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All Journal International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) POLYGLOT JURNAL MEDIA INFORMATIKA BUDIDARMA Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Building of Informatics, Technology and Science Journal of Electronics, Electromedical Engineering, and Medical Informatics eProceedings of Engineering
Wikky Fawwaz Al Maki
Universitas Telkom
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Big Cats Classification Based on Body Covering Fernanda Januar Pratama; Wikky Fawwaz Al Maki; Febryanti Sthevanie
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (442.897 KB) | DOI: 10.29207/resti.v5i5.3328

Abstract

The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. To overcome the decline in the animal population, a classification model was built to classify images that focuses on the pattern of body covering possessed by animals. However, in designing an accurate classification model with an optimal level of accuracy, it is necessary to consider many aspects such as the dataset used, the number of parameters, and computation time. In this study, we propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Initially, the input image is processed to take the body covering pattern of the animal and converted it into a grayscale image. Then, the image is segmented by employing the median filter and the Otsu method. Therefore, the noise contained in the image can be removed and the image can be segmented. The results of the segmentation image are then extracted by using the PHOG and then proceed with the classification process by implementing the SVM. The experimental results showed that the classification model has an accuracy of 91.07%.
Pengembangan Media Pembelajaran pada Pemrograman Terstruktur dan Pemrograman Berorientasi Objek dengan Visualisasi Bangun Datar Menggunakan Processing [Developing Learning Media Instruction on A Structured Programming and An Object-oriented Programming with Two-dimensional Figure Visualization Using Processing] Dodi Dodi; Elvira Wardah; Wikky Fawwaz Al Maki
Polyglot Vol 14, No 1 (2018): January
Publisher : Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/pji.v14i1.522

Abstract

In general, many people learn structured programming first and then learn object-oriented programming. This makes it difficult for someone to learn object-oriented programming. Because of this difficulty, researchers are interested in developing a learning medium that can display structured programming code and object-oriented code simultaneously and then visualize it with a flat wake object. It is hoped that this learning media can help students in studying object-oriented programming. The type of research used is Research and Development. Questionnaires were distributed to 30 students to find out their response to the media that were developed. The results of this research are displayed in the form of percentages of each aspect on the questionnaire and the percentage of the whole aspect. The results obtained in this research show that structured programming and object-oriented programming that are displayed and visualized simultaneously can help students in understanding object-oriented programming. It can be seen from the percentage obtained during user trials which was 83,33% and for percentage of student response to learning media which was a large as 84,92%.BAHASA INDONESIA ABSTRAK: Pada umumnya kebanyakan orang mempelajari pemrograman terstruktur terlebih dahulu dan baru kemudian mempelajari pemrograman berorientasi objek. Kebanyakan orang juga mengalami kesulitan dalam mempelajari pemrograman berorientasi objek. Dari permasalahan tersebut peneliti tertarik untuk mengembangkan sebuah media pembelajaran yang dapat menampilkan kode pemrograman terstruktur dan berorientasi objek secara bersamaan kemudian memvisualisasikannya dengan objek bangun datar. Media pembelajaran ini diharapkan dapat membantu seseorang dalam mempelajari pemrograman berorientasi objek. Jenis penelitian ini adalah Research and Development. Angket dibagikan kepada 30 orang mahasiswa untuk mengetahui respons mereka terhadap media pembelajaran yang dikembangkan. Hasil pengolahan data dalam penelitian berupa persentase setiap aspek pada angket dan persentase keseluruhan aspek.  Hasil yang didapatkan dalam penelitian ini adalah pemrograman terstruktur dan pemrograman berorientasi objek yang ditampilkan dan divisualisasikan secara bersamaan dapat membantu mahasiswa dalam memahami pemrograman berorientasi objek. Hal tersebut dapat dilihat dari persentase yang didapatkan pada saat uji coba pengguna dengan persentase sebesar 83,33% dan untuk persentase respons mahasiswa terhadap media pembelajaran secara keseluruhan sebesar 84,92%.
Classification of Glaucoma Using Invariant Moment Methods on K-Nearest Neighbor and Random Forest Models Athalla Rizky Arsyan; Wikky Fawwaz Al Maki
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): Maret 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (523.941 KB) | DOI: 10.47065/bits.v3i4.1244

Abstract

One of the cardiovascular diseases that can interfere with eye vision is glaucoma. This disease is caused by high pressure on the inside of the eyeball to cause blindness slowly. In general, screening or early diagnosis can help prevent glaucoma, specifically by analyzing several eye components affected by pressure, including the optical disc, optical cup, and blood vessels. Thus, by blending machine learning algorithms and computer vision technology, glaucoma classification and identification can be accelerated and improved. This study applied the Invariant Moment method to extract the optical cup and blood vessel segmentation's shape, scale, and rotation features. To obtain segmentation results for these two objects, we threshold two image datasets, DrishtiGS-1 and REFUGE, and implemented the approach described in this study to analyze system performance on these datasets. For the classification method used in this study, we proposed KNN and RF models to evaluate the suitability of the methods we used on the REFUGE and DrishtiGS-1 datasets and demonstrated that both models could be used to identify glaucoma through the use of fundus images. When the datasets were merged, we obtained 81.86% and 75.86% of accuracy when using blood vessel and optical cup segmentation results, respectively
Single Object Tracking with Minimum False Positive using YOLOv4, VGG16, and Cosine Distance Galuh Ramaditya; Wikky Fawwaz Al Maki
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4827

Abstract

Siamese network is a solution to the problem of single object tracking. Siamese network is a comparison method that uses a neural network in it. In this case, the siamese network extracts several images with the same weight, then compares them. There have been many studies that use the siamese network for single object detection. However, many still have high false positives when the target object is in and out of the frame or is entirely blocked by something so that the target data stored has a high level of damage. This study aims to create a method to track an object (single object tracking), even though the object is blocked by something, temporarily exits the frame with minimum false positive to keep the target data clean. The authors developed the method based on YOLOv4, VGG16, and cosine distance. Furthermore, researchers combine these methods to solve these problems with the concept of a siamese network. The result is that the system can track a person even if the target is entirely blocked by something or even in and out of the frame and reappears in a different location with minimum false positive.
Support vector machine with a firefly optimization algorithm for classification of apple fruit disease Wikky Fawwaz Al Maki; Amien Jafar Makrufi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2365

Abstract

Fruit diseases became one of the serious problems that the farmer faced because it could threaten their economic outcome. The main focus of this study is apples. Apple fruit is very susceptible to disease, in general diseases that usually attack the apple are blotch apple, rot apple, and scab apple. In this study, the author is classifying these three apple diseases and normal apples. Classification is a process that we can do manually by human power, which costs a lot of fortune, takes a long time, and it's also very vulnerable to false identification. This study takes advantage of computer vision technology and machine learning to overcome the classification problem. By using the SVM method and parameter FA optimization algorithm, we can get the highest result only in the first experiment and also with 94% accuracy.
Klasifikasi Penyakit Pada Tanaman Kopi Robusta Berdasarkan Citra Daun Menggunakan Convolutional Neural Network Savira Anggita Sabrina; Wikky Fawwaz Al Maki
eProceedings of Engineering Vol 9, No 3 (2022): Juni 2022
Publisher : eProceedings of Engineering

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Abstract

Abstrak Tanaman kopi merupakan salah satu komoditas perkebunan andalan di Indonesia, dengan sebagian besar hasil produksinya berasal dari tanaman kopi robusta. Salah satu permasalahan dalam produksi kopi robusta, yang dapat menyebabkan kerugian yang cukup signifikan, adalah rendahnya mutu hasil panen tanaman kopi robusta akibat serangan hama dan penyakit. Proses penanganan hama dan penyakit pada tanaman kopi robusta memerlukan sumber daya yang tidak sedikit. Terdapat beberapa cara untuk mengatasi permasalahan tersebut, salah satunya adalah dengan membangun sistem yang dapat melakukan identifikasi terhadap hama dan penyakit pada tanaman kopi robusta. Penelitian ini menggunakan Convolutional Neural Network (CNN) dengan arsitektur EfficientNet-B0 untuk menghasilkan sistem klasifikasi penyakit pada citra daun tanaman kopi robusta. Teknik augmentasi data dan pendekatan fine-tuning diterapkan untuk membantu menghasilkan sistem dengan performansi optimal. Evaluasi performansi dilakukan pada dua algoritma optimasi berbeda, yaitu Adam dan Root Mean Square Propagation (RMSProp). Kedua algoritma memperoleh hasil terbaik yang sama dengan nilai akurasi sebesar 91%Kata kunci : tanaman kopi robusta, CNN, Adam, RMSProp
Peringkas Teks Otomatis Bahasa Indonesia Secara Abstraktif Menggunakan Metode Long Short-term Memory Muhammad Alfhi Saputra; Wikky Fawwaz Al Maki; Nur Andini
eProceedings of Engineering Vol 8, No 2 (2021): April 2021
Publisher : eProceedings of Engineering

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Abstract

bstrak Salah satu topik dalam bidang Natural Language Processing (NLP) yang cukup menantang adalah peringkas teks otomatis. Dalam praktiknya peringkas teks otomatis terbagi menjadi dua pendekatan, yaitu ekstraktif dan abstraktif. Pendekatan abstraktif dinilai lebih baik karena cara kerjanya mendekati cara kerja manusia ketika meringkas teks atau yang disebut parafrase. Metode yang digunakan pada penelitian ini adalah Long Short-Term Memory (LSTM) yang mana metode tersebut telah sukses melakukan peringkasan dalam Bahasa Inggris. Dataset yang digunakan adalah kumpulan artikel berita media daring Bahasa Indonesia. Hasil terbaik yang didapatkan pada pengujian dengan metode LSTM menggunakan metode evaluasi ROUGE-1 adalah 0.13846. Kata kunci: peringkas teks otomatis, abstraktif, Bahasa Indonesia, long short-term memory, ROUGE Abstract One topic about natural language processing that is quite challenging is automatic text summarization. Automatic-text-summarization is practically divided into two kinds of approach, namely extractive and abstractive. Abstractive-approach is considered better since it resembles how humans work in terms of text summarizing or paraphrasing. A method used in this study is Long Short-Term Memory (LSTM) which has succeeded to summarize texts in English. Datasets that have been used are a number of online news articles in Bahasa Indonesia. The best result gained using LSTM based on the ROUGE-1 evaluation is 0.13846. Keywords: automatic text summarization, Bahasa Indonesia, long short-term memory, ROUGE
Deteksi Glaukoma Menggunakan Metode Convolutional Neural Network Dan Grabcut Segmentation Dicky Hidayat; Wikky Fawwaz Al Maki
eProceedings of Engineering Vol 9, No 3 (2022): Juni 2022
Publisher : eProceedings of Engineering

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Abstract

Abstrak Glaukoma adalah jenis gangguan yang menyerang penglihatan. Glaukoma terjadi karena kerusakan saraf optik yang dapat menyebabkan kebutaan. Cara yang dapat dilakukan untuk mendeteksi glaukoma adalah melalui gambar retina. Ada banyak cara untuk memproses gambar retina sebelum dapat mendeteksi glaukoma. Proses ini sangat penting karena dapat mempengaruhi tingkat keberhasilan sistem pendeteksian glaukoma. Dalam penelitian ini, kami menggunakan Convolution Neural Network sebagai metode klasifikasi. Gambaran retina dibagi menjadi dua kelas, yaitu glaukoma positif dan negatif. kemudian kami menerapkan metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dan segmentasi Grabcut. Hasil akurasi tertinggi dengan menggunakan data uji pada percobaan ini adalah 75.71%, precision sebesar 75.47%, recall sebesar 76.19%, dan F1- score sebesar 75.82% untuk arsitektur CNN InceptionV3. Kata kunci: convolutional neural network, CLAHE, pemrosesan gambar, segmentasi grabcut
Klasifikasi Ikan Cupang Menggunakan Support Vector Machine Irsyad Rafi Diesta; Wikky Fawwaz Al Maki
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

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Abstract

Ikan cupang merupakan ikan hias yang umum dijumpai di wilayah Indonesia. Dibandingkan dengan ikan secara umum ikan cupang memiliki bentuk, ukuran, serta warna yang jauh lebih menarik. Minimnya riset terkait ikan laut maupun ikan hias dan sulitnya membedakan jenis ikan cupang yang unik. Dengan menerapkan ilmu pengolahan citra, visi komputer, dan pembelajaran mesin dapat dibuat model yang dapat mengidentifikasi jenis ikan cupang berdasarkan ciri-ciri yang dimiliki ikan cupang. Beberapa penelitian terkait ikan masih meneliti berdasarkan beberapa bagian ikan saja seperti kepala, kulit atau sisik, ekor atau sirip saja. Dalam Tugas Akhir (TA) ini ikan cupang diteliti berdasarkan bentuk sirip, ekor, bentuk tubuh, dan warna sisik yang beragam dan lebih kompleks dibanding ikan secara umum. Dataset yang digunakan sebanyak 3,082 citra ikan cupang yang telah diaugmentasi menjadi 12.328 yang diterapkan beberapa teknik pengolahan citra, lalu metode fitur ekstraksi PHOG dan Color Moments, dan diklasifikasi menggunakan SVM dengan beberapa jenis kernel. Dari beberapa model yang telah dibuat, dihasilkan akurasi paling besar 87% sedangkan akurasi terkecil 73%. Dengan model yang berhasil dibuat diharapkan penelitian terkait ikan cupang yang memiliki bentuk dan warna kompleks dapat menginspirasi para peneliti lain untuk meneliti terhadap ikan hias maupun ikan laut yang memiliki informasi yang lebih kompleks lagi. Kata kunci : ikan cupang, color moments, PHOG, Klasifikasi SVM, pengolahan citra
Broccoli leaf diseases classification using support vector machine with particle swarm optimization based on feature selection Yulio Ferdinand; Wikky Fawwaz Al Maki
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.951

Abstract

Broccoli is a plant that has many benefits. The flower parts of broccoli contain protein, calcium, vitamin A, vitamin C, and many more. However, in its cultivation, broccoli plants have obstacles such as the presence of pests and diseases that can affect production of broccoli. To avoid this, the authors build a model to identify diseases in broccoli through leaf images with a size of 128x128 pixels. The model is constructed to classify healthy leaves, and disease leaves using the image processing method that uses machine learning stages. There are several stages, including K-Means segmentation, colour feature extraction, and classification using SVM (Support Vector Machine) with RBF kernel and PSO (Particle Swarm Optimization) for reduce dimensionality data. The model that has been built compares the SVM model and the SVM-PSO model. It produces good accuracy in the training of 97.63% and testing accuracy of 94.48% for SVM-PSO and 85.82% for training, and 86.25% for testing in the SVM model. Therefore, this proposed model can produce good results in categorizing healthy and diseased leaves in broccoli.
Co-Authors Amien Jafar Makrufi Athalla Rizky Arsyan Azizah Cahya Kemila Dicky Hidayat Dodi Dodi Elvira Wardah Febryanti Sthevanie Fernanda Januar Pratama Galuh Ramaditya Indra Bayu Muktyas Irsyad Rafi Diesta Khaidir Mauladan Muhammad Alfhi Saputra Nur Andini Savira Anggita Sabrina Yulio Ferdinand