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All Journal Jurnal Simetris JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Informatika: Jurnal Pengembangan IT
Pergiwati, Dewi
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Computer Science & IT Decision Sciences, Operations Research & Management

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Peningkatan Deteksi Posisi Wajah Manusia dengan Metode Normal PDF berbasis Algoritma Viola-Jones Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Alzami, Farrikh; Prabowo, Dwi Puji; Pergiwati, Dewi; Sinaga, Daurat
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 15, No 1 (2024): JURNAL SIMETRIS VOLUME 15 NO 1 TAHUN 2024
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v15i1.10617

Abstract

Deteksi kulit manusia dalam pengolahan citra memiliki peran penting dalam aplikasi seperti analisis gerakan, pencarian citra berbasis konten, interaksi manusia komputer, dan analisis pelacakan gerakan manusia. Meskipun banyak penelitian telah dilakukan, masih ada kendala dalam menghadapi variasi warna kulit manusia yang kompleks. Dalam penelitian ini, diusulkan peningkatan kinerja deteksi kulit manusia dengan memanfaatkan algoritma deteksi wajah Viola-Jones untuk menentukan posisi wajah dalam citra. Selain itu, diterapkan juga teknik pemisahan region kasar dan halus pada wajah guna meningkatkan hasil deteksi kulit manusia. Penggunaan Normal PDF digunakan untuk mencari probabilitas piksel kulit dalam citra. Metode yang diusulkan berhasil mencapai tingkat akurasi tinggi, di mana sebagian besar citra uji memiliki akurasi di atas 90%. Meskipun terdapat beberapa citra yang memiliki akurasi lebih rendah dibandingkan metode sebelumnya, secara keseluruhan metode yang diusulkan mampu meningkatkan kinerja deteksi kulit manusia. Oleh karena itu, penelitian ini memberikan kontribusi berharga dalam pengembangan metode deteksi kulit manusia yang lebih baik.
Adaptive Inertia Weight Particle Swarm Optimization for Augmentation Selection in Coral Reef Classification with Convolutional Neural Networks Prabowo, Dwi Puji; Rohman, Muhammad Syaifur; Megantara, Rama Aria; Pergiwati, Dewi; Saraswati, Galuh Wilujeng; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar; Andono, Pulung Nurtantio
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2726

Abstract

Indonesia possesses the world's largest aquatic resources, with 17,504 islands and 6.49 million square kilometers of sea. Located in the coral triangle, Indonesia is home to diverse marine life, including vital coral reefs. However, these reefs face threats from climate change, pollution, and human activities, endangering biodiversity and coastal communities. Therefore, monitoring and preservation are crucial. This study evaluates various augmentation methods for classifying underwater coral reef images using Convolutional Neural Networks (CNNs). Effective augmentation methods are essential due to the unique characteristics of these images. The methodology includes testing different augmentation methods, epoch parameters, and CNN parameters on a coral reef image dataset. Five optimization algorithms (AIWPSO, GA, GWO, PSO, and FOX) are compared. The highest accuracy, 95.64%, is achieved at the 10th epoch. AIWPSO and GA show the highest average accuracies, 93.44%, and 93.50%, respectively, with no significant performance differences among the algorithms. Statistical analysis using the Wilcoxon test indicates a significant difference between training and validation accuracy (p-value = 0.0020). These findings underscore the importance of selecting augmentation methods that align with the characteristics of each optimization algorithm to enhance classification performance. The results provide valuable insights into improving the quality and diversity of input data for classification algorithms in underwater image analysis. They highlight the necessity of matching augmentation methods to specific optimization algorithms to boost accuracy and effectiveness significantly. Future research should explore additional augmentation methods and optimization algorithms further to enhance the robustness and accuracy of underwater image classification.
Implemenatasi Algoritma K-Nearest Neighbor (KNN) dalam Memprediksi Penyakit: Implemenatasi Algoritma K-Nearest Neighbor (KNN) dalam Memprediksi Penyakit Agyztia Premana; Pergiwati, Dewi
Jurnal Teknik Informatika UMUS Vol 7 No 1 (2025): Mei
Publisher : Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46772/intech.v8i1.1800

Abstract

Stroke merupakan gangguan fungsi otak yang disebabkan oleh terganggunya aliran darah ke otak. Penyakit stroke dapat menyebabkan kecacatan pada penderitanya atau bahkan kematian. Data Organisasi Stroke Dunia menyatakan bahwa setiap tahun terdapat 13,7 juta penderita stroke dan 5,5 juta kasus kematian akibat stroke. Penyakit ini merupakan penyakit mematikan nomor tiga dunia. Berdasarkan hal tersebut maka penelitian ini bertujuan untuk melakukan penerapan algoritma K-Nearest Neighbor dalam memprediksi penyakit stroke serta dapat mengetahui akurasi yang dihasilkan algoritma KNN dalam memprediksi penyakit stroke. Melalui hasil perhitungan klasifikasi-prediksi pada data penyakit stroke dengan data latih sebanyak 80 data dan data uji sebanyak 20 data dapat diketahui bahwa algoritma KNN dapat melakukan prediksi penyakit stroke berdasarkan jenis kelamin, umur, hipertensi, riwayat penyakit jantung, status menikah, tipe pekerjaan, tipe tempat tinggal, rerata kadar glukosa , bmi dan status merokok dengan akurasi yang didapatkan sebesar 95% dengan nilai k=9.
Enhancing Entity Extraction in E-Government Complaint Data using LDA-Assisted NER Umam, Ahmad Khotibul; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Rohmani, Asih; Prabowo, Dwi Puji; Pergiwati, Dewi; Megantara, Rama Aria; Iswahyudi, Iswahyudi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15292

Abstract

With the rapid development of information technology, governments are increasingly challenged to provide digital channels that enhance public participation in governance. LaporGub, an official platform managed by the Central Java Provincial Government, accommodates citizens' aspirations and complaints, but faces challenges in processing large amounts of unstructured text. Manual analysis is time-consuming and error-prone, resulting in delayed responses and decreased service quality. Conventional Named Entity Recognition (NER) models struggle to handle informal Indonesian-language text, while transformer-based approaches require substantial computing resources that are not widely available in local government environments. Therefore, this study aims to develop a lightweight NER approach by integrating Latent Dirichlet Allocation (LDA) as a semantic pre-annotation tool to improve the accuracy of entity extraction in Indonesian e-government complaint data. To achieve this goal, a dataset of 53,858 complaint reports from the LaporGub platform (2022–2025) was processed using LDA topic modeling (k=10) to provide semantic context during annotation. Next, the enriched dataset was used to train a spaCy-based NER model targeting three entity types: LOCATION, ORGANIZATION, and PERSON, with a training-validation-test split ratio of 70:15:15 using stratified sampling. The evaluation showed that the proposed NER+LDA model achieved a precision of 90.03%, a recall of 81.86%, and an F1-score of 85.75%, representing improvements of +5.78, +2.55, and +4.04, respectively, compared to the baseline NER model (F1-score: 81.71%). Furthermore, the most significant improvements occurred in the detection of ORGANIZATION and PERSON entities. These findings confirm that the integration of LDA as a pre-annotation strategy effectively improves NER performance on informal complaint texts in Indonesia, thus offering a practical and resource-efficient alternative to transformer-based methods for e-government applications.
Aplikasi Prediksi IHSG Berbasis Web Dengan Integrasi Multi-Algoritma Waluyo, Dwi Eko; Paramita, Cinantya; Kinasih, Hayu Wikan; Pergiwati, Dewi; Rafrastara, Fauzi Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6193

Abstract

The four regression algorithms used in predicting the Composite Stock Price Index (IHSG) have contributed significantly, as the test results show that the Decision Tree algorithm outperforms k-Nearest Neighbor, Linear Regression, and Random Forest, especially in terms of Mean Squared Error (MSE) and R2 score. The stages of data collection, pre-processing, and modeling, followed by model performance measurement, have provided valuable insights into the effectiveness of each algorithm. The success of the Decision Tree in this testing has further propelled its development into a web-based application. This conversion process, following the outlined flowchart, integrates various essential aspects of the model, including user interface and back-end integration, ensuring that the application can be accessed and used efficiently and effectively. Furthermore, the black box testing and User Acceptance Testing (UAT) results, using the Mean Opinion Score (MOS), enhance the validity and reliability of the application. Black box testing involving 2 features with 37 steps demonstrates the system's effectiveness in producing valid outputs, from the initial menu display to the prediction results. Additionally, UAT involving students and entrepreneurs as respondents provides in-depth insights into user acceptance. With a focus on functionality at 97.08%, reliability at 96.09%, and usability at 98.09%, UAT yields high scores in all three aspects, with usability achieving the highest score. These results not only confirm the efficiency of the system in performing its functions but also indicate a high level of user satisfaction, strongly suggesting the potential for widespread adoption of this application in the future.
Komparasi dan Implementasi Algoritma Regresi Machine Learning untuk Prediksi Indeks Harga Saham Gabungan Waluyo, Dwi Eko; Kinasih, Hayu Wikan; Paramita, Cinantya; Pergiwati, Dewi; Nohan, Rajendra; Rafrastara, Fauzi Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.6105

Abstract

Indeks Harga Saham Gabungan (IHSG) or Indonesia Composite Index (ICI) is part of the macro indicators of a country that describes the economic condition of a country. ICI is an interesting study to research since its existence will be able to show market sentiment regarding an event that occurred in a country. This research tries to predict the ICI in the future based on historical data. The dataset used in this research is publicly available in Yahoo Finance. The experiment is conducted by implementing some regression machine learning algorithms, such as Decision Tree, Random Forest, k-Nearest Neighbor (kNN), and Linear Regression. As a result, Decision Tree has the lowest MSE value compared to other methods: 1268.242. In this research, a website-based application prototype was also developed that can be used to view IHSG graphs and make future predictions, using the 4 (four) tested algorithms.
PENCAPAIAN KLASIFIKASI TERBAIK BERBASIS PERBAIKAN CITRA CLAHE DAN DARK CHANNEL PRIOR PADA SPESIES IKAN Pergiwati, Dewi; Anggi Pramunendar, Ricardus; Puji Prabowo, Dwi; Alzami, Farrikh; Megantara, Rama Aria
Jurnal Teknik Informatika UMUS Vol 7 No 2 (2025): November
Publisher : Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ikan merupakan bahan pangan lauk-pauk utama yang dikonsumsi manusia untuk menunjang protein hewani dan zat-zat lain yang diperlukan tubuh. Ikan merupakan lauk-pauk pilihan utama yang memiliki harga relative murah dan mudah didapat. Namun pada nyatanya konsumsi ikan di Indonesia sangat rendah dibandingkan dengan negara-negara yang memiliki potensi sumberdaya perikanan yang jauh lebih rendah seperti negara Jepang, Korea Selatan, serta negara-negara di Asia lainnya. Di sisi lain, salah satu kekayaan Indonesia yang sangat berlimpah pada sector perairan adalah biota ikan. Dengan kondisi demikian, upaya peningkatan konsumsi ikan akan memberikan multiflier effect dalam lingkungan masyarakat. Selain meningkatkan tingkat kesehatan serta kecerdasan, juga semakin menggairahkan sektor perikanan untuk dapat mendorong peningkatan penyerapan tenaga kerja, meningkatkan pendapatan serta kesejahteraan pada masyarakat khususnya profesi nelayan, pembudidaya ikan, pengolah hasil ikan serta pihak terkait lainnya. Maka, perlu ditingkatkan kemampuan pengenalan ikan secara otomatis dengan bantuan computer untuk mengenali jenis-jenis ikan yang sangat beragam guna mempermudah proses pengelolaan dan distribusi ikan. Oleh karena itu dalam penelitian ini, peneliti ini mengusulkan untuk melakukan analisis dampak pre-processing dari kombinasi algoritma CLAHE dan DCP yang diterapkan dalam klasifikasi ikan dengan Random Forest.
Co-Authors Agyztia Premana Ahmad Khotibul Umam, Ahmad Khotibul Alzami, Farrikh Anggi Pramunendar, Ricardus Asih Rohmani, Asih Dwi Eko Waluyo Dwi Puji Prabowo, Dwi Puji Fauzi Adi Rafrastara Guruh Fajar Shidik Hayu Wikan Kinasih ISWAHYUDI ISWAHYUDI Megantara, Rama Aria Nohan, Rajendra Paramita, Cinantya Puji Prabowo, Dwi Pulung Nurtantio Andono Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Saraswati, Galuh Wilujeng Sinaga, Daurat