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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika Dinamika Informatika Jurnal Teknologi MAGISTRA Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Seminar Nasional Informatika (SEMNASIF) INFORMATIKA Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Creative Information Technology Journal Jurnal Sains dan Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ CSRID (Computer Science Research and Its Development Journal) Informasi Interaktif JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Suara Keadilan Technologia: Jurnal Ilmiah KURVATEK Jurnal Tecnoscienza Respati Jurnal Sistem Komputer & Kecerdasan Buatan Journal of Computer System and Informatics (JoSYC) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Madani : Indonesian Journal of Civil Society JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Journal of Applied Sciences, Management and Engineering Technology (JASMET) Jurnal Ekonomi dan Teknik Informatika Transformasi Journal of Social Research Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) PELS (Procedia of Engineering and Life Science) JAIA - Journal of Artificial Intelligence and Applications Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi EXPLORE Prosiding University Research Colloquium COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Jurnal of Information Technology and Society (JITS) Teknomatika: Jurnal Informatika dan Komputer Explore Jurnal Teknologi SWAGATI: Journal of Community Service
Andi Sunyoto
Universitas Amikom Yogyakarta
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation Other

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PENGARUH ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN TOMAT Rizky Arya Kurniawan; Sunyoto, Andi; Nasiri, Asro
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.111

Abstract

Plant disease is one of the crucial factors in plant survival. Tomato plants also need early help to be able to deal with disease problems. One of the organs of the tomato plant that is commonly attacked by diseases is the leaves. By providing assistance early on, it can prevent crop failure. Of course, having a trained system can reduce the cost of a farmer in dealing with diseases without expert assistance. In this research, we will test the ability of the CNN architecture to classify tomato leaf disease images. The dataset used is 4079 image data which are divided into 3 disease classes. From the results of experiments that have been carried out the InceptionV3 architecture gets the best results with an accuracy rate of 100%, ResNet50 has 97,36% accuracy and MobileNet 85,81%.
PEMANFAATAN DEEP LEARNING UNTUK SEGMENTASI PARU-PARU DARI CITRA X-RAY DADA Putranto, Dinar Wakhid; Andi Sunyoto; Asro Nasiri
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 2 (2023): Desember 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i2.114

Abstract

Chest or thoracic X-ray examination is the most commonly used supporting examination in the diagnosis of lung diseases. In addition to being quick, X-rays are more economical than CT scans or laboratory blood tests. Before determining the disease that appears from the lung image in the chest X-ray image, the doctor first determines the boundaries of the lung area. Not every chest X-Ray image has a normal lung image, some display abnormal images that look white haze or morphological changes due to lung disease processes. As one of the CNN architectures that can be used in segmenting the lungs is UNET which is an encode-decoder architecture. This research tries to train a deep learning model with U-Net CNN architecture. From the results of our experiments, the proposed model can show the ability to recognize lung boundaries even though there are abnormal or foggy lung images. The performance of the model is calculated by measuring the pixel accuracy value and the overlap value with Jaccard Index (IoU), the values of both are 98.25% and 94.54% respectively.
Klasifikasi Penyakit Alzheimer pada Citra Medis Magnetic Resonance Images dengan Arsitektur DenseNet121 Ismail, Muhamad Yusuf; Sunyoto, Andi; Purwanto, Agus
Jurnal Ilmiah Komputasi Vol. 23 No. 2 (2024): Jurnal Ilmiah Komputasi : Vol. 23 No 2, Juni 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.23.2.3600

Abstract

Penyakit Alzheimer merupakan penyakit neurodegeneratif yang paling umum terjadi pada manusia, terutama pada lansia, dan merupakan masalah kesehatan global yang serius. Citra Magnetic Resonance Imaging (MRI) ini dapat membantu membedakan kondisi normal pada pasien penyakit Alzheimer. Tetapi interpretasi MRI secara manual gambar oleh profesional medis masih memiliki keterbatasan tertentu. Proses interpretasi manual memakan waktu dan bergantung pada keterampilan dan pengalaman individu. Selain itu, kemungkinan kesalahan manusia juga meningkatkan risiko kesalahan diagnostik. Masalah tersebut bisa diatasi dengan menggunakan kecerdasan buatan, yaitu dengan menerapkan metode Convolutional Neural Network (CNN) dengan model Densenet121 yang dilatih untuk membedakan suatu objek bedasarkan gambar. Untuk tujuan tersebut, penelitian ini menerapkan metode Densenet121 untuk melakukan proses klasifikasi jenis penyakit Alzheimer melalui citra MRI otak. Hasil dari penelitian tersebut adalah metode Densenet 121 dapat digunakan untuk proses klasifikasi penyakit Alzheimer melalui citra MRI otak dengan tingkat akurasi 97,83%.
Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa Gori, Takhamo; Sunyoto, Andi; Al Fatta, Hanif
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241118074

Abstract

Pendidikan merupakan aspek penting dalam kehidupan masyarakat dan memiliki peran yang sangat vital untuk menciptakan sumber daya manusia yang handal dan berkualitas dalam menghadapi berbagai tantangan pada era modernisasi. Namun, putus sekolah dan retensi siswa menjadi tantangan serius bagi perkembangan pendidikan saat ini. Salah satu faktor pemicu putus sekolah adalah kinerja akademik siswa yang rendah, mendorong perlunya tindakan pencegahan yang efektif untuk mengurangi tingkat kegagalan pendidikan. Penelitian ini bertujuan untuk memprediksi kinerja akademik siswa dengan mengintegrasikan metode Correlation-Based Feature Selection (CFS) dan Algoritma Naïve Nayes pada gabungan dataset pelajaran Matematika dan Bahasa Portugis dua sekolah menengah di Portugal. Proses preprocessing data melibatkan integrasi data, pelabelan data, transformasi data, dan pembersihan data diterapkan pada tahap awal penelitian. Hasil penelitian menunjukkan bahwa atribut signifikan yang mempengaruhi kinerja akademik siswa meliputi G2, G1, Higher, Medu, Studytime, goout, Absences, dan Failures. Melalui pemodelan algoritma Naïve Bayes, metode CFS terbukti meningkatkan nilai accuracy, recall, precision, dan f1-score dalam memprediksi kinerja akademik siswa. Sebelum CFS, model Naïve Bayes menunjukkan accuracy sebesar 89.27%, dengan recall, precision, danf1-score masing-masing sebesar 89.27%, 89.86%, dan 89.47%. Setelah implementasi CFS, evaluasi model prediksi mengalami peningkatan signifikan menjadi 91.22%, 91.22%, 92.24%, dan 91.48%.
Implementasi Metode Deep Learning CNN Dalam Klasifikasi Tajong (Sarung) Samarinda Muhartini, Sitti; Sunyoto, Andi; Muhammad, Anva Hendi
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 6, No 1 (2024): Jurnal SENOPATI Vol 6, No 1
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2024.v6i1.6573

Abstract

Samarinda sarongs are one of Indonesia's traditional fabrics that are famous for their beautiful motifs and textures. This fabric is made using traditional weaving techniques using non-machine looms (ATBMs), resulting in a unique and distinctive diversity of textures. The difference between the loom, namely the machine and the non-machine, resulting in a difference in the texture of the Samarinda sarong. This difference can be seen from the thread density, texture smoothness, and sharpness of the motif. On certain Samarinda sarong motifs that do not require special details. This study aims to develop a classification model of Samarinda sarong texture based on the loom (machine and non-machine) using the Deep Learning method. This model is expected to help, increase the selling value of Samarinda sarongs, preserve and promote traditional fabrics In this context, the choice between DenseNet121 and VGG16 can depend on user preferences or specific needs, such as computing speed or model size.
Knowledge-Based Decision Support System for Determining Types of Agricultural Crops According to Soil Conditions Wibowo, Ferry Wahyu; Sunyoto, Andi; Setiaji, Bayu; Wihayati, Wihayati
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6254

Abstract

Selecting the right crop for a particular land condition is one of the significant challenges in the agricultural sector. Each crop type has specific needs related to environmental factors such as soil type, pH, humidity, rainfall, and temperature. Mistakes in determining the appropriate crop type can result in decreased production, wastage of resources, and losses for farmers. This paper aims to determine the best model for use as a knowledge base to choose suitable plants for soil conditions. Machine learning algorithms were used to identify patterns of relationships between land conditions and the success of certain crop types to assist in selecting suitable crops and then made a knowledge-based decision support system. Algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) have been applied to solve this problem. In this paper, 30 experiments were conducted to test the stability of the model in determining suitable crops based on land conditions. The results of the experiments showed that the Support Vector Machine (SVM) has a more stable performance than other algorithms, with accuracy values of mean and standard deviation of 1 and 0, respectively.
Analisis Convolutional Neural Network LeNet-5 Dalam Klasifikasi Daun Mangga Fitri Handayani; Andi Sunyoto; Bayu Anugerah Putra
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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

Abstract

Mango is one of the agricultural productions. Like other agricultural crops, diseased mango leaves are a production problem. As a result, agricultural productivity decreases. This research aims to classify healthy or diseased mango leaves by developing a Convolutional Neural Network (CNN) based system with LeNet-5 feature extraction. The dataset used is sourced from Mendeley consisting of healthy leaf types totaling 265 images and diseased totaling 170 images. The data division used consists of 80% training data and 20% test data. The augmentation process is carried out to reduce over fitting. The results showed that the epoch process stopped at the 20th epoch and resulted in 93% accuracy. This shows that the CNN method for image classification can produce accurate accuracy in solving real-world problems.
Optimasi Peletakan Watermark pada Citra Digital Menggunakan Algoritma Genetika Supomo, Eko; Sunyoto, Andi; Kurniawan, Mei P
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 2, No 2 (2021): Metaverse dan Masa Depan Interaksi Digital: Perspektif Teknologi dan Sosial
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.839 KB) | DOI: 10.33650/coreai.v2i2.3216

Abstract

Pengolahan citra digital termasuk bagian dari ilmu komputer, banyak peneliti yang tertarik untuk meneliti watermark citra digital karena dengan adanya teknologi watermark citra digital sangat dibutuhkan. Namun pada prakteknya para peneliti kesulitan untuk menentukan letak watermark yang paling baik karena jumlah piksel atau koordinat citra digital yang sangat banyak, misalnya pada citra digital yang berukuran 512 x 512 terdapat 262.144 piksel atau titik. Untuk itu diperlukan suatu metode yang dapat menyelesaikannya, salah satunya adalah metode optimasi Algoritma Genetika (Genetic Algorithm). Pada algoritma ini terdapat beberapa tahapan yaitu: Evaluasi nilai fitnes, pemilihan individu, Kombinasi (Crossover), mutasi, populasi baru. Untuk memudahkan pemilihan individu sehingga diperoleh kromosom yang unngul maka penulis menggunakan metode seleksi Roulette Wheel Selection (RWS). Berdasarkan pemilihan piksel dari ribuan piksel, dengan metode yang dipilih penulis telah menemukan 9 titik koordinat terbaik pada citra digital pertama, 12 titik koordinat terbaik sedangkan pada citr digital ketiga ditemukan 10 titik koordinat terbaik dan titik-titik tersebut merupakan sebagai rekomendasi tempat watermark citra digital
Deteksi Serangan SQL Injection Menggunakan Hidden Markov Model *, Pramono; Sunyoto, Andi; Pramono, Eko
JURNAL TECNOSCIENZA Vol. 5 No. 2 (2021): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/tecnoscienza.v5i2.432

Abstract

Serangan aplikasi web terus meningkat jumlahnya dan dalam tingkat keparahan. Information besar tersedia di internet memotivasi penyerang untuk melakukan serangan jenis baru. Di dalam konteks, penelitian intensif tentang keamanan aplikasi web telah dilakukan. Serangan berbahaya yang menargetkan web aplikasi adalah Structured Query Language Injection (SQLI). Serangan ini merupakan ancaman serius bagi web aplikasi. Beberapa pekerjaan penelitian melakukan cara untuk mengurangi serangan ini baik dengan mencegahnya dari awal tahap atau mendeteksinya saat itu terjadi. Dalam tulisan ini, kami sajikan gambaran umum tentang serangan injeksi SQL dan klasifikasi dari solusi deteksi dan pencegahan yang baru diusulkan. Dalam penelitian ini kami menggunakan Hidden Markov Model (HMM) untuk melakukan metode deteksi dan pencegahan dari serangan SQLI untuk mengurangi serangan ini khususnya yang didasarkan pada ontologi dan pembelajaran mesin. Kata kunci: HMM, SQL Injection, Web Security
Implementation of YOLOv7 Model for Human Detection in Difficult Conditions B, Arijal; Sunyoto, Andi; Hanafi, M.
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10662

Abstract

The rapid development of artificial intelligence technology in recent decades has led to the development of highly efficient object detection algorithms, including human detection under difficult conditions. Human detection is one of the major challenges in computer vision as it involves various complex factors such as obstructed human objects, pose variations, small low-resolution human objects, as well as the presence of fake human objects such as statues or images. This research uses the SLR (Systematic Literature Review) method to determine the algorithm used, namely YOLOv7. The three YOLOv7 models tested in this study are YOLOv7x.pt, YOLOv7-w6-person.pt, and YOLOv7-w6-pose.pt. These models were selected based on their excellence in detecting human objects and their relevance for complex scenarios. Tests were conducted using 100 images obtained from the internet and divided into four categories of human objects under difficult conditions, which represent various challenges in human detection. Analysis was performed using convusion matrix to evaluate performance metrics such as accuracy, precision, recall, and F1-score. Based on the test results, the YOLOv7-w6-person.pt model showed the best overall performance, especially in detecting humans in obstructed conditions and complex lighting with a precision of 90.4%, Recall 88.7%, and F1-Score 89.5%. This model has higher accuracy, precision, and F1-score than the other models, making it a reliable choice for human detection in difficult scenarios. These findings not only demonstrate the relevance of YOLOv7 as a reliable human detection algorithm, but also provide a basis for further optimization of YOLOv7-based human detection systems, both through improving the model architecture and adapting to more specific datasets. This research makes an important contribution to the development of human detection technologies for real-world applications, such as surveillance, crowd analysis, and automated safety systems.
Co-Authors *, Pramono A.A. Ketut Agung Cahyawan W Aam Shodiqul Munir Abdul Jalil Rozaqi Abdul Jalil Rozaqi Abdul Mizwar A. Rahim Abidarin Rosidi Abidarin Rosidi Ade Kurniawan kurniawan Ade Pujianto, Ade Afis Julianto Afis Julianto Agus Harjoko Agus Harjoko AGUS PURWANTO Aidina Ristyawan, Aidina Alva Hendi Muhammad Alva Hendi Muhammad Alva Hendi Muhammad Muhammad Ana Wati Ndarbeni Anna Baita Annas Al Amin Arif Sutikno Asro Nasiri Asro Nasiri Asro Nasiri Astria, Kadek Kiki B, Arijal Bambang Soedijono Bambang Soedijono WA Banu Dwi Putranto Bayu Anugerah Putra Bayu Setiaji Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Cahyo, D. Diffran Nur Dhanar Intan Surya Saputra Diansyah, Ahmad Febri Dwi Sari Widyowaty Dwi Yuli Prasetyo Eka Yulia Sari Eko Pramono Eko Pramono Ema Utami Emha Taufiq Luthfi Emha Taufiq Luthfi Ferry Wahyu Wibowo Ferry Wahyu Wibowo Firdiyan Syah Fitri Handayani Gagah Gumelar Gori, Takhamo Hanafi Hanafi Hani Atun Mumtahana Hani Atun Mumtahana Hani Setiani Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Harianto, Harianto Hidayat Hidayat Huda, Luthfi Nurul Ibnu Hadi Purwanto Ikhwan Baidlowi Sumafta Ikmah Ikmah Indah Nofikasari Irawanto, Indra Ismail, Muhamad Yusuf K Kusrini Kapti . kurniawan, Ade Kurniawan Kurniawan, Mei P Kurniawan, Mei P. Kusnawi Kusnawi Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini, K Kusrini, Kusrini Liana Trihardianingsih Licantik M rudyanto Arief M. Afriansyah M. Rudyanto Arief M. Suyanto M. Suyanto, M. M. Syukri Mustafa Maie Istighosah Mashuri, Ahmad Sanusi Masruri, Nizar Haris Mohammad Suyanto Mudawil Qulub Muhammad Rudyanto Arief Muhammad Setiyawan Muhammad, Anva Hendi Muhartini, Sitti Mumtahana, Hani Atun Mursyid Ardiansyah Nalendra, Adimas Ketut Nasiri, Asro Noordin Asnawi Norlaila2 Nugraha, Anggit Ferdita Nulngafan, Nulngafan Nur Arifin Akbar Parsiyono Parsiyono Patmawati Patmawati, Patmawati Pramono * Putra, Reyhan Dwi Putranto, Dinar Wakhid Quratul Ain Rafli Junaidi Kasim Rahim Jamal Rahmat Hidayat Raynaldi Fatih Amanullah Ria Andriani Rifda Faticha Alfa Aziza Riyanto, Thomas Pramuji Singgih Riza Marsuciati Rizfi Syarif Rizky Arya Kurniawan Rohim, Ni’matur Rudi Prietno Sahirul Muklis Saiful Bahri Salmuasih - Samsul Bahri Sari, Rita Novita Setiawan Budiman Sholihin, Iasya Silvi Agustanti Bambang Singgih Arif Widodo Slamet Triyanto Soedijono W A, Bambang Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudiana Sudiana Sukresno Sukresno Sulistyowati Sulistyowati Suliswaningsih Suliswaningsih Sumafta, Ikhwan Baidlowi Supomo, Eko Supraba, Laksmita Dewi Sutejo, Danang Syah, Firdiyan Syah, Firdiyan Syukirman Amir TONNY HIDAYAT Tribiakto, Herlandro Ulinuha, Hinova Rezha W., Bambang Soedijono WA, Bambang Soedijono Wahyu Caesarendra Wahyu Hidayat Wihayati, Wihayati Windarni, Vikky Aprelia Windha Mega Pradnya Dhuhita Wing Wahyu Winarno Yoga Dwi Pambudi Yoga Pristyanto Yohanes Setyo Prabowo, Yohanes Setyo Yusuf Sutanto Zaipin, Zaipin