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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknik Informatika (JUTIF) Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Herdianti Darwis
Informatics Engineering, Faculty Of Computer Science, Universitas Muslim Indonesia
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Other

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Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Support Vector Machine dengan Fitur Fourier Descriptor Putri Regina Prayoga; Purnawansyah Purnawansyah; Tasrif Hasanuddin; Herdianti Darwis
Jurnal Pendidikan Informatika (EDUMATIC) Vol 7 No 1 (2023): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v7i1.17521

Abstract

Indonesia is a rich country in herbal plants that can be used as traditional medicine. Leaves are one of the main components of herbal plants that are difficult to distinguish in texture and shape. This study aims to classify two types of herbal leaves, namely Sauropus androgynus and Moringa leaves using the K-nearest neighbor (KNN) and Support vector machine (SVM) with fourier descriptor (FD) feature extraction on texture and shape features. The research uses primary data collected through a smartphone camera as much as 480 image data with light and dark scenarios which are then divided into 80:20 training and testing data. Based on the research that has been done, it is found that the KNN for light scenario data and dark scenarios get 92% and 94% accuracy respectively. The test results using SVM with FD feature extraction obtain an accuracy of 96% for light and dark scenarios. Thus, SVM is more recommended in the classification of herbal leaf images.
Implementasi Aplikasi Augmented Reality untuk Media Pembelajaran Flora di SD Inpres Desa MarindingToraja Fitriyani Umar; Herdianti Herdianti; Wistiani Astuti; Nurul Alfiyyah; Sarah Fila Nurul
Ilmu Komputer untuk Masyarakat Vol 4, No 1 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v4i1.1545

Abstract

Banyak upaya yang dapat dilakukan untuk membantu proses pembelajaran. Transfer pengetahuan akan optimal jika didukung dengan media pembelajaran yang tepat. Sekolah Dasar Inpres Marinding dalam proses pembelajaran selama ini belum dapat memanfaatkan teknologi terkini yang sesuai untuk meningkatkan pemahaman siswa tentang suatu mata pelajaran. Padahal pesatnya perkembangan teknologi informasi dapat dimanfaatkan sebagai media tambahan untuk mendukung pembelajaran yang merangsang imajinasi, interaktif dan menumbuhkan minat belajar sehingga proses belajar mengajar menjadi lebih baik lagi.Demi menunjang proses pembelajaran di SDN 294 Inpres Marinding, siswa diharapkan memiliki banyak buku yang berisi satu tema tertentu sebanyak jumlah tema yang ada. Akan tetapi, pembelajaran masih berpusat pada buku tersebut dan tidak ada alat peraga khusus tentang Flora. Siswa tidak dapat melihat objek Flora secara langsung, hanya melalui gambar di buku dan kurang detailnya informasi tentang objek tersebut. Akibatnya, pembelajaran cenderung monoton, dan kurang kreatifitas.Solusi yang diusulkan adalah memberikan pelatihan untuk implementasi aplikasi Augmented Reality Pengenalan dengan tujuan untuk meningkatkan pengetahuan guru dan siswa dalam pemanfaatan teknologi informasi untuk media pembelajaran alternatif melalui Augmented Reality khususnya pembelajaran Flora.Tercapainya tujuan kegiatan telah menghasilkan luaran berupa modul dan aplikasi yang dapat menjadi alternatif media pembelajaran, publikasi pada media online dan jurnal yang diterbitkan di ILKOMAS.
K-Nearest Neighbor dan Convolutional Neural Network pada Klasifikasi Penyakit Tanaman Bawang Merah - Nurhikma; - Purnawansyah; Herdianti Darwis; Harlinda L
Techno.Com Vol 22, No 3 (2023): Agustus 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i3.8533

Abstract

Bawang merah merupakan suatu kebutuhan masyarakat terutama pada bahan makanan dan juga digunakan untuk Kesehatan. Dengan banyaknya manfaat bawang merah, dibalik itu juga memiliki suatu kendala atau resiko pada penanaman bawang merah salah satu resikonya adalah hama atau penyakit yang dapat merugikan petani bawang merah. Tujuan dari penelitian ini yaitu mengklasifikasi penyakit daun bercak ungu dan moler pada tanaman bawang merah, yang di implementasikan menggunakan metode ekstraksi fitur Gray Level Co-Occurance Matix (GLCM) yang digunakan untuk ekstraksi fitur tekstrur. Selain itu ada lima jarak yaitu Eucludiean, Manhattan, Chebyshev, Minkowski, Hamming digunakan dalam metode klasifikasi  K-Nearest Neighbor (KNN). Penelitian ini juga menggunakan metode klasifikasi Convolutional Neural Network (CNN). Hasil dari penelitian ini yang diperoleh menggunakan metode GLCM dan KNN dengan jarak Euclidean, Manhattan, Chebyshev, dan Minkowski mendapatkan hasil akurasi yang tinggi yakni sebesar 100%, sedangkan nilai akurasi terendah terdapat pada KNN jarak Hamming nilai akurasi yaitu sebesar 42%, adapun klasifikasi dari gabungan dari metode GLCM dan CNN mendapatkan hasil akurasi sebesar 100% dan pada metode CNN yang tanpa metode ekstraksi memiliki nilai akurasi sebesar 100%.
Max Feature Map CNN with Support Vector Guided Softmax for Face Recognition Herdianti Darwis; Zahrizhal Ali; Yulita Salim; Poetri Lestari Lokapitasari Belluano
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1751

Abstract

Face recognition has made significant progress because of advances in deep convolutional neural networks (CNNs) in addressing face verification in large amounts of data variation. When image data comes from different sources and devices, the identifiability of other classes and the presence of profile face data can lead to inaccurate and ambiguous classification because other classes lack discriminatory power. Furthermore, using a complex architecture with many deep convolutional layers can become very slow in the training process due to a huge amount of Random Access Memory (RAM) usage during the reverse pass of backpropagation. In this paper, we design a light CNN architecture that addresses these challenges. Specifically, we implemented Max-feature-map (MFM) into each convolutional layer to improve the accuracy and efficiency of the CNN. The strength of the support vector-guided SoftMax (SV-SoftMax) is also used in the proposed method to emphasize misclassified points and adaptively guide feature learning. Experimental results show that the 9-Layers CNN with MFM layer and SV-SoftMax outperform VGG-19 with 96.22% validation accuracy and the second rank below FaceNet tested on the same dataset with fewer parameters. Moreover, the model performed well on data that is obtained from various capture devices such as webcam, CCTVs, phone cameras, and DSLR cameras. The implications of this research could extend to scenarios requiring face recognition technology implementation with light size, such as surveillance and authentication systems
Perbandingan Metode Naïve Bayes dan K-NN dengan Ekstraksi Fitur GLCM pada Klasifikasi Daun Herbal A. Nurjulianty; Purnawansyah Purnawansyah; Herdianti Darwis
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6262

Abstract

Indonesia is a country with various types of herbal plants that have the potential to be very effective medicines. Herbal plants have been used since ancient times as natural medicines. One part that has health benefits is the leaves, however, there are many similarities between the different types of leaves. This research aims  to classify digital images of herbal leaves implementing the Naïve Bayes and K-Nearest Neighbor (KNN) methods with Gray Level Co-occurrence Matrix (GLCM) feature extraction. The dataset consisted of sauropus androgynus and moringa leaves with data collection in bright and dark scenarios. A total of 480 data which was divided into two parts, namely 80% for training data and 20% for testing images. The KNN distances used for comparison are Euclidean, Manhattan, Chebyshev, Minkowski, and Hamming. Meanwhile, Naïve Bayes uses Gaussian, Multinomial, and Bernoulli kernels. The results of the study showed that the KNN method with the Manhattan distance obtained the best results with an accuracy rate of up to 94% in bright scenarios.
Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN Purnawansyah Purnawansyah; Aji Prasetya Wibawa; Triyanna Widyaningtyas; Haviluddin Haviluddin; Cholisah Erman Hasihi; Ming Foey Teng; Herdianti Darwis
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.v15i2.1759.382-389

Abstract

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.
Hybrid Fourier Descriptor Naïve Bayes dan CNN pada Klasifikasi Daun Herbal Sunarti Passura Backar; Purnawansyah Purnawansyah; Herdianti Darwis; Wistiani Astuti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5186

Abstract

Plants are vital to human life on earth, and the leaves and their whole parts have many benefits. These parts of the plant can help distinguish between different species. The leaf identification can be performed at any time, while the other parts of the plants can only be identified at a certain time. The study aims to classify two types of herbs i.e. saur-opus androgynous and moringa oleifera, implementing the Fourier Descriptor method to extract the shape and texture features. In the process of classification using the Naïve Bayes method with three types of nuclei (Gaussian, Bernoulli, and Multinomial) and a Convolutional Neural Network. The testing process was carried out using two scenarios, dark and light, where each scenario consisted of 240 images for a total of 480 images divided into 20% of the data testing and 80% of the training data. The Fourier Descriptor-Bernoulli Naive Bayes method gives the lowest accuracy in both light and dark scenarios, at 46% and 52%, respectively. As for the classification of herbal leaves using a combination of the Fourier Descriptor-Convolutional Neural Network method, it is recommended to be used in light image scenarios and Fourier Descriptor-Gaussian Naive Bayes in the dark scenarios because it is able to detect herbal leaf types with 100% accuracy.
Fourier Descriptor Pada Klasifikasi Daun Herbal Menggunakan Support Vector Machine Dan Naive Bayes Mutmainnah Samir; Purnawansyah; Herdianti Darwis; Fitriyani Umar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 6: Desember 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1067309

Abstract

Daun herbal bermanfaat sebagai obat alternatif karena kandungan alaminya dapat menyembuhkan berbagai penyakit dan menjaga kesehatan tubuh. Klasifikasi citra daun herbal digunakan untuk membedakan jenis tanaman herbal berdasarkan bentuk daun. Penelitian ini Penelitian menggunakan Fourier Descriptor (FD) untuk mengekstraksi fitur pada daun herbal dan mengklasifikasikannya menggunakan metode Support Vector Machine (SVM) dan Naive Bayes (NB). SVM diimplementasikan dengan empat kernel yaitu Linear, polynomial, Radial Basis Function (RBF), dan sigmoid sementara Naive bayes diaplikasikan dengan tiga jenis kernel yaitu Gaussian, Multinomial, Bernoulli. Evaluasi kinerja menggunakan Precision, accuracy F1-Score dan Recall. Citra daun herbal terdiri dari daun katuk (Sauropus Androgynus) dan daun kelor (Moringa) dengan total 480 citra. Data tersebut dibagi menjadi 80% untuk training dan 20% untuk testing. Terdapat dua skenario pencahayaan yaitu kondisi gelap dan terang. Hasil penelitian menunjukkan bahwa perbandingan metode SVM dengan ekstraksi FD dimana kernel Linear mencapai akurasi sebesar 98% pada skenario gelap, sementara kernel Sigmoid memberikan akurasi terendah sebesar 44% pada scenario gelap maupun terang. Adapun hasil dari metode Naive bayes dengan ekstraksi FD pada kernel multinomial menghasilkan akurasi tertinggi sebesar 83% pada terang, sedangkan kernel Bernoulli memberikan akurasi terendah sebesar 46% pada skenario gelap dan terang. Berdasarkan perbandingan hasil klasifikasi dari kedua metode, disarankan bahwa metode SVM pada ekstraksi FD lebih direkomendasikan dalam proses klasifikasi daun herbal. Penelitian ini dapat memberikan rekomendasu pengembang sistem untuk menetapkan metode yang tepat dalam klasifikasi citra daun herbal.   Abstract Herbal leaves are beneficial as alternative medicine because their natural content can cure various diseases and maintain a healthy body. The classification of herbal leaf images is used to differentiate types of herbal plants based on leaf shapes. This study utilizes Fourier Descriptor (FD) to extract features from herbal leaves and classify them using the Support Vector Machine (SVM) and Naive Bayes (NB) methods. SVM is implemented with four kernels namely linear, polynomial, Radial Basis Function (RBF), and Sigmoid while Naive bayes is applied with three types of kernels namely Gaussian, multinomial, Bernoulli. Performance evaluation includes precision, accuracy, F1- score and recall. Herbal leaf images consist of leaves (Sauropus Androgynus) and moringa leaves with a total of 480 images. The data is divided into 80% for training and 20 % for testing. There are two lighting scenarios, namely dark and light conditions. The result of this study shows a comparison of the SVM method with FD extraction where the Linear kernel achieves the highest accuracy of 98% in dark scenarios, while the Sigmoid kernel provides the lowest accuracy of 44% in both dark and light scenarios. The result of the naïve bayes method with FD extraction on the Multinomial kernel yield the highest accuracy of 83% in light scenarios while the Bernoulli kernel provides the lowest accuracy 46% in both dark and light scenarios. Based on the comparison of the classification result of the two methods, it is suggested that the SVM method for FD extraction is more recommended in the herbal leaf classification process. This research can provide recommendation for system developers to determine the appropriate method for classifying herbal leaf images.  
DIGITAL IMAGE CLASSIFICATION OF HERBAL LEAVES USING KNN AND CNN WITH GLCM FEATURES Dinna Zahirah; Purnawansyah Purnawansyah; Nia Kurniati; Herdianti Darwis
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1162

Abstract

Geographical position and having a tropical climate make Indonesia known for its abundant biodiversity, one of which is herbal leaves. Indonesia has more than 2039 species that fall into the category of herbal medicinal plants. Herbal leaves are plants that are used as an alternative to natural disease healing. The large number of herbal leaf plants makes it difficult for people to distinguish between herbal plants and non-herbal plants, except when herbal leaf plants bear fruit or bloom. With advances in technology, many studies have been conducted to identify types of herbal plants, one of which is to identify the characteristics of the leaves. In this study, image recognition of herbal leaves was carried out using the K-Nearest Neighbor and Convolutional Neural Network methods with feature extraction of the Gray Level Co-occurance Matrix. By using these 2 methods, the data collected in this study were 480 leaf images which were then divided into 80% testing data and 20% training data. The data used are in the form of Sauropus androgynus and Moringa leaves. Based on the test results, the Convolutional Neural Network method which is suggested in the herbal leaf image classification which has an accuracy value of 96%..
Co-Authors - Nurhikma A. Nurjulianty Adela Regita Azzahra Aji Prasetya Wibawa Alfitriana Riska Annisa Febrianti Cholisah Erman Hasihi Dedy Atmajaya Dewi Widyawati Dian Dinna Zahirah Fina Febriana Fitrah Wahyu Ningsih Fitriani Umar Fitriyani Umar Fitriyani Umar Harlinda L Harlinda Lahuddin Haviluddin Haviluddin Hilma Aszahrah Huzain Azis Jumrayanti Arfah lilis nurhayati Lokapitasari Belluano, Poetri Lestari Masita Balla Ming Foey Teng Mutmainnah Samir Nia Kurniati Nur Afra Dimitri Pratiwi Nur Almar' Atussaliha Nur Chaerunnisa Nurul Alfiyyah Purnawansyah Purnawansyah Purnawansyah Purnawansyah Putri Regina Prayoga Ramdan Sastra Sarah Fila Nurul Siska Anraeni Sunarti Passura Backar Tasrif Hasanuddin Triyanna Widyaningtyas Wistiani Astuti Wistiani Astuti Yulita Salim Zahrizhal Ali