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All Journal Jupiter IPTEK Journal of Proceedings Series PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnas Nasional Teknologi dan Sistem Informasi 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 Kumawula: Jurnal Pengabdian Kepada Masyarakat Infotekmesin Jurnal Pengabdian Masyarakat Multidisiplin 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) Jurnal Pengabdian Kepada Masyarakat
Wilda Imama Sabilla
Politeknik Negeri Malang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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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.
Online Expedition Services System for Customer at XYZ Ltd. Nasa Zata Dina; Wilda Imama Sabilla; Irvandy Handoyo
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 8 No. 1 (2020): Maret 2020
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v8i1.2015

Abstract

XYZ Ltd. is an expedition company that uses container ships and container trucks as transportation modes. There are three business process transactions that are carried out by the company, i.e. customer registration, ordering expedition services, and payment expedition services. Currently, all business process transactions are carried out manually, so process automation is needed in order for the business processes to run more effectively and efficiently. For this purpose, an Online Expedition Services Customer Service Information System was proposed to be built. This system consists of three main processes, namely the customer registration process, the customer order process, and the payment process and report generation. XYZ Ltd. Customer Service Information System was built following the waterfall development model. The stages of system development consist of five stages, namely planning, requirements analysis, database, and interface design, implementation of program code writing and testing and system maintenance. In implementing and testing the system, whole system requirements have been checked; the three main business processes in the proposed online system were running precisely, effectively and efficiently.
PENINGKATAN EKSPOSUR DIGITAL UMKM DESA WRINGINSONGO KABUPATEN MALANG MELALUI PERANCANGAN KATALOG DIGITAL Ade Ismail; Vipkas Al Hadid Fiardaus; Luqman Affandi; M. Hasyim Hasyim Ratsanjani; Wilda Imama Sabilla
Jurnal Pengabdian kepada Masyarakat Vol. 13 No. 1 (2026): JURNAL PENGABDIAN KEPADA MASYARAKAT 2026
Publisher : P3M Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/abdimas.v13i1.6383

Abstract

This community service program aimed to enhance the digital exposure of small and medium enterprises (UMKM) in Wringinsongo Village through the development of a digital catalog. The program involved analyzing the unique characteristics and market potential of local products, followed by collaboration with UMKM to create high-quality promotional materials. Key methods included digital marketing training, catalog development, and professional enhancements of promotional materials. The results showed significant improvements in product visibility through online platforms and enhanced UMKM knowledge of digital marketing strategies. Additionally, the quality of product promotion materials, including product descriptions, pricing, and high-quality images, was notably increased, leading to broader market reach and improved consumer engagement.
IMPLEMENTASI APLIKASI BANTUAN SEBAGAI PEMAHAMAN PENGGUNA PADA WEB UKM SUYTEESTORE: IMPLEMENTATION BUILD APPLICATION USER GUIDE HELP FOR UNDERSTANDING USERS OF WEB UKM SUYTEESTORE Ariadi Retno Hayati; Habibie Ed; Wilda Imama Sabilla; Vit Zuraida; Candra Bella
Jurnal Pengabdian Masyarakat Multidisiplin Vol 8 No 1 (2024): Oktober
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/jpm.v8i1.5215

Abstract

This activity research implementation application with tools help for users implementation based web for UKM SuyteeStore in Malang. This application have content for understanding by user how to operate the application web for catalogue and order system where each content has explanation for each content as detailed in web based. Design in this application has simply design and easy understanding for users with HTML and PHP programming. The contents of help application in this application are information how to access web for the use of find products and detailed product as catalogue product, the use how to order in system, the use of how process after order in system, the information of UKM SuyteeStore and location and contact and the information in this application can help users to operate the web that building for catalogue and order system in application web based. This application is build as tool for user that link to web UKM SuyteeStore and help users to understand the contents in application web UKM SuyteeStore with different menus and content in this tools application as sub of the web in UKM SuyteeStore. This application is designed with the concept of theory for build application for users as help user guide that in the concept of build application user guide must be have fews contents that different with the application and the contents must be explanation the application contents as detail.
Pemodelan Akustik HMM–GMM untuk Pengenalan Ucapan Kata Darurat Bahasa Indonesia Candra Bella Vista; Endah Septa Sintiya; Wilda Imama Sabilla; Adevian Fairuz Pratama
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.369-376

Abstract

Keamanan dan keselamatan merupakan kebutuhan dasar manusia. Situasi darurat seperti pencurian, kebakaran, maupun ancaman lainnya sering kali memerlukan respons yang cepat untuk mengurangi dampak negatifnya. Seiring berkembangnya teknologi, sistem keamanan berbasis suara menjadi solusi potensial untuk meningkatkan efektivitas deteksi dini dan tanggapan terhadap keadaan darurat. Penelitian ini bertujuan untuk mengembangkan model akustik berbasis Hidden Markov Model–Gaussian Mixture Model (HMM–GMM) guna mendeteksi kata-kata darurat dalam Bahasa Indonesia, seperti tolong, jangan, maling, kebakaran, dan kecelakaan. Dataset yang digunakan terdiri atas 1.100 berkas audio yang seimbang antara kelas darurat dan non-darurat. Fitur akustik dari dataset diekstraksi menggunakan Mel-Frequency Cepstral Coefficients (MFCC) beserta fitur turunan delta dan delta-delta. Evaluasi dilakukan dengan metode 10-fold cross-validation dan model terbaik diujikan dengan data holdout test untuk memastikan kemampuan generalisasi model. Hasil penelitian menunjukkan bahwa model HMM–GMM dengan 4 state dan 16 komponen Gaussian memberikan performa terbaik dengan akurasi 94,5%, presisi 94,3%, recall 94,5%, dan F1-score 94,4% pada hold. Temuan ini membuktikan bahwa model HMM–GMM efektif dalam mengenali ucapan darurat Bahasa Indonesia secara akurat dan efisien.
Co-Authors Ade Ismail Adevian Fairuz Pratama Ariadi Retno Hayati 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 Endah Septa Sintiya Firdaus, Aji Akbar Habibie Ed Habibie Ed Dien Habibie Ed Dien Handoyo, Irvandy Hayati, Ariadi Retno Ika Kusumaning Putri Irvandy Handoyo Kirana, Puspa Luqman Affandi M. Hasyim Hasyim Ratsanjani 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 Vipkas Al Hadid Fiardaus Vit Zuraida Yunita Ardilla Yuri Ariyanto Yusron, Rizqi Darma Rusdiyan