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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Begawe Teknologi Informasi AMPLITUDO: Journal of Science & Technology Innovation
Nugraha, Gibran Satya
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16, Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition Nugraha, Gibran Satya; Darmawan, Muhammad Ilham; Dwiyansaputra, Ramaditia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 2, May 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i2.1667

Abstract

The Arabic script is written from right to left and consists of 28 characters, with no capital or lowercase letters. The Arabic script has several orthographic and morphological properties that make handwriting recognition of the Arabic script challenging. In addition, one of the biggest challenges in recognizing Arabic script patterns is the different handwriting styles and characters of each person's writing. The authors propose a study to compare the accuracy of handwriting pattern recognition in Arabic script which has been done previously by comparing five CNN architectures, namely GoogleNet, AlexNet, VGG-16, LeNet-5, and ResNet-50. Considering that previous research has not obtained excellent accuracy. The number of datasets used is 8400 image data and the most optimal comparison of testing and training data is 80:20. Based on the research that has been done, there are several things that the author can conclude. The model is made using 64 filters for each convolution layer because the optimal size is used for 5 architectures, kernel size is 3x3, neurons is 128, dropout weight is 50% to reduce overfitting, learning rate is 0.001, image size is 64x64, the normalization method with the ReLU activation function, and 1-dimensional input image (grayscale), and with a comparison of testing and training data of 80:20. The VGG-16 architectural model is the architecture that gets the highest score, namely 83.99%. This can have good potential to be developed as a medium for learning Arabic script.
Multiclass Text Classification of Indonesian Short Message Service (SMS) Spam using Deep Learning Method and Easy Data Augmentation Latifah, Nurun; Dwiyansaputra, Ramaditia; Nugraha, Gibran Satya
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3835

Abstract

The ease of using Short Message Service (SMS) has brought the issue of SMS spam, characterized by unsolicited and unwanted. Many studies have been conducted utilizing machine learning methods to build models capable of classifying SMS Spam to overcome this problem. However, most of these studies still rely on traditional methods, with limited exploration of deep learning-based approaches. Whereas traditional methods have a limitation compared to deep learning, which performs manual feature extraction. Moreover, many of these studies only focus on binary classification rather than multiclass SMS classification which can provide more detailed classification results. The aim of this research is to analyze deep learning model for multiclass Indonesian SMS spam classification with six categories and to assess the effectiveness of the text augmentation method in addressing data imbalace issues arising from the increased number of SMS categories. The research method used were Indonesian version of Bidirectional Encoder Representations from Transformers (IndoBERT) model and exploratory data analysis (EDA) augmentation technique to address imbalance dataset issue. The evaluation is conducted by comparing the performance of the IndoBERT model on the dataset and applying EDA techniques to enhance the representation of minority classes. The result of this research shows that IndoBERT achieves 91% accuracy rate in classifying SMS spam. Furthermore, the use of EDA technique results in significant improvement in f1-score, with an average 12% increase in minority classes. Overall model accuracy also improves to 93% after EDA implementation. This research concludes that IndoBERT is effective for multiclass SMS spam classification, and the EDA is beneficial in handling imbalanced data, contributing to the enhancement of model performances.
Implementasi Fuzzy C-Means untuk Pengelompokan Daerah berdasarkan Persebaran Penularan Covid-19 Nugraha, Gibran Satya; Dwiyansaputra, Ramaditia; Bimantoro, Fitri; Aranta, Arik
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023105796

Abstract

Peningkatan kasus Covid-19 di Indonesia memberikan rasa khawatir bagi hampir seluruh masyarakat, Dilihat dari persebaran tiap provinsi untuk kasus positif, sembuh, dan meninggal tidak menunjukkan sebuah grafik yang linier. Seperti pada data harian kasus per provinsi di akhir bulan April 2021 dimana kasus positif dan sembuh terbanyak terdapat pada Provinsi DKI Jakarta, untuk kasus meninggal Provinsi Jawa Timur berada di posisi pertama, dan di posisi empat untuk kasus positif dan meninggal. Data persebaran yang abstrak ini membuat pengelompokan persebaran Covid-19 di Indonesia menjadi sukar untuk dilakukan. Penelitian ini mengelompokkan provinsi-provinsi berdasarkan persebaran Covid-19 di Indonesia dengan cara mengimplementasikan metode Fuzzy C-means serta metode Elbow. Fuzzy C-means adalah metode pengelompokan berbasis fuzzy yang dapat melakukan persebaran data pada seluruh cluster berdasarkan derajat keanggotaan yang dimilikinya. Sedangkan untuk menentukan jumlah cluster terbaik akan diimplementasikan metode Elbow. Metode Elbow membandingkan perbandingan hasil sum square error (SSE) dari setiap cluster dan mendapatkan jumlah cluster terbaik dari perubahan nilai SSE yang signifikan atau membentuk siku (elbow). Penggunaan Fuzzy c-means sebagai metode pengelompokan untuk mencari tahu seberapa besar pengaruh yang dimiliki setiap data terhadap masing-masing cluster. Karera metode-metode sebelumnya yang digunakan pada objek yang sama hanya melakukan pengelompokan saja secara tegas, tanpa memperhatikan besarnya pengaruh sebuah data terhadap seluruh cluster. Pengelompokan dilakukan menjadi tiga buah cluster atau kelompok berdasarkan parameter kasus positif, sembuh, dan meninggal Covid-19 per 27 April 2021. Cluster 1 hanya terdiri tiga provinsi yaitu Jawa Barat, Jawa Tengah, dan Jawa Timur. Cluster 2 DKI Jakarta, dan sisanya masuk ke cluster 3. AbstractThe increase in Covid-19 cases in Indonesia raises concerns for all parties, When viewed for the distribution of each province, positive, recovered and dead cases do not show a linear graph. As in the daily data of cases per province at the end of April 2021 where the most positive and recovered cases were in DKI Jakarta Province, while for dead cases, East Java Province was in first position, and in fourth position for positive and dead cases. This abstract distribution data makes it difficult to classify the distribution of Covid-19 in Indonesia. This study will group provinces based on the spread of Covid-19 in Indonesia using the Fuzzy C-means method and the Elbow method. Fuzzy C-means is a fuzzy-based grouping method that allows all data to be members of all clusters formed with their respective degrees of membership. Meanwhile, to determine the best number of clusters, the Elbow method will be implemented. The Elbow method compares the sum square error (SSE) results from each cluster and gets the best number of clusters from a significant change in the SSE value or forms an elbow. The use of Fuzzy c-means as a grouping method to find out how much influence each data has on each cluster. Because the previous methods used on the same object only grouped it explicitly, without paying attention to the effect of one data on the entire cluster. The grouping was carried out into three clusters or groups based on the parameters of positive cases, recovered, and died of Covid-19 as of 27 April 2021. Cluster 1 only consisted of three provinces, namely West Java, Central Java, and East Java. Cluster 2 DKI Jakarta, and the rest go to cluster 3. It takes a grouping test to determine how accurate the results are.
CLASSIFICATION OF DENTAL CARIES DISEASE IN TOOTH IMAGES USING A COMPARISON OF EFFICIENTNET-B0, MOBILENETV2, RESNET-50, INCEPTIONV3 ARCHITECTURES Wahyuningsih, Wahyuningsih; Nugraha, Gibran Satya; Dwiyansaputra, Ramaditia
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2187

Abstract

Dental caries is a global metabolic disorder, influenced by complex interactions between the body and microbes, it's caused by prolonged exposure to a low pH environment, leading to demineralized carious lesions. If untreated, it can cause pain and eating difficulties, requiring emergency care and significantly impacting overall quality of life. Diagnosis process can be conducted through physical assessment and analyzing laboratory testing. Image-based artificial intelligence systems, particularly the EfficientNet-B0 model, is suggested as a resolution for classifying dental caries using tooth images. The study's goal is to assess EfficientNet-B0's performance in comparison to other CNN architectures and play a role in advancing medical image classification technology. The original dataset comprising 1554 images was initially collected. After augmentation, the dataset expanded to 6348 images. The data was then divided into three subsets of training, validation, and testing datasets with a distribution ratio of 70:15:15, respectively. From all the evaluated models, the EfficientNet-B0 demonstrated a quite commendable accuracy of 0.98% with overfitting tolerance of less than 2%. Having the same accuracy as the MobileNetV2 (0.98%). Despite its inability to exceed the accuracy achieved by ResNet-50 (0.99%), EfficientNet-B0 accomplished its accuracy level with roughly a quarter of the parameters than ResNet-50 and highger than InceptionV3 (0.97%), highlighting its efficiency in parameter utilization and computational resource management. These findings hold promise for enhancing models and guiding clinical decision-making.
Matlab Program for Sharpening Image due to Lenses Blurring Effect Simulation with Lucy Richardson Deconvolution Muhammad, Fathony Arroisy; Nugraha, Gibran Satya; Dwiyansaputra, Ramaditia
AMPLITUDO : Journal of Science and Technology Innovation Vol. 2 No. 1 (2023): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v2i1.57

Abstract

This research was conducted to simulate digital image sharpening using the Lusi Richardson deconvolution method. Sharpening was then performed by Lusi richardson deconvolution of the pint spread function of the lens effect. This point spread function is modeled mathematically with a mathematical function approach. The results of the convolution between the Digital Image from a photo of an object are then convolved with the point spread function so as to produce a blurry image. The blurry image is then re-sharpened by deconvolution using the Lucy Richardson convolution method. The results of this deconvolution are then compared with the image of an object photo of reference and then the difference is calculated. The slight difference between the deconvolution result image and the original object photo image indicates that the program is running well. Peak Signal to Noise Ratio (PSNR) Is used to determine image sharpening recovery. The optimum sharpening recovery of deconvolution iteration is obtained in the maximum PSNR value
Sosialisasi Pemasaran Digital Bagi Petani dan UMKM di Desa Mujur, Lombok Tengah, NTB: Digital Marketing Socialization for Farmers and UMKM in Desa Mujur,Lombok Tengah, NTB Nugraha, Gibran Satya; Dwiyansaputra, Ramaditia; Bimantoro, Fitri; Aranta, Arik
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 5 No. 1 (2024): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v5i1.1180

Abstract

Upaya Pemerintah Republik Indonesia dalam mendorong kemandirian desa melalui optimalisasi pemasaran hasil bumi menggarisbawahi pentingnya integrasi strategi pemasaran digital di pedesaan. Pengabdian ini mengeksplorasi kasus Desa Mujur di Pulau Lombok, yang dikenal sebagai "bumi Sasak" dan memiliki potensi sumber daya alam yang melimpah. Dengan mayoritas penduduknya bekerja sebagai petani, Desa Mujur menghadapi tantangan dalam memperluas pasar lokalnya, yang sebagian besar terbatas pada penjualan hasil bumi di pasar atau kerja sama dengan toko dan kios lokal. Pengabdian ini menyoroti pentingnya pemasaran digital sebagai alat untuk meningkatkan potensi pendapatan lokal dengan mencapai konsumen di luar wilayah geografis desa. Temuan dari survei Badan Pusat Statistik pada tahun 2022 menunjukkan bahwa penggunaan platform pesan instan, media sosial, dan marketplace merupakan strategi pemasaran digital yang paling banyak digunakan oleh pedagang online. Pengabdian ini menggaris bawahi bagaimana sosialisasi dan pemanfaatan teknologi digital dapat menjadi faktor krusial dalam era digital saat ini, tidak hanya untuk perusahaan besar tetapi juga untuk UMKM (Usaha Mikro, Kecil, dan Menengah), dalam meningkatkan penjualan dan memperluas pasar dalam lingkungan yang kompetitif. Platform digital seperti media sosial, e-commerce, dan aplikasi mobile menawarkan peluang baru yang belum pernah ada sebelumnya. Proses sosialisasi memungkinkan pelaku usaha memahami dan mengimplementasikan strategi digital yang efektif, termasuk pemasaran digital, SEO (Search Engine Optimizer, dan penggunaan media sosial untuk promosi, serta mengoptimalkan pengelolaan toko online. Oleh karena itu, sosialisasi teknologi dalam penjualan tidak hanya fokus pada adopsi alat baru, tapi juga pada transformasi mindset dan model bisnis, yang esensial untuk pertumbuhan dan keberlangsungan usaha di masa depan.
SOSIALISASI PEMASARAN DIGITAL BAGI PELAKU UMKM DI DESA JURIT, LOMBOK TIMUR, NTB: Digital Marketing Socialization for Small and Medium Enterprises on Jurit Village East lombok Bimantoro, Fitri; Wijaya, I Gede Pasek Suta; Dwiyansaputra, Ramaditia; Nugraha, Gibran Satya; Husodo, Ario Yudo; Hamidi, Mohammad Zaenuddin; Akhyar, Halil; Darmawan, Riski
Jurnal Begawe Teknologi Informasi (JBegaTI) Vol. 5 No. 2 (2024): JBegaTI
Publisher : Program Studi Teknik Informatika, Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jbegati.v5i2.1272

Abstract

Terletak di sebelah Selatan kaki gunung Rinjani, desa Jurit yang terletak di Lombok Timur merupakan salah satu desa yang memiliki sumber daya yang melimpah. Desa Jurit dengan mayoritas petani memiliki produk unggulan berupa Nanas. Saat ini, dengan perkembangan teknologi yang begitu pesat, tentu penggunaan teknologi menjadi salah satu faktor yang mampu mendongkrak kualitas hidup Masyarakat pada umumnya. Tentu hal itu juga menjadi fokus utama pengabdian di desa Jurit, yakni akan menyoroti tentang penggunaan digital marketing sebagai alat untuk meningkatkan penjualan berbasis digital, tentunya harapannya dapat meningkatkan pendapatan para petani dan pelaku usaha kecil dan menengah yang ada di desa Jurit. Pada prosesnya, sosialisasi ini memperkenalkan dan melatih pelaku usaha untuk menggunakan media pemasaran seperti media sosial, e-commerce, dan aplikasi mobile lainnya, dengan tujuan pelaku usaha mampu memahami dan menggunakan strategi digital yang baik seperti pemasaran digital, search engine optimizer, penggunaan media sosial dan tentunya e-commerce. Sehingga pada praktiknya, kegiatan ini tidak hanya berfokus pada cara penggunaan teknolginya, namun juga bagaimana mengenalkan dan menanamkan mindset dan model bisnis digital yang akan membantu peningkatan dan keberlangsungan pelaku usaha pada masa depan.
Co-Authors Akhyar, Halil Aranta, Arik Ario Yudo Husodo, Ario Yudo Darmawan, Muhammad Ilham Darmawan, Riski Dwiyansaputra, Ramaditia Fitri Bimantoro Hamidi, Mohammad Zaenuddin I Gede Pasek Suta Wijaya Latifah, Nurun Muhammad, Fathony Arroisy Wahyuningsih Wahyuningsih