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All Journal International Journal of Advances in Applied Sciences Jurnal Edukasi dan Penelitian Informatika (JEPIN) CogITo Smart Journal Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Infotech: Journal of Technology Information Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Nuansa Informatika Riwayat: Educational Journal of History and Humanities
Ahya Radiatul Kamila
Universitas Bunda Mulia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Predictive Maintenance of Heavy Equipment Machines using Neural Network Based on Operational Data Ahya Radiatul Kamila; Derhass, Gerry Hudera; Andry, Johanes Fernandes; Lee, Francka Sakti; Budiyanto, Very; Anatasia, Velly
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.555.229-241

Abstract

Preventive maintenance is a routine maintenance strategy that aims to maximize equipment life cycle and prevent unplanned downtime which causes increased repair costs. When carrying out this maintenance, error in selecting machines need to be anticipated to avoid company losses. This research aims to reduce human error in machine selection for preventive maintenance using deep learning. The dataset used in this research is operational data of heavy equipment machine dataset from one of the palm oil companies in Indonesia with 9 independent features and 1 dependent feature. Dependent feature is a target feature contain two target classes representing effective and ineffective machines. The dataset in this study contains outlier, feature scales that are very different, and imbalanced data class. To handle outlier and standardise data scale, the Z-score method is used. Meanwhile, the over sampling method is used to handle imbalanced data classes. To obtain the best model performance, the number of epochs and two types of optimizers (adam&adamax) of neural network are selected. In selecting the number of epochs, experiments were carried out using 100 epochs. This research obtained the linearity relationship between the number of epochs and accuracy with the accuracy values using Adam and Adamax optimizers were 94.82% and 93.11% at the 100th epoch.
Information system architecture for healthcare company based on TOGAF Vania Christy; Johanes Fernandes Andry; Ahya Radiatul Kamila; Francka Sakti Lee
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp810-817

Abstract

In 2020 COVID-19 cases have entered Indonesia causing public health problems and millions of deaths. To prevent transmission of COVID-19, an air purifier is needed whose function is to remove small droplets that can carry the virus. One of them is a medical device company located in Jakarta. The purpose of this research is to produce a design that can improve business processes in the health sector and achieve company goals. The current business process is not very optimal because it is still done conventionally and the existing system has not been integrated with other divisions. To achieve business goals, it is necessary to integrate business processes with information technology (IT) and technology development that will be proposed based on the design of information system architecture that will produce a blueprint and assisted by the open group architecture framework (TOGAF) framework which is very helpful in the process of analyzing company needs. In this research, data collection through interviews with directors and direct observation of health service companies. The results of this study are recommendations given to help health.
PENINGKATAN PERFORMA MODEL MACHINE LEARNING UNTUK DETEKSI DINI POLYCYSTIC OVARY SYNDROME MELALUI KOMBINASI METODE PREPROCESSING Ahya Radiatul Kamila; Francka Sakti Lee; Johanes Fernandes Andry
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.448

Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most common hormonal disorders experienced by women of reproductive age and can lead to various health problems, including menstrual irregularities, infertility, and an increased risk of metabolic diseases. Early detection of PCOS is essential to minimize long-term impacts and improve the quality of life for patients. This study aims to identify effective data preprocessing strategies to enhance the performance of classification models for PCOS detection. The dataset used is open source, consisting of 541 participants with 45 clinical and laboratory features. The main challenges encountered include the presence of many missing values, an imbalanced target class distribution, and a large number of independent features. To address these issues, a series of preprocessing steps were applied, including missing value imputation, data balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and dimensionality reduction using Principal Component Analysis (PCA). A classification model was built using the Random Forest algorithm, and its performance was compared before and after applying PCA. The evaluation results show that before PCA, the model achieved an accuracy of 87.5%, precision of 86%, recall of 86%, and an F1-score of 86%. After applying PCA, performance improved to an accuracy of 90%, precision of 89%, recall of 89%, and an F1-score of 89%. These findings indicate that the right combination of preprocessing strategies, particularly SMOTE and PCA, can significantly improve the efficiency and effectiveness of models in detecting PCOS, thereby supporting the development of more reliable medical decision support systems.
Co-Authors Adikara, Fransiskus Anatasia, Velly Ariq Cahya Wardhana Derhass, Gerry Hudera Deswin Auliyaa Rabbani EKO WAHYU PRASETYO Francka Sakti Lee Francka Sakti Lee Francka Sakti Lee Francka Sakti Lee Geasela, Yemima Monica Gerry Hudera Derhass Herdian, Cevi Johanes Fernandes Andry Johanes Fernandes Andry Kusuma, Adi Wahyu Candra Meyliana, Sintia Revaldo, Danny Samuel Winata Surianto Surianto Sutrisno, Sutrisno Tampinongkol, Felliks F. Tampinongkol, Felliks Feiters Vania Christy Very Budiyanto