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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science Wahana : Tridarma Perguruan Tinggi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Antivirus : Jurnal Ilmiah Teknik Informatika CAHAYA tech Journal of Applied Data Sciences Insyst : Journal of Intelligent System and Computation
F.X. Ferdinandus
Institut Sains Dan Teknologi Terpadu Surabaya (ISTTS)
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology

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Identifikasi Serangan Denial Of Service (Dos) Di Jaringan Dengan Algoritma Decision Tree C4.5 Pramana, Meirza; Endang Setyati; F.X. Ferdinandus
WAHANA Vol 73 No 2 (2021): Wahana
Publisher : LPPM Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/wahana.v73i2.4071

Abstract

DoS and DDoS are attacks on computer networks that flood network traffic with continuous requests. For this reason, efforts to secure computer networks and preventive measures need to be carried out by installing firewalls, IDS / IPS devices. The IDS acts as an alarm to the admin that there is abnormal activity on the network, so that the admin can take immediate preventive action. In detecting attacks, IDS uses methods or algorithms to identify anomalies that occur in the network. The algorithm is expected to be able to classify between dangerous traffic and normal traffic. Data mining is suitable to be applied in the classification of network traffic because of the large size of the data and the various types of attacks. The C4.5 decision tree algorithm is expected to be able to be used in the traffic classification process with the aim of identifying DoS attacks. The results of the trial with dataset testing, C.45 yielded an accuracy of 90,68% in classifying traffic for the identification of DoS attacks, and yielded an accuracy of 74,99% in classifying all types of traffic. The Naïve Bayes algorithm is used as a comparison, the accuracy is 86,56% in classifying DoS attack identification traffic, and produces an accuracy of 69,50% in classifying all types of traffic. The C4.5 algorithm is superior in terms of accuracy but takes longer to build the model than the Naïve Bayes algorithm.
Deteksi Jumlah Leukosit Bersentuhan Pada Citra Mikroskopis Leukemia Limfoblastik Akut Menggunakan Multiple K-Means Clustering Andrey Kartika Widhy Hapantenda; F.X. Ferdinandus; Reddy Alexandro Harianto
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2018
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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

Abstract

Leukemia merupakan salah satu penyebab kematian di antara beberapa jenis kanker. Leukemia disebabkan oleh neoplasma maligna atau tumor ganas sel darah putih. Umumnya jenis kanker ini banyak diderita oleh anak-anak dan dewasa di atas usia 50 tahun. Menurut klasifikasi French- American-British (FAB) salah satu tipe Leukemia akut yaitu Leukemia Limfoblastik Akut (LLA). Keberadaan LLA ditandai dengan penyimpangan proliferasi Limfoblast pada sumsum tulang. Salah satu permasalahan pada segmentasi citra LLA adalah pemisahan grup sel yang saling bersentuhan. Hal ini diperlukan untuk analisa kuantitatif yang sangat penting untuk klasifikasi tipe LLA. Beberapa metode pernah digunakan untuk memisahkan grup sel termasuk K-Means Clustering, namun masih sering terjadi over maupun under segmen. Pada Metode Multiple K-Means, nilai K awal merupakan estimasi awal jumlah Leukosit bersentuhan. Proses K-Means dilakukan dengan melakukan iterasi sebanyak tiga hingga lima kali dengan nilai K sama dengan nilai K awal minus dua hingga nilai K awal plus dua, dimana nilai K awal, dengan nilai K minimal dua. Metode yang diusulkan ini mampu mengatasi kelemahan metode K- Means sebelumnya, dengan rata-rata relative error 0 pada 11 gambar yang terdapat 21 grup sel-sel yang saling bersentuhan dalam database ALL-IDB1.
Sistem Pendukung Keputusan Rekomendasi Penerima Bantuan Siswa Miskin Menggunakan Metode Simple Additive Weighting (Saw) Muh Burhanudin; FX Ferdinandus; Muhaji Bayu
CAHAYAtech Vol 8, No 2 (2019): SEPTEMBER 2019
Publisher : STT Cahaya Surya Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.782 KB) | DOI: 10.47047/ct.v8i2.53

Abstract

Tidak sedikit masyarakat Indonesia khususnya di Kota Kediri yang tidak bisa melanjutkan pendidikan sampai jenjang perguruan tinggi karena keterbatasan biaya. Oleh sebab itu, peran pemerintah pusat dalam mengatasi permasalahan ini adalah dengan diberikan Bantuan Siswa Miskin atau BSM. BSM adalah bantuan dari pemerintah berupa sejumlah uang tunai yang diberikan secara langsung kepada siswa sesuai criteria yang telah ditetapkan. Alasan lain bantuan ini diberikan pemerintah adalah untuk kompensasi atas kenaikan harga BBM (Bahan Bakar Minyak). Perangkingan siswa penerima bantuan siswa miskin. Metode yang digunakan dalam perhitungan adalah Simple Additive Weighting perhitungan manual dan perhitungan sistem menunjukkan nilai yang sama. Metode Simple Additive Weighting dipilih karena mampu menyeleksi alternative terbaik dari sejumlah alternatif, dalam hal ini alternative yang dimaksudkan yaitu yang berhak menerima BSM berdasrkan criteria-kriteria yang telah ditentukan. Penelitian dilakukan dengan mencari nilai bobot untuk setiap atribut, kemudian membuat rangking yang akan menentukan alternative yang optimal, yaitu penerima BSM. Sistem ini dapat menjadi alat bantu kerja tim penyeleksi bantuan dalam melakukan penyeleksian. Telah diimplementasikan sistem pendukung keputusan penerima bantuan siswa miskin dengan menggunakan Simple Additive Weighting (SAW) di SMK Plus Darus Salam Kediri. Aplikasi SPK yang dibuat dapat memberikan solusi terbaik dalam penentuan rekomendasi untuk mendukung keputusan penerima bantuan siswa miskin. Kata Kunci : Sistem Pendukung, BSM, Simple Additive Weighting
Sistem Pendukung Keputusan Pemilihan Paket Pernikahan Dengan Metode Saw Berbasis Web Irvan Sulistiya Putra; FX Ferdinandus; Muhaji Bayu
CAHAYAtech Vol 8, No 2 (2019): SEPTEMBER 2019
Publisher : STT Cahaya Surya Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (684.695 KB) | DOI: 10.47047/ct.v8i2.50

Abstract

Pernikahan merupakan acara sakral dimana dua orang saling mengikat janji dalam ikatan perkawinan yang sah. Mempersiapkan momen pesta pernikahan yang berkesan merupakan hal yang tidak mudah bagi calon pengantin, sehingga calon pengantin memerlukan jasa Wedding Organizer. Akan tetapi, berbagai paket pernikahan yang disediakan oleh Wedding Organizer tidak jarang menyulitkan pelanggan untuk memilih. Dari analisa tersebut, sebuah aplikasi pendukung keputusan sangat dibutuhkan guna membantu Wedding Organizer mengelola paket pernikahan dan membantu pelanggan dalam memilih paket yang sesuai dengan buget. Simple Additive Weighting menjadi metode yang dipakai pada sistem ini. PHP dan Html sebagai bahasa pemrograman, server Laragon dengan MySQL sebagai databasenya. Penelitian dilakukan pada Sulistiya Wedding Organizer dengan menggunakan wawancara dan observasi sebagai metode untuk mengumpulkan data. Hasil dari sistem ini nantinya dapat  menampilkan alternatif paket pernikahan berdasarkan peringkat.         Kata kunci : HTML, MySQL, SPK, Simple Additive Weighting, Paket Pernikahan, PHP.
Sistem Pendukung Keputusan Pemilihan Handphone Dengan Metode Simple Additive Weighting Berbasis WEB Aji Amijaya; FX Ferdinandus; Muhaji Bayu
CAHAYAtech Vol 8, No 2 (2019): SEPTEMBER 2019
Publisher : STT Cahaya Surya Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (322.208 KB) | DOI: 10.47047/ct.v8i2.47

Abstract

Pemilihan handphone sangatlah penting bagi kebutuhan setiap orang. Karena masing-masing orang memiliki kriteria tertentu dalam menentukan pilihannya. Ada sejumlah kasus yang ditemukan pada beberapa pengguna handphone mengaku salah dalam pemilihan handphone karena tidak mengetahui apa yang menjadi pertimbangan mereka untuk menentukan pilihan handphone yang tepat sesuai dengan kebutuhan mereka. Dengan adanya program “Sistem Pendukung Keputusan Pemilihan (SPK) Handphone Dengan Metode Simple Additive Weighting (SAW)” ini, peneliti berharap dapat membantu menyelesaikan masalah dalam pemilihan handphone yang sesuai dengan kebutuhan pengguna secara cepat dan efisien. Penelitian ini bertujuan untuk menghasilkan suatu sistem yang mampu membantu pengguna dalam menentukan pilihan handphone yang sesuai dengan kebutuhan mereka berdasarkan perhitungan dari bobot kriteria yang telah ditentukan untuk mendapatkan hasil perhitungan yang akurat..        Kata kunci: Pemilihan handphone, SAW, SPK
OPTIMASI TEKNIK STEGANOGRAFI AMELSBR PADA EMPAT BIT TERAKHIR DENGAN COVER IMAGE BERWARNA Muhammad Alfin Fikri; F. X. Ferdinandus
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 16 No 1 (2022): Mei 2022
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v16i1.1967

Abstract

The dynamic growth of data transmission in the modern age needs a safe interchange of data. Usually, the answer is between steganography and cryptography. Steganography is a well-established technique for protecting hidden data from unauthorized access into a cover object in such a fashion that it is easily imperceptible to human eyes. Purpose: This work proposes an optimization towards Adaptive Minimum Error Least Significant Bit Replacement (AMELSBR) by modifying at least four bits to be replaced. Sadly, steganography is often detectable by human eyes, especially if underwent such bit replacement modification. To solve this problem, we have since optimized AMELSBR. Method: Specifically, we optimized AMELSBR so it can use the last 4 bit in cover image. We divided the experiment scheme based on per replaced bit, namely one bit, two bits, three bits, and four bits. The object covered in this work is colored images, divided into four classes: abstract images, landscape images, animal images, and fruit images.. Results: We were able to minimize Mean Squared Error into at least 1 and Peak-to-Signal Ratio into at least under 35 dB in this work. We also tested the stego images as the end result of the AMELSBR steganography process using brightness, contrast, resize, noise, and blur. Conclusion: Our experiment proves that the optimized AMELSBR as steganography technique can be reliable for text embedding in the cover image without using cryptography as intended in standard use.
3D Visualization for Lung Surface Images of Covid-19 Patients based on U-Net CNN Segmentation FX Ferdinandus; Esther Irawati Setiawan; Eko Mulyanto Yuniarno; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.709

Abstract

The Covid-19 infection challenges medical staff to make rapid diagnoses of patients. In just a few days, the Covid-19 virus infection could affect the performance of the lungs. On the other hand, semantic segmentation using the Convolutional Neural Network (CNN) on Lung CT-scan images had attracted the attention of researchers for several years, even before the Covid-19 pandemic. Ground Glass Opacity (GGO), in the form of white patches caused by Covid-19 infection, is detected inside the patient’s lung area and occasionally at the edge of the lung, but no research has specifically paid attention to the edges of the lungs. This study proposes to display a 3D visualization of the lung surface of Covid-19 patients based on CT-scan image segmentation using U-Net architecture with a training dataset from typical lung images. Then the resulting CNN model is used to segment the lungs of Covid-19 patients. The segmentation results are selected as some slices to be reconstructed into a 3D lung shape and displayed in 3D animation. Visualizing the results of this segmentation can help medical staff diagnose the lungs of Covid-19 patients, especially on the surface of the lungs of patients with GGO at the edges. From the lung segmentation experiment results on ten patients in the Zenodo dataset, we have a Mean-IoU score = of 76.86%, while the visualization results show that 7 out of 10 patients (70%) have eroded lung surfaces. It can be seen clearly through 3D visualization.
Indonesian Language Term Extraction using Multi-Task Neural Network Joan Santoso; Esther Irawati Setiawan; Fransiskus Xaverius Ferdinandus; Gunawan Gunawan; Leonel Hernandez
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p160-167

Abstract

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two multilayer perceptron neural networks to perform Part-of-Speech (PoS) labeling and Noun Phrase Chunking. Our models were trained as a joint model to solve this problem. Our proposed method, with an f-score of 86.80%, can be considered a state-of-the-art algorithm for performing term extraction in the Indonesian Language using noun phrase chunking.
Aspect-Based Sentiment Analysis of Healthcare Reviews from Indonesian Hospitals based on Weighted Average Ensemble Setiawan, Esther Irawati; Tjendika, Patrick; Santoso, Joan; Ferdinandus, FX; Gunawan, Gunawan; Fujisawa, Kimiya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.328

Abstract

Public assessments are essential for evaluating hospital quality and meeting patient demand for superior medical treatment. This study offers a novel approach to aspect-based sentiment analysis (ABSA), which consists of aspect extraction, emotion categorization, and aspect classification. The goal is to examine patient reviews (6,711 reviews) from Google assessments of 20 Indonesian hospitals, broken down by categories including cost, doctor, nurse, and other categories. For example, there are 469 good, 66 negative, and 7 neutral ratings for cleanliness and 93 positive, 125 negative, and 19 neutral reviews for pricing in the sample, which covers a range of attitudes. Using the Conditional Random Field (CRF) approach, aspect phrase extraction was refined and word characteristics and positional tags were adjusted, resulting in an improvement in the F1-score from 0.9447 to 0.9578. The Support Vector Machine (SVM) model had the greatest F1-score of 0.8424 out of two strategies used for aspect categorization. With the addition of sentiment words, sentiment classification improved and led by SVM to an ideal F1-score of 0.7913. For aspect and sentiment classification, a Weighted Average Ensemble approach incorporating SVM, Naïve Bayes, and K-Nearest Neighbors was employed, yielding F1-scores of 0.7881 and 0.8413, respectively. The use of an ensemble technique for sentiment and aspect classification and the incorporation of hyperparameter optimization in CRF for aspect term extraction, which led to notable performance gains, are the innovative aspects of this work.
MultiResUNet for COVID-19 Lung Infection Segmentation Based on CT Image Ferdinandus, F.X.; Setiawan, Esther Irawati; Santoso, Joan
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.85386

Abstract

Image segmentation plays a crucial role in medical image analysis, facilitating the identification and characterization of various pathologies. During the COVID-19 pandemic, this technique has proven valuable for detecting and assessing the severity of infection. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have significantly enhanced the efficacy of image segmentation. Numerous CNN-based architectures have been proposed in the literature, with MultiResUNet emerging as a promising approach. This study investigates the application of the MultiResUNet architecture for segmenting regions of COVID-19 infection within patient lung CT images. Experimental results demonstrate the effectiveness of MultiResUNet, achieving an average Dice score of 73.10%.
Co-Authors Afandi, Acxel Derian Aji Amijaya Cahyadi, Billy Kelvianto Eko Mulyanto Yuniarno Endang Setyati Esther Irawati Setiawan Fujisawa, Kimiya Gunawan Gunawan Gunawan Gunawan Hapantenda, Andrey Kartika Widhy Irvan Sulistiya Putra Joan Santoso Leonel Hernandez Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mikhael Setiawan Muh Burhanudin Muhaji Bayu Muhammad Alfin Fikri Pramana, Meirza Reddy Alexandro Harianto Saputra, Daniel Gamaliel Setiawan, Esther Tjendika, Patrick