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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT JSiI (Jurnal Sistem Informasi) Bulletin of Computer Science Research The Indonesian Journal of Computer Science
Ilham Firman Ashari
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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PENGEMBANGAN ANTARMUKA PENGGUNA KOLEPA MOBILE APP MENGGUNAKAN METODE DESIGN THINKING DAN SYSTEM USABILITY SCALE Ilham Firman Ashari; Rahmat Rizky Muharram
Jurnal Sistem Informasi Vol 9 No 2 (2022)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v9i2.4993

Abstract

Kolepa Mini Golf & Coffee Shop merupakan sebuah usaha yang bergerak di bidang penjualan makanan dan minuran disertai dengan hiburan yang menghadirkan jasa hiburan mini golf. Saat ini, jumlah pengunjung Kolepa dapat mencapai hingga 3000 orang per bulannya. Sebagai bentuk digitalisasi dan peningkatan layanan terhadap pelanggan, Kolepa ingin mengembangkan sebuah aplikasi berbasis mobile yang dapat memudahkan pelanggan yang ingin berkunjung dengan menyediakan fitur reservasi meja, serta fitur perhitungan skor mini golf untuk menggantikan kertas fisik untuk menulis skor. Sebelum aplikasi dikembangkan, dibutuhkan perancangan tampilan antarmuka pengguna (user-interface) sesuai hasil dari wawancara dengan pihak perusahaan. Dalam perancangan antarmuka pengguna, penulis menggunakan Figma sebagai tools dan metode design thinking untuk merumuskan arsitektur sistem termasuk fitur yang terdapat pada aplikasi. Dari hasil perancangan yang dilakukan, dilakukan pengujian dengan System Usability Scale (SUS) dan didapatkan hasil bahwa rancangan antarmuka pengguna Kolepa Mobile App mendapatkan nilai 'A' pada metode grading dan nilai 'good' pada metode ajektiva berdasarkan matriks konversi penilaian SUS. Kata kunci: Antarmuka, Mobile, Design Thingking, System Usability Scale, Grading.
Analisis Kombinasi Sistem Parkir dengan Pengenalan Wajah dan QR Code Menggunakan Metode Histogram Oriented Gradient Ilham Firman Ashari; Idri, Mohamad; Nasrulah, M. Anas
IT Journal Research and Development Vol. 7 No. 1 (2022)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2022.9958

Abstract

Security is very important everywhere, including in the campus environment. To provide security and comfort for those who park their vehicles, a parking application is needed that can provide vehicle security while undergoing academic activities on campus. QR code (Quick Response Code) is a technology for converting written data into a two-dimensional code, which is printed on a more compact medium capable of storing various types of data. The most common individual part used to identify a person is the face because it has the unique characteristics of everyone. Histogram of Oriented Gradient (HOG) is a feature extraction used for face identification based on histogram of gradient orientation and gradient magnitude. This application is implemented using the Dlib library for facial recognition. The implementation of this method is expected to improve parking security and provide a record of parked vehicles. The results of testing the implementation of facial recognition methods into android applications show very satisfactory results. With the results of testing the QR code scanning accuracy of 100% and an accuracy of 90% for a 7% damage rate and an accuracy of 85% for a 15% damage rate, and the results of facial recognition testing of 90% on face photos wearing helmets and an accuracy of 92% on photo of face without helmet.
Sentiment Analysis of Tweets About Allowing Outdoor Mask Wear Using Naïve Bayes and TextBlob Ilham Firman Ashari; A, Fadhillah; M. Daffa; Sekar A
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3238

Abstract

Covid-19, a virus that attacks the respiratory tract and has a fairly high mortality rate, has spread throughout the country. On March 11, 2020, WHO declared Covid-19 a global pandemic. The government is trying various efforts to reduce the number of sufferers of this virus. Starting from the implementation of the lockdown, PPKM, to making Government Regulations related to the use of masks and so on for personal protection. In June 2021, there was a spike in Covid-19 cases in Indonesia and Covid-19 patients increased drastically. Conditions at that time were very chaotic, and left trauma for some people. On May 17, 2022, the government made concessions in the use of masks in open spaces while maintaining social distance. Even though masks play an important role in preventing the spread of the virus. With this, a research related to "Analysis of Sentiment on Tweets regarding Allowance for the Use of Masks in Outdoors using Naive Bayes was carried out" to find out public opinion. The research was conducted using Text Mining through Twitter sentiment and Naive Bayes for classification. Based on research, the majority of twitter users give a neutral response. This is indicated by the number of neutral sentiments of 75.76% or about 757 tweets. The data used in this study, namely 1000 Indonesian tweets with the keyword 'jokowi mask'. Testing data of 20% resulted in a more accurate model, which resulted in an accuracy of about 85%, while the model using testing data of 30% only produced an accuracy of about 83%.
The Evaluation of Audio Steganography To Embed Image Files Using Encryption and Snappy Compression Ilham Firman Ashari
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3050

Abstract

Images are messages that can be kept secret, so security measures are needed. Techniques that can be used are cryptography and steganography. Steganography can be combined with cryptography to increase security. Images have a relatively large size; Therefore, a compression algorithm is needed. The compression algorithm used is lossless compression. MP3 audio is used as the cover media because it is the most popular audio file. In this study, aspects of imperceptibility, fidelity, recovery, payload, and robustness will be evaluated. The imperceptibility aspect is carried out by observing the RGB Histogram of the image and the audio frequency spectrum, the test results show that there is no significant difference between the audio before and after the image message is inserted. In the fidelity aspect, the PSNR result is above 30 dB. In the payload aspect, the file size after being encrypted with AES and RC4 is larger than just encoded using the base64 encoder. From the recovery aspect, the test results show a BER value of 0. Testing the robustness aspect by manipulating the bitrate, channel mode, and sample frequency, the test results show that the message cannot be extracted.
Analisis Perbandingan Metode Convolutional Neural Network (CNN) untuk Deteksi Warna pada Objek Prastita, Dila Ayu; Andika Setiawan; Ilham Firman Ashari
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.617

Abstract

This research aims to evaluate and compare the performance of three Convolutional Neural Network (CNN) architectures, namely VGG16, Xception, and NASNet Mobile, in detecting colors on objects. The main problem in this research is to determine the architecture with the most effective and efficient combination of hyperparameters to detect colors on objects. The research process includes problem identification, object color dataset collection, image preprocessing, training of three CNN models (VGG16, Xception, and NASNet Mobile), and performance evaluation using accuracy, precision, recall, and f1-score metrics. In addition, a comparative analysis of the performance of each model based on the combination of hyperparameters used, such as optimizer, batch size, and learning rate. The analysis also includes evaluating computational efficiency by measuring the training time and prediction time of each model, as well as examining the relationship between architectural complexity and classification performance. The results of the analysis are used to determine the most optimal model that is feasible to implement in an object color detection system. The test results show that NASNet Mobile provides the best performance with an accuracy of 88% and a prediction time of 2 minutes 22 seconds for 2904 images. The Xception model produced an accuracy of 86% with a prediction time of 4 minutes 22 seconds, while VGG16 recorded an accuracy of 90% with a prediction time of 10 minutes 9 seconds.
Co-Authors A, Fadhillah Andika Setiawan Ayu Soleha Idri, Mohamad M. Daffa Nasrulah, M. Anas Prasetyawan, Purwono Prastita, Dila Ayu Rahmat Rizky Muharram Raizummi Fil’aini Sekar A