Article Per Year (5 Year)
p-Index From 2020 - 2025
0.92
P-Index
This Author published in this journals
All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Robotics and Control (JRC) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Pengabdian Masyarakat Sains dan Teknologi
ALYA MASITHA
INSTITUT TEKNOLOGI STATISTIKA DAN BISNIS MUHAMMADIYAH SEMARANG
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Mechanical Engineering Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset Alya Masitha; Muhammad Kunta Biddinika; Herman Herman
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2984

Abstract

Heart failure is included in the category of cardiovascular disease. Heart disease is not easy to detect, and its detection needs to be done by experienced and skilled medical professionals. Most patients with heart failure require hospitalization. Common symptoms of heart disease, such as chest pain and high or low blood pressure, vary from person to person. This study aims to find the most optimal k value based on the accuracy obtained based on calculations by testing different k values, namely 1, 3, 5, 7, and 9. After getting the results of the accuracy of the five k values, compare which accuracy has the highest value, best for K-Nearest Neighbor (K-NN) models. The classification process uses the K-NN algorithm. This algorithm is quite easy to use because some parameters work using distance metrics and k values. Therefore, the value of k in the K-NN algorithm greatly affects the accuracy that will be produced. In the results of this study, the accuracy obtained was k = 7 and k = 9, which are the most optimal results because they have the highest accuracy compared to other k values, with an accuracy of 88%. The expected benefit of this research is that it can make a scientific contribution to research in the field of machine learning classification, especially in predicting heart failure
Preparing Dual Data Normalization for KNN Classfication in Prediction of Heart Failure Alya Masitha; Muhammad Kunta Biddinika; Herman
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1382

Abstract

Heart failure disease is a serious condition that is significant in affecting both a person's quality of life and health. Therefore, it is important to develop classification methods that can help detect this disease. In this research, a data preprocessing stage is performed before being used to classify heart failure diseases using machine learning models, such as K-NN. Data preprocessing is an effort to simplify data analysis and ensure accurate results, and it is a very essential step in analyzing data to improve the quality of the data used. The dataset used in this research is raw data that has not gone through the preprocessing stage. The dataset consists of 918 data with target attributes of 0 and 1, where a value of 0 indicates a normal condition and a value of 1 indicates a potential heart failure condition. Data preprocessing includes data cleaning, data transformation, and data normalization. The main objective of this research is to carry out the preprocessing stage on data derived from heart failure disease datasets. Based on the comparison between two normalization methods, namely Min-Max and Simple Feature Scale, it is found that the Simple Feature Scale normalization method has the best performance, with an accuracy rate of 85%, while the Min-Max normalization method only reaches 84%.
Budidaya Lele Dalam Ember dan Upaya Pemasaran Digital Menggunakan Media Sosial Alya Masitha; Tri Stiyo Famuji; Adiyah Mahiruna; Rahmat Riansyah; Maulana Muhammad Jogo Samodro
Jurnal Pengabdian Masyarakat Sains dan Teknologi Vol. 3 No. 4 (2024): Desember : Jurnal Pengabdian Masyarakat Sains dan Teknologi
Publisher : Fakultas Teknik Universitas Cenderawasih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58169/jpmsaintek.v3i4.639

Abstract

The community service program titled 'Catfish Farming in Buckets and Digital Marketing Efforts Using Social Media' aims to provide participants with both theoretical knowledge and practical skills on simple, cost-effective, and appropriate methods for catfish farming in limited spaces. This initiative responds to the needs of the Tembalang community, which seeks to engage in farming activities on limited land while also exploring ways to market their agricultural products via social media platforms. The training was conducted using a participatory approach, which incorporated theory, hands-on practice, and interactive discussions. The content of the training included, among other things, techniques for catfish farming in buckets as well as strategies for utilizing social media to market the cultivated catfish products. Participants were guided to apply proper aquaculture practices using the provided cultivation buckets and equipment, and were then asked to capture images of their farming outcomes to be used as marketing content on social media. The implementation of this activity proceeded smoothly, and the participants showed strong enthusiasm throughout the process. It is anticipated that this training will enable the Tembalang community to effectively leverage technology, particularly social media platforms such as WhatsApp, as a tool for digital marketing of their catfish farming products.
Meningkatkan Keterampilan Profesional Mahasiswa: Strategi Penguatan Soft Skills untuk Sukses di Era Digital Huda, Nurul; Istiawan, Deden; Masitha, Alya; Mahiruna, Adiyah
Jurnal Pengabdian Masyarakat Sains dan Teknologi Vol. 3 No. 4 (2024): Desember : Jurnal Pengabdian Masyarakat Sains dan Teknologi
Publisher : Fakultas Teknik Universitas Cenderawasih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58169/jpmsaintek.v3i4.660

Abstract

This community service program aims to equip students with knowledge and skills relevant to facing the challenges of the digital workforce. The activity was attended by 40 students, with materials covering four main topics: Understanding the Digital Era Workforce, Key Soft Skills for the Digital Era, Building Competencies for the Digital Workforce, and Preparing for Career Success. In addition, participants were guided in creating an individual Action Plan to prepare themselves for career success in the future. The methods used in this program included material presentations, interactive discussions, and the development of personal action plans, aiming to enhance both technical and non-technical skills needed in the digital workforce. Evaluation results show that 75% of students rated the material as excellent, 20% as good, and 5% found it somewhat beneficial. This indicates the program’s success in providing significant value to participants. This program is expected to enhance students' readiness to enter the increasingly digital and technology-driven job market, while providing valuable insights for planning and developing their careers. Moving forward, this activity can serve as a model for developing more effective community service programs that align with the needs of the modern workforce.
Concerns of Ethical and Privacy in the Rapid Advancement of Artificial Intelligence: Directions, Challenges, and Solutions Furizal, Furizal; Ramelan, Agus; Adriyanto, Feri; Maghfiroh, Hari; Ma'arif, Alfian; Kariyamin, Kariyamin; Masitha, Alya; Fawait, Aldi Bastiatul
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.24090

Abstract

AI is a transformative technology that emulates human cognitive abilities and processes large volumes of data to offer efficient solutions across various sectors of life. Although AI significantly enhances efficiency in many areas, it also presents substantial challenges, particularly regarding ethics and user privacy. These challenges are exacerbated by the inadequacy of global regulations, which may lead to potential abuse and privacy violations. This study provides an in-depth review of current AI applications, identifies future needs, and addresses emerging ethical and privacy issues. The research explores the important roles of AI technologies, including multimodal AI, natural language processing, generative AI, and deepfakes. While these technologies have the potential to revolutionize industries such as content creation and digital interactions, they also face significant privacy and ethical challenges, including the risks of deepfake abuse and the need for improved data protection through platforms like PrivAI. The study emphasizes the necessity for stricter regulations and global efforts to ensure ethical AI use and effective privacy protection. By conducting a comprehensive literature review, this research aims to provide a clear perspective on the future direction of AI and propose strategies to overcome barriers in ethical and privacy practices.
Co-Authors Adiyah Mahiruna Adriyanto, Feri Aldi Bastiatul Fawait Fawait Alfian Ma’arif Furizal, Furizal Herman Herman Herman Istiawan, Deden Kariyamin, Kariyamin Maghfiroh, Hari Mahiruna, Adiyah Maulana Muhammad Jogo Samudro Muhammad Kunta Biddinika Muhammad Kunta Biddinika Nurul Huda Rahmat Riansyah Ramelan, Agus Tri Stiyo Famuji