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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) INFORMAL: Informatics Journal CSRID
Lutfi Budi Ilmawan
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Computer Science & IT Control & Systems Engineering

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Aplikasi Mobile untuk Analisis Sentimen pada Google Play Lutfi Budi Ilmawan; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 1 (2015): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.6640

Abstract

AbstrakGoogle dalam application store-nya, Google Play, saat ini telah menyediakan sekitar 1.200.000 aplikasi mobile. Dengan sejumlah aplikasi tersebut membuat pengguna memiliki banyak pilihan. Selain itu, pengembang aplikasi mengalami kesulitan dalam mencari tahu bagaimana meningkatkan kinerja aplikasinya. Dengan adanya permasalahan tersebut, maka dibutuhkan sebuah aplikasi analisis sentimen yang dapat mengolah sejumlah komentar untuk memperoleh informasi.Sistem yang dibangun memiliki tujuan untuk menentukan polaritas sentimen dari ulasan tekstual aplikasi pada Google Play yang dilakukan dari perangkat mobile. Perangkat mobile memiliki portabilitas yang tinggi dan sebagian dari perangkat tersebut memiliki resource yang terbatas. Hal tersebut diatasi dengan menggunakan arsitektur sistem berbasis client server, di mana server melakukan tugas-tugas yang berat sementara client-nya adalah perangkat mobile yang hanya mengerjakan tugas yang ringan. Dengan solusi tersebut maka Analisis sentimen dapat diaplikasikan pada mobile environment.Adapun metode klasifikasi yang digunakan adalah Naïve Bayes untuk aplikasi yang dikembangkan dan Support Vector Machine Linier sebagai pembanding. Nilai akurasi dari Naïve Bayes classifier dari aplikasi yang dibangun sebesar 83,87% lebih rendah jika dibandingkan dengan nilai akurasi dari SVM Linier classifier sebesar 89,49%. Adapun penggunaan semantic handling untuk mengatasi sinonim kata dapat mengurangi akurasi classifier. Kata kunci— analisis sentimen, google play, klasifikasi, naïve bayes, support vector machine AbstractGoogle's Google Play now providing approximately 1.200.000 mobile applications. With these number of applications, it makes the users have many options. In addition, application developers have difficulties in figuring out how to improve their application performance. Because of these problems, it is necessary to make a sentiment analysis applications that can process review comments to get valuable information.The purpose of this system is determining the polarity of sentiments from applications’s textual reviews on Google Play that can be performed on mobile devices. The mobile device has high portability and the majority of these devices have limited resource. That problem can be solved by using a client server based system architecture, where the server performs training and classification tasks while clients is a mobile device that perform some of sentiment analysis task. With this solution, the sentiment analysis can be applied to the mobile environment.The classification method that used are Naive Bayes for developed application and Linear Support Vector Machine that is used for comparing. Naïve Bayes classifier’s accuracy is 83.87%. The result is lower than the accuracy value of Linear SVM classifier that reach 89.49%. The use of semantic handling can reduce the accuracy of the classifier. Keywords—sentiment analysis, google play, classification, naïve bayes, support vector machine
Analisis Keamanan Sistem Informasi Akademik (SIAKAD) Universitas XYZ Menggunakan Metode Vulnerability Assessment Erick Irawadi Alwi; Lutfi Budi Ilmawan
INFORMAL: Informatics Journal Vol 6 No 3 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i3.27053

Abstract

The use of academic information systems (siakad) has become mandatory for universities in providing user convenience in online academic administrative activities. However, sometimes college siakad has security holes that irresponsible people can take advantage of by hacking. This study aims to identify security vulnerabilities at XYZ Siakad University. The method used in this study is a vulnerability assessment method. A university syakad will conduct an initial vulnerability assessment by doing footprinting to get information related to XYZ syakad after that a vulnerability scan is carried out using vulnerability assessment tools to identify vulnerabilities and the level of risk found. Based on the vulnerability of the XYZ university's vulnerabilities, it is quite good, with a high risk level of 1, a medium risk level of 6 and a low risk level of 14. Researchers provide recommendations for improvements related to the findings of security holes in XYZ university Siakad from XSS (Cross Site Scripting) attacks, Clickjacking, Brute Force, Cross-site Request Forgery (CSRF) and Sniffing.
Klasifikasi Citra Digital Daun Herbal Menggunakan Support Vector Machine dan Convolutional Neural Network dengan Fitur Fourier Descriptor Aulia Rezky Rahmadani Darmawati; Purnawansyah; Herdianti Darwis; Lutfi Budi Ilmawan
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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

Abstract

Leaves are one component of plants that contain natural properties and are useful for maintaining human health. However, several types of leaves have the same characteristics and characteristics that make it difficult to distinguish. This study aims to classify types of herbal leaves using the SVM method with four kernels (Linear, RBF, Polynomial, Sigmoid) and CNN with Fourier descriptor (FD) feature extraction. The processed dataset is katuk leaf images, and Moringa leaf images of 480 images which are divided into 80% training data and 20% testing data using two scenarios, namely dark and light. From the testing process, it was found that FD + CNN in the light and dark scenarios obtained an accuracy value of 98%. Thus, the FD + SVM algorithm with Linear, RBF, polynomial kernels can be recommended in classifying herbal leaf images to have the best accuracy value of 100%.
Klasifikasi Citra Digital Daun Herbal Menggunakan Support Vector Machine dan Convolutional Neural Network dengan Fitur Fourier Descriptor Darmawati, Aulia Rezky Rahmadani; Purnawansyah; Herdianti Darwis; Lutfi Budi Ilmawan
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.1.2024.01-12

Abstract

Leaves are one component of plants that contain natural properties and are useful for maintaining human health. However, several types of leaves have the same characteristics and characteristics that make it difficult to distinguish. This study aims to classify types of herbal leaves using the SVM method with four kernels (Linear, RBF, Polynomial, Sigmoid) and CNN with Fourier descriptor (FD) feature extraction. The processed dataset is katuk leaf images, and Moringa leaf images of 480 images which are divided into 80% training data and 20% testing data using two scenarios, namely dark and light. From the testing process, it was found that FD + CNN in the light and dark scenarios obtained an accuracy value of 98%. Thus, the FD + SVM algorithm with Linear, RBF, polynomial kernels can be recommended in classifying herbal leaf images to have the best accuracy value of 100%.
Co-Authors Aulia Rezky Rahmadani Darmawati Darmawati, Aulia Rezky Rahmadani Edi Winarko Erick Irawadi Alwi Herdianti Darwis Purnawansyah