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  • The Functionalization of Law and Criminal Procedures to Confront Health Care Fraud in Hospitals

    The Functionalization of Law and Criminal Procedures to Confront Health Care Fraud in Hospitals

    Musa Darwin Pane ; Diah Pudjiastuti

    DOI: 10.22304/pjih.v8n3.a2

    Abstract:

    Fraud is a systematic crime that has a very broad impact. It can happen in any fields, including in hospitals. Fraud is a form of corruption. Hospital is a health service institution. Corruption in hospitals has the potential to lead to ineffective health services for people. The phenomenon of health care fraud in hospital is an indication the law does not function in accordance with the objective. This study aims to determine the functionalization of law and sentence for fraudulent acts as a form of corruption in hospitals based on justice values. This study is a descriptive study with normative juridical method that employed statutory and conceptual approaches. The data were collected through a literature study. It was subsequently analyzed qualitatively. This study is of the position to view that prosecution of criminal acts of corruption requires functionalization of law. The functionalization of law must be interpreted as positioning everything in its proper place. It is the synergy of the legal system, which consists of formulative, judicial, and executive policies. The criminal procedures can apply the punishment system for perpetrators of fraudulent acts in hospitals that includes extended alternative punishment. © 2021, Padjadjaran University. All rights reserved.

    Author Keywords:

    fraud; legal functionalization; punishment system

    This article can be accessed at https://www.scopus.com/pages/publications/85137481599?

  • Odour Pollution Control Using Type-2 Fuzzy Logic Controller

    Odour Pollution Control Using Type-2 Fuzzy Logic Controller

    Authors: Muhammad Aria Rajasa Pohan, Dhiyaa Rifqi Taufiqurrahman, Fernando Sitanggang, Vania Retha Luthfia Fitriani

    Abstract

    This investigation aims to develop an odour reduction system simulation using a Type-2 Fuzzy Logic Controller (T2FLS). T2FLC is a further development of the classic fuzzy logic (Type-1 Fuzzy Logic Controller or T1FLC). Where the membership function of T2FLC is also fuzzy. A test by several references indicates that the performance of T2FLC is better than the T1FLC. The proposed T2FLC is tested and compared to the PID controller and T1FLC. The test is based on simulation using MATLAB and Simulink®. Three attempts are carried out to compare the performance of the three controllers using three different gain (C), time delay (L), and time constant (T) values. This shows that type-2 fuzzy is the most reliable method for magnifying C, L, and T values with rise time = 1.368 seconds, maximum overshoot = 60%, and settling time = 8.989 seconds. © School of Engineering, Taylor’s University.

    Author keywords

    Fuzzy logic Type 2; MATLAB; Odour pollution; Simulink

    This article can be accessed at https://www.scopus.com/pages/publications/85147503914

  • Designing Virtual Reality Game for Learning Al-Quran

    Designing Virtual Reality Game for Learning Al-Quran

    Authors: Rizky Jumansyah, Senny Luckyardi

    Abstract

    This study aims to design a new horror game by utilizing virtual reality technology. This game was developed as an entertainment medium and also a learning medium where in addition to being able to play games, users can also learn to memorize verses of the Qur’an. In data collection, this research uses a qualitative descriptive method, then in the application design process, we used Adobe XD to make the initial design of the game. After getting an idea related to the game that will be made, Unity Software will be used for the virtual reality design stage. The result of this research is that with the presence of this game application, users are offered a new experience because players can feel that deepening spiritual conditions can also be achieved through games. The conclusion is that the “Tadhakar” game application using VR technology can be a new concept for those who are interested. © School of Engineering, Taylor’s University.

    Author keywords

    Design; Developing; Games; Horror games; Prototype; Virtual reality

    This article can be accessed at https://www.scopus.com/pages/publications/85147517696

  • Pupil Center Detection Using Radial Symmetry Transform to Measure Pupil Distance in the Eye

    Pupil Center Detection Using Radial Symmetry Transform to Measure Pupil Distance in the Eye

    Authors: Utama J, Fitriani V. R. L

    DOI: 10.5829/ije.2023.36.05b.04

    Abstract

    In patients with refractive errors or impaired vision, light rays received by the pupil do not fall directly onto the retina. This can be corrected by wearing monocled glasses. The focal point of the eyeglass lens needs to be adjusted to the center of the user’s pupil. This can be known through the measured pupil distance (PD) value information. The measurement of the PD is very important to determine the center distance of the pupils in both eyes. where the eye does not experience the prism effect. This study aims to apply the radial symmetry transformation (RST) method combined with self-quotient (SQI) imagery to detect the pupillary center and measure PD. This algorithm combines to get more optimal results in detecting the center of the pupil in dark conditions or those exposed to shadow illumination. The program created using the MATLAB software simulates PD measurements for pupillary center detection in bright and dark images conditions. The test was carried out ten times, and the results showed that the system was able to measure PD on low-resolution images of 300 x 300 pixels at 72 dpi in bright image conditions; with measurement uncertainty values in each image of 0.60 mm. As for testing on dark images, the uncertainty values are 0.80 mm. In this case, the standard deviation value is obtained from the effect of the different dimensions of the face object on the tested image. © 2023 Materials and Energy Research Center. All rights reserved.

    Author keywords

    Euclidean Distance; Pupil Distance; Radial Symmetry Transform; Self Quotient Image

    This article can be accessed at https://www.scopus.com/pages/publications/85158848120

  • A Bert Model to Detect Provocative Hoax

    A Bert Model to Detect Provocative Hoax

    Authors: Rio Yunanto, Eri Prasetyo Wibowo, Rianto R

    Abstract

    The information flood makes social media users vulnerable to becoming victims of provocative hoaxes or even spreading hoaxes themselves. This research examines the capabilities of two variants of Bidirectional Encoder Representations from Transformers (BERT) models for the Indonesian language (IndoBERT Base Model and Indonesian BERT base model 522M) in developing the detection of provocative hoaxes in the Indonesian language. The proposed method used two variants of the monolingual BERT model for the Indonesian language from the Huggingface library. The proposed method’s architectural flow starts with data collection and labelling from community hoax collector websites, followed by pre-processing. The cleaned data is then divided into training and test data to proceed to the fine-tuning stage, where several layers and weights of the BERT model are adjusted to fit the desired classification task. The experimental results of the study show that the recommended Indonesian BERT variant for the detection of provocative hoaxes is the IndoBERT Base Model with a learning rate of 1e-5, a batch size of 32, and a maximum length limit of 128 tokens, achieving an average training accuracy of 99,22%, with a training time of 21min 52s. The research findings also indicate that a learning rate 1e-5 can produce better test accuracy than a learning rate of 2e-5 or 3e-5. The detection model of provocative hoaxes using Indonesian BERT variants needs to be improved, especially in terms of collecting a large amount of hoax data, to enhance the accuracy of the provocative hoax detection model. © School of Engineering, Taylor’s University.

    Author keywords

    Accuracy; Classification; Hoax; Provocative; Transformer

    This article can be accessed at https://www.scopus.com/pages/publications/85180943734

  • Application of Data Mining for Predicting Horticultural Commodities Price

    Application of Data Mining for Predicting Horticultural Commodities Price

    Authors: Dian Dharmayanti, Arsy Opraza Akma, Eddy Soeryanto Soegoto, Lia Warlina

    Abstract

    In Garut Regency, farmers receive their commodities at a selling price that collectors determined. This issue causes farmers to lose money because their income does not correspond with the market price for their goods. This loss has an impact on reducing farmer productivity. Therefore, this research aims to provide recommendations to farmers regarding commodity selling prices for particular periods based on forecast results. This research implements data mining so commodities selling prices can be predicted. Data mining is a way to find patterns or knowledge from past data. Multiple linear regression methods or algorithms are used to find patterns or knowledge with data mining. Multiple linear regression can be used to predict commodities selling prices based on rainfall factors and total production. The application developed can produce recommendations for farmers for commodity selling prices for specific periods based on prediction results, thereby reducing losses for farmers. © 2024 Taylor’s University. All rights reserved.

    Author keywords

    Data mining; Horticultural commodities; Multiple linear regression; Price prediction

    This article can be accessed at https://www.scopus.com/pages/publications/85184573404

  • Risk Assessment of Work Posture in Manufacture Industry for Drilling Pipes

    Risk Assessment of Work Posture in Manufacture Industry for Drilling Pipes

    Authors: Herry Saputra, Purnama Dicki Gilar, Putra Muhamad Luki, Ramdoni Mochamad Sahri, Suryatno Wiganepdo Soegoto

    Abstract

    The objective of this research is to examine the working postures of individuals in the manufacturing sector involved in drilling pipe production, utilizing the REBA and RULA methodologies. These methods serve to identify potential risks associated with the working positions adopted by workers, distinguishing between correct postures and those that may lead to problems. The research involved data collection through field observations and photographs capturing operators’ postures during work. Data processing entailed creating sketches of the operator’s posture and measuring the angles of each part using CorelDRAW. This facilitated aligning the measurement results with the RULA and REBA worksheets. The results indicated that the RULA method, when assessing the operator’s working posture while handling the spindle, yielded a final score of 5. The assessment outcome indicates a need for a thorough examination of the operator’s body positioning, and prompt implementation of alterations in working posture is essential. Conversely, the application of the REBA method to the operator’s stance while dealing with the iron pipe resulted in a score of 8, signifying a significant risk level. Immediate improvements in the working position are considered imperative. © 2024 Taylor’s University. All rights reserved.

    Author keywords

    REBA; Risk assessment; RULA; Work posture

    This article can be accessed at https://www.scopus.com/pages/publications/85184616974

  • Prediction System for Vitamin A Requirements in West Java Indonesia Using Linear Regression

    Prediction System for Vitamin A Requirements in West Java Indonesia Using Linear Regression

    Authors: Sri Nurhayati, Wicaksono M. F, Diana Effendi

    Abstract

    This research aims to analyse the accuracy of the linear regression method in predicting the amount of vitamin A needed and providing information about predicting vitamin A needs within a specific period. Vitamin A is identified as the most crucial nutrient, necessitating external supplementation due to insufficient and low food consumption. The linear regression method is employed in this study, serving as a data analysis technique to predict unknown data values based on related and known data values. The dataset involves the number of vitamin A administrations in each district/city in West Java, Indonesia. Mean absolute perception error (MAPE) was utilized to assess prediction errors. The system requirements analysis used an object-oriented approach with Unified Modeming Language (UML) tools. The comprehensive prediction results yield an average accuracy of 86%, indicating that the linear regression method effectively predicts vitamin A needs in the subsequent period. Functional testing of the system demonstrates a 100% success rate, aligned with the needs analysis, thereby providing information on predicting vitamin A requirements within a specific period for each district/city in West Java. © 2024 Taylor’s University. All rights reserved.

    Author keywords

    Linear regression; Object-oriented based system; Prediction; Vitamin A

    This article can be accessed at https://www.scopus.com/pages/publications/85184606841

  • Prototype Design for a Microcontroller- Based Parking Information System

    Prototype Design for a Microcontroller- Based Parking Information System

    Authors: Euis Neni Hayati, Taufan Ismail Mihrab, Fadlan Alfarezh Hartono, Dida Prambudi, Bobi Kurniawan

    Abstract

    In a parking area, numerous issues exist, such as the lack of information to locate available parking spaces, leading to prolonged search times. To streamline and expedite the search for parking, a parking system can be designed to provide information about full and empty parking spaces. Employing an agile process to address parking challenges, the aim is to shorten queues and provide timely parking information. The envisioned outcome is a system enabling drivers, especially those with specialty vehicles, to identify parking spaces without manual searching. The parking information system prototype incorporates a 16×2 LCD for displaying parking space addresses, LED indicators, and the ability to showcase the number of available parking spaces, enhancing effectiveness and user accessibility. By preventing excessive queues through real-time information on vacant parking spaces, the system reduces congestion and provides smoother parking experiences for drivers. Each parking lot has an infrared sensor to determine occupancy status, while the LCD at the entrance relays information on available spaces. The LED is an indicator light, illuminating when a parking lot is vacant. The microcontroller is the central system, seamlessly connecting and coordinating each component. © 2024 Taylor’s University. All rights reserved.

    Author keywords

    Information system; Microcontroller; Parking

    This article can be accessed at https://www.scopus.com/pages/publications/85184575827

  • Best Classifier Over Feature Selection and Delta Error in Time Series Dataset

    Best Classifier Over Feature Selection and Delta Error in Time Series Dataset

    Authors: Nugroho Widyanto, Jin-Whan Kim, Agus Nursikuwagus

    Abstract

    The selection of the best classifier on conventional machine learning is often made intuitively by observing existing research. It becomes an obstacle when stating high accuracy, regardless of whether the machine suits the dataset. This research proposes a method to deal with the accuracy of using a learning machine so that the accuracy generated can be aligned with the usage of the dataset. This method uses the feature selection method combined with the machine learning classifier. SelectKbest and principal component analysis (PCA) methods combined with the classifier machine by counting confirmed MSE, MAE, R2, and delta errors can predict the appropriate machine learning for the OTHERS datasets. Delta error and R2 by SelectKBest and the PCA can see the Delta Error tendency of its classifier learning. The observed classifiers confirmed the best R2 values on linear and Bayesian regression. The R2 values of Bayesian and linear regression are 0.634 and 0.687, respectively. Average Delta Error of MAE of SelectiKbest < Average Delta Eror of MAE of PCA, 3.21×1015 < 1.37×1016. In future research, we found the challenge of the best classifier in Deep Learning and the method of selecting features appropriate for the time series dataset. © 2024 Taylor’s University. All rights reserved.

    Author keywords

    Chi-square; Classifier; Delta error; Principal component analysis; SelectKBest

    This article can be accessed at https://www.scopus.com/pages/publications/85196260915