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Development of a Research and Service Information System to Optimize Monitoring of Research and Service Activities at Semarang State University Muh. Sholeh; Anggyi Trisnawan Putra; Martanto Setyo Husodo
Journal of Economic Education Vol 12 No 1 (2023): June 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jeec.v12i1.74913

The aim of this research is to build a module that contains monitoring features for the stages of research implementation and community service at SIPP to realize effectiveness and efficiency in the process of monitoring the stages of activity implementation by utilizing information system-based technology to support orderly administration and service improvement at LPPM UNNES. This type of research is research and development. Research and development is a process or steps to develop a new product or improve an existing product that can be accounted for. The results of the research show that with the development of the Research and Community Service Information System (SIPP), deficiencies in the previous piecemeal system which was carried out manually or electronically but not in the application of databases can be overcome properly, thereby making the data management process easier. effective and efficient. The SIPP being developed already has data storage media in the form of a database so it is hoped that it can minimize the possibility of data loss. With the development of SIPP, it is hoped that the process of incoming mutations, outgoing mutations, majors, can run well so that data errors do not occur and make it easier for researchers and staff to organize data

Optimizing Random Forest for Predicting Thoracic Surgery Success in Lung Cancer Using Recursive Feature Elimination and GridSearchCV Putra, Deonisius Germandy Cahaya; Putra, Anggyi Trisnawan
Recursive Journal of Informatics Vol 2 No 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v2i2.73154

Abstract. Lung cancer is one of the deadliest forms of cancer, claiming numerous lives annually. Thoracic surgery is a strategy to manage lung cancer patients; however, it poses high risks, including potential nerve damage and fatal complications leading to mortality. Predicting the success rate of thoracic surgery for lung cancer patients can be accomplished using data mining techniques based on classification principles. Medical data mining involves employing mathematical, statistical, and computational methods. In this study, the prediction of thoracic surgery success employs the random forest algorithm with recursive feature elimination for feature selection. The feature selection process yields the top 8 features. The 8 best features include 'DGN', 'PRE4', 'PRE5', 'PRE6', 'PRE10', 'PRE14', 'PRE30', and 'AGE'. Hyperparameter using GridSearchCV is then applied to enhance classification accuracy. The results of this method implementation demonstrate a predictive accuracy of 91.41%. Purpose: The study aims to develop and evaluate a Random Forest model with a Recursive Feature Elimination feature selection and applies hyperparameter GridSearchCV for predicting thoracic surgery success rate. Methods: This study uses the thoracic surgery dataset and applies various data preprocessing techniques. The dataset is then used for classification using the Random Forest algorithm and applies the Recursive Feature Elimination feature selection to obtain the best features. GridSearchCV is used in this study for hyperparameter. Result: The accuracy using the Random Forest algorithm and Recursive Feature Elimination feature selection with hyperparameters tuning GridSearchCV resulted in an accuracy of 91,41%. The accuracy was obtained from the following parameters values: bootstrap set to false, criterion set to gini, n_estimator equal to 100, max_depth set to none, min_samples_split equal to 4, min_samples_leaf equal to 1, max_features set to auto, n_jobs set to -1, and verbose set to 2 with 10-fold cross validation. Novelty: This study comparison and analysis of various dataset preprocessing methods and different model configurations are conducted to find the best model for predicting the success rate of thoracic surgery. The study also employs feature selection to choose the best feature to be used in classification process, as well as hyperparameter tuning to achieve optimal accuracy and discover the optimal values for these hyperparameters.

Optimization of the Convolutional Neural Network Method Using Fine-Tuning for Image Classification of Eye Disease Wulandari, Vivi; Putra, Anggyi Trisnawan
Recursive Journal of Informatics Vol 2 No 1 (2024): March 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v2i1.73625

The eye is the most important organ of the human body which functions as the sense of sight. Most people wish they had healthy eyes so they could see clearly about life around them. However, some people experience eye health problems. There are many types of eye diseases ranging from mild to severe. With advances in technology, artificial intelligence can be used to classify eye diseases accurately, one of which is deep learning. Therefore, this study uses the Convolutional Neural Network (CNN) algorithm to classify eye diseases using the VGG16 architecture as a base model and will be combined using a fine-tuning model as an optimization to improve accuracy.

Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning Adityatama, Resta; Putra, Anggyi Trisnawan
Recursive Journal of Informatics Vol 1 No 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v1i2.70774

Abstract. The development of information technology in facial recognition is influenced by a faster and more accurate authentication system. This allows the computer system to identify a person's face. Purpose: Similar to fingerprints and the retina of the human eye, each person's face has a different shape and contour. Since it is known that the human face provides a lot of information, as well as topics that attract attention make it studied intensively. Methods/Study design/approach: Several studies examining information from human faces are facial recognition. One of the approaches used to recognize facial imagery is through the use of a Convolutional Neural Network (CNN). CNN is a method in the field of Deep Learning that can be used to recognize and classify objects in digital images. In this study, the method used to implement facial image classification is the Xception architecture CNN algorithm with a transfer learning approach. Result/Findings: The dataset used in this study was obtained from Kaggle, namely the Face Shape Dataset which contains 5000 data. After testing, an accuracy rate of 96.2% was obtained in the training process and 81.125% in the validation process. This study also uses new data to test the model that has been made, and the results show an accuracy rate of 85.1% in classifying facial imagery. Novelty/Originality/Value: Therefore, it can be said that the model created in this study has the ability to classify images of facial shapes Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning.

Implementasi Algoritma Klasterisasi K-Medoids untuk Segmentasi Pengguna E-Ujian.Com Putra, Anggyi Trisnawan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.528

User behaviour management and analysis is very important in business. A good marketing strategy needs to be done so that the loyalty of old users is maintained. This can be initiated by segmenting users so that a good marketing strategy can be formulated. Customer segmentation can be done with the help of one of the data mining methods, which is clustering. In this study, k-medoids algorithm is used to cluster e-ujian.com users based on the behavioral data of each user. The first step will be analyzing data attributes that can be used. Next, the clustering process was carried out with the experimentally determined value of k. Finally, the cluster results will be evaluated using the Davies Bouldin Index (DBI) to determine the best number of clusters. The results showed that the value of k = 4 became the optimal number of clusters with a DBI value of 3,017.

Implementasi Algoritma Klasterisasi K-Medoids untuk Segmentasi Pengguna E-Ujian.Com Putra, Anggyi Trisnawan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.528

User behaviour management and analysis is very important in business. A good marketing strategy needs to be done so that the loyalty of old users is maintained. This can be initiated by segmenting users so that a good marketing strategy can be formulated. Customer segmentation can be done with the help of one of the data mining methods, which is clustering. In this study, k-medoids algorithm is used to cluster e-ujian.com users based on the behavioral data of each user. The first step will be analyzing data attributes that can be used. Next, the clustering process was carried out with the experimentally determined value of k. Finally, the cluster results will be evaluated using the Davies Bouldin Index (DBI) to determine the best number of clusters. The results showed that the value of k = 4 became the optimal number of clusters with a DBI value of 3,017.

Integration of Smart Machine Presence Using RFID E-Money Cards for Employee Attendance Management at Universitas Negeri Semarang Alfath Yanuarto; Dinar Diaz Septian; Akhmad Munawar; Anggyi Trisnawan Putra; Andika Enggal Ramadhan; Bhekti Kumorowati
International Journal of Active Learning Vol. 9 No. 1 (2024): April 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

The study aims to improve the effectiveness of the online attendance system at Universitas Negeri Semarang (UNNES). The existing system, reliant on web-based input via tokens and employee identification numbers, suffers from inefficiencies and potential inaccuracies due to proxy attendance. These limitations hinder productivity and the accuracy of performance appraisals. To address these challenges, a Smart Machine Presence system was designed and implemented, utilize RFID e-money cards which become employee Identification cards (ID Card) to streamline the process and eliminate the need for manual input. This novel system employs Raspberry Pi 4 Model B technology, integrated with RFID readers and camera modules for robust authentication. The research utilized a three-stage approach: system needs analysis, prototype design, and system development. Usability testing conducted with 20 participants using the System Usability Scale (SUS) yielded a score of 86.8, indicating high user satisfaction and effectiveness. The proposed system demonstrated significant advantages, including improved data validity, enhanced operational efficiency, and reduced resource costs compared to traditional systems. This study concludes that the Smart Machine Presence system is a cost-effective, efficient, and scalable solution for modern attendance management systems.

Analysis of User Behavior in Face Recognition Boarding Gate Services Towards the Satisfaction of Long-Distance Train Passengers Using the UTAUT2 Model Amaliyah, Tazkiyatun Aeni; Putra, Anggyi Trisnawan
Journal of Advances in Information Systems and Technology Vol. 7 No. 1 (2025): April
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v7i1.9879

In the era of Industry 4.0, the adoption of technology has become crucial for enhancing performance, efficiency, usability, and sustainability in various sectors, including transportation. PT Kereta Api Indonesia (Persero), the largest railway company in Indonesia, has implemented Face Recognition Boarding Gate technology to improve the passenger experience by simplifying the train departure process. However, the deployment of facial recognition technology in the transportation sector also raises significant concerns regarding data security and privacy. This study aims to analyze the impact of using Face Recognition Boarding Gate technology on the satisfaction of long-distance train passengers, employing the UTAUT2 model as a theoretical framework. The research utilizes a quantitative method with purposive sampling to select respondents, focusing on users aged 17 and above who have used the service at least once. A total of 166 valid responses were collected through online questionnaires. The collected data was processed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the aid of Google Colab. The analysis results stated that 7 out of 9 proposed hypotheses were accepted, corresponding to an acceptance rate of 77%. Specifically, performance expectancy, effort expectancy, and perceived security were found to have a direct positive impact on passenger satisfaction. Additionally, hedonic motivation and habit significantly influenced behavioral intention, which in turn had a positive effect on satisfaction and use behavior. The findings of this research provide valuable insights for service providers, highlighting the importance of addressing security and privacy concerns while enhancing the overall user experience. Furthermore, the study offers a useful reference for future researchers interested in exploring the adoption and impact of advanced technologies in the transportation sector.

Literacy, Compliance, and Digital Legal Awareness: The Role of JDIH UNNES in Disseminating Legal Information Wulandari, Cahya; Sugianto, Sugianto; Putra, Anggyi Trisnawan; Emha, Zidney Ilma Fazaada; Hassan, Muhamad Sayuti
Indonesian Journal of Advocacy and Legal Services Vol. 7 No. 1 (2025): The Global Challenges on Advocacy and Law Enforcement
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ijals.v7i1.26600

Public legal awareness is an important foundation in creating a fair and effective legal system. The low level of legal understanding is the main trigger for public non-compliance with the law. Therefore, legal literacy is a strategic key in shaping legal awareness. The novelty of this article discusses the urgency of legal literacy and compliance in realizing a law-aware society and highlights the role of JDIH UNNES as an important tool in disseminating legal information. The method used in this study is normative juridical. The results of this study show that legal literacy not only includes the ability to understand the content of the law, but also encourages proactive legal attitudes and behavior. JDIH UNNES has proven to play a strategic role in providing legal information that is easily accessible, accurate, and relevant so as to support information disclosure and public participation in law enforcement and encourage the development of a legal culture in Indonesia. Thus, strengthening legal literacy through means such as JDIH has practical implications in shaping a society that understands, obeys, and is aware of the law in a sustainable manner.

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