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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) SEMIRATA 2015 Sistemasi: Jurnal Sistem Informasi Jurnal Bina Komputer International Journal of Advances in Artificial Intelligence and Machine Learning
Astried, Astried
Universitas Riau
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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PEMBUATAN KATALOG SKRIPSI DAN TUGAS AKHIR BERBASIS WEB PADA PERPUSTAKAAN JURUSAN MATEMATIKA FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM UNIVERSITAS RIAU Muhammad Anshori, Astried, Fiza Febriyani,
SEMIRATA 2015 Prosiding Bidang Iptek dan Multi Disiplin
Publisher : SEMIRATA 2015

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (258.67 KB)

Abstract

Penelitian ini bertujuan untuk membuat menu katalog Skripsi dan Tugas  Akhir di perpustakaan Jurusan Matematika. Menu katalog Skripsi dan Tugas  Akhir dibuat menggunakan PHP (Hypertext Prepocessor) dan  basisdata MySQL, dan selanjutnya program tersebut akan diintegrasikan dengan perangkat lunak pengelola perpustakaan yang digunakan saat ini.   KataKunci : Katalog, PHP, MySQL
PENINGKATAN KECEPATAN PROSES PADA METODE COLOR ORDERING DAN MAPPING DENGAN PENDEKATAN DELAPAN-KETETANGGAAN Astried Astried; Tri Basuki Kurniawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 7, No 1: April 2009
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v7i1.576

Abstract

The development of digital technology and internet, nowadays, has given the facility for easy access and distribution of a lot of information in digital form. The facilities in distributing digital data also has emerged the negative impact such as the violence of copy right. One of techniques developed from negative impact is water making technique. Several techniques have been mentioned by many researches in water making, one of them is the use of color palette and color index which is also know as ordering and mapping color method. In this method, the substitute color found through all colors in the pallets so this process gives the longer time in bit watermark inserting process. In this paper, ordering and mapping color which use color pallet of an image will be modified to increase the speed of the process by using eight neighborhoods approximation. From experimental result conducted to 30 images, it can be concluded that the proposed approximation has taken the shorter time than the conventional method. 
MODEL SIMULASI PENYELESAIAN MASALAH PERJALANAN PENJUAL MENGGUNAKAN PENDEKATAN KECERDASAN BUATAN, OPTIMISASI KOLONI SEMUT Misinem, Misinem; Kurniawan, Tri Basuki; Astried, Astried; Widians, Joan Angelina
Jurnal Bina Komputer Vol 3 No 1 (2021): Jurnal Bina Komputer
Publisher : Jurnal Ilmiah Terpadu Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1624.207 KB) | DOI: 10.33557/binakomputer.v2i2.974

Abstract

Salah satu hal yang menarik dalam bidang perangkat lunak adalah ditemukannya algoritma pengoptimisasian. Banyak pekerjaan yang rumit dan kompleks yang akan mustahil untuk dilakukan secara manual ataupun kalau terpaksa dilakukan dengan cara manual akan membutuhkan waktu dan tenaga yang sangat besar. Dengan adanya algoritma optimisasian pekerjaan yang rumit dan kompleks tadi dapat dilakukan dengan lebih mudah dan lebih cepat. Bahkan juga memeberikan jaminan secara teoritis, untuk mendapatkan solusi yang terbaik. Dalam penelitian ini akan dibangun sebuah model simulasi perangkat lunak untuk menyelesaikan masalah perjalanan penjual dengan menggunakan algoritma optimisasi koloni semut, untuk memberikan visual bagi pengguna bagaimana masalah tersebut dapat diselesaikan secara Langkah demi Langkah. Pembangunan program simulasi menggunakan metode pengembangan perangkat lunak Extreme Programming (XP) pada lingkungan system operasi Windows dengan menggunakan Bahasa pemrograman C# pada Visual Studio 2019. Hasil dari penelitian didapati, program dapat memberikan visualisasi/simulasi yang baik kepada pengguna.
Regression Analysis to Predict the Length of Time to Complete a Thesis based on the Title Aminuddin, Al; Hidayat, Rahmat; Sastria, Gita; Astried, Astried
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i2.4994

Abstract

The selection of thesis titles by students is an important thing to do as part of the graduation requirements in completing undergraduate studies. In general, the difficulty or complexity of the thesis can be reflected through the title of the thesis that is appointed. This can indicate that the more difficult a thesis title, the longer it will take to complete the thesis research. This study utilizes the data mining method using machine learning, namely linear regression, in predicting how long it will take to complete a thesis title. The data used is obtained from the words or text in the thesis title as a feature or independent variable and the completion time in days as the dependent variable to predict the time required for students starting from a thesis proposal seminar to a comprehensive seminar or thesis final session. The regression model produces an evaluation value of the coefficient of determination of 0.999, which is close to the maximum value equal to 1.
Diabetic Retinopathy Prediction Using Deep Learning: Insights From CNN Sirisati, Ranga Swamy; Navyasri, V.; Swathi, T.; Akhila, M.; Astried, Astried
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 3 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i3.420

Abstract

Background of study: Diabetic Retinopathy (DR) is a severe microvascular complication of diabetes mellitus that can lead to vision loss if not detected early. With over 93 million individuals affected globally, the need for accurate and efficient diagnostic systems has become urgent. Traditional screening methods depend on manual interpretation of fundus images by ophthalmologists, which is time-consuming and prone to subjectivity.Aims: This research seeks to create and assess a deep learning diagnostic model designed to reliably identify the severity levels of diabetic retinopathy using retinal fundus images. The research also explores model interpretability using Shapley Additive Explanations (SHAP) to increase transparency in AI-assisted medical diagnosis.Methods: Convolutional Neural Networks (CNNs) were implemented using transfer learning with pretrained architectures such as ResNet50 and InceptionV3. The EyePACS dataset, containing images categorized into five DR severity levels, was used for model training. Preprocessing techniques, including contrast enhancement, histogram equalization, and data augmentation, improved image quality and model generalization. The models were optimized with the Adam and assessed through accuracy, precision, recall, F1-score, and AUC. Additionally, SHAP analysis was employed to interpret and illustrate the model’s predictions.Results: The proposed CNN-based model achieved 98.5% accuracy, with a sensitivity and specificity of 0.99, demonstrating strong performance across multiple DR stages. Comparison with existing studies revealed a notable improvement in diagnostic accuracy. SHAP visualizations confirmed that critical retinal features such as microaneurysms, hemorrhages, and cotton-wool spots were key predictors influencing model decisions.Conclusion: The findings validate the efficacy of deep learning, particularly CNNs, in enhancing early detection and classification of diabetic retinopathy. The integration of SHAP interpretability bridges the gap between AI predictions and clinical trust, making this approach a promising tool for large-scale automated DR screening and supporting ophthalmologists in timely diagnosis and treatment.
Co-Authors Akhila, M. Al Aminuddin Gita Sastria Joan Angelina Widians, Joan Angelina Kurniawan, Tri Basuki Misinem, Misinem Navyasri, V. Rahmat Hidayat Sirisati, Ranga Swamy Swathi, T. Tri Basuki Kurniawan