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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization 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 Journal of Health and Nutrition Research eProceedings of Engineering
Wikky Fawwaz Al Maki
Universitas Telkom
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Medicine & Pharmacology Nursing Public Health

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Parameter Optimization of Support Vector Machine using River Formation Dynamic on Brain Tumor Classification Cahya Kemila, Azizah; Fawwaz Al Maki, Wikky
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i3.312

Abstract

Brain tumor is a condition that can interfece with brain function due to abnormal cell growth in the brain. MRI is used as a diagnostic tool when a patient has a brain tumor. The images obtained through MRI will be analyzed by the doctor to determine the type of tumor. Therefore, it is necessary to have a system that can classify tumor types based on MRI images. The image will be extracted using the HOG method, then classified using SVM. Certain measures can be taken to improve SVM performance, such as optimizing its parameters. This research develops a system that uses a novel combination, the SVM with the River Formation Dynamic (RFD) algorithm. RFD is being used to optimize parameters of SVM (C and gamma). The basic idea of RFD is to imitate the movement of water droplets flowing from high to low areas. This research compares the accuracy produced by SVM with the accuracy produced by SVM-RFD. The result is that SVM-RFD provides the better accuracy than only using SVM. The accuracy result by SVM on the MRI dataset is 74.37%. When we compared it with SVM-RFD, the accuracy increased by 13.19% to 87.56%. Further work will be carried out on the implementation of RFD on other SVM parameters to find other parameter combinations that can improve the accuracy of SVM.
Enhancing Pneumonia Disease Classification using Genetic Algorithm-Tuned DCGANs and VGG-16 Integration Putri, Kania Ardhani; Fawwaz Al Maki, Wikky
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.349

Abstract

Diagnostic complications arise from pneumonia, characterized by lung inflammation caused by alveolar fluid accumulation, particularly in regions with limited radiologists. To tackle this issue, a new method utilizes the VGG16 methodology for categorization, bolstered by genetic algorithms. In addition, Deep Convolutional Generative Adversarial Networks (DCGANs) improve the dataset by adding fake X-rays of pneumonia. Genetic algorithms are used to optimize hyperparameters in classification tasks. In contrast, DCGANs are employed to increase data augmentation techniques, boosting models' accuracy in identifying and categorizing pneumonia cases. The study partitioned a dataset into training, testing, and validation sets for pneumonia X-ray pictures. The training of GANs entails utilizing both generators and discriminators to produce increasingly realistic pictures gradually. The genetic algorithm enhances the hyperparameter tuning process, resulting in a substantial increase in accuracy. Initially, VGG16 achieved a success rate of 89.50% and a fitness score of 87.50%. Post-optimization and DCGAN augmentation, accuracy climbed to 95.50%, and F1-Score improved to 94.75%. This study combines genetic algorithms and DCGANs to create a model that can produce genuine pneumonia X-ray pictures and enhance categorization accuracy.
Boosting Performance of SVM in Koi Classification Using Direct Methods-Based Optimization Arkananta, Muhammad Hafizh; Fawwaz Al Maki, Wikky
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2474

Abstract

Many koi fish enthusiasts keep or buy them just for their attractive colors without knowing what type of koi fish they are. The manual classification of koi fish species is still frequently incorrect. As a result, it is critical to apply a machine learning technique to identify various koi fish species. This research implemented a computer vision algorithm to classify koi fish species using the Support Vector Machine (SVM) as the classifier. However, the maximum accuracy SVM can achieve in our koi fish classification system is 79%.  To achieve better performance, the SVM was optimized by applying various optimization methods from the Direct Method group, i.e., the Generalized Pattern Search (GPS), the Powell method, and the Nelder-Mead method. Three optimization methods from the Direct Method group have successfully improved the performance of SVM in this task. Experimental results demonstrated that using the Generalized Pattern Search (GPS) in our classification system can increase the accuracy to 98%. Also, implementing the Powell and the Nelder-Mead method can make the koi classification system obtain a better accuracy of 99%. These results indicate that the proposed approach is a viable solution to overcome the limitations of the SVM algorithm.
Optimizing Support Vector Machine for Avocado Ripeness Classification Using Moth Flame Optimization Crisannaufal, Kemal; Fawwaz Al Maki, Wikky
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.652

Abstract

Avocado is a fruit from Mexico and Central America that is widely distributed worldwide for production and consumption. In avocados, ripeness is crucial because it is the primary factor consumers consider, significantly influencing their purchasing decisions. The manual ripeness selection is inefficient and inconsistent, so the classification system is essential for determining ripeness due to its effectiveness and efficiency compared to manual selection. In this study, we aim to develop a model that can classify avocado ripeness using machine learning with optimization. The data consists of avocado images categorized into five ripeness stages: underripe, breaking, ripe (first stage), ripe (second stage), and overripe. We utilize a Support Vector Machine (SVM) for the classification. Instead of manually choosing the model’s hyperparameters, we use Moth Flame Optimization (MFO) to optimize the SVM hyperparameters. The MFO ensures that the proposed model has optimal performance. For the input of SVM, we extract the HSV, GLCM, and HOG and apply PCA to the data. In this study, we use three SVM kernels: RBF, polynomial, and sigmoid. The MFO finds the model’s hyperparameters based on kernel requirements, including C, gamma, degree, and coef0. The MFO-SVM obtains optimal performance with an accuracy of 82.55%, 82.68%, and 81.23% for SVM kernel RBF, polynomial, and sigmoid, respectively. The results show that our proposed model demonstrates adequate performance in identifying the ripeness levels of avocados. The MFO increases model performance on all evaluation metrics compared to the baseline model and can be an excellent strategy to improve model performance.
Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 Artika, Artika; Al Maki, Wikky Fawwaz
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1925

Abstract

The primary goal of this research is to develop a feasible and efficient method for identifying the disease and to advocate for an appropriate system that provides an early and cost-effective solution to this problem. Due to their superior computational capabilities and accuracy, computer vision and machine learning methods and techniques have garnered significant attention in recent years for classifying various leaf diseases. As a result, Resnet50 and Resnet101 were proposed in this study for the classification of bitter gourd disease. The 2490 images of bitter gourd leaves are classified into three categories: Healthy leaf, Fusarium Wilt leaf, and Yellow Mosaic leaf. The proposed ResNet50 architecture accomplished 98% accuracy with the Adam optimizer. The ResNet101 architecture achieves an average accuracy of 94% with the Adam optimizer. As a result, the proposed model can differentiate between healthy and diseased bitter gourd leaves. This research contributes to the development of methods for detecting bitter melon leaf disease using computer vision and machine learning, achieving high accuracy and supporting automatic disease diagnosis. The results can help farmers quickly and cost-effectively detect diseases early, thereby increasing agricultural productivity.
Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization Wikky Fawwaz Al Maki; Khaidir Mauladan; Indra Bayu Muktyas
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.4839

Abstract

Various cultivars of date fruits distributed throughout exhibit diverse complexity and unique attributes, including color, flavor, shape, and texture. These distinctive characteristics and appearance occasionally lack variability in date fruits, since various kinds of date fruit may have subtle differences in color, shape, and texture. To overcome the difficulty of sorting and classifying multiple types of date fruit, a classification model was developed to categorize date fruit according to their visual appearances and digital characteristics. This study proposes a classification system that categorizes date fruit into five distinct types. The system achieves this by extracting features related to date fruit images' color, shape, and texture. Specifically, color moments, HOG descriptors, and circularity are used for feature extraction. The resulting high-quality training data is then used to train a K-Nearest-Neighbor (KNN) classifier. Considering the parameters applied to develop the proposed classification model is essential. Therefore, the proposed KNN model will be optimized by Principal Component Analysis (PCA) and Binary Particle Swarm Optimization (BPSO). PCA is employed for dimensionality reduction, whereas BPSO is implemented to discover the optimal neighbors. The experimental results demonstrated that the classification model achieved an accuracy of 93.85%, a considerable improvement of 12% over barebone KNN.
Klasifikasi Genus Burung Hantu Berdasarkan Suara Menggunakan Convolutional Neural Network Ihsanti, Soraya; Al Maki, Wikky Fawwaz
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

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Abstract

Abstrak — Burung memiliki peran penting dalam sikluskehidupan yang kebanyakan aktif di siang hari. Burung hantumerupakan salah satu jenis burung yang aktif di malam hari.Burung hantu berperan penting di bidang pertanian dalammengusir hama. Meskipun penelitian tentang burung hantusudah banyak, namun banyak aspek biologi, sejarah evolusi,dan taksonomi burung hantu yang tetap kurang diketahui.Penelitian ini dilakukan untuk mengenali suara burung hantudan menggunakan Convolutional Neural Network (CNN) untukmengklasifikasikannya berdasarkan genusnya. Trimming dannoise reduction dilakukan untuk membantu menghasilkansistem dengan performansi yang optimal. Pengujian sistemdilakukan dengan skenario mencari nilai epoch, learning rate,dan optimizer dengan akurasi terbaik. Evaluasi performansidilakukan dengan hyperparameter terbaik yang sudah didapat.Hasil dari pengujian mendapat akurasi sebesar 99.8%. Kata kunci— audio processing, CNN, sound classification
Medical Eligibility as a Predictor of Continued Copper Intrauterine Device (IUD-Cu) Use: A Correlational Analysis Suazini, Esa Risi; Al Maki, Wikky Fawwaz; Cahyati, Widya Hary
Journal of Health and Nutrition Research Vol. 4 No. 2 (2025)
Publisher : Media Publikasi Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56303/jhnresearch.v4i2.427

Abstract

Approximately 60% of acceptors expressed dissatisfaction with contraceptive counseling services, which has resulted in a 0.7% decrease in modern contraceptive use in the last five years. One important factor in the sustainability of contraceptive use is the suitability of the acceptor's medical condition. This study aimed to determine the relationship between medical appropriateness and plans for continued use of the IUD-Cu. This study used a descriptive correlational design with a cross-sectional approach. Data were collected through structured interviews using a Medical Eligibility Criteria (MEC) application-based questionnaire. This application was created by WHO based on the 5th edition of the 2015 MEC for Contraceptive use guidebook. The sample was purposively selected, and 280 IUD-Cu acceptors were obtained. Univariate analysis was performed with frequency distribution and categorization of medical eligibility, while bivariate analysis used Chi-Square test and Prevalence Ratio (PR) with 95% CI. The results showed that 92.9% of acceptors were medically fit to use IUD-Cu, while 7.1% were not fit. A total of 12.1% of acceptors planned to change contraceptives. There was a significant association between medical eligibility and plans to continue using the IUD-Cu (p = 0.000; PR = 3.077; 95% CI: 1.574-6.015), indicating that medically eligible acceptors had 3.077 times greater potential to continue using the IUD-Cu than those who were not eligible. Therefore, it is recommended to optimize pre-installation screening of IUD-Cu with medical criteria-based tools so that the sustainability of contraceptive use can be maintained and women's reproductive rights are maximally fulfilled.
Deteksi Penyakit Tanaman Cabai Menggunakan Metode Convolutional Neural Network Dzaky, Athallah Tsany Rakha; Maki, Wikky Fawwaz Al
eProceedings of Engineering Vol. 8 No. 2 (2021): April 2021
Publisher : eProceedings of Engineering

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Abstract

Abstrak Penyakit dari suatu tanaman akan sangat mempengaruhi hasil panen dari tanaman tersebut. Jika penyakitnya tidak segera ditangani maka penyakit tersebut dapat merusak tanaman dan mengakibatkan gagal panen yang akan berpengaruh ekonomi. Maka dari itu deteksi penyakit tanaman menjadi hal yang sangat penting dalam proses perawatan tanaman. Tanaman cabai merupakan salah satu bahan makanan yang paling sering digunakan dalam berbagai masakan di Indonesia. Banyaknya permintaan maka akan diperlukan adanya penambahan lahan cabai. Semakin luas lahannya maka akan semakin besar usaha yang diperlukan untuk merawat dan mengawasi tanaman. Dengan perkembangan teknologi saat ini, memungkinkan dilakukannya pengawasan terhadap tanaman secara otomatis menggunakan sistem komputer. Dengan menggunakan image processing penyakit yang dapat dilihat dan direkam oleh kamera akan dapat dianalisis dan diidentifikasikan oleh komputer. Dengan begitu pengawasan tanaman akan menjadi lebih mudah dan efisien. Beberapa penyakit tanaman cabai yang terlihat dan cukup sering ditemui adalah virus kuning, keriting mosaik, dan layu. Ciri penyakit tersebut bisa dilihat dari bentuk dan warna daun. Pada penelitian ini dilakukan pengenalan penyakit dengan menggunakan algoritma CNN. CNN digunakan agar model dapat mengekstrak fitur dan melakukan klasifikasi citra secara otomatis. Data citra yang digunakan diambil langsung dari perkebunan cabai di Jawa Tengah. Data diambilsekitar pukul 10.00 – 12.00 siang, sehingga citra memiliki warna yang jelas. Model ini akan bisa mengklasifikasikan 4 jenis kondisi daun dengan 3 penyakit dan 1 kondisi normal. Model CNN ini bisa menghasilkan akurasi diatas 90% menggunakan arsitektur AlexNet. Kata kunci : Tanaman Cabai, Penyakit Tanaman, CNN, Neural Network, AlexNet. Abstract Disease from a plant will greatly affect the harvest of that plant. If the disease is not treated immediately, it can damage the crops and resulting in crop failure which will affect the economy. Therefore detection of plant diseases is very important. Chili plants are one of the most commonly used food ingredients in various dishes in Indonesia. With a large number of requests, it will require additional chili fields. The more extensive the land, the greater the effort required to care for and maintain the plants. With current technological developments, it is possible to observe plants disease automatically using a computer system. By using image processing, disease that can be seen and recorded by the camera can be analyzed and identified by a computer. That way, crop disease identification will become easier and more efficient. Some of the diseases that are seen and often encountered are yellow virus, curl mosaic, and wilting. The characteristics of the disease can be seen from the shape and color of the leaves. In this study, disease recognition was carried out using the CNN algorithm. CNN is used so that the model can extract features and perform image classification automatically. The image data used were taken directly from chili plantations in Central Java. Data is taken around 10:00 - 12:00 AM, so the image has a clear color. This model will be able to classify 4 types of leaf conditions with 3 diseases and 1 normal condition. This CNN model can produce an accuracy over 90% using AlexNet architecture. Keyword : Chili Plant, Plant Disease, CNN, Neural Network, AlexNet
Peringkas Teks Otomatis Bahasa Indonesia Secara Abstraktif Menggunakan Metode Long Short-term Memory Saputra, Muhammad Alfhi; Maki, Wikky Fawwaz Al
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
Co-Authors Amien Jafar Makrufi Arkananta, Muhammad Hafizh Arsyan, Athalla Rizky Artika, Artika Cahya Kemila, Azizah Crisannaufal, Kemal Dicky Hidayat Dodi Dodi Dzaky, Athallah Tsany Rakha Elvira Wardah Febryanti Sthevanie Fernanda Januar Pratama Galuh Ramaditya Ihsanti, Soraya Indra Bayu Muktyas Irsyad Rafi Diesta Khaidir Mauladan Nurdenara, Ciara Putri, Kania Ardhani Saputra, Muhammad Alfhi Savira Anggita Sabrina Suazini, Esa Risi Widya Hary Cahyati Yulio Ferdinand