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All Journal Jurnal Paradigma Ekonomika JUSIM (Jurnal Sistem Informasi Musirawas) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Industri Terintegrasi (JUTIN) Elektriese: Jurnal Sains dan Teknologi Elektro
Rio, Rio
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology

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Journal : Jurnal Teknik Industri Terintegrasi (JUTIN)

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Hyperparameter Tuning Model Artificial Neural Network untuk Klasfikasi Penyakit Breast Cancer Sidqi, M Nejatullah; Marsehan, Ahmad; Rio, Rio; Felani, Rani Okta
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 7 No. 3 (2024): July
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v7i3.31469

Abstract

Cancer is a deadly disease that is often experienced unexpectedly. Cancer does not only attack adults and the elderly, even newborn babies can get cancer. Breast cancer is the number one cancer disease in Indonesia and is one of the main causes of death due to cancer. According to Globocan 2020, the number of new cases of breast cancer in Indonesia increased from 396,914 new cancer cases to 68,858 cases (16.6%). There was a trial for early diagnosis of breast cancer carried out on the Breast Cancer dataset, totaling 569 patients with target categories namely Malignant (malignant cancer) and Benign (benign cancer). This diagnosis aims to carry out classification using data mining to extract new information and knowledge. The method applied uses the Artificial Neural Network (ANN) algorithm, using parameters, 1 layer and 250 to 500 epochs. Meanwhile, the activation functions used are ReLU and Sigmoid activation. Then the best classification results were produced by 250 epochs with an accuracy of 98.24%.
Analisis Keberhasilan Sistem Informasi Sentuh Tanahku Badan Pertanahan Nasional Menggunakan Metode ISSM Fadli, Muhamad; Rio, Rio; Marsehan, Ahmad; Oktafelani, Rani
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 7 No. 3 (2024): July
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v7i3.31473

Abstract

The use and usefulness of information technology is an important issue in today's life. This can be seen as a way of using technology to support human activities in various sectors or fields, including government and user security. Of course, the use and usefulness of information technology in various agencies is very necessary to support the implementation of better government programs. The use and utilization of technology in the government environment can be carried out with the help of a research information system. Research is located at two government information agencies: Lubuk-Linggau City National Land Agency; The information system used by the National Land Agency is called Touch My Land based on Android. The implementation of an information system is said to be successful if it can be proven that the information system produces significant positive impacts or benefits for some of its users. The DeLone and McLean evaluation concept is considered a successful information system model. The research method used in this research is a quantitative method with a sample size as many as 61 people from National Land Agency. This research uses six variables and nine hypotheses.  The sixth variable consists of information quality, system quality, service quality, usage intention, user satisfaction and net profit. The research results show that several research hypotheses are accepted because the t-statistic value is greater than 1.96.
Co-Authors Felani, Rani Okta Hidayat, Asep Toyib Junaidi Junaidi M. Nejatullah Sidqi Marsehan, Ahmad Muhamad Fadli Mustika, Candra Oktafelani, Rani Santosa, I Gede Olka Siti Aminah