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IPTEK The Journal for Technology and Science
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 08534098     EISSN : 20882033     DOI : -
Core Subject : Science,
Computer Science & IT
IPTEK The Journal for Technology and Science (eISSN: 2088-2033; Print ISSN:0853-4098), is an academic journal on the issued related to natural science and technology. The journal initially published four issues every year, i.e. February, May, August, and November. From 2014, IPTEK the Journal for Technology and Science publish three times a year, they are in April, August and December in online version.
Arjuna Subject : -
Articles 6 Documents
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Search results for , issue "Vol 33, No 2 (2022)" : 6 Documents clear
Exposure Fusion Framework in Deep Learning-Based Radiology Report Generator Hilya Tsaniya; Chastine Fatichah; Nanik Suciati
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13572

Abstract

Writing a radiology report is time-consuming and requires experienced radiologists. Hence a technology that could generate an automatic report would be beneficial. The key problem in developing an automated report-generating system is providing a coherent predictive text. To accomplish this, it is important to ensure the image has good quality so that the model can learn the parts of the image in interpreting, especially in medical images that tend to be noise-prone in the acquisition process. This research uses the Exposure Fusion Framework method to enhance the quality of medical images to increase the model performance in producing coherent predictive text. The model used is an encoder-decoder with visual feature extraction using a pre- trained ChexNet, Bidirectional Encoder Representation from Transformer (BERT) embedding for text feature, and Long-short Term Memory (LSTM) as a decoder. The model’s performance with EFF enhancement obtained a 7% better result than without enhancement processing using an evaluation value of Bilingual Evaluation Understudy (BLEU) with n-gram 4. It can be concluded that using the enhancement method effectively increases the model’s performance.
Upgrading The Web-Based Credit Score Calculation System Nanda Lidya Fadillah; Vivianti Vivianti
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13741

Abstract

The development of the credit score system is a development that aims to streamline the time and performance of Widyaiswara at the Makassar Religious Education and Training Center. However, the previous system development did not have the Overload task verification and credit score features, so Widyaiswara needed manual input. The Abundant Task feature is a feature that allows Widyaiswara to input credit numbers above the position following the provisions for reducing credit scores following applicable regulations. Meanwhile, the credit score verification feature ensures that the credit score entered follows the specified conditions. This study aims to develop these two features for upgrading the system using the Waterfall model development. This model was chosen because it fits the needs of systematic and sequential system development. The test results show that the system has met the ISO/IEC 25010 standard; the functional feasibility is worth 1 (one), which means the system is working well. While the results of the usability test of the system are 90.4%, the system is declared very feasible to input and calculate credit scores efficiently. The result shows that the upgrading of the web-based credit score calculation system has been effectively used for inputting overflow assignments and verifying credit scores.
A Comparative Analysis between Food Affordability and Healthy Life Among The Rural and Urban People of Bangladesh Prodipto Bishnu Angon; Md. Shafiul Islam
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13552

Abstract

Good health depends on moderate and proper nutritional food. There is a noticeable difference in eating patterns between individuals living in rural and urban regions. The central aspect of this study is to make a comparative analysis of the health of rural and urban people with their eating habits. In February 2022, data were collected from 1,400 people in Bangladesh’s major cities through offline and online surveys and analyzed using IBM SPSS Statistics 25 and Microsoft Excel. Three types of correlation are brought out among the selected parameters, such as local people will be able to protect themselves from heart disease by consuming vegetables. The paper highlights the staple food of people of all ages in Bangladesh as well as their daily food intake time and quantity through a comprehensive survey. People will be able to adjust their health depending on the type and amount of food they consume, and they will also be able to know whether urban people are ahead of rural people and vice versa. This research can bring good health to the people by reviewing the food habits of the people of the village and the city.
Variety of Characteristic Magnetic Material on Permanent Magnet Synchronous Generator (PMSG) Syamsyarief Baqaruzi; Amrina Mustaqim; Putty Yunesti; Gde KM Atmajaya; Ali Muhtar; Sabhan Kanata
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13532

Abstract

Wind energy conversion system, one of the main components is a Permanent Magnet Synchronous Generator (PMSG). During the past two decades, many types of per- manent magnet generators for wind power applications have been the research topic. This study focuses primarily on designing a PMSG to create, simulate, and analyze an internal permanent magnet topology with twelve plots and eight poles. We limit with the simulation was carried out at a rotational speed of 1000rpm, and a type of permanent magnet material, Ceramic 11, SmCo 26/26, and NdFeB 48/11. The result of the analysis is that permanent magnets applied in the design of a generator impact its output power and efficiency. At 15 Ω and 60 Ω loads, SmCo 26/26 and NdFeB 48/11 are the only ones that fulfill the specified requirements in this investigation. The permanent magnet type with the most optimal characteristics is Neodymium Iron Boron 48/11 because it has a high flux density, thus causing the electrical energy generated to be greater than other types of permanent magnets. The 48/11 NdFeB permanent magnet generates the most output power, 2110.86 W when loaded with 15 Ω. The best efficiency of 89.38 percent for the PMSG 12 slot eight poles occurs when the load is 15 on the 48/11 NdFeB permanent magnet.
Student Behaviour Analysis To Detect Learning Styles Using Decision Tree, Naïve Bayes, And K-Nearest Neighbor Method In Moodle Learning Management System Santi Tiodora Sianturi; Umi Laili Yuhana
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13665

Abstract

A learning management system (LMS) manages online learning and facilitates inter- action in the teaching and learning processes. Teachers can use LMS to determine student activities or interactions with their courses. Everyone learns uniquely. It is necessary to understand their learning style to apply it in students’ learning activi- ties. One factor contributing to learning success is the use of an appropriate learning style, which allows the information received to be appropriately conveyed and clearly understood. As a result, we require a mechanism to identify learning styles. This study develops a learning style detection system based on learning behavior at the LMS of Christian Vocational School Petra Surabaya for the subject of Network System Administration using the Decision Tree, Naïve Bayes, and K-Nearest Neigh- bor. The results of the study showed that the Decision Tree method could better detect and predict learning styles, namely using the 80:20 train split test, which obtained an accuracy of 0.96 process time of 0.000998 seconds, while the K-Fold 10 Cross-Validation test obtained an accuracy of 0.98 and a processing time of 0.04033 seconds.
Feature Selection Using Hybrid Binary Grey Wolf Optimizer for Arabic Text Classification Muhammad Bahrul Subkhi; Chastine Fatichah; Agus Zainal Arifin
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13769

Abstract

Feature selection in Arabic text is a challenging task due to the complex and rich nature of Arabic. The feature selection requires solution quality, stability, conver- gence speed, and the ability to find the global optimal. This study proposes a feature selection method using the Hybrid Binary Gray Wolf Optimizer (HBGWO) for Ara- bic text classification. The HBGWO method combines the local search capabilities or exploratory of the BGWO and the search capabilities around the best solutions or exploits of the PSO. HBGWO method also combines SCA’s capabilities in finding global solutions. The data set used Arabic text from islambook.com, which consists of five Hadith books. The books selected five classes: Tauhid, Prayer, Zakat, Fasting, and Hajj. The results showed that the BGWO-PSO-SCA feature selection method with the fitness function search and classification method using SVM could per- form better on Arabic text classification problems. BGWO-PSO with fitness function and the classification method using SVM (C=1.0) gives a high accuracy value of 76.37% compared to without feature selection. The BGWO-PSO-SCA feature selec- tion method provides an accuracy value of 88.08%. This accuracy value is higher than the BGWO-PSO feature selection and other feature selection methods.

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