AD). However, such information is often recorded in unstructured clinical notes rather than in an administrative database. We developed and validated a natural language processing (NLP) system to extract cognitive test results from clinical notes in the Veterans Affair (VA) Healthcare System.MethodAn NLP system was developed using regular expression‐based rules and Python to extract results for six tests that have been used most frequently in VA: Mini‐Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA), Saint Louis University Mental Status Examination (SLUMS), Mini‐cog, Boston Naming Test (BNT), and Benton Visual Retention Test (BVRT). The system extracted test results from each note in two steps: (1) searched a test name, or variations and abbreviation of the test name and, if successful, (2) searched the quantitative results (e.g., 12/30 for MMSE, 74th percentile for BNT) and/or descriptive results (e.g., “borderline impairment” for BVRT) within 5 words or one sentence before or after the test name. To balance the system performance and speed, we developed 3‐8 extraction rules per test based on a manual review of 30‐50 notes for each test. We further validated NLP performance on 6 held‐out datasets (50‐200 notes/test). We automatically sampled the development and held‐out notes by searching the test name and its variations/abbreviation to increase positive cases, i.e., notes that contained the above test results.ResultThe NLP system achieved 0.72‐0.92 predictive positive values (PPV), 0.96‐1.00 recall, and 0.83‐0.95 F1 scores on the validation test sets (Table 1). In addition, it demonstrated a scalable performance (processed 200,000 notes in 7 min), allowing an extraction of millions of notes from ∼1 million patients within hours.ConclusionRule‐based NLP can extract cognitive test results with adequate performance and scalable capability for clinical notes from patients with MCI or AD within the VA Healthcare System.

Using an integrated multi‐omics approach to predict dementia: Explainable variational autoencoder (E‐VAE) classifier model

AbstractBackgroundAlzheimer’s disease (AD) and AD related dementias (ADRD) are complex multifactorial neurodegenerative diseases. Application of deep learning methods to analyze large scale whole genome‐wide SNP data and transcriptomic data may be a powerful approach to utilize early molecular changes for early detection of ADRD and elucidate the biological mechanisms in AD/ADRD.MethodWe harmonized the definition of dementia in the Health and Retirement Study (HRS) and an external study Religious Orders Study/Memory and Aging Project (ROSMAP) using a diagnostic algorithm previously validated in the HRS based on cognitive tests. The cognitive tests included immediate and delayed word recall, and backward counting that measure working memory and episodic memory. We developed an explainable variational autoencoder (E‐VAE) classifier model using genetic variants (genome‐wide SNP data, 5474 SNPs) and RNAseq data (592 differentially expresses genes in ADRD) from 204 study participants (102 participants with dementia and 102 controls) in the HRS to predict dementia. We validated the prediction performance in a test set of the HRS participants (26 participants with dementia and 26 controls), and further assessed the generalizability of the multi‐omics‐based VAE classifier model in the ROSMAP data (12 participants with dementia and 222 controls).ResultOur results showed a predictive accuracy of 0.73 (95% CI [0.61, 0.85]) with an AUC of 0.80 in the HRS validation data and an accuracy of 0.58 (95% CI [0.51, 0.64]) with an AUC of 0.66 in the ROSMAP study data. The E‐VAE model outperformed a penalized logistic regression model (Figure in supplementary file). We identified genes that involved in the apoptotic process, neuron and nervous system development were highly weighted in the multi‐omics‐based VAE model and found that genes in ‘Butyrophilin interactions pathway’ were over represented in ADRD (FDR adjusted p <0.05).ConclusionThis is the first study showing the generalizability of a deep learning prediction model for dementia in an independent dataset using an integrated multi‐omics approach. The more precise and definitive latent variables identified using E‐VAE can help us understand the complex biology in ADRD and characterize the disease status.

Novel Nanocarrier - A Research Article's Review on Nanosponges"

The process of modern medication delivery system development is smooth. Any delivery method that directs a molecule to a specific place is always valued. A specialised drug delivery system can easily target the medicine at the site of action without sacrificing its efficacy and quality, satisfying this criterion. Most novel medications can be administered effectively using a conventional dose form. Benefits from localised, focused distribution of therapeutic substances are thus additional factors influencing the current situation. With the introduction of nanosponge technology, systemic toxicity and extreme reactions can be reduced by helping the drug release gradually and in a controlled manner. With cavities that can be filled with a wide range of hydrophilic and hydrophobic drugs, nanosponges, which are tiny sponges about the size of a virus (250 nm-1µm), can be further incorporated into a pharmaceutical dosage form such as oral, parenteral, topical, or inhalation. The discovery of nanosponge has proven to be a significant stride in the fight against issues including patient unacceptability, medication toxicity, poor bioavailability, and physiochemical instability. There are six different methodologies for the preparation of the nanosponges which follows a particular mechanism for the release of the drug from it. Further, the nanosponges can be subjected to several characterization.

Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.

Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse, because of their narrative structure. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. To develop an NLP-based approach for extracting and categorising data from echocardiography reports by accurately converting continuous (e.g., LVOT VTI, AV VTI and TR Vmax) and discrete (e.g., regurgitation severity) outcomes in a semi-structured narrative format into a structured and categorised format, allowing for future research or clinical use. 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and was iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Continuous outcomes including LVOT VTI, AV VTI and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC = 1.00, p < 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. A good level (ICC = 0.75-0.9, p < 0.05) of inter-rater reliability was observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to a structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

Single Candidate: A Setback to Indonesian Democracy?

The increase in regional head candidates was held in 2015. There were 4 single candidates. In 2017, there were 9 single candidates; in 2018, there were 16 single candidate pairs; for the 2020 regional elections, there was an increase to 25. The phenomenon of single candidates in the Pilkada and the increasing trend of each provincial head election is increasingly attractive to be studied more deeply. This research uses a qualitative approach to analysis by emphasizing process and meaning. Various literature studies related to research on implementing single candidates in simultaneous regional elections from 2015, 2017, 2018, and 2020. Some of the factors of a single candidate for the regional head are the failure of political parties in the cadre process, the high cost of political dowries, the legitimacy of the law, and the difficulty of the requirements for independent candidates to run for regional head elections. The phenomenon of a single candidate in the democratic system in Indonesia certainly attracts the attention of academics, politicians, and the public to study and follow the development process of the local democratic system. The presence of independent candidates will provide an alternative solution to regions in regional head elections that do not have competitors offered by political parties.

Maturity degree assessment of hospital ward system using integrated fuzzy AHP-TOPSIS model.

The hospital ward system is the core service unit of a hospital and an important aspect of hospital management. The maturity of the hospital ward system represents the level of development and improvement in ward management and services. In order to improve the quality of hospital services, it is significant to assess the maturity of the ward system. Although various assessment methods have been proposed in existing studies, there are some issues such as a single-dimensional factor system, subjectivity in qualitative factor values, and insufficient objectivity in the evaluation methods. Therefore, based on the data collection of the factors used in the existing literature, this study made correlation analysis, determined the similarity of factors, and established a maturity assessment factor system satisfying distinctiveness and comprehensiveness. Furthermore, an evaluation method for the weights of each factor was proposed based on the fuzzy analytic hierarchy process, while taking account of the ambiguity of expert information. In addition, through the technique for order of preference by similarity to ideal solution method, an objective evaluation method for the maturity of the hospital ward system was established. Finally, a case study involving 6 hospitals in a specific location was conducted. There are a total of 16 factors used to assess the maturity of the hospital ward system, among which the most important is Doctor service. The maturity degree of the 6 researched hospitals are 0.4517, 0.0035, 0.4254, 0.8681, 0.6636, 0.1586, and the maturity degree of the 6 researched hospitals are II, I, II, IV, III, I. The factor system constructed in this study effectively reflected the importance of human-related factors in the development process of ward system. The evaluation results were consistent with the actual situation, demonstrating the effectiveness of the proposed method.

Evaluating virtualization for fog monitoring of real-time applications in mixed-criticality systems

Technological advances in embedded systems and the advent of fog computing led to improved quality of service of applications of cyber-physical systems. In fact, the deployment of such applications on powerful and heterogeneous embedded systems, such as multiprocessors system-on-chips (MPSoCs), allows them to meet latency requirements and real-time operation. Highly relevant to the industry and our reference case-study, the challenging field of nuclear fusion deploys the aforementioned applications, involving high-frequency control with hard real-time and safety constraints. The use of fog computing and MPSoCs is promising to achieve safety, low latency, and timeliness of such control. Indeed, on one hand, applications designed according to fog computing distribute computation across hierarchically organized and geographically distributed edge devices, enabling timely anomaly detection during high-frequency sampling of time series, and, on the other hand, MPSoCs allow leveraging fog computing and integrating monitoring by deploying tasks on a flexible platform suited for mixed-criticality software, leading to so-called mixed criticality systems (MCSs). However, the integration of such software on the same MPSoC opens challenges related to predictability and reliability guarantees, as tasks interfering with each other when accessing the same shared MPSoC resources may introduce non-deterministic latency, possibly leading to failures on account of deadline overruns. Addressing the design, deployment, and evaluation of MCSs on MPSoCs, we propose a model-based system development process that facilitates the integration of real-time and monitoring software on the same platform by means of a formal notation for modeling the design and deployment of MPSoCs. The proposed notation allows developers to leverage embedded hypervisors for monitoring real-time applications and guaranteeing predictability by isolation of hardware resources. Providing evidence of the feasibility of our system development process and evaluating the industry-relevant class of nuclear fusion applications, we experiment with a safety-critical case-study in the context of the ITER nuclear fusion reactor. Our experimentation involves the design and evaluation of several prototypes deployed as MCSs on a virtualized MPSoC, showing that deployment choices linked to the monitor placement and virtualization configurations (e.g., resource allocation, partitioning, and scheduling policies) can significantly impact the predictability of MCSs in terms of Worst-Case Execution Times and other related metrics.

Development of a multifunctional real-time data processing system for interferometers on EAST

In the latest campaign of EAST experiments, two new interferometers were installed, a dispersion interferometer (DI) based on a Carbon Dioxide laser and a solid source interferometer (SSI) based on microwave multiplier sources. To make them available for the Plasma Control System (PCS) system, each of them needs to be provided with a real-time processing system to extract the detector output signal and afterward obtain the electron density information through signal processing. To obtain interferometer data quickly and reliably, a unified hardware template was applied to both interferometers. Three main parts are included in this hardware template — digitization, digital signal processing, and output modules. In particular, the digitization section uses a multi-channel Analog-to-Digital Convertor (ADC) to acquire the signals that need to be calculated. The digital signal processing part is implemented by an FPGA, which is the hardware basis of this multifunctional template that can be used for multiple interferometers. This section includes two main parts, wrapped density signal extraction and signal unwrapping. For DI it is a phase calculation based on the intensity ratio, while for SSI it is a phase calculation by means of FFT or phase demodulation. Both computational approaches form independent IP cores to build systems quickly, while these IP cores provide parameter interfaces to adapt to different application scenarios. The output module provides a variety of data transmission methods, including fiber optic transmission and Digital-Analog-Convertor (DAC) based analog transmission, to interface with PCS or other subsequent systems.This data processing system template has been applied to each of the above two interferometers and valid data were obtained in a recent EAST experiment campaign. It demonstrates the usefulness of the template and provides a reference for the design of data processing systems on future devices.

Perancangan Sistem Informasi Presensi Online Karyawan Berbasis Website dengan Face Record dan Geo Location

This research discusses the development of an online employee attendance system in the context of the company. Attendance data includes employee attendance, arrival and return information, as well as absence status such as sickness or clearance. Traditionally, some organizations still use physical methods to collect attendance data, which is considered less efficient and effective. The research uses classical waterfall development method to facilitate the process of system development up to release. With face recognition and geo location methods, an online attendance system is proposed with IT infrastructure using private hosting and virtual machines with Red Hat Enterprise Linux 8.7 Operating System as a web server. With the implementation of this system, it is expected that benefits will be realized in the ease for employees to conduct attendance and management capabilities in managing employee attendance data in a structured manner.

Digitalisasi Informasi Manajemen Aset sebagai Sarana Penunjang Operasional Kampus Menggunakan Metode Relational Unified Process

Campus asset management is the processing of operational supporting infrastructure on campus. So far, the asset management system at the Baubau Polytechnic Campus still uses the conventional method, namely by recording through albums and control cards attached to the walls of each room. This method is prone to loss of assets because it is not controlled. For this reason, software for digitizing asset management information is needed using the Relational Unified Process (RUP) system development method. The approach of this method is iterative and use case driven which focuses on solving asset management problems. This system works to control in real time the process of borrowing or transferring assets. this system is very helpful for the Baubau Polytechnic Campus in managing assets, especially on the problem of losing assets. This system also provides information about the physical condition of assets in each room so that it can assist the Asset Technical Implementation Unit (UPT) to transfer assets so that campus operations can run optimally.