Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

2.475

P-Index

This Author published in this journals

All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Informatika dan Teknik Elektro Terapan Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science INTEGER: Journal of Information Technology JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jusikom: Jurnal Sistem Informasi Ilmu Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Nusantara Science and Technology Proceedings Journal of Computer, Electronic, and Telecommunication (COMPLETE) KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika

Agung Mustika Rizki, Agung Mustika

Brawijaya University

Author-ID : 610131

Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Other

Published : 23 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3

PENERAPAN MODEL HIBRIDA CNN-KNN UNTUK KLASIFIKASI PENYAKIT MATA Wardhani, Adil Sandy; Anggraeny, Fetty Tri; Rizki, Agung Mustika
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 3 (2024): JATI Vol. 8 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i3.9774

Penyakit mata merupakan gangguan yang menyerang organ mata akibat dari virus, bakteri, dan kebiasaan buruk. Saat ini, penggunaan teknologi kecerdasan buatan populer dalam mendiagnosa penyakit mata untuk memungkinkan penanganan lebih dini agar tidak memicu kebutaan. Convolutional Neural Network (CNN) merupakan algoritma klasifikasi yang paling umum digunakan karena dapat menghasilkan akurasi yang baik dalam memproses data yang berformat gambar. K-Nearest Neighbor (KNN) juga termasuk algoritma untuk klasifikasi dengan menggunakan parameter nilai tetangga terdekat. Pada penelitian ini, peneliti akan melakukan hibrida atau menggabungkan algoritma CNN dan KNN dengan CNN sebagai proses ekstraksi fitur serta KNN sebagai klasifikasi. Penelitian akan dilakukan dengan menggunakan beberapa parameter pada CNN dan KNN untuk mencari akurasi terbaik. Hasil akurasi terbaik dari penerapan model hibrida CNN-KNN pada penyakit mata diperoleh dengan menggunakan optimasi adam learning rate 0,001 dan nilai tetangga terdekat 9 dengan akurasi sebesar 94,03%.

Algoritme Particle Swarm Optimization (PSO) untuk Optimasi Perencanaan Produksi Agregat Multi-Site pada Industri Tekstil Rumahan Rizki, Agung Mustika; Nurlaili , Afina Lina
Journal of Computer Electronic and Telecommunication Vol. 1 No. 2 (2020): December
Publisher : Institut Teknologi Telkom Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/complete.v1i2.73

In the industrial world, companies need to manage their production areas well. One way is to implement aggregate production planning. The goal is that the production costs incurred by the company can be controlled properly. However, production planning cannot be formulated quickly. The problem is more complicated if the company has several production locations. The difference in location also affects the production references and standards applied in each location. Based on these problems, the authors propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the problem of aggregate production planning in order to obtain the optimal solution for each production location. As a result, the algorithm proposed by the author can produce optimal and efficient solutions for 6 production sites. This is evidenced by the relatively short time required compared to the previous planning by the company.

PERAMALAN TINGKAT INFLASI DI INDONESIA MENGGUNAKAN ARTIFICIAL BEE COLONY DAN XGBOOST Mohammad, Farrel Adel; Rizki, Agung Mustika; Sihananto, Andreas Nugroho
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4827

Pertumbuhan ekonomi dan stabilitas harga merupakan fokus utama bagi negara-negara, termasuk Indonesia. Inflasi, sebagai indikator fluktuasi harga barang dan jasa, memainkan peran penting dalam stabilitas ekonomi. Peramalan inflasi menjadi kunci bagi pemerintah dan pemangku kepentingan ekonomi untuk merancang kebijakan yang responsif. Model pembelajaran mesin, seperti XGBoost, telah digunakan untuk tujuan ini, namun penyetelan hiperparameter yang optimal menjadi kunci keberhasilannya. Algoritma optimisasi seperti Artificial bee colony (ABC) dapat mengotomasi proses penyetelan hiperparameter XGBoost, meningkatkan efisiensi dan kinerja model. Penelitian ini membuktikan bahwa kombinasi Artificial bee colony dan XGBoost berhasil meramalkan tingkat inflasi bulanan di Indonesia dengan hasil yang akurat. Implementasi metode ini memberikan rata-rata skor RMSE 0.155066, skor MAE 0.115655, dan skor MAPE 0.795767.

SEGMENTASI SEL PAP SMEAR SERVIKS BERTUMPUK MENGGUNAKAN LOCAL ADAPTIVE THRESHOLDING DAN WATERSHED Lutfia, Qonita; Mandyartha, Eka Prakarsa; Rizki, Agung Mustika
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4811

Kanker serviks merupakan ancaman kesehatan global serius, dengan WHO melaporkan sekitar 604.000 kasus baru dan 342.000 kematian pada tahun 2020. Penelitian ini mengeksplorasi kombinasi metode local adaptive threshold dan segmentasi watershed untuk meningkatkan akurasi deteksi dini kanker serviks dengan lebih akurat mengidentifikasi sel-sel yang saling tumpang tindih pada Pap Smear. Metode Local Adaptive Threshold menyesuaikan nilai ambang berdasarkan karakteristik lokal gambar, dan segmentasi watershed diaplikasikan untuk memisahkan sel-sel yang saling tumpang tindih. Kombinasi ini menunjukkan hasil yang menjanjikan dalam meningkatkan efisiensi dan akurasi skrining kanker serviks, mendukung strategi WHO untuk eliminasi kanker serviks. Namun, adopsinya menghadapi tantangan di negara berkembang karena keterbatasan sumber daya dan kesenjangan digital. Tes menggunakan K-Fold Cross Validation (5 dan 7) menunjukkan akurasi 90.93% untuk k=5, dengan rata-rata precision 97.97%, recall 49.22%, dan F1-Score 65.50%. Pada k=7, hasil sedikit meningkat dengan precision 97.99%, recall 49.24%, dan F1-Score 65.53%. Rata-rata PSNR adalah 43.4341 dB dan MSE 3.45061, menegaskan efektivitas metode.Kata Kunci: Local Adaptive Thresholding, Watershed, Cervical Cancer, Pap Smear

Prediksi Harga Rumah Di Jabodetabek Menggunakan Metode Artificial Neural Network Hafizh, Muhammad Abdullah; Subairi, Subairi; Libriawan, Raditya Dimas; Maulana, Naufal Duta; Rizki, Agung Mustika
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 5, No 2 (2024)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2024.v5i2.6806

A house is a fundamental need for humans. Determining house prices is a crucial aspect of property transactions, especially in major areas like Jabodetabek, where property prices are consistently rising. Prediction is a suitable tool to assist in decision-making for determining house prices. There are numerous methods that can be applied for prediction; the author employs the Artificial Neural Network (ANN) method. ANN is known as a highly flexible predictive algorithm capable of accommodating various input features. The results of using the ANN method for predicting house prices in the Jabodetabek area show a Mean Absolute Error (MAE) of 0.209, Mean Squared Error (MSE) of 0.159, and Mean Absolute Percentage Error (MAPE) of 4.951.

Implementation of Genetic Algorithm to Solve Travelling Salesman Problem with Time Window (TSP-TW) for Scheduling Tourist Destinations in Malang City Yuliastuti, Gusti Eka; Mahmudy, Wayan Firdaus; Rizki, Agung Mustika
Journal of Information Technology and Computer Science Vol. 2 No. 1: June 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

In doing travel to some destinantions, tourist certainly want to be able to visit many destinations with the optimal scheduling so that necessary in finding the best route and not wasting lots of time travel. Several studies have addressed the problem but does not consider other factor which is very important that is the operating hours of each destination or hereinafter referred as the time window. Genetic algorithm proved able to resolve this travelling salesman problem with time window constraints. Based on test results obtained solutions with the fitness value of 0,9856 at the time of generation of 800 and the other test result obtained solution with the fitness value of 0,9621 at the time of the combination CR=0,7 MR=0,3.

Determining Optimum Production Quantity on Multi-Product Home Textile Industry by Simulated Annealing Yuliastuti, Gusti Eka; Rizki, Agung Mustika; Mahmudy, Wayan Firdaus; Tama, Ishardita Pambudi
Journal of Information Technology and Computer Science Vol. 3 No. 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Production planning is a plan aimed at controlling the quantity of products produced. Production planning is very important to be carried out by the company so that the production will always be controlled. It is very difficult to plan production with a variety of product variations because each product certainly has a different demand value from its customers. This has become a complex problem so an algorithm is needed to overcome these problems. Simulated Annealing can produce optimal solutions more effectively and efficiently. Production costs generated by applying Simulated Annealing are Rp. 6,902,406,000, - for all types of products, which is better than existing condition.

Variable Neighborhoods Search for Multi-Site Production Planning Rizki, Agung Mustika; Yuliastuti, Gusti Eka; Mahmudy, Wayan Firdaus; Tama, Ishardita Pambudi
Journal of Information Technology and Computer Science Vol. 3 No. 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

In the home textile industry, production planning needs to be done so that the production costs incurred by the company can be well controlled. Production planning is a problem that cannot be solved in a short time. Problems are more complex if the company has several production branches in other cities, with rules and standards that are certainly very different from one city to another. Based on this background, an algorithm is needed that can solve production planning problems for companies with many production branches in order to obtain optimal solutions. VNS is applied by the author and produces an optimal and efficient solution because the time needed is relatively short compared to the planning carried out previously by the company.

Penerapan Convolutional Neural Network (CNN) dalam Klasifikasi Citra MRI untuk Deteksi Tumor Otak Manusia Dimara, Denis Lizard Sambawo; Putri, Shintyadhita Wirawan; Amelia, Rizky; Arishandy, Zalfa Ibtisamah; Rizki, Agung Mustika
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 4, No 2 (2023)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2023.v4i2.6960

Brain tumors are deadly diseases with a high mortality rate, making early diagnosis crucial to improving patient survival rates. However, manual diagnosis through Magnetic Resonance Imaging (MRI) often requires significant time and is prone to errors. This study developed an MRI image classification method using the EfficientNetB3-based Convolutional Neural Network (CNN) architecture to detect brain tumors. The dataset used was obtained from Kaggle, consisting of 253 brain MRI images, including 98 normal and 155 abnormal images. The data were preprocessed through normalization and resizing to 224x224 pixels. The model employed transfer learning techniques using pretrained weights from ImageNet, enhanced with additional layers to improve performance. Evaluation was conducted using metrics such as accuracy, precision, recall, F1-score, AUC, as well as confusion matrix and classification report analyses. The results showed that the EfficientNetB3 model achieved an overall accuracy of 86%, demonstrating its capability to support brain tumor diagnosis processes quickly and accurately. This implementation is expected to provide a significant contribution to early detection of brain tumors and improve patient care quality in the medical field.

A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting Shiddiqi, Ary Mazharuddin; Ardi, Bagaskoro Kuncoro; Amaliah, Bilqis; Mogi, I Komang Ari; Rizki, Agung Mustika; Nuralamsyah, Bintang; Adillion, Ilham Gurat; Alzamzami, Moch. Nafkhan
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1264

Time-series forecasting plays a crucial role in various fields, including economics, healthcare, and meteorology, where accurate predictions are essential for informed decision-making. As data volume and complexity continue to grow, the need for efficient and reliable forecasting methods has become more critical. iTransformer, a recent innovation, improves interpretability while effectively handling multivariate data. In this study, the author proposes Dual-Net iTransformer, a novel approach that integrates iTransformer with a dual-network framework to enhance both accuracy and efficiency in time-series forecasting. This research aims to evaluate and compare the performance of traditional methods, iTransformer, and Dual-Net iTransformer, highlighting the advantages of the proposed model in improving forecasting outcomes.

Co-Authors Adillion, Ilham Gurat Adityawan, Firza Prima Alzamzami, Moch. Nafkhan Andreas Nugroho Sihananto Anggraini Puspita Sari Ardi, Bagaskoro Kuncoro Arishandy, Zalfa Ibtisamah Ary Mazharuddin Shiddiqi ASHARI, FAISAL Bilqis Amaliah Citra Nurina Prabiantissa Dimara, Denis Lizard Sambawo Eka Prakarsa Mandyartha EKO WAHYUDI Eva Yulia Puspaningrum Fetty Tri Anggraeny Firza Prima Aditiawan Gusti Eka Yuliastuti Hafizh, Muhammad Abdullah Henni Endah Wahanani Hermadiputri, Firdausa Yasmin Hidra Amnur I Komang Ari Mogi, I Komang Ari IMANDAYANTI, NUR EZA indrawanti, annisaa sri Ishardita Pambudi Tama Libriawan, Raditya Dimas Lutfia, Qonita Mandyarth, Eka Prakarsa Maulana, Naufal Duta Mina, Nora Mohammad, Farrel Adel Muhammad Muharrom Al Haromainy Muhammad Rivai Nuralamsyah, Bintang Nurlaili , Afina Lina Pratama, Muhammad Lutfi Putri, Shintyadhita Wirawan Rizky Amelia Saputra, Rendi Cahya Setianto, Christian Wahyu Subairi Subairi Taufiqqurrahman, Husain Titin Sumarni Wardhani, Adil Sandy Wayan Firdaus Mahmudy

Title

Found 23 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 23 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 23 Documents
Search