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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Penelitian Pendidikan Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) SISFOTENIKA JIKO (Jurnal Informatika dan Komputer) MATAPPA: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Universitas Pamulang JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Manajemen Informatika dan Sistem Informasi JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Abdimasku : Jurnal Pengabdian Masyarakat Infotech: Jurnal Informatika & Teknologi Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Journal of Intelligent Software Systems Jurnal Teknologi Informasi Jurnal INFOTEL Scientific Journal of Informatics Journal of Education and ICT
Rikie Kartadie
Universitas Teknologi Digital Indonesia
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Prediction of Cyber Attack Losses by Attack Type and Country with Visual Approach and Quantitative Statistics Sepfanner Kabahing; Rikie Kartadie; Sigit Aditomo; Ivònia Fàtima Ruas da silva; Francisco Xavier; Nur aini Nur aini
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2002

Abstract

Cyberattacks continue to be a major threat to the digital infrastructure of countries around the world, significantly impacting economic stability, data security and public trust. This research aims to analyze financial losses due to cyberattacks by country, attack type, and affected industry sector, utilizing a visual exploratory approach through interactive dashboards and descriptive statistical analysis. The data used includes 3,000 cyber incidents from 10 countries, covering various attack types such as DDoS, Phishing, Malware, and Man-in-the-Middle. Visualization was developed using Power BI with DAX (Data Analysis Expressions) SUMX aggregation formula to calculate Total_Loss in order to dynamically estimate the cost of loss based on user interaction. The analysis showed that DDoS and Phishing attacks were the most frequent attack types, while the Information Technology, Banking and Government sectors recorded the highest accumulative losses. Geographically, the UK, Germany and Brazil were the countries with the largest total losses, with the highest average loss per incident found in Man-in-the-Middle and Phishing attacks. The findings underscore the urgency for the government and private sector to develop more responsive and data-driven mitigation strategies. This research confirms that the integration of dynamic visualization systems with quantitative analysis not only improves understanding of attack patterns, but also supports the decision-making process in efforts to strengthen national cybersecurity in a sustainable manner
CLASSIFICATION OF OIL PALM FRUIT CROSS-SECTIONS USING HSV FEATURE EXTRACTION AND GAUSSIAN NAÏVE BAYES Teguh Junian Kuswanto; WIDYASTUTI ANDRIYANI; Rikie Kartadie; Bambang Purnomosidi D.P; Danny Kriestanto
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2310

Abstract

Accurate identification of oil palm fruit varieties is essential for supporting breeding programs and optimizing seed quality in plantation operations. Manual approaches often lead to inconsistencies due to the high visual similarity among fruit types, particularly between dura and tenera. This study proposes an automatic classification model for oil palm fruit cross-sections using HSV-based color feature extraction combined with a Gaussian Naïve Bayes classifier. A dataset of 186 cross-sectional fruit images was used, consisting of 90 training samples and 96 testing samples representing the dura, pisifera, and tenera varieties. The methodology includes preprocessing, segmentation, HSV feature extraction, model training, and performance evaluation through a confusion matrix. Experimental results show that the proposed model achieves an accuracy of 85%, with misclassifications primarily occurring in the tenera class due to its close resemblance to the dura variety. Compared to Linear Discriminant Analysis (LDA), the proposed approach demonstrates faster computation time and competitive accuracy. These findings indicate that Gaussian Naïve Bayes, supported by HSV feature descriptors, provides an efficient solution for lightweight and cost-effective digital classification of oil palm fruit varieties
Co-Authors Abda Abda Ahmad Fitri Boy Ali Impron Aloysius Agus Subagyo Andriyani, Widyastuti Anggoro, Pius Dian Widi Aprian Pradipta Eka Sihono Ardeana Galih Mardika Asyahri Hadi Nasyuha Atni Hadi, Teguh Hayatul Auliadi, Anissa Sahda Azanuddin Azanuddin Bagas Triaji Bagus Fahrudin Jamal Bambang Purnomosidi Dwi Putranto Danny Kriestanto Dara Kusumawati Dewa, Eddy Putra Eddy Putra Dewa Edy Prayitno Elyas, Ananda Hadi Erna Hudianti Fauzi Erwis Femi Dwi Astuti FIRMANDA, EKA ADI Francisco Xavier Handayani, Latifah Nurrohmah Hanifa, Sizka L. Hendra Maryanto Hikmanto, Riki Israel Mnsen, Heman Koreri Ivònia Fàtima Ruas da silva kholik, Moh. Abdul KURNAEDI, DIDI Leswanto, Tomi Lucia Nugraheni Harnaningrum Lucia Nugraheni Harnaningrum Mardika, Ardeana Galih Muhammad Guntara Muhammad Haries Muhammad Kurnia Ramadhan Nanis Hairunisya Nenen Isnaeni Ngubaidillah, Ahmad Ngubaidillah, Ahmad Nugroho, Ari Fauzi Mukti Nugroho, Muhammad Agung Nur aini Nur aini Panggayuh, Vertika Prayitna, Adiyuda Purnomosidi, Bambang Ragil Murdiantoro Aji Rangga Warsito Rizky, Firahmi Robby Cokro Buwono Robertus Saptoto Robertus Saptoto Rozi, Fahrur Sepfanner Kabahing Sigit Aditomo Sizka L. Hanifa Subiyantoro, Hari Syabani, Dwi Nur Teguh Junian Kuswanto Tommy Suryanto Totok Suprawoto Vertika Panggayuh Winarno, Basuki Heri Wiwi Widayani Y Yohakim Mawarta Yudhi Kusnanto Yudhi Kusnanto Yuli Astuti Zulham Zulham