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IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Published by Universitas Kristen Satya Wacana
ISSN : 28287940     EISSN : 28291727     DOI : https://doi.org/10.24246/itexplore.v1i1.2022
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi merupakan jurnal ilmiah tentang penelitian penerapan Teknologi Informasi dalam berbagai bidang, terbit tiga kali dalam setahun, yaitu pada bulan Januari, Mei, dan September untuk masing-masing volumenya. IT-Explore menerima artikel ilmiah hasil-hasil penelitian di bidang penerapan Teknologi Informasi, yang harus didasarkan pada hasil penelitian yang mengetengahkan urgensi, manfaat dan tujuan penelitian. Ruang Lingkup artikel ilmiah yang dapat diterbitkan di IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi mencakup, namun tidak terbatas pada Teknologi Informasi, Sistem Informasi, Bisnis Digital, Komunikasi Visual, Teknologi Komunikasi, Pendidikan Teknologi Informasi, Multimedia, Sains Informasi, dan Penerapan TI dalam berbagai bidang.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 84 Documents
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Penerapan algoritma K-Nearest Neighbors (KNN) untuk klasifikasi citra medis Ujianto, Nur Tulus; Gunawan; Fadillah, Haris; Fanti, Azizah Permata; Saputra, Aryan Dandi; Ramadhan, Ilham Gema
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 1 (2025): IT-Explore Februari 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i1.2025.pp33-43

Abstract

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.
Perancangan WebAR sebagai media hybrid-education mengenai tujuan ke-13 dari SDGS Prestiliano, Jasson; Jayanto, Lydia Rosita; Prasida, T. Arie Setiawan
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 1 (2025): IT-Explore Februari 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i1.2025.pp1-15

Abstract

The compulsive impact of human-caused climate change is now considered more of an environmental issue than a national security threat. The latest 2030 Agenda, the Sustainable Development Goals (SDGs), has a transformational vision to achieve universal literacy worldwide, including Indonesia. Addressing climate change is Goal 13 of 17 SDGs. This study uses a qualitative research method with a linear research strategy to develop hybrid-education media in the form of printed pamphlet media with Augmented Reality (AR) technology applications that run on the Web platform, namely WebAR. The procedure is triggered by the target image of the pamphlet, and the basic HTML structure uses the A-Frame 3D framework. This framework allows for 3D modeling and animation to be loaded into WebAR. This study shows that WebAR-based hybrid-education media can be used as an interactive means to study climate change issues and increase awareness and understanding of climate actions relevant to Goal 13 of the SDGs. The results also show that this media can increase public engagement in combating climate change issues.
Analisis perbandingan machine learning untuk prediksi kelayakan kredit perbankan pada Bank BRI Tegal Andriani, Wresty; Gunawan; Naja, Naella Nabila Putri Wahyuning
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 1 (2025): IT-Explore Februari 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i1.2025.pp82-92

Abstract

Predicting credit worthiness is an important step for banks to reduce the risk of bad credit. This research compares the performance of four classification algorithms, namely SVM, Naïve Bayes, Random Forest and Decision Tree using simulated datasets. The results obtained on the metrics of accuracy, precision, recall, F1 score, and AUC-ROC, show that Decision Tree has the best performance with 42.5% accuracy, 48.3% precision, 47.5% recall, 47.5% F1 score, and AUC 0.60, indicating its ability to is in differentiating credit worthiness. Random Forest achieved an accuracy of 37.5% and an AUC of 0.493, while Naïve Bayes had the lowest accuracy with an accuracy of 27.5% and an AUC of 0.425. SVM gives better results than Naïve Bayes but is still inferior to Decision Tree. This research recommends implementing a Decision Tree as the main model with optimization through hyperparameter tuning, adding relevant features, and handling data accounting. These results are expected to support banking decision making more effectively and efficiently.
Clustering zonasi daerah rawan bencana alam Provinsi Jawa Tengah menggunakan algoritma k-means dan library geopandas Faqih, Muhammad Faiq Adhitya; Mailoa, Evangs
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 1 (2025): IT-Explore Februari 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i1.2025.pp116-127

Abstract

Based on the 2016-2020 Central Java Disaster Risk Assessment, floods and landslides are the most frequent disasters, with 818 flood cases accounting for 31.33% of the total disasters and landslides accounting for 29.57%. This study aims to cluster disaster-prone areas in Central Java using the K-Means algorithm and the GeoPandas library. Data on disaster events for the period 2019-2023 was obtained from the National Disaster Management Agency, while administrative map data of Central Java was downloaded from the Geoportal of Central Java Province. The research stages include data collection, data cleaning, standardization using the Standard Scaler method, application of the K-Means algorithm for regional clustering, and visualization of results using GeoPandas. The results showed that Central Java was divided into four clusters, namely: cluster 0 (disaster-prone areas) includes 3 regions, cluster 1 (non-disaster-prone areas) has 22 regions, cluster 2 (flood-prone areas) consists of 7 regions, and cluster 3 (landslide-prone areas) has 3 regions. The results of this research provide spatial data-based information that can be used as a basis in decision-making for disaster mitigation in Central Java.
Teknik dan aplikasi data mining di Indonesia: tinjauan literatur satu dekade (2015-2024) Saputri, Eliana
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 2 (2025): IT-Explore Juni 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i2.2025.pp138-149

Abstract

The importance of data mining in Indonesia is increasing along with the growth of big data in various strategic sectors. Data mining plays an important role in transforming complex data into useful information to support data-driven decision making, which is urgently needed in the face of competitive challenges and operational complexity. This research aims to examine the development of data mining techniques and applications in Indonesia over the last decade (2015-2024). Through a systematic literature review approach, data was collected from academic publications in SCOPUS indexed databases. From the initial 95 papers found, a further selection was made based on accessibility, title, and abstract until 64 papers were included in the article review. The results show that techniques such as K-Means, Naive Bayes, and Decision Tree are most commonly used. In the business sector, clustering through K-Means is widely applied for market segmentation and consumer pattern analysis. The healthcare sector mainly utilizes classification techniques, such as Naive Bayes and Decision Tree, for disease risk prediction and early diagnosis. Meanwhile, the education sector uses data mining to assess student performance and predict potential dropouts, assisting institutions in optimizing learning strategies.
Pengelompokan wilayah produksi tuna, cakalang, tongkol dan udang di Indonesia menggunakan algoritma K-Means Dwiasnati, Saruni; eliyani, Eliyani; Arif, Sutan Mohammad; Avrizal, Reza
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 2 (2025): IT-Explore Juni 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i2.2025.pp128-137

Abstract

The research was intended to cluster the production areas of Indonesia's fishery products especially Skipjack Tuna, Tuna, Mackarel Tuna, and shrimp using data science techniques. The algorithm used was K-means Clustering. The data used was annual production data for each province for the last 3 years (2019 – 2021). Determination of the number of clusters using the Elbow Method. For each commodity, three clusters were obtained, namely clusters with low production, medium production, and high production. For Skipjack Tuna, there were 19 provinces belonging to the low cluster, 13 provinces being medium, and 2 provinces being high. For Tuna, there were 22 provinces in the low cluster, 9 provinces in the middle, and 3 provinces in the high cluster. For Mackarel Tuna, low was 19 provinces, medium was 12 provinces, and high was 3 provinces. For shrimp, 23 provinces were low, 7 provinces were medium, and 4 provinces were high. High production clusters for Skipjack Tuna were North Sulawesi and North Maluku Provinces, Tuna were North Sulawesi, North Maluku and Maluku Provinces, for Mackarel Tuna were Aceh, East Java and Maluku Provinces, and for shrimp were North Sumatra, West Kalimantan, South Kalimantan and East Kalimantan Provinces.
Analisis forensik jaringan serangan ARP Spoofing menggunakan metode National Institute of Justice (NIJ) Latifah Iriani; Muhammad Nasir Hafizh; Khairina Eka Setyaputri
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 2 (2025): IT-Explore Juni 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i2.2025.pp150-160

Abstract

This study aims to identify evidence of Address Resolution Protocol (ARP) Spoofing attacks in the form of the attacker's and victim's Media Access Control (MAC) addresses, as well as the time of the attack. With the increasing use of computer networks, ARP Spoofing attacks have become a serious threat that can lead to data theft, communication interception, and service disruption. ARP Spoofing can serve as a means to launch more complex attacks, such as Denial of Service (DoS) and Man in the Middle (MITM), which can cripple network systems and steal sensitive information. This study utilizes the Wireshark tool to monitor network traffic, specifically ARP, and applies the National Institute of Justice (NIJ) method as a framework for forensic investigation. The NIJ method was chosen because it provides a systematic approach to identifying, collecting, analyzing, and reporting digital evidence, enabling enhanced attack mitigation and supporting legal aspects in network security investigations. The specific approaches used in this forensic analysis include log analysis, packet capture and analysis using Wireshark, and traffic correlation to identify attack patterns based on time and involved devices. The attack simulation was conducted on Personal Computer (PC) 1 and a routerboard, where communication between these two devices was redirected through the attacker. Based on the test results, it was found that every device in the network experienced ARP Spoofing attacks, which could be detected and analyzed using the NIJ method. The contribution of this study is to provide a more systematic approach to forensic network investigations using the NIJ method, which not only aids in attack detection but also establishes a strong foundation for mitigation actions and legal enforcement in computer network security.
Manajemen risiko teknologi informasi pada PT. XYZ menggunakan framework COBIT 5 Maharani, Mutia; Klasmanto, Andreas; Simanjuntak, Bima Aprianto; Andayani, Sri
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 2 (2025): IT-Explore Juni 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i2.2025.pp173-185

Abstract

PT. XYZ  applies Information Technology in business processes to achieve company goals. achieve company goals. However the use of Technology brings risks that can reduce business effectiveness and efficiency of the business. PT. XYZ faced several problems in its IT department, including network connectivity issues, human error, hardware failure, ERP system integration failure, data security, and human error, hardware failure, ERP system integration failure, data security, data backup failures, system downtime, and power outages. This research applies data collection methods through interviews and risk analysis, evaluating the steps taken by the company in managing IT risks systematically. Research results show that the application of the COBIT 5 Framework is effective in identifying, assessing, and managing IT risks, thus helping the company to minimize losses and ensure smooth operations. This research provides recommendations for improved risk management that is more structured and data-driven risk management, as well as the importance of training for IT staff to improve risk management capabilities in the company. improve risk management capabilities in the company.
Analisis persebaran dan visualisasi penyakit Infeksi Saluran Pernapasan Akut (ISPA) dengan metode K-Means clustering pada Provinsi Jawa Tengah Lazuardi, Febrian Bagaskara; Prillysca Chernovita , Hanna
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 2 (2025): IT-Explore Juni 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i2.2025.pp161-172

Abstract

Acute Respiratory Infection (ARI) is an infectious disease that often affects the upper and lower respiratory tract. This disease is one of the main causes of death in children under five, especially in areas with less favourable environmental conditions. This study aims to map the distribution of ARI in Central Java Province using the K-Means clustering method. Through data analysis that includes inputting, transforming, processing, and visualisation, this study successfully identified three clusters of areas with different levels of ARI distribution. Cluster 0 indicates areas with low risk, such as Demak and Semarang Regency, Cluster 1 indicates areas with medium risk, such as Klaten, Magelang Regency, Pati, while cluster 2 indicates areas with high risk, including Semarang City and Surakarta City. The results of this analysis are presented as a map using QGIS to spatially visualise the distribution of ARI across Central Java. Thus, local governments can design more effective and targeted ARI prevention and control strategies.
Perancangan sistem informasi manajemen gudang berbasis web pada Cozy Mart Prandiska, Kelvin; Nataliani, Yessica
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 2 (2025): IT-Explore Juni 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i2.2025.pp186-195

Abstract

PT. Surya Cahaya Kembar or known as Cozy Mart aims to improve the delivery process and inventory management by using a web-based warehouse management information system. Companies have difficulty monitoring stock and distribution accurately as their business grows rapidly. The system analysis and software development method used in this research is Agile, which allows quick adjustments to user needs. The system is designed to include delivery tracking, stock management features and reports to assist staff in making decisions. System design uses Unified Modeling Language (UML) which describes system functionality as a whole. Test results show significant improvements in user satisfaction, as well as the speed and accuracy of warehouse management. PT. Surya Cahaya Kembar (Cozy Mart) hopes to reduce operational errors and optimize business processes by implementing this system. This helps the overall growth of the company.

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