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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Bulletin of Electrical Engineering and Informatics ILKOM Jurnal Ilmiah Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Pengabdian Kepada Masyarakat Darma Saskara
Susetianingtias, Diana Tri
Gunadarma University
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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PEMBUATAN APLIKASI KLASIFIKASI CITRA DAUN MENGGUNAKAN RUANG WARNA RGB DAN HSV Sanusi, Haura; H. S., Suryadi; Susetianingtias, Diana Tri
Jurnal Ilmiah Informatika Komputer Vol 24, No 3 (2019)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2019.v24i3.2323

Abstract

Bagi orang awam, mengetahui tumbuhan berdasarkan warna daun tentu tidak mudah, mengingat semua warna daun relatif sama yaitu warna hijau, sehingga akan sulit juga untuk mengetahui manfaat dari tumbuhan tersebut. Oleh karena itu dibutuhkan sebuah sistem klasifikasi berdasarkan warna daun untuk mengetahui apa nama dan manfaat tumbuhan tersebut. Penelitian ini bertujuan untuk menghasilkan ekstraksi nilai red, green, blue, hue, saturation, dan value pada citra daun, menghasilkan klasifikasi citra daun berdasarkan hasil ekstraksi nilai RGB dan HSV, serta menghasilkan nilai akurasi hasil klasifikasi citra daun. Pada penelitian ini, peneliti akan melakukan klasifikasi terhadap daun berdasarkan warna daun. Peneliti menggunakan 200 buah citra dari 10 jenis daun. Klasifikasi daun berdasarkan warna dilakukan peneliti menggunakan ruang warna RGB dan HSV. Hasil klasifikasi citra daun memiliki rata-rata akurasi yang tinggi yaitu 90,08%.
KOMBINASI LOGISTIC MAP DAN PSEUDO-RANDOM NUMBER GENERATOR PADA PEMBANGKITAN KUNCI UNTUK ENKRIPSI CITRA DIGITAL Arianty, Rini; Susetianingtias, Diana Tri
Jurnal Ilmiah Teknologi dan Rekayasa Vol 25, No 3 (2020)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2020.v25i3.3120

Abstract

Informasi berbentuk gambar yang bersifat sensitif atau rahasia, seperti data pribadi, dokumen penting yang dikirimkan melalui internet belum tentu aman dari serangan pihak luar. Kerugian yang cukup besar dapat ditimbulkan apabila data tersebut diakses dan dimanipulasi oleh orang yang tidak bertanggung jawab. Salah satu metode dalam mengamankan suatu informasi adalah kriptografi. Logistic map adalah salah satu algoritma chaos yang sering digunakan dalam kriptografi citra karena algoritma ini mampu menghasilkan deretan bilangan acak yang kompleks dengan persamaan polinomial rekursif yang sederhana. Pada penelitian ini, akan diimplementasikan algoritma chaos logistic map dan pseudo-random number generator (PRNG) dalam pengenkripsian citra. Citra input akan diubah bentuknya kedalam array lalu proses difusi dilakukan secara selektif dengan mensubstitusi 4 bit MSB setiap nilai warna citra dengan kunci logistic map. Hasil difusi tersebut akan dikonfusi dengan cara mensubstitusikan indeks arraynya dengan kunci prng sehingga didapat sebuah array baru yang teracak indeksnya. Array tersebut diubah kembali menjadi sebuah citra sehingga didapat citra terenkripsi yang aman.
Implementation of the prophet model in COVID-19 cases forecast Rodiah Rodiah; Eka Patriya; Diana Tri Susetianingtias; Ety Sutanty
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1219.99-111

Abstract

One of the steps to understanding this pandemic is to look at the spread of the data by predicting an increase in cases in various countries so that prevention can be carried out as early as possible. One way to see fluctuations in COVID-19 pandemic data is to predict the rate of cases using forecasting methods so that conclusions can be drawn on the spread of COVID-19 pandemic data around the world to be processed using statistical models. This study will implement the use of the Prophet Model in seeing the rate of development of COVID-19 in the world using four features in the forecasting process such as the number of confirmed cases, the number of cases of recovered patients, the number of cases of death, and the number of active cases. The results of this study produce forecasting data on the number of cases of the COVID-19 pandemic that can be viewed daily, weekly, and even monthly. Forecasting results show the first spike at the end of March until the number of cases reached around 10,275,800 million as of June 29, 2020, where the number of cases grew exponentially until June 29, 2020. The case rate of growth in many instances experienced significant growth until the end of October, touching the number in the range of 34,507,150 million as of October 25, 2020. After June 29, 2020, a very high spike was different from the increase in cases in the previous months. Forecasting results show no point decline because historical data on the number of daily confirmed cases of the COVID-19 pandemic has not decreased. The forecasting results in this study are expected to be able to systematically predict events or events that will occur in the COVID-19 pandemic around the world with the help of valid periodic data so that some information can be obtained for preventive measures related to the COVID-19 pandemic.
Combination of YOLOv3 Algorithm and Blob Detection Technique in Calculating Nile Tilapia Seeds Diana Tri Susetianingtias; Eka Patriya; Rini Arianty
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1634.317-325

Abstract

Baby Fish counting must be counted accurately so it will not cause any loss, especially for fish seeds or fingerlings that have a small size. Generally, people still use conventional counting methods that produce low accuracy values. This research will make a Nila Baby Fish fingerlings counter program using the YOLOv3 algorithm and Blobb detection technique. The annotation data process will use LabelImg, and the dataset training will use Google COLABoratory with the Darknet framework in an online environment. Images that will predict in this program will be called and detected with an object detector. The object with a confidence score of more than 0.3 will be converted into a blob. The blob value will be forwarded to the output layer for scaling the bounding box objects. The output of this program is the predicted image, blob value, prediction time, and the number of Nila seeds. The model performance is evaluated using a confusion matrix and got a 98.87% for accuracy score.
Biometric System for Person Authentication Using Retinal Vascular Branching Pattern Diana Tri Susetianingtias; Sarifuddin Madenda; Rodiah; Rini Arianty
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 2 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i2.1156

Abstract

The person’s retina has its uniqueness that can be used as biometric recognition. The use of the retina as a marking feature in biometrics is more accurate in making calls, verification, and authentication. Retinal biometric characteristics are unique and difficult to manipulate, thus making the retinal biometric system one of the most reliable biometrics compared to other biometric characteristics. The retinal biometric system can be formed using extracted retinal vessels. The difficulty in extracting retinal vessels is a characteristic of retinal vessels. itself includes (central artery, central branch artery, central vein and central branch vein), the ratio of the thickness ratio between the different retinal arteries and veins (2:3), the location of the retinal artery and vein and the color. This complexity often results in errors in the retinal blood vessel extraction process, where not all blood vessel objects can be extracted properly which can reduce the accuracy of the retinal biometric system. This study will address the problem of extracting retinal vessels by proposing the use of an extraction method to produce truly unique retinal features to be included in the retinal biometric system by tracing all branches of the retinal vessels (consisting of: bifurcation, trifurcation and crossover). ). The accuracy results show that 99.81% of the images were correctly detected. The blood pattern is obtained by doing extraction which includes the preprocessing stage and is continued by doing the blood extraction stage. This pattern extraction result is used as a unique pattern to be included in the feature vector of the biometric system in identifying person based on the retina.
Perbandingan Metode Recurrent Neural Network (RNN) dengan Linear Regression untuk Prediksi Saham Netflix Duriyawati; Rodiah; Susetianingtias, Diana Tri
Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Vol. 10 No. 4 (2024): Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton
Publisher : Lembaga Jurnal dan Publikasi Universitas Muhammadiyah Buton

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35326/pencerah.v10i4.6381

Abstract

Saham merupakan suatu surat berharga atas bukti penyertaan atau kepemilikan individu maupun institusi dalam suatu perusahaan. Alasan banyak investor menanamkan modalnya saat berinvestasi adalah return yang tinggi. Namun pergerakan harga saham sulit untuk ditebak arah kelanjutannya. Banyak metode dan cara yang dilakukan untuk memprediksi jual beli saham, salah satunya adalah perkembangan komputasi untuk pembelajaran mesin yang sangat pesat, yaitu teknik kecerdasan buatan. Dalam penelitian ini, metode kecerdasan buatan yang digunakan adalah Recurrent Neural Network (RNN) dan Linear Regression. Penelitian ini menggunakan dataset saham Netflix dari tahun 2022 sampai dengan 2023. Penelitian ini bertujuan untuk membandingkan metode RNN dan linear regression dengan melihat nilai Koefisien determinasi (R2) dan Mean Absolute Percentage Error (MAPE). Setelah dilakukan pengujian, didapatkan hasil dari R2 pada metode RNN sebesar 85.11%, menandakan bahwa hasil prediksi sudah baik dan nilai MAPE nya sebesar 5.74% yang berarti bahwa tingkat errornya sudah sangat rendah. Sedangkan pada metode linear regression, nilai R2 yang didapat sebesar 100% menandakan bahwa hasil prediksi sudah sangat baik dan nilai MAPE nya sebesar 1.23%, menandakan bahwa tingkat error nya sudah sangat rendah.
The Implementation of Artificial Neural Network (ANN) on Offline Cursive Handwriting Image Recognition Fitrianingsih, Fitrianingsih; Susetianingtias, Diana Tri; Pernadi, Dody; Patriya, Eka; Arianty, Rini
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1113.63-73

Abstract

Identifying a writing is an easy thing to do for human, but this does not apply to computers, in particular if it is handwriting. Handwriting recognition, especially cursive handwriting is a research in the area of image processing and pattern matching that is challenging to complete, following the different characteristics of each person's cursive handwriting style. In this study, the use of the ANN model will be implemented in performing offline handwriting image recognition. The cursive handwriting image that has been obtained is then preprocessed and segmented using bounding box rectangle and contour techniques. Evaluation of system performance using global performance metrics in this study resulted in a percentage of 93% where the bounding box and contour succeeded in determining the segmentation point correctly, so that the ANN model worked optimally.
Deep learning based identification of Crocidolomia pavonana larvae on mustard plants using Grad-CAM Diana Tri Susetianingtias; Sarifuddin Madenda; Risnawati Risnawati; Maukar Maukar; Eka Patriya; Rodiah Rodiah
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11343

Abstract

Mustard greens are an important vegetable commodity, but their production is often affected by pest attacks, especially the cabbage worm Crocidolomia pavonana (C. pavonana). The larvae damage leaf tissues and cause significant yield losses, while chemical control is often ineffective due to differences in insecticide sensitivity across larval instars. This study proposes a deep learning based classification approach combined with gradient weighted class activation mapping (Grad-CAM) to identify larval instars of C. pavonana on mustard plants. A dataset of 684 images covering instars 1 to 4 was collected from laboratory rearing and field observations, then processed using resizing and augmentation techniques and divided into training, validation, and testing sets with an 8 to 1 to 1 ratio. Two convolutional neural network (CNN) models, visual geometry group 19 (VGG19), and Xception, were implemented with additional fully connected layers. The VGG19 model achieved 94.20% accuracy and outperformed Xception. Grad-CAM successfully highlighted larval regions and supported visual interpretation. The results show that the proposed method can improve pest identification accuracy and support more effective pest management.
Program Pendidikan Ilmu Teknologi Informasi dan Pelatihan Komputer ”Pemanfaatan Media Online Dalam Pemasaran” Untuk Peserta Didik Divisi Pendidikan Publik Lembaga AgenKultur Kp Babakan Ds Binong, Karawaci, Tangerang, Banten Missa Lamsani; Maulana Mujahidin; Mochamad Wisuda Sardjono; Rini Arianty; Siti Chodijah; Sri Wulan Windu Ratih; Agung Slamet Riyadi; Any Kurniawaty Yapie; Diana Tri Susetianingtias; Diny Wahyuni
Jurnal Pengabdian Kepada Masyarakat Darma Saskara Vol. 5 No. 2 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/abdimasug.2025.v5i2.211

Abstract

Pelaksanaan Pengabdian kepada Masyarakat merupakan salah satu bentuk nyata dari penerapan Tri Dharma Perguruan Tinggi. Dalam kegiatan ini, Universitas Gunadarma menjalin kerja sama dengan Yayasan AgenKultur, sebuah lembaga independen yang bergerak di bidang sosial, budaya, dan pendidikan, di mana PKBM Tan Malaka menjadi divisi yang berfokus pada layanan pendidikan. Kolaborasi tersebut diwujudkan melalui pemberian pelatihan komputer dan ekonomi kepada peserta didik program Paket B dan C di yayasan tersebut. Mengingat pentingnya penguasaan teknologi informasi pada masa sekarang, materi pelatihan yang diberikan diarahkan pada pengenalan dan pemanfaatan internet untuk kegiatan pemasaran digital. Selain pelatihan teknis, Universitas Gunadarma juga menyelenggarakan Kuliah Umum secara daring untuk menambah wawasan para siswa mengenai perkembangan teknologi informasi, khususnya dalam bidang ilmu komputer dan penerapannya di bidang ekonomi. Seluruh kegiatan ini bertujuan untuk meningkatkan pengetahuan dan keterampilan digital para siswa di PKBM Tan Malaka sehingga mereka memiliki bekal yang lebih baik dalam menghadapi kebutuhan pembelajaran dan dunia kerja. One of the ways to actualize the Tri Dharma of Higher Education is through community service activities, and this initiative is reflected in the collaboration between Gunadarma University and the AgenKultur Foundation, an independent organization focusing on social, cultural, and educational development. Within this foundation, PKBM Tan Malaka serves as the main educational unit. Through this partnership, training sessions in computer science and basic economics were provided for students enrolled in Package B and C programs. Given the increasing reliance on digital technology today, the training modules emphasized the introduction and practical use of the internet as a tool for digital marketing. In addition to technical training, Gunadarma University also offered an online Public Lecture designed to give students insight into the latest trends in information technology, particularly within computer science and its relevance to economic activities. The overall goal of this community service program was to enhance the digital literacy and practical IT skills of students at the AgenKultur Foundation.
Model Convolutional Neural Network (CNN) Custom Sequential untuk Klasifikasi Citra Ikan Hias Carassius Auratus pada Industri Akuakultur Rodiah; Diana Tri Susetianingtias; Eka Patriya
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 5 No. 3: NOVEMBER 2025
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v5i3.1229

Abstract

Identifikasi varietas ikan hias Carassius auratus dengan keanekaragaman morfologi yang tinggi masih sering dilakukan secara manual, sehingga menimbulkan kesulitan bagi para pecinta ikan hias maupun pelaku akuakultur dalam membedakan jenisnya secara tepat. Kondisi ini menuntut adanya solusi berbasis teknologi yang mampu memberikan hasil identifikasi lebih cepat, praktis, dan presisi. Penelitian ini bertujuan untuk mengembangkan model klasifikasi citra menggunakan pendekatan Convolutional Neural Network (CNN) dengan arsitektur custom sequential sebagai sistem identifikasi visual otomatis. Tahapan penelitian dilakukan melalui pengumpulan 2.080 citra ikan yang mewakili delapan kelas varietas, dilanjutkan dengan proses pra-pemrosesan data agar citra siap digunakan pada tahap pelatihan model. Arsitektur CNN yang dibangun terdiri dari tujuh lapisan konvolusi dengan jumlah filter bertingkat mulai dari 32 hingga 256, disertai proses pooling, aktivasi ReLU, transformasi flatten, serta lapisan fully connected dengan mekanisme dropout untuk mengurangi overfitting, dan diakhiri dengan fungsi aktivasi softmax sebagai klasifikasi multi-kelas. Hasil eksperimen menunjukkan bahwa model mampu mengklasifikasikan citra ikan hias dengan tingkat akurasi mencapai 93,03% serta menghasilkan prediksi data uji secara konsisten tepat. Penelitian ini berkontribusi dalam menghadirkan pendekatan klasifikasi berbasis citra yang efisien, akurat, dan aplikatif, sehingga dapat mendukung proses identifikasi varietas ikan hias dalam industri akuakultur secara lebih modern dan berbasis teknologi.
Co-Authors Agung Slamet Riyadi Any Kurniawaty Yapie Diny Wahyuni Dody Pernadi Dody Pernadi Duriyawati Eka Patriya Ety Sutanty Fitrianingsih Fitrianingsih Maukar Maukar Maulana Mujahidin Missa Lamsani Mochamad Wisuda Sardjono Patriya, Eka Rini Arianty Rini Arianty Risnawati Risnawati Rodiah Rodiah Rodiah Sanusi, Haura Sarifuddin Madenda Siti Chodijah Sri Wulan Windu Ratih Suryadi, Soleh