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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Teknologi dan Rekayasa Matematika: Jurnal Teori dan Terapan Majalah Ilmiah Matematika Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) Journal of the Indonesian Mathematical Society Journal of Information Technology and Computer Science (JOINTECS) MIND (Multimedia Artificial Intelligent Networking Database) Journal Journal of Applied Data Sciences
Ernastuti Ernastuti
Gunadarma University
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics

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MRI Sagittal Image Segmentation from Patients with Abdominal Aortic Aneurysms Desti Riminarsih; Cut Maisyarah Karyati; Achmad Benny Mutiara; Bambang Wahyudi; E. Ernastuti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3520

Abstract

Early detection in patients with abdominal aortic aneurysm (AAA) is esdential to reduce the risk of rupture of aortic wall that causes bleeding and often lead to death. Information about the condition of AAA is indispendable to complete the diagnosis of doctors in decision making. The position and shape of AAA can be obtained by sagittal image from an MRI examination. Characteristics of MRI sagittal image are having a gray level that is almost teh same between one organ to another. Therefore, to separate between one organ to another is difficult. This research is conducted MRI sagittal iamge segmentation in patients to obtain information on morphology and location of abdominal aortic aneurysm (AAA). To Segmenting the MRI Image we comobine thresholding method and Haralick Method. Under this proposed method, obtained sagittal images of the aorta are used to gain information about the location and shape of the aneurysm in abdominal aorta.
ALGORITMA PARALEL ODD EVEN TRANSPOSITION PADA MODEL JARINGAN NON-LINIER Ernastuti .; Ravi A. Salim; Haryanto .
Jurnal Ilmu Komputer dan Informasi Vol 3, No 2 (2010): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (977.741 KB) | DOI: 10.21609/jiki.v3i2.144

Abstract

Odd-even-transposition adalah suatu algoritma paralel yang merupakan pengembangan dari algoritma sekuensial “bubble sort”. Algoritma odd-even-transposition ini didesain khusus untuk model jaringan array linier (homogen). Untuk n elemen data, kompleksitas waktu dari algoritma bubble sort adalah O(n2), sedangkan pada odd-even-transposition yang bekerja di atas n prosesor adalah (n). Ada peningkatan kecepatan waktu pada kinerja algoritma paralel ini sebesar n kali dibanding algoritma sekuensialnya. Hypercube dimensi k adalah model jaringan non-linier (non-homogen) terdiri dari n = 2k prosesor, di mana setiap prosesor berderajat k. Model jaringan Fibonacci cube dan extended Lucas cube masing-masing merupakan model subjaringan hypercube dengan jumlah prosesor < 2k prosesor dan maksimum derajat prosesornya adalah k. Pada paper ini, diperlihatkan bagaimana algoritma odd-even-transposition dapat dijalankan juga pada model jaringan komputer cluster non-linier hypercube, Fibonacci cube, dan extended Lucas cube dengan kompleksitas waktu O(n). Odd-even-transposition is a parallel algorithm which is the development of sequential algorithm “bubble sort”. Odd-even transposition algorithm is specially designed for linear array network model (homogeneous). For n data elements, the time complexity of bubble sort algorithm is O(n2), while the odd-even-transposition that works with n processor is (n). There in an increase in the speed of time on the performance of this parallel algorithms for n times than its sequential algorithm. K-dimensional hypercube is a non-linear network model (non-homogeneous) consists of n = 2k processors, where each processor has k degree . Network model of Fibonacci cube and extended Lucas cube are the hypercube sub-network model with the number of processors
Analysis of Deauthentication Attack on IEEE 802.11 Connectivity Based on IoT Technology Using External Penetration Test Yogi Kristiyanto; Ernastuti Ernastuti
CommIT (Communication and Information Technology) Journal Vol. 14 No. 1 (2020): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v14i1.6337

Abstract

The research aims to know the level of security of WiFi connectivity against deauthentication attacks on Internet of Things (IoT)-based devices. It is done through testing using an external penetration test method. The external penetration test simulates a real external attack without information about the target system and network given. The process starts from accessing the device through Internet or WiFi by the test target. At the same time, the attacker performs Denial-of-Service (DoS) attacks onWiFi. The attacker uses Arduino ESP8266 NodeMCU WiFi with Lua programming. To record WiFi activities, the researchers use CommView for WiFi V. 7.0, and the target is Internet Protocol (IP) camera device. The result shows that the communication of the test target with the gateway is lost, but the Media Access Control (MAC) of the test target is still registered at the gateway. Deauthentication attacks cause communication paralysis, and several changes occur, such as an increase in data rate, and change in frequency channel, Distribution System (DS) status, retry bits in frame management, and the sequence number.
JENIS-JENIS ORDINAL BARISAN CACAH SEBAGAI PERLUASAN BILANGAN HINGGA BESERTA HIRARKI-HIRARKINYA Ravi Ahmad Salim; Ernastuti Ernastuti; Erwin Harahap; Norawati Norawati
Matematika Vol 6, No 1 (2007): Jurnal Matematika
Publisher : Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jmtm.v6i1.3376

Abstract

Sifat menyerap pada penjumlahan ordinal adalah fenomena menghilangnya seluruh atau sebagian ordinal pada penjumlahan, misalnya 2+w = w, (w+2)+w = w2. Orca adalah singkatan dari ordinal barisan cacah yaitu barisan-barisan bilangan cacah tertentu sebagaimana didefinisikan di bawah. Pertama tulisan ini mengemukakan upaya melenyapkan sifat menyerap operasi penjumlahan ordinal melalui pembentukan korespondensi F antara sistem ordinal dengan sistem orca. Bertolak dari barisan-barisan konstan sebagai wakil bilangan cacah, dengan merumuskan “tambah satu” (+1) dan penjumlahan berbentuk S+S+S+... atas barisan-barisan bilangan cacah diperoleh orca-orca. Penjumlahan perluasan atas barisan ternyata mewakili penjumlahan ordinal tanpa penyerapan.        Kedua tulisan ini membahas bagaimana orca-orca tersusun dalam sebuah hirarki yang didasarkan atas polinom-polinom pendefinisinya. Dari hirarki ini dapat dikaitkan dengan hipotesis kontinum serta konsep kardinal-kardinal tak terjangkau dalam teori ordinal. Dengan demikian diperoleh sumbangan teori ordinal terhadap teori orca. Ketiga tulisan ini menjelaskan bagaimana mengembangkan sistem bilangan yang sejajar dengan sistem-sistem bilangan bulat, pecahan, dan real namun dengan memasukkan seluruh sistem orca dari bagian pertama ke dalamnya.
EXTENDED LUCAS TUBE: GRAF HAMILTONIAN BARU Ernastuti .; Djati Kerami; Belawati H Widjaya
Journal of the Indonesian Mathematical Society Volume 14 Number 1 (April 2008)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.14.1.59.25-35

Abstract

A Hamiltonian cycle in a connected graph G is defined as a closed walk that traverses every vertex of G exactly once, except the starting vertex at which the walk also terminates. If an edge from a Hamiltonian cycle is removed, it forms a path calleda Hamiltonian path. A graph G is called Hamiltonian if there is a Hamiltonian cyclein G. It is known that every hypercube graph is Hamiltonian. But when one or more vertices are removed from a hypercube graph, will it still be Hamiltonian? Some induced subgraphs of a hypercube graph such as the Fibonacci cube (FC), the extended Fibonaccicube (EFC), and the Lucas cube (LC) have been introduced and their Hamiltonicities have been investigated. Research results showed that less than a third of FC graphs are Hamiltonian although all of them have Hamiltonian path. All EFC graphs are Hamiltonian and none of LC graphs is Hamiltonian although some still have Hamiltonian paths.This paper introduces another subgraph of a hypercube graph called the Extended Lucas Cube (ELC). The ELC is shown to be Hamiltonian by using the approach of k-Gray Code and Bipartition Property.DOI : http://dx.doi.org/10.22342/jims.14.1.59.25-35
Implementasi Metode CNN Multi-Scale Input dan Multi-Feature Network untuk Dugaan Kanker Payudara Ghifari Prameswari Natakusumah; Ernastuti Ernastuti
JOINTECS (Journal of Information Technology and Computer Science) Vol 7, No 2 (2022)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v7i2.3637

Abstract

Menurut WHO, kanker payudara merupakan penyumbang angka morbiditas tertinggi pada tahun 2020 dengan jumlah 2,26 juta kasus. Dalam menentukan prognosis pasien diperlukan berbagai pemeriksaan, salah satunya adalah analisis histopatologi. Namun, analisis histopatologi adalah proses yang relatif melelahkan dan memakan waktu. Dengan berkembangnya metode deep learning, ilmu computer vision dapat diterapkan untuk pendeteksian kanker pada citra medis, yang diharapkan dapat membantu meningkatkan akurasi prognosis dan kecepatan identifikasi yang dilakukan oleh para ahli. Berdasarkan pengetahuan tersebut, penelitian ini bertujuan untuk menerapkan klasifikasi multi-kelas (normal, benign, in situ, invasif) dan prediksi citra jaringan digital normal atau telah diduga memiliki sel kanker menggunakan Convolutional Neural Network dengan multi-scale input dan multi-feature network (CNN-G). Dataset yang digunakan adalah 400 data citra jaringan payudara yang diklasifikasikan menjadi empat kelas dan diberi label oleh ahli patologi. Hasil akurasi yang diperoleh dari pelatihan adalah 0,5375~0,54 dan berhasil membuat peningkatan jika dibandingkan dengan model tunggal (CNN14, CNN42, CNN84). Metode evaluasi model lain yang dilakukan adalah confusion matrix, precision, recall, dan f-1 score. 
ANALISIS KESADARAN MASYARAKAT MENGENAI ISU STUNTING PADA TWITTER MENGGUNAKAN ANALISIS JEJARING SOSIAL Ernastuti Ernastuti; Sulistyo Puspitodjati; D. L. Crispina Pardede; Henny Widowati Farida
Jurnal Ilmiah Teknologi dan Rekayasa Vol 28, No 1 (2023)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2023.v28i1.8329

Abstract

Stunting, menurut WHO, merupakan masalah bagi suatu negara jika nilai prevelansinya di atas 20%. Indonesia, dalam nilai prevalensi termasuk tinggi, sempat menduduki tertinggi ke-tiga di dunia. Walau nilai prevalensi stunting di Indonesia membaik, namun masih di atas standar WHO tersebut. Pemerintah sudah merumuskan dan mengambil keputusan untuk memperkecil nilai stunting ini, salah satunya adalah meningkatkan kepedulian masyarakat tentang bahaya stunting. Penelitian ini melakukan analisa keterlibatan masyarakat melalui pendekatan analisa jaringan sosial untuk melihat sejauh mana masyarakat terlibat melalui ukuran modularitas dan sentralitas jaringan sosial melalui scraping data media sosial Twitter. Hasil penelitian berupa topik utama yang dibahas berkenaan stunting, dan statistik jaringan masyarakat yang membicarakan stunting di Twitter.
Ornamental Plants Classification Using Integration of Convolution With Capsule Network FATONI, MOHAMMAD; ERNASTUTI, ERNASTUTI
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 8, No 2 (2023): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v8i2.158-172

Abstract

AbstrakKlasifikasi tanaman hias bertujuan untuk mempermudah media sosial tanaman hias dalam mengategorikan citra, sehingga sistem dapat merekomendasikan konten sesuai dengan preferensi pengguna. Pengguna berpotensi merasa cepat bosan apabila konten hanya ditampilkan secara acak. Penelitian ini melakukan Integration of Convolution with Capsule Network (ICCN) dengan menggabungkan beberapa lapisan strided convolution dan Capsule Network (CapsNet) untuk menghasilkan model klasifikasi yang memiliki komputasi lebih rendah dibandingkan original CapsNet dan mampu mengatasi permasalahan invariant of translation pada Convolutional Neural Network (CNN). Sebanyak 3 lapisan convolution dengan kernel berukuran 3x3 dan stride 2 ditambahkan pada CapsNet untuk membantu mengekstraksi citra dan mengurangi jumlah parameter yang dilatih. Hasil penelitian menunjukkan ICCN yang diusulkan memiliki jumlah parameter 2 kali lebih sedikit daripada original CapsNet dan memiliki akurasi lebih tinggi dibandingkan dengan CNN yaitu sebesar 95% sementara CNN berakurasi 93%.Kata kunci: tanaman hias, klasifikasi citra, cnn, capsnet, integrasiAbstractThe aim of ornamental plant classification is to assist ornamental plant social media in categorizing images, so the system is able to recommend content based on user preferences. Showing content randomly can lead to user boredom. This research implements Integration of Convolution with Capsule Network (ICCN) by combining several layers of strided convolution with Capsule Network (CapsNet) to create a classification model that has lower computation compared to the original CapsNet and able to address the issue of invariant of translation in Convolutional Neural Network (CNN). There are 3 convolutional layers with 3x3 kernel and stride of 2 added to CapsNet to assist in image extraction and reduce the number of trainable parameters. The research results showed that the proposed ICCN has 2 times fewer trainable parameters than the original CapsNet and achieves higher accuracy than CNN, with 95% accuracy, while CNN has an accuracy of 93%.Keywords: ornamental plants, image classification, cnn, capsnet, integration
Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability Andryana, Septi; Mantoro, Teddy; Mutiara, Achmad Benny; Ernastuti, Ernastuti; Prihandoko, Prihandoko; Gunaryati, Aris
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1024

Abstract

This study lies in the field of decision support systems, focusing on the application of Multi-Criteria Decision Making (MCDM) for ranking alternatives based on predefined organizational criteria. A persistent challenge in this domain is the instability and subjectivity of ordinal weighting methods - such as Rank Order Centroid (ROC), Rank Sum (RS), Rank Reciprocal (RR), and Rank Order Distribution (ROD), which derive weights solely from rank positions, often leading to inconsistent and unreliable outcomes. To address this, this study introduces Rank Geometric (RG) weights, a geometric mean aggregation of ROC, RS, RR, and ROD designed to reduce subjectivity, stabilize weight distribution, and enhance robustness. By using the Combined Compromise Solution (CoCoSo) method, the RG against Times Higher Education’s (THE) official weights were evaluated, and the four individual ordinal methods, applied to the top 10 Indonesian universities across five THE 2025 ranking criteria. Empirical results show that RG-CoCoSo produces stronger and more consistent correlations with THE’s rankings than THE-CoCoSo, as validated by Spearman and Pearson correlation tests, with a p-value of 0.0251. This study contributes a practical, data-driven weighting framework that strengthens the reliability of MCDM-based institutional performance evaluation and can be generalized to other ranking contexts.
Co-Authors Achmad Benny Mutiara Achmad Benny Mutiara Achmad Fahrurozi Aris Gunaryati, Aris Bambang Wahyudi Belawati H Widjaya Belawati H Widjaya Cut Maisyarah Karyati D. L. Crispina Pardede Desti Riminarsih Dewi Putrie Lestari Djati Kerami Djati Kerami Djati Kerami Eri Prasetyo Wibowo, Eri Prasetyo Ericks Rachmat Swedia Erwin Harahap Fitriani, Risma Rahmalia Fitrianingsih Fitrianingsih Ghifari Prameswari Natakusumah Haryanto . Henny Widowati Farida Kristiyanto, Yogi Muhammad Agus Muljanto Norawati Norawati Pribadi, Hanafi Indra Prihandoko Prihandoko Ravi A. Salim Ravi Ahmad Salim Ravi Ahmad Salim Rodiah Rodiah Sarifuddin Madenda Sarifudin Madenda Septi Andryana Sulistyo Puspitodjati Suryarini Widodo Teddy Mantoro Trini Saptariani Widya Silfianti