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All Journal Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer EXPLORE Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi INFOKUM Infotek : Jurnal Informatika dan Teknologi Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat EXPLORE Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Computer and Technology
erniwati, surni
Universitas Teknologi Mataram
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences

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IDENTIFIKASI KEMIRIPAN FOTO ASLI DAN SKETSA MENGGUNAKAN MODEL GENERATIF ADVERSARIAL NETWORK (GANs) Satriawan, Andre; Imran, Bahtiar; Erniwati, Surni
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 2 No. 3 (2023): September 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v2i3.36

Abstract

Perkembangan seni semakin bertumbuh khususnya dalam bidang seni lukis, pertumbuhan tersebut terlihat dari banyaknya pemula yang mulai belajar melukis secara otodidak diawali dengan belajar membuat sketsa menggunakan metode yang beragam, tetapi masalah umum yang sering dihadapi oleh pemula dalam seni Lukis adalah seringkali sketsa dan foto asli terlihat serupa tetapi tidak tahu seberapa mirip sketsa yang telah dibuat. Penlitian ini bertujuan untuk mengidentifikasi persentase kemiripan foto asli dan sketsa menggunakan metode diskriminatif dari model Generative Adversarial Networks (GANs) memantkan library atau modul ssim. Diskriminator merupakan CNN yang menerima input gambar berukuran sama atau memiliki dimensi yang sama dan menghasilkan angka yang menyatakan apakah input merupakan gambar yang sama atau memeiliki kemiripan. Untuk mendapatkan persentase kemiripan yang tepat antara dua gambar memanfaatkan Struktural Similarity Index (SSIM) yang telah terlatih pada library scikit-image.
Sistem Pakar Mendiagnosis Penyakit Mata Manusia Menggunakan Metode Fuzzy Mamdani Dwinita Arwidiyarti; Juhartini, Juhartini; Surni Erniwati
Jurnal PROCESSOR Vol 19 No 1 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.1.1627

Abstract

The medical field has utilized technology in an effort to improve better services in diagnosing diseases, one of which is eye disease in humans. Because the eye is one of the five senses that is important to interact with the surrounding environment. The doctor's work is very busy and busy resulting in delays in serving the community. To overcome this, an expert system is needed to assist patients in diagnosing early eye disease, so that patients can find out the eye disease they are suffering from and its severity. This study discusses the creation of an expert system using the fuzzy mamdani concept to diagnose eye disease. The Fuzzy Mamdani method has been successfully implemented into an expert system for diagnosing eye diseases. This expert system uses the fuzzy mamdani method for diagnosing eye diseases which can provide fast diagnosis results along with the level of certainty for each disease. Expert systems can diagnose diseases based on the trust value of the disease using the Fuzzy Mamdani formula. From the results of the diagnosis calculations performed by the system, it can be seen that the accuracy of the system diagnosis with doctors reaches 93.3%
SemetonBug: Next-Generation Machine Learning-Powered Code Analyzer for Precision Bug Detection and Dynamic Error Localization Erniwati, Surni; Imran, Bahtiar; Muahidin, Zumratul; Zaeniah, Zaeniah; Juhartini, Juhartini
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11837

Abstract

Bug detection in Python programming is a crucial challenge in software development. This research proposes SemetonBug, a machine learning-based system for automatically detecting bugs in Python code. The system utilizes a Random Forest Classifier as the main model, with features extracted from the syntactic structure of the code using an Abstract Syntax Tree (AST). The dataset consists of 200 Python files, divided into 100 files with bugs and 100 files without bugs. The model is optimized using Grid Search Cross Validation, with the best combination of n_estimators = 300, max_depth = 20, min_samples_split = 5, and min_samples_leaf = 2. Evaluation results show that the model achieves 85% accuracy, 0.84 precision, 0.87 recall, and 0.86 F1-score. The detected bugs are stored in an Excel file for further analysis. By leveraging machine learning, SemetonBug enhances efficiency and accuracy in bug identification compared to traditional rule-based methods. These findings highlight the potential of machine learning models in improving software quality and reducing coding errors automatically.
Co-Authors - Sarjan - Sriasih Apsih, Denda Arwidiyarti, Dwinita Hambali Hambali Imran, Bahtiar indriana, pathul Karim, Muh Nasirudin Lalu Isnaeni Rahman Muahidin, Zumratul Muhammad Multazam Mutaqin, Zaenul Salman Salman Sarjan, - Satriawan, Andre Sriasih, Sriasih St Tuhpatussania Subki, Ahmad Tuhpatussania, Siti wahyuni, wenti ayu Zaeniah Zaeniah zaeniah zaeniah Zaeniah Zaeniah