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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) KAPAL Jurnal Ilmu Pengetahuan dan Teknologi Kelautan SITEKIN: Jurnal Sains, Teknologi dan Industri Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Mechanical Engineering for Society and Industry Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat)
Kurnianingtyas, Diva
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Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Transportation Other

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The necessity of implementing AI for enhancing safety in the Indonesian passenger shipping fleet Rahadi, Shinta J.A.; Prasetyo, Dimas Fajar; Hakim, Muhammad Luqman; Sari, Dian Purnama; Virliani, Putri; Rahadi, Cakra W.K.; Rina, Rina; Yulfani, R. D.; Mohammad, Luthfansyah; Kurnianingtyas, Diva
Kapal: Jurnal Ilmu Pengetahuan dan Teknologi Kelautan Vol 21, No 1 (2024): February
Publisher : Department of Naval Architecture - Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/kapal.v21i1.58868

Abstract

The shipping industry, grappling with escalating challenges, increasingly adopts Artificial Intelligence (AI) to enhance efficiency, safety, and environmental impact. Experts endorse ship automation and AI implementation for safety, navigation, and operational efficiency in ferry networks. This paper underscores AIS technology's role in maritime safety and environmental protection, emphasizing AI's potential in navigation and knowledge gap bridging. Indonesia, with its numerous islands and significant population, faces complex challenges in ensuring safe maritime transportation. Collaborative efforts among the government, industry, and stakeholders are vital for enhancing safety standards across the archipelago. Despite regulations, Indonesia contends with a high ferry accident rate, prompting the need for preventive measures. The study reviews AI's application in preventing sea accidents, recognizing its contributions and potential effectiveness in maritime safety. Acknowledging challenges like data quality and cybersecurity, the paper emphasizes the necessity of AI development for passenger ship safety. It concludes by highlighting significant research efforts, endorsing AI's promising role in reshaping the industry for improved efficiency and safety. Further exploration of AI applications, particularly in passenger ship safety, is recommended to meet evolving challenges in the maritime sector.
Perbandingan Kinerja Model YOLOv6 dan YOLOv7 dalam Mendeteksi Sampah Perairan Kirana, Naufal Laksana; Kurnianingtyas, Diva; Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 8 No 13 (2024): Publikasi Khusus Tahun 2024
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Naskah ini akan diterbitkan di Jurnal Internasional INASS
Rekomendasi Menu dengan Bahan Makanan Alternatif Berdasarkan Kategorisasi Nutrisi menggunakan K-Means dan BERT Naufal, Muhammad Jilan; Kurnianingtyas, Diva; Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Naskah ini akan diterbitkan di jurnal Applied Clinical Informatics
Implementasi Sistem Deteksi Anomali pada Jaringan Komputer dengan Pendekatan XGBoost dan Data SNMP Rudianto, Amadeo Muhammad Augie; Pramukantoro, Eko Sakti; Kurnianingtyas, Diva
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 2 (2025): Februari 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Reliabilitas jaringan sangat bergantung pada kemampuan sistem untuk mendeteksi dan merespons anomali secepat mungkin. Anomali dalam jaringan dapat berupa aktivitas mencurigakan, kegagalan perangkat keras, atau serangan siber yang mengancam ketersediaan layanan. Penelitian ini bertujuan untuk mengembangkan sistem deteksi anomali jaringan berbasis data Simple Network Management Protocol (SNMP) menggunakan model XGBoost. Data SNMP yang digunakan pada penelitian ini mencakup berbagai metrik jaringan, sehingga memungkinkan sistem untuk mendeteksi pola-pola kompleks secara akurat. Penelitian ini mencakup proses pengembangan model klasifikasi, implementasi sistem inferensi deteksi anomali berbasis SNMP, dan evaluasi kinerja sistem melalui skenario pengujian yang realistis. Hasil pengujian menunjukkan bahwa model XGBoost mampu mendeteksi berbagai jenis serangan dengan akurasi mencapai 99,82% pada data uji yang belum pernah dilihat. Sistem inferensi yang diimplementasikan juga mampu bekerja secara kontinu untuk memproses data SNMP dan memberikan hasil prediksi yang konsisten. Namun, penelitian ini juga menemukan bahwa sistem memiliki keterbatasan dalam hal fleksibilitas untuk menangani pola serangan baru yang tidak terwakili dalam data pelatihan. Penelitian ini berhasil mengembangkan dan mengimplementasikan sistem deteksi anomali berbasis SNMP dengan model XGBoost yang andal dan akurat. Temuan ini diharapkan dapat menjadi landasan bagi pengembangan lebih lanjut untuk meningkatkan fleksibilitas dan adaptabilitas sistem dalam lingkungan jaringan yang dinamis.
Penjadwalan Makan Otomatis untuk Ibu Hamil Menggunakan Algoritma Genetika pada Aplikasi Mobile Berbasis Jetpack Compose Putra, I Gusti Ngurah Mayun Suryatama Giri; Kurnianingtyas, Diva; Huda, Fais Al
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Naskah ini akan diterbitkan di e-Informatica Software Engineering Journal
Eksplorasi Skema Reproduksi Algoritma Genetika untuk Vehicle Routing Problem dengan Time Windows (Studi Kasus: Anekapay) Hanif, Haidar; Kurnianingtyas, Diva; Wahyu Widodo, Agus
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Naskah ini akan diterbitkan di Engineering Optimization Journal
A review of machine learning methods to build predictive models for male reproductive health Adimoelja, Ariawan; Firdaus Mahmudy, Wayan; Kurnianingtyas, Diva
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3739-3749

Abstract

Developing of artificial intelligence (AI) technology in the medical sector, especially in the part of male reproduction and infertility, is growing rapidly. In both supervised learning and unsupervised learning, AI has been tested and applied to medical personnel to treat their patients. Calculations from simple to complex probability and a combination of some different methods have conducted results of accurate and precise. The results can help determine the condition of male infertility. Artificial neural network (ANN) and fuzzy inference system (FIS) are AI techniques applied to male health issues. ANN is adequate for processing large amounts of combined data in a short time. ANN also has a high level of accuracy and excellent adaptive capabilities. Afterwards, FIS can reflect problems using models with easy to understand, flexible, and also competent to model complex linear functions for decision-making. Based on the advantages of ANN and FIS, it is hoped acquiring prediction results of better and more accurate in male health issues.
Digital Based Branding of Tourism and MSME Product in Tasikmadu and Gemaharjo Village, Trenggalek Regency Dewi, Candra; Rahayudi, Bayu; Mahmudy, Wayan Firdaus; Kurnianingtyas, Diva
Journal of Innovation and Applied Technology Vol 10, No 2 (2024)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2024.10.02.002

Abstract

Tasikmadu and Gemaharjo villages are two villages in Watulimo have considerable potential in tourism and MSME products. However, rural communities including tourism actors, village officials, and MSME players are still unable to maximize the potential of the village, due to the lack of ability of tourism managers and MSME actors in promoting tourism and its products widely. Therefore, this activity intends to increase the knowledge and ability of tourism actors, village officials, and MSME actors from Tasikmadu and Gemaharjo villages in promoting tourism and MSME products. This is done by developing tourism profiles and MSMEs digitally.  The evaluation results showed that the activity was quite successful in increasing tourism promotion in Tasikmadu by utilizing the village's Instagram social media. In addition, the success of MSME promotion can also be seen by making logos and packaging for MSME products, both in Tasikmadu and Gemaharjo.
Chest X-Ray Images Clustering using Convolutional Autoencoder for Lung Disease Detection Syafira, Putri Amanda; Yudistira, Novanto; Kurnianingtyas, Diva
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2478

Abstract

In healthcare, medical imaging is commonly used for health assessments. One of the most commonly used types of medical imaging is X-ray imaging. One area that often undergoes examination using this modality is the lungs, where healthcare professionals use X-ray images to interpret the results. However, prolonged interpretation of X-ray results by healthcare professionals and other work activities can lead to errors and potentially result in invalid disease identification. There is a need for a system that can classify the detection results from these images to assist healthcare professionals in their tasks. Various methods can be used for this purpose, such as classification, clustering, segmentation, etc. However, data labeling requires significant resources and costs, especially with large-scale datasets. One possible solution is to use an unsupervised learning approach to address this. One method under unsupervised learning is clustering, which allows the system to process and understand data patterns without needing external annotations or manual labeling. This research uses an autoencoder as a subcategory of unsupervised learning. This is because autoencoders can automatically extract relevant features from the data without needing external label guidance. The research utilizes a dataset consisting of 700 X-ray images of the chest, including 500 images showing disease and 200 normal X-ray images. This research aims to determine the effectiveness of clustering methods using an autoencoder model in grouping X-ray image results. The research conducted two experiments. In the first experiment, an autoencoder with 18 Layers was used, resulting in the best performance with a value of K=15 and a rand index of 76%. In the second experiment, an autoencoder with a reduced number of Layers (11 Layers) was used, and it achieved the best performance with a value of K=15 and a rand index of 87%.
Opportunities of Artificial Intelligence for Authentication and Assurance of Halal Products Mahmudy, Wayan; Kurnianingtyas, Diva
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101661

Abstract

The need for Muslims to obtain halal food encourages the development of systems that can assist in the authentication and assurance of halal products. Various Artificial Intelligence (AI) based systems have been developed to meet these needs. AI is a technology that enables computers to understand, learn, and perform tasks that would typically require human intelligence and reasoning to act and make decisions. With the help of high-speed computers, AI algorithms learn patterns in available data and perform assigned tasks with high accuracy and efficiency. This study focusses on the application of AI for the identification, monitoring, verification and validation of halal products. Also, this paper limits the discussion to food products with the topics of (1) Inspection of raw materials and detection of contamination to ensure the halalness of raw materials (2) Market monitoring and recommendation of halal restaurants to help Muslim consumers. The consumers were looking for a restaurant that suits their needs and preferences (3) Verification of halal certification to ensure that the certification given to the product comes from a competent and trustworthy authority.
Co-Authors Achfas Zacoeb Adimoelja, Ariawan Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Anindito Purnowidodo Ariadi, Yudhi Bajsair, Fath' Hani Sarli Bayu Rahayudi Candra Dewi Daud, Nathan Dian Purnamasari Dimas Kurniawan Dyan Putri Mahardika, Dyan Putri Edy Santoso Eko Sakti Pramukantoro, Eko Sakti Fabiano, Stefanus Gembong Edhi Setyawan Geoffrey Manurung, Daniel Hanif, Haidar Huda, Fais Al Ika Amalia Kartika Imam Cholissodin Indriati Indriati Indriati K., Dwi Angraeni Kirana, Naufal Laksana Kohei Arai Lailil Muflikhah Mahmudy, Wayan Marji Marji Moch. Agus Choiron Mohammad, Luthfansyah Muh. Arif Rahman Muhammad Luqman Hakim, Muhammad Luqman Mukhrodi, Dillah Lyra Naufal, Muhammad Jilan Novanto Yudistira Prasetyo, Dimas Fajar Putra, I Gusti Ngurah Mayun Suryatama Giri Rahadi, Cakra W.K. Rahadi, Shinta J.A. Rahardian, Brillian Aristyo Rennie, Allan E.W. Rina Rina Rudianto, Amadeo Muhammad Augie Soebroto, Arief Andi Sukma, Lintang Cahyaning Syafira, Putri Amanda Tusty Nadia Maghfira, Tusty Nadia Virliani, Putri Wayan Firdaus Mahmudy Wirawan, Willy Artha Yulfani, R. D.