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All Journal Jupiter IPTEK Journal of Proceedings Series PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Journal of Information Systems Engineering and Business Intelligence CESS (Journal of Computer Engineering, System and Science) Sistemasi: Jurnal Sistem Informasi Jurnal Komputer Terapan Jurnal Informatika Universitas Pamulang Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Kumawula: Jurnal Pengabdian Kepada Masyarakat Infotekmesin Jurnal Pengabdian UNDIKMA Jurnal Pengabdian Polinema Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Informatika dan Multimedia Jurnal Informatika Polinema (JIP)
Wilda Imama Sabilla
Politeknik Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna Ardilla, Yunita; Imama Sabilla, Wilda; Ulinnuha, Nurissaidah
Infotekmesin Vol 12 No 2 (2021): Infotekmesin: Juli 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i2.616

Abstract

Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90.
Membandingkan Analisa Kesalahan Metode K-Means Clustering dan Canopy K-Means Clustering Dengan Data Gambar Terfilter Hayati, Ariadi Retno; Imama, Wilda; Kirana, Puspa; Zuraida, Vit
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.50978

Abstract

Pada penelitian ini menganalisa kesalahan yang diperoleh pada data pembelajaran pada data gambar dengan membandingkan metode K-Means Clustering dan metode Canopy K-Means Clustering. Data yang digunakan adalah data gambar yang diujikan pada aplikasi yang dibangun dan menelaah nilai kesalahan pada setiap iterasi. Analisa kesalahan dengan memahami karakteristik formula pada metode K-Means Clustering dan Canopy K-Means Clustering dan menganalisa angka kesalahan berdasarkan formula kedua metode dengan demikian maka karakteristik perolehan error pada metode Canopy K-Means Clustering diperoleh berdasarkan karakteristik formula dari metode tersebut. Dari hasil beberapa uji coba dengan dataset yang data berbeda maka diperoleh rata-rata bahwa metode Canopy K-Means Clustering memiliki nilai kesalahan lebih sedikit sejumlah 0,0264% dibandingkan metode K-Means Clustering dengan Euclidean distance dan rata-rata keberhasilan 85% sesuai kelompok.
Proliferative Diabetic Retinopathy Detection Using Convolutional Neural Network with Enhanced Retinal Image Wilda Imama Sabilla; Mamluatul Hani'ah; Ariadi Retno Tri Hayati Ririd; Astrifidha Rahma Amalia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.4976

Abstract

Proliferative Diabetic Retinopathy (PDR) is the most severe stage of Diabetic Retinopathy (DR), carrying the highest risk of complications. Automatic detection can help provide earlier and more accurate PDR diagnosis, but prediction accuracy may decline due to limitations in retinal images. Therefore, image enhancement techniques are often applied to improve DR classification. This study aims to detect PDR from retinal images using Convolutional Neural Networks (CNNs) and to evaluate the impact of three enhancement methods. This research method is based on a CNN architecture, including ResNet34, InceptionV2, and DenseNet121, as well as enhancement methods such as CLAHE, Homomorphic Filtering (HF), and Multiscale Contrast Enhancement (MCE). The results of this research show that CNN performance varies across architectures and enhancement methods. The highest performance was achieved using ResNet34 with HF, yielding an accuracy of 0.976, precision of 0.934, and recall of 0.904. CLAHE generally improved performance across architectures, achieving the best average accuracy of 0.953, whereas MCE decreased classification accuracy. Overall, the findings highlight the importance of selecting appropriate enhancement methods to improve PDR detection accuracy. Implementing such systems in clinical screening could help reduce the risk of vision impairment among diabetic patients.
Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction Mamluatul Hani'ah; Moch Zawaruddin Abdullah; Wilda Imama Sabilla; Syafaat Akbar; Dikky Rahmad Shafara
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2287

Abstract

The stock market often attracts investors to invest, but it is not uncommon for investors to experience losses when buying and selling shares. This causes investors to hesitate to determine when to sell or buy shares in the stock market. The accurate stock price prediction will help investors to decide when to buy or sell their shares. In this study, we propose a new approach to predicting stocks using machine learning with a combination of features from stock price features, technical indicators, and Google trends data. Three well-known machine learning algorithms such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear regression are used to predict future stock prices. The test results show that the SVR outperformed the MLP and Multiple Linear Regression to predict stock prices for Indonesian stocks with an average MAPE is 0.50%. The SVR can predict the stock price close to the actual price.
Co-Authors Ariadi Retno Tri Hayati Ririd Ariadi Retno Tri Hayati Ririd As Sidiq, Zidan Shabira Astrifidha Rahma Amalia Candra Bella Vista Chastine Fatichah Dhebys Suryani Hormansyah, Dhebys Suryani Dika Rizky Yunianto Dikky Rahmad Shafara Dimas Wahyu Wibowo Dwi Puspitasari Firdaus, Aji Akbar Habibie Ed Dien Habibie Ed Dien Handoyo, Irvandy Hayati, Ariadi Retno Ika Kusumaning Putri Kirana, Puspa Mamluatul Hani'ah Mamluatul Hani’ah Moch Zawaruddin Abdullah Mulyo, Muhammad Faris Hadi Nasa Zata Dina Nurhayati, Rafika Nurissaidah Ulinnuha Perkasa, Muhammad Adisa Putra Rokhimatul Wakhidah Rosa Andrie Asmara Rully Soelaiman Sarah Astiti S.Kom., M.MT Syafaat Akbar Tesa Eranti Putri Ulla Delfana Rosiani Utama, Andhika Vit Zuraida Yunita Ardilla Yuri Ariyanto Yusron, Rizqi Darma Rusdiyan