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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang JOURNAL OF SCIENCE AND SOCIAL RESEARCH KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknik Elektro dan Komputer TRIAC JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) JISKa (Jurnal Informatika Sunan Kalijaga) INOVTEK Polbeng - Seri Informatika INJECT Interdisciplinary Journal of Communication Jurnal Repositor
Christian Sri Kusuma Aditya
Universitas Muhammadiyah Malang
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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Semi-supervised approach for detecting distributed denial of service in SD-honeypot network environment Fauzi Dwi Setiawan Sumadi; Christian Sri Kusuma Aditya; Ahmad Akbar Maulana; Syaifuddin Syaifuddin; Vera Suryani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1094-1100

Abstract

Distributed Denial of Service (DDoS) attacks is the most common type of cyber-attack. Therefore, an appropriate mechanism is needed to overcome those problems. This paper proposed an integration method between the honeypot sensor and software defined network (SDN) (SD-honeypot network). In terms of the attack detection process, the honeypot server utilized the Semi-supervised learning method in the attack classification process by combining the Pseudo-labelling model (support vector machine (SVM) algorithm) and the subsequent classification with the Adaptive Boosting method. The dataset used in this paper is monitoring data taken by the Suricata sensor. The research experiment was conducted by examining several variables, namely the accuracy, precision, and recall pointed at 99%, 66%, and 66%, respectively. The central processing unit (CPU) usage during classification was relatively small, which was around 14%. The average time of flow rule mitigation installation was 40s. In addition, the packet/prediction loss occurred during the attack, which caused several packets in the attack not to be classified was pointed at 43%.
Peringkasan Berita Online Corona Virus dengan Metode Lexical Chain dan Word Sense Disambiguation Nisrina Arintia Maghfiroh; Galih Wasis Wicaksono; Christian Sri Kusuma Aditya
Komputika : Jurnal Sistem Komputer Vol 10 No 2 (2021): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v10i2.4499

Abstract

Automatic news summarizes are the activity of extracting the core from the news without compromising the significance contained in the news. In the automatic news release there are several methods that can be used, one of which is the Lexical Chain method. This method performs well in summarizes text by determining the highest chain. However, this method has the disadvantage of not being able to identify ambiguous words contained in news sentences. Therefore, to correct the shortcomings of the Lexical Chain method weaknesses, the study was equipped with Word Sense Disambiguation to identify ambiguous words. This study used 100 news about Covid-19 sourced from the most popular online news portals. Accuracy testing of automatic news releases used in this study using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), while the evaluation used in this study there are three kinds of precission, recall, and f-measure. The evaluation results obtained an average precission value of 0.62, recall of 0.20, and f-measure of 0.30.
Implementasi Website Profil Madrasah Muhammadiyah Al-Munawarroh Malang Sebagai Media Informasi Bagi Masyarakat Didih Rizki Chandranegara; Christian Sri Kusuma Aditya; Fauzi Dwi Setiawan Sumadi
JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) VOL. 4 NOMOR 2 SEPTEMBER 2020 JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat)
Publisher : Lembaga Publikasi Ilmiah dan Penerbitan (LPIP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (572.798 KB) | DOI: 10.30595/jppm.v4i2.6095

Abstract

Madrasah Muhammadiyah Al-Munawarroh adalah madrasah yang terletak di Malang dan salah satu madrasah yang berkembang. Untuk menunjang perkembangan tersebut, dibuat sebuah website profile yang dapat memberikan informasi kepada masyarakat secara cepat. Metode yang digunakan dalam pembuatan website adalah metode RAD (Rapid Application Development). Metode ini digunakan karena kami ingin melibatkan pihak madrasah dalam pembuatan website, agar website yang dibuat dapat bermanfaat secara penuh dan sesuai dengan kebutuhan penyebaran informasi bagi madrasah. Hasil dari pembuatan website profile menunjukkan bahwa pihak madrasah dapat dengan mudah menyebarkan informasi-informasi penting seperti jadwal pendaftaran siswa, informasi mengenai madrasah hingga informasi lowongan pekerjaan yang ada di madrasah.
Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking Hasbi Atsqalani; Nur Hayatin; Christian Sri Kusuma Aditya
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.669

Abstract

Sentiment analysis is a computational study of a sentiment opinion and an overflow of feelings expressed in textual form. Twitter has become a popular social network among Indonesians. As a public figure running for president of Indonesia, public opinion is very important to see and consider the popularity of a presidential candidate. Media has become one of the important tools used to increase electability. However, it is not easy to analyze sentiments from tweets on Twitter apps, because it contains unstructured text, especially Indonesian text. The purpose of this research is to classify Indonesian twitter data into positive and negative sentiments polarity using Support Vector Machine and Query Expansion Ranking so that the information contained therein can be extracted and from the observed data can provide useful information for those in need. Several stages in the research include Crawling Data, Data Preprocessing, Term Frequency – Inverse Document Frequency (TF-IDF), Feature Selection Query Expansion Ranking, and data classification using the Support Vector Machine (SVM) method. To find out the performance of this classification process, it will be entered into a configuration matrix. By using a discussion matrix, the results show that calcification using the proposed reached accuracy and F-measure score in 77% and 68% respectively.
Analisis Sentimen Tweet Tentang UU Cipta Kerja Menggunakan Algoritma SVM Berbasis PSO Trifebi Shina Sabrila; Yufis Azhar; Christian Sri Kusuma Aditya
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 7 No. 1 (2022): Januari 2022
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (232.782 KB) | DOI: 10.14421/jiska.2022.7.1.10-19

Abstract

Support Vector Machine (SVM) is one of the most widely used classification algorithms for sentiment analysis and has been shown to provide satisfactory performance. However, despite its advantages, the SVM algorithm still has weaknesses in selecting the right SVM parameters to optimize the performance. In this study, sentiment analysis was done with the use of data called tweets about Undang-Undang Cipta Kerja which reap many pros and cons by the people in Indonesia, especially the laborers. The classification method used in this study is the Support Vector Machine algorithm which is optimized using the Particle Swarm Optimization method for the SVM parameters selection in the hope of optimizing the performance generated by the SVM algorithm in sentiment analysis. The results of the study using 10 k-fold cross-validations using the SVM algorithm resulted in an accuracy of 92,99%, a precision of 93,24%, and a recall of 93%. Meanwhile, the SVM and PSO algorithms produce an accuracy of 95%, precision of 95,08%, and recall of 94,97%. The results show that the Particle Swarm Optimization method can overcome the weaknesses of the Support Vector Machine algorithm in the problem of parameter selection and has succeeded in improving the resulting performance where the SVM-PSO is more superior to SVM without optimization in sentiment analysis.
Prediksi Harga Saham Jakarta Islamic Index Menggunakan Metode Long Short-Term Memory Didih Rizki Chandranegara; Raffi Ainul Afif; Christian Sri Kusuma Aditya; Wildan Suharso; Hardianto Wibowo
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 1 (2023): Volume 9 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i1.57561

Abstract

Saat ini investasi sudah sangat menyebar luas dan banyak dari kita sedang melakukannya. Investasi ini berguna untuk mengatasi kebutuhan hidup dimasa mendatang yang tidak menentu. Salah satu penyebab tidak menentunya kebutuhan dimasa mendatang adalah inflasi. Salah satu contoh investasi adalah saham. Di dalam jual beli saham di Indonesia terdapat Jakarta Islamic Index (JII). JII adalah salah satu index yang ada di pasar modal Indonesia yang mengelompokkan beberapa saham yang masuk dalam kriteria syariah dan dihitung rata-rata dari harga saham – saham tersebut. Dalam berinvestasi saham, kita tidak bisa melakukan pergerakan yang sembarangan karena saham yang relatif berubah-ubah menjadi penyebab kegagalan dalam berinvestasi saham. Dengan demikian ketika melakukan investasi saham harus dilakukan analisa yang tepat. Perkembangan teknologi saat ini sangat maju dan juga dapat membantu kita dalam melakukan analisa dalam berinvestasi dengan melakukan prediksi harga. Pada penelitian ini, akan dimanfaatkan kemajuan teknologi tersebut dengan melakukan penelitian prediksi, penelitian ini dilakukan menggunakan metode Long short Term-Memory (LSTM). Model LSTM yang diusulkan dapat memperoleh performa yang cukup baik dengan hasil RMSE mencapai 5.20877667554, dan MAPE 0.08658576985.
Game Design for Mobile App-Based IoT Introduction Education in STEM Learning Indra Puja Laksana; Evi Dwi Wahyuni; Christian Sri Kusuma Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.5007

Abstract

STEM education has received considerable attention in recent years. However, developing valid and reliable assessments in interdisciplinary learning in STEM has been a challenge. Therefore, many students ranging from junior high school to university students are only familiar with the Internet of Things (IoT) from social media but do not know its concept and function in STEM learning. This is also supported by the absence of educational applications about IoT. This research aims to introduce IoT by using mobile applications. This research refers to the multimedia development method according. The data collection method in this study was carried out by means of observation and interviews randomly to high school students to university students. This data collection was carried out using the experimental method of application testing to analyze user needs from several aspects such as features, images, and fonts. This research is also supported by the existence of literature studies derived from several journals. The results show that the functions in the application can operate as expected. Based on the survey results of the application, 75.37% of respondents rated this application in the very good category and gave positive responses so that this application could be well received by users
Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance Muhammad Abizar; Muhammad Ferry Septian Ihzanor Syahputra; Ahmad Rizky Habibullah; Christian Sri Kusuma Aditya; Fauzi Dwi Setiawan Sumadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1974-1984

Abstract

One of the main challenges in developing the internet of things (IoT) is the existence of availability problems originated from the low-rate distributed denial of service attacks (LRDDoS). The complexity of IoT makes the LRDDoS hard to detect because the attack flow is performed similarly to the regular traffic. Integration of software defined IoT (SDN-Enabled IoT) is considered an alternative solution for overcoming the specified problem through a single detection point using machine learning approaches. The controller has a resource limitation for implementing the classification process. Therefore, this paper extends the usage of Feature Importance to reduce the data complexity during the model generation process and choose an appropriate feature for generating an efficient classification model. The research results show that the Gaussian Naïve Bayes (GNB) produced the most effective outcome. GNB performed better than the other algorithms because the feature reduction only selected the independent feature, which had no relation to the other features.
Sentiment Analysis of the 2024 Presidential Candidates Using SMOTE and Long Short Term Memory Christian Sri Kusuma Aditya; Galih Wasis Wicaksono; Hilman Abi Sarwan Heryawan
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.32210

Abstract

Numerous political leaders participate in elections since they are a crucial component of the political process. Since electability is an issue, steps are taken to make political candidates running in general elections more electable. The media, including internet news media, has emerged as one of the key strategies for raising electability. Reader comments can be analyzed for sentiment to provide an evaluation of political figures. However, because the comments contain unstructured content, particularly in Indonesian text, it is difficult to interpret the sentiments of different comments in online news media. In this research, an analysis of public sentiment towards the 2024 presidential candidates will be carried out which is expressed through the Twitter social network. There are several stages to carry out sentiment analysis, including the stages of data collection, data preprocessing, balancing the distribution of the number of datasets, and sentiment classification using the LSTM method with word2vec feature representation. The results of this study show that the LSTM method combined with SMOTE due to the limited amount of data is able to produce a fairly good LSTM model with an average accuracy of 89.42% and a loss value of 0.24, the ideal scenario is when the accuracy is high and the loss is minimal, in which case the LSTM model only exhibits minor errors on a subset of the data. 
Implementation of Convolutional Neural Network Method in Identifying Fashion Image Christian Sri Kusuma Aditya; Vinna Rahmayanti Setyaning Nastiti; Qori Raditya Damayanti; Gian Bagus Sadewa
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17372

Abstract

The fashion industry has changed a lot over the years, which makes it hard for people to compare different kinds of fashion. To make it easier, different styles of clothing are tried out to find the exact and precise look desired. So, we opted to employ the Convolutional Neural Network (CNN) method for fashion classification. This approach represents one of the methodologies employed to utilize computers for the purpose of recognizing and categorizing items. The goal of this research is to see how well the Convolutional Neural Network method classifies the Fashion-MNIST dataset compared to other methods, models, and classification processes used in previous research. The information in this dataset is about different types of clothes and accessories. These items are divided into 10 categories, which include ankle boots, bags, coats, dresses, pullovers, sandals, shirts, sneakers, t-shirts, and trousers. The new classification method worked better than before on the test dataset. It had an accuracy value of 95. 92%, which is higher than in previous research. This research also uses a method called image data generator to make the Fashion MNIST image better. This method helps prevent too much focus on certain details and makes the results more accurate.
Co-Authors Abdul Hadiy Dyo Fatra Adam Novrisal Ahmad Akbar Maulana Ahmad Rizky Habibullah Aminudin Anindya Samantha Prayoga Arolina Rinardi Asfa Maghfiratunnisa Bayu Agung Setiawan Chandranegara, Didih Rizki Danny Ferdiansyah Dati Nafa Alfiana Didih Rizk`i`` Chandranegara Dignifo Nauval Muhammady Dio Richard Prastiyo Dzur Rifqi Aziz Erlita Setio Widianti Evi Dwi Wahyuni Excel Bima Evansyah Fatahillah Arsyad Fathul Agit Darmawan Fatimah Defina Setiti Alhamdani Ferdy Yoga Permana Garin Muhammad Akbar Gian Bagus Sadewa Gita Indah Marthasari Haidar Aldy Eka Nugraha Hamdan Nafiatur Rosyida Hardianto Wibowo Hasbi Atsqalani Hilman Abi Sarwan Heryawan Indra Puja Laksana Iriandi Riski Kusuma Putra Kelvin Aprilio Luthfiya Indah Fithriani M Bagus Triyadi Maylani Kusuma Wardhani Moch Fuad Nasvian Muchamad Thaqifullah Muh. Ridha Agam Muhammad Abizar Muhammad Alif Nasrulloh Muhammad Bima Mauludin Muhammad Ferry Septian Ihzanor Syahputra Muhammad Robby Fathuda Muhammad Zaky Darajat Nisrina Arintia Maghfiroh Nur Hayatin Nuryasin, Ilyas Putri Maharani Isnainiyah Qori Raditya Damayanti Raffi Ainul Afif Rakha Pradana Susilo Putra Roni Hadi Wijaya S, Vinna Rahmayanti Sabrila, Trifebi Shina Saniyya Ruzzy Marwa Sumadi, Fauzi Dwi Setiawan Syaifuddin Syaifuddin Vera Suryani Vita Amalia Herlinda Wicaksono, Galih Wasis Wildan Suharso Yufis Azhar