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All Journal International Journal of Electrical and Computer Engineering Jurnal Dedikasi Jurnal Gamma TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Inovasi Teknologi Pendidikan International Journal of Advances in Intelligent Informatics POSITIF CESS (Journal of Computer Engineering, System and Science) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control KOMPUTIKA - Jurnal Sistem Komputer Jurnal AbdiMas Nusa Mandiri Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Algoritma Makara Journal of Technology Jurnal Repositor

Agus Eko Minarno

Universitas Muhammadiyah Malang

Author-ID : 320354

Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Klasifikasi Resting-State Dan Task-State Pada Functional Magnetic Resonance Imaging Menggunakan Cross Correlation dan Support Vector Machine Agus Eko Minarno
Jurnal Gamma Vol. 10 No. 1 (2014): September
Publisher : Jurnal Gamma

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Klasifikasi Resting-State Dan Task-State Pada Functional Magnetic Resonance Imaging Menggunakan Cross Correlation dan Support Vector MachineClassification of Resting-State and Task-State In Functional Magnetic Resonance Imaging Using Cross Correlation and Support Vector MachineAgus Eko MinarnoJurusan Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah MalangJl. Tlogomas 246 Malang (0341) 464318 Email : agoes.minarno@gmail.com ABSTRACTIn the previous study identified several overlapping voxel during the resting-state and state task. Methods for determining the connectivity map involves overlapping areas, less than optimal in describing patterns of task-state as a feature. In this study, proposed a new method for the selection of features to improve accuracy and reduce computational time in determining significant voxel-state task when using a non-overlapping area. Selection of the features to determine significant voxel using cross Correlation, and take voxel correlation value which is above the average correlation. The next stage is the determination of the threshold value to determine the number of voxels are chosen as a feature. The selected features will be labeled in accordance with the given stimulus, namely picture and sentence, and then selected voxel to obtain non-overlapping between the stimulus picture with the stimulus sentence. The average yield of 6 subjects, methods that involve overlapping area using SVM classifier obtained precision, recall, and accuracy respectively 94.2%, 95.1%, 94.6% and computation time 0.021 seconds. While the method has a non-overlapping area of precision, recall, and accuracy respectively 95.0%, 95.3%, 95.1% and computation time 0.019 seconds. Feature selection methods using non-overlapping area has the accuracy and computation time better than methods that involve overlapping area, in determining the connectivity map.Keywords: feature selection, task-state, cross-correlation, voxel-based selection, non-overlappingABSTRAKPada penelitian sebelumnya teridentifikasi beberapa voxel yang overlapping pada saat resting-state dan task state. Metode untuk menentukan connectivity map melibatkan daerah yang overlapping, kurang optimal dalam menggambarkan pola task-state sebagai ciri. Pada penelitian ini, diusulkan sebuah metode baru untuk pemilihan fitur untuk meningkatkan akurasi dan mengurangi waktu komputasi dalam menentukan voxel yang signifikan pada saat task-state menggunakan metode non-overlapping area. Pemilihan fitur untuk menentukan voxel yang signifikan menggunakan cross corelation, dan mengambil voxel dengan nilai korelasi yang berada diatas korelasi rata-rata. Tahapan berikutnya adalah penentuan nilai ambang batas (threshold) untuk menentukan jumlah voxel yang dipilih sebagai fitur. Fitur yang terpilih akan diberi label sesuai dengan stimulus yang diberikan, yaitu picture dan sentence, kemudian diseleksi untuk mendapatkan voxel yang non-overlapping antara stimulus picture dengan stimulus sentence. Hasil rata-rata dari 6 subyek, metode yang melibatkan overlapping area menggunakan classifier SVM diperoleh precision, recall, dan accuracy masing–masing 94.2%, 95.1% , 94.6% dan waktu komputasi 0.021 detik. Sedangkan metode non-overlapping area memiliki precision, recall, dan accuracy masing–masing 95.0%, 95.3% , 95.1% dan waktu komputasi 0.019 detik. Pemilihan fitur menggunakan metode non-overlapping area memiliki akurasi dan waktu komputasi yang lebih baik dari metode yang melibatkan overlapping area, dalam menentukan connectivity map.Kata kunci : feature selection, task-state, cross-correlation, voxel-based selection, non-overlapping

PENERAPAN ALGORITMA GENETIKA MENGGUNAKAN METODE SELEKSI ROULETTE WHEEL PADA OPTIMASI PENENTUAN KOMPOSISI BAHAN PAKAN AYAM BROILER Iqbal Fairus Zamani; Galih Wasis Wicaksono; Agus Eko Minarno
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 4 No 1 (2018): POSITIF : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v4i1.505

Along with many various human activity to fulfill the food nutritional in take vegetable nutrition and animal nutrition must be fulfilled. Animal nutrition is a needs that aften consumed by many people and one of those is broiler chicken. Broiler chicken is a livestuck chicken that easily produce, so there are so many broiler chicken farm that we could see in many place. But there are some problem that occur in most broiler breeders such as the composition of food ingredients and high ration costs. To fulfill the right composition of ingredients with minimum cost, researchers offer a solution by using genetic algorithm in deter mining the composition of broiler food ingredients. This aplication uses 50 ingredients of broiler chicken livestock along with nutrition. This study used roulette wheel methods. The result of this study obtained optimum parameters with population size of 400, various generation of 350 and pc 0.1 and pm 0.8 combinations of 10 times test. This result is a composition of broiler food ingredients that meet the nutritional needs with minimal cost.

Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network Tsabitah Ayu; Vizza Dwi; Agus Eko Minarno
CESS (Journal of Computer Engineering, System and Science) Vol 6, No 2 (2021): Juli 2021
Publisher : Universitas Negeri Medan

Pertanian adalah salah satu sektor ekonomi yang terpenting di negara-negara Asia Tenggara. Saat ini, pembangunan ekonomi sangat bergantung pada pertanian. Seperti contoh Mangga, Manga juga merupakan bahan makanan yang dapat diolah menjadi berbagai jenis makanan yang lezat. Karena banyaknya manfaat pada buah ini tak jarang masyarakat ingin menanam pohon mangga untuk dibudidayakan dengan tujuan komersil maupun pribadi. Salah satu masalah utama yang menurunkan kualitas dan kuantitas manufaktur pertanian adalah penyakit tanaman. Oleh karena itu bidang penelitian pertanian menarik para peneliti dan ilmuwan untuk memberikan teknik untuk mengidentifikasi penyakit tanaman dengan menggunakan pengolahan gambar dan visi komputer seperti dalam kertas ini yang menggunaka model Convolutional Neural Network (CNN) untuk mengklasifikasi jenis daun mangga yang sakit (terserang hama) dan sehat berdasarkan bentuk dan tekstur daun. Pada penelitian yang dihasilkan tingkat akurasi sebesar 0,96.

Re-Ranking Image Retrieval on Multi Texton Co-Occurrence Descriptor Using K-Nearest Neighbor Yufis Azhar; Agus Eko Minarno; Yuda Munarko
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Some features commonly used to conduct image retrieval are color, texture and edge. Multi Texton Co-Occurrence Descriptor (MTCD) is a method which uses all three features to perform image retrieval. This method has a high precision when doing retrieval on a patterned image such as Batik images. However, for images focusing on object detection like corel images, its precision decreases. This study proposes the use of KNN method to improve the precision of MTCD method by re-ranking the retrieval results from MTCD. The results show that the method is able to increase the precision by 0.8% for Batik images and 9% for corel images.

Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor Trifebi Shina Sabrila; Veronica Retno Sari; Agus Eko Minarno
Fountain of Informatics Journal Vol 6, No 2 (2021): November
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v6i2.5536

Analisis sentimen merupakan salah satu bidang dari pengolahan data berbentuk teks untuk mengidentifikasi isi yang terkandung dalam teks pada dataset dengan membagi dataset ke dalam dua kelas yaitu sentimen positif dan sentimen negatif. Pada penelitian ini akan dilakukan analisis sentimen terhadap data yang diperoleh dari jejaring sosial Twitter mengenai penanganan Covid-19 oleh pemerintah di Indonesia yang menuai banyak pro dan kontra oleh masyarakat di Indonesia. Tujuan dari penelitian ini adalah untuk mengetahui kecenderungan masyarakat terkait topik tersebut. Metode klasifikasi yang digunakan dalam penelitian ini adalah Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN) dengan ekstraksi fitur Word Embedding. Pengklasifikasian yang dilakukan dengan menggunakan algoritma Support Vector Machine (SVM) dengan menggunakan ekstraksi fitur Word Embedding yaitu Word2Vec menghasilkan akurasi sebesar 85%, presisi 86% , recall 85%, dan nilai AUC sebesar 0.92. Sementara pada algoritma K-Nearst Neighbor (KNN) dengan ekstraksi fitur yang sama, dihasilkan akurasi sebesar 76%, presisi 77%, recall 76% dan nilai AUC sebesar 0.87. Hasil perbandingan dari kedua metode menunjukkan bahwa algoritma Support Vector Machine (SVM) mendapatkan performa yang lebih baik dibandingkan algoritma K-Nearest Neighbor (KNN).

Klasifikasi Buah Zaitun Menggunakan Convolution Neural Network Toton Dwi Antoko; Muhammad Azhar Ridani; Agus Eko Minarno
Komputika : Jurnal Sistem Komputer Vol 10 No 2 (2021): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v10i2.4475

Buah zaitun merupakan tanaman produk hortikultura rumpun oleaceae dengan genus Olea yang memiliki berbagai macam jenis dan fitur yang unik. Satu dari sekumpulan species Olea yang ditemukan di wilayah tropis dan subtropis yang menjadikan tanaman subur dan melimpah. Hasil panen yang sangat melimpah sebanding dengan kebutuhan pasar. Pemanenan produk secara acak membuat pemilihan produk pasca panen sangat penting dalam mengelompokkan jenis buah zaitun. Sehingga perlu adanya sistem yang dapat mengklasifikasi secara otomatis. Sebelumnya sudah ada penelitian yang diusulkan untuk mengklasifikasi buah zaitun dengan akurasi yang cukup tinggi. Namun kecepatan yang diperlukan butuh waktu yang sangat lama karena menggunakan model pretrained yang begitu kompleks. Oleh karena itu, penelitian ini bertujuan untuk melakukan klasifikasi buah zaitun dengan waktu yang lebih cepat dan akurasi yang tidak kalah dari sebelumnya. Metode yang akan digunakan adalah Convolutional Neural Network (CNN) dengan rangkaian arsitektur sendiri. Hasil dari penelitian ini mendapatkan akurasi sebesar 92% dengan 30 epoch.

Vehicle Classification using Haar Cascade Classifier Method in Traffic Surveillance System Moch Ilham Ramadhani; Agus Eko Minarno; Eko Budi Cahyono
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 1, February-2018
Publisher : Universitas Muhammadiyah Malang

Object detection based on digital image processing on vehicles is very important for establishing monitoring system or as alternative method to collect statistic data to make efficient traffic engineering decision. A vehicle counter program based on traffic video feed for specific type of vehicle using Haar Cascade Classifier was made as the output of this research. Firstly, Haar-like feature was used to present visual shape of vehicle, and AdaBoost machine learning algorithm was also employed to make a strong classifier by combining specific classifier into a cascade filter to quickly remove background regions of an image. At the testing section, the output was tested over 8 realistic video data and achieved high accuracy. The result was set 1 as the biggest value for recall and precision, 0.986 as the average value for recall and 0.978 as the average value for precision.

Music Information Retrieval Based on Active Frequency Wibowo, Hardianto; Suharso, Wildan; Azhar, Yufis; Wicaksono, Galih Wasis; Minarno, Agus Eko; Harmanto, Dani
Makara Journal of Technology Vol. 25, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Music is the art of combining frequencies. A balance of frequencies gives rise to a harmonious tone. Several features of music can be analyzed, and they include sociocultural background, lyrics, mood, tempo, rhythm, harmony, melody, timbre, and instrumentation. In this study, we use the frequency of instrumentation as a feature for classification because each instrument has a frequency range. To test this frequency range, we use five music genres and one music playing skill. The five genres are dangdut, electronic dance music (EDM), metal, pop/rock, and reggae. The music playing skill is acoustic. Active frequencies are tested using the k-nearest neighbor method, and the results serve as basis of the accuracy of music classification. The classification accuracy for EDM, metal, and acoustic is over 70%, whereas that for dangdut, pop/rock, and reggae is less than 60%. In sum, the accuracy of music classification is influenced by the similarities in the music instruments used and the tempo.

Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16 Bella Dwi Mardiana; Wahyu Budi Utomo; Ulfah Nur Oktaviana; Galih Wasis Wicaksono; Agus Eko Minarno
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4550

Herbal leaves are a type that is often used by people in the health sector. The problem faced is the lack of knowledge about the types of herbal leaves and the difficulty of distinguishing the types of herbal leaves for ordinary people who do not understand plants. If any type of plant is used, it will have a negative impact on health. Automatic classification with the help of technology will reduce the risk of misidentification of herbal leaf types. To make identification, a precise and accurate herbal leaf detection process is needed. This research aims to facilitate the classification model of herbal leaf images with a higher accuracy value than previous research. Therefore, the proposed method in this classification process is one of the Transfer Learning methods, namely Convolutional Neural Network (CNN) with a pretrained VGG16 model. This research uses a dataset of herbal leaves with a total of 10 classes: Belimbing Wuluh, Jambu Biji, Jeruk Nipis, Kemangi, Lidah Buaya, Nangka, Pandan, Pepaya, Seledri and Sirih. The performance of the results of the proposed classification method on the test dataset using Classification Report shows an increase in the results of the previous research accuracy value from 82% to 97%. This research also applies Image Data Generator in the augmentation process which aims to improve the image of herbal leaves, reduce overfitting, and improve accuracy.

Convolutional Neural Network featuring VGG-16 Model for Glioma Classification Agus Eko Minarno; Sasongko Yoni Bagas; Munarko Yuda; Nugroho Adi Hanung; Zaidah Ibrahim
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1230

Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%.

Co-Authors Abu Abbas Mansyur Abu Abbas Mansyur Achmad Fauzi Saksenata Ahmad Annas Al Hakim Ahmad Annas Al Hakim Ahmad Faiz Ahmad Faiz, Ahmad Ahmad Heryanto, Ahmad Akbi, Denar Regata Alfarizy, Muhammad Rifal Alfian Yuniarto Ali Sofyan Kholimi Anbiya, Dhika Rizki Andhika Pranadipa Andhika Pranadipa Andrian Rakhmatsyah Aria Maulana Eka Mahendra Arif Bagus Nugroho Aripa, Laofin Arrie Kurniawardhani arrie kurniawardhany, arrie AULIA ARIF WARDANA Ayu Septya Maulani Bagaskara, Andhika Dwija Basuki, Setio Bayu Yudha Purnomo Bella Dwi Mardiana Chandranegara, Didih Rizki Cokro Mandiri, Mochammad Hazmi Deris Stiawan Devi Dian Aprilia Kusuma Sari Dwi Rahayu Dyah Ayu Irianti Dyah Ayu Irianti Eko Budi Cahyono Elsyah Ayuningrum Fachry Abda El Rahman Feny Aries Tanti Feny Aries Tanti Firdhansyah Abubekar Firdhansyah Abubekar Fitri Bimantoro Galang Aji Mahesa Gita Indah Marthasari Hanung Adi Nugroho Haqim, Gilang Nuril Hardianto Wibowo Hariyady Hariyady Harmanto, Dani Hasanuddin, Muhammad Yusril Hazmi Cokro Mandiri, Mochammad Ibrahim, Zaidah Indah Soesanti Iqbal Fairus Zamani Irfan, Muhammad irma fitriani Izzah, Tsabita Nurul Lailis Syafa'ah Lailis Syafa’ah Linggar Bagas Saputro Luqman Hakim Lusianti, Aaliyah Mandiri, Mochammad Hazmi Cokro Moch Ilham Ramadhani Moch. Chamdani Mustaqim Muhammad Afif Muhammad Azhar Ridani Muhammad Ferry Fernanda Muhammad Hussein Muhammad Nafi Maula Hakim Muhammad Nasrul Tsalatsa Putra Muhammad Nasrul Tsalatsa Putra Muhammad Nuchfi Fadlurrahman Nanik Suciati Naser Jawas, Naser Nia Dwi Nurul Safitri Nia Dwi Nurul Safitri Noor Aini Mohd Roslan Norizan Mat Diah Oktavia Dwi Megawati Prabowo, Christian Ramadhani, Moch Ilham Rangga Kurnia Putra Wiratama Ratna Sari Riksa Adenia Rizalwan Ardi Ramandita Rizka Nurlizah Sabrila, Trifebi Shina Sari, Veronica Retno Sari, Zamah Sasongko Yoni Bagas Setiyo Kantomo, Ilham Sulastria Sulastria Sumadi, Fauzi Dwi Setiawan Suryani Rachmawati Suseno, Jody Ririt Krido Syaifuddin Syaifuddin Toton Dwi Antoko Tsabitah Ayu Ulfah Nur Oktaviana Veronica Retno Sari Vizza Dwi Wahyu Andhyka Kusuma Wahyu Budi Utomo Wicaksono, Galih Wasis Wicaksono, Galih Wasis Widya Rizka Ulul Fadilah Wildan Suharso Yesicha Amilia Putri Yoga Anggi Kurniawan Yuda Munarko Yudhono Witanto Yufis Azhar Yundari, Yundari Zaidah Ibrahim Zaidah Ibrahim Zamah Sari Zamani, Iqbal Fairus

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