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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Proceedings of KNASTIK Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika SPEKTRUM INDUSTRI Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Jurnas Nasional Teknologi dan Sistem Informasi JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Teknologi Elektro INFORMAL: Informatics Journal Proceeding SENDI_U Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Bulletin of Electrical Engineering and Informatics JOIN (Jurnal Online Informatika) Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika Jurnal Khatulistiwa Informatika Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT InComTech: Jurnal Telekomunikasi dan Komputer Insect (Informatics and Security) : Jurnal Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Applied Information System and Management ILKOM Jurnal Ilmiah Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) JUMANJI (Jurnal Masyarakat Informatika Unjani) JURTEKSI RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Ilmiah Mandala Education (JIME) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Systemic: Information System and Informatics Journal EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Journal of Robotics and Control (JRC) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Cyber Security dan Forensik Digital (CSFD) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) International Journal of Advances in Data and Information Systems International Journal of Marine Engineering Innovation and Research Edunesia : jurnal Ilmiah Pendidikan Journal of Innovation Information Technology and Application (JINITA) Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information Jurnal Teknologi Informatika dan Komputer Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Algoritma Techno Jurnal Pengabdian Informatika (JUPITA) Jurnal INFOTEL Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) Scientific Journal of Informatics Control Systems and Optimization Letters Signal and Image Processing Letters Scientific Journal of Engineering Research SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Edumaspul: Jurnal Pendidikan Methods in Science and Technology Studies JOCHAC
Abdul Fadlil
Ahmad Dahlan University
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Perbandingan Metode Smart dan Maut untuk Pemilihan Karyawan pada Merapi Online Corporation Nasution, Musri Iskandar; Fadlil, Abdul; Sunardi, Sunardi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 6: Desember 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021863583

Abstract

Penelitian ini merancang sistem untuk menentukan pemilihan karyawan terbaik menggunakan Sistem Pendukung Keputusan (SPK). Perhitungan sistem menggunakan metode SMART dan MAUT. SMART merupakan metode pengambilan keputusan multiatribut yang setiap alternatif terdiri dari sekumpulan atribut dan setiap atribut mempunyai nilai-nilai. Sedangkan MAUT didasarkan pada konsep dimana pembuat keputusan dapat menghitung utilitas dari setiap alternatif menggunakan fungsi MAUT dan dapat memilih alternatif dengan utilitas tertinggi. Metode SMART digunakan karena perhitungannya lebih sederhana dan memungkinkan penambahan serta pengurangan alternatif tanpa mempengaruhi perhitungan pembobotan mengingat jumlah karyawan bisa berkurang dan bertambah secara tidak teratur. Sedangkan metode MAUT digunakan karena memunculkan hasil urutan peringkat dimana akan muncul hasil nilai terbesar sampai nilai terkecil sehingga dapat diketahui karyawan dengan terbaik dengan nilai tertinggi. Sehingga dapat mengambil keputusan dengan efektif atas persoalan yang kompleks dengan menyederhanakan dan mempercepat proses pengambilan keputusan. Metode penelitian yang digunakan adalah metode pengembangan sistem model waterfall, metodologi ini terdapat tahapan-tahapan kegiatan yang harus dilakukan dalam merancang suatu sistem. Perhitungan menggunakan 30 sampel data karyawan dan empat kriteria penilaian. Empat kriteria tersebut adalah presensi dengan bobot 40, masa kerja dengan bobot 30, ijin dengan bobot 20, dan disiplin dengan bobot 10. Data karyawan yang digunakan adalah karyawan yang sama dalam kedua metode serta mempunyai data penilaian yang sama. Hasil perhitungan menggunakan SMART dan MAUT menunjukkan bahwa keduanya dapat diimplementasikan dan berfungsi dengan baik untuk menentukan karyawan terbaik. Dengan menggunakan data alternatif, nilai alternatif, dan bobot kriteria yang sama diperoleh hasil bahwa metode SMART memberikan hasil yang lebih baik dengan 22 peringkat, sedangkan metode MAUT menghasilkan 18 peringkat. Semakin banyak jumlah peringkat yang muncul maka semakin baik karena mampu meminimalisir nilai preferensi yang sama, sehingga perankingan alternatif dapat dilakukan dengan baik. AbstractThis study designed a system to determine the best employee selection using a Decision Support System (SPK). System calculations using the SMART and MAUT methods. SMART is a multi-attribute decision making method in which each alternative consists of a set of attributes and each attribute has values. Whereas MAUT is based on the concept where decision makers can calculate the utility of each alternative using the MAUT function and can choose alternatives with the highest utility. The SMART method is used because the calculation is simpler and allows the addition and subtraction of alternatives without affecting the weighting calculation given the number of employees can be reduced and increased irregularly. While the MAUT method is used because it raises the ranking order results in which the largest value will appear until the smallest value so that it can be known by the employee with the highest value. So that they can make decisions effectively on complex issues by simplifying and accelerating the decision making process. The research method used is the method of developing the system waterfall model, this methodology there are stages of activities that must be carried out in designing a system. The calculation uses 30 employee data samples and four assessment criteria. The four criteria are presence with a weight of 40, tenure with a weight of 30, permission with a weight of 20, and discipline with a weight of 10. Employee data used are the same employees in both methods and have the same assessment data. The results of calculations using SMART and MAUT indicate that both can be implemented and function properly to determine the best employees. By using alternative data, alternative values, and the same criteria weights, the results obtained that the SMART method gives better results with 22 ratings, while the MAUT method yields 18 ratings. The more number of ratings that appear, the better because it is able to minimize the same preference value, so that alternative ranking can be done well. 
Penentuan Guru Berprestasi Menggunakan Metode Analytical Hierarchy Process (AHP) dan VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) Hanif, Kharis Hudaiby; Yudhana, Anton; Fadlil, Abdul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 6: Desember 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022934628

Abstract

Guru merupakan bagian penting dalam memajukan suatu bangsa, karena guru dapat membimbing generasi penerus bangsa. Oleh karena itu penilaian guru berprestasi dibutuhkan untuk menciptakan generasi penerus bangsa yang baik. Penelitian AHP dan VIKOR bertujuan menghasilkan alternatif guru berprestasi di SMA N 2 Purbalingga. Metode AHP digunakan karena  mempunyai kelebihan dalam pembobotan setiap kriteria yang tidak dimiliki oleh VIKOR, metode VIKOR digunakan untuk perangkingan alternatif guru berprestasi. Tahapan-tahapan penelitian yaitu pengumpulan data, menentukan bobot kriteria, metode AHP, metode VIKOR, perhitungan manual dan sistem. Kriteria yang digunakan ada empat kriteria yaitu pedagogik, kepribadian, sosial, dan profesional. Nilai setiap kriteria akan diproses menggunakan metode AHP untuk mendapatkan bobot kriteria. Bobot kriteria selanjutnya dihitung menggunakan  metode VIKOR untuk mendapatkan alternatif guru berprestasi. Hasil perhitungan bobot prioritas kriteria dengan metode AHP dari kriteria pedagogik sampai profesional adalah 0,2236; 0,4187; 0,1162; 0,2414. Nilai-nilai tersebut merupakan nilai bobot kriteria yang akan digunakan untuk metode VIKOR. Bobot kriteria digunakan bersama dengan hasil dari pengisian kuesioner dalam VIKOR dengan data alternatif guru berprestasi untuk mendapatkan alternatif guru berprestasi. Metode AHP dan VIKOR diuji kebenarannya agar bobot dan perangkingan dapat dinyatakan benar. Hasil pengujian menggunakan black box didapatkan persentase 100%, oleh karena itu penelitian SPK dapat dinyatakan sesuai dan hasil perangkingan dapat menyelesaikan masalah yang ada di SMA N 2 Purbalingga. AbstractTeachers are an important part of advancing a nation, because teachers can guide the nation's future generations. Therefore, the assessment of outstanding teachers is needed to create a good future generation of the nation. AHP and VIKOR research aims to produce an alternative for outstanding teachers at SMA N 2 Purbalingga. The AHP method is used because it has advantages in weighting each criterion that VIKOR does not have, the VIKOR method is used for alternative ranking of outstanding teachers. The stages of the research are data collection, determining the criteria weights, the AHP method, the VIKOR method, manual and system calculations. There are four criteria used, namely pedagogic, personality, social, and professional. The value of each criterion will be processed using the AHP method to obtain the weight of the criteria. The weight of the criteria is then calculated using the VIKOR method to obtain an alternative for outstanding teachers. The results of the calculation of the criteria priority weight using the AHP method from pedagogic to professional criteria are 0.2236; 0.4187; 0.1162; 0.2414. These values are the criteria weight values that will be used for the VIKOR method. The weights of the criteria are used together with the results of filling out the questionnaire in VIKOR with alternative data for outstanding teachers to get alternatives for outstanding teachers. The AHP and VIKOR methods are tested for accuracy so that the weight and ranking can be declared correct. The results of the test using a black box obtained a percentage of 100%, therefore the SPK research can be declared appropriate and the ranking results can solve the problems that exist in SMA N 2 Purbalingga. 
Center of Pressure Control for Balancing Humanoid Dance Robot Using Load Cell Sensor, Kalman Filter and PID Controller Wulandari, Cisi Fitri; Fadlil, Abdul
Control Systems and Optimization Letters Vol 1, No 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v1i2.22

Abstract

Balance control on the Lanage Jagad humanoid dance robot is one of the means to create flexible dance movements in the robot movement system to make it more stable and can reduce the frequency of the robot falling or being unable to maintain balance when performing the dance. For the position of the robot, it can use a weight sensor or load cell sensor, the sensor measures the resistance value that can control the weight of 4 weight points on each robot leg which will later be converted into a pressure value at each point, in the study. This test was carried out with the same control behavior using an inertial sensor MPU6050. The balance on the robot uses a balance based on a load cell, which is a situation where the position of the robot in coordinates approaches the center of balance or CoP (Center of Pressure) at coordinates (0,0) or if using MPU6050 it is in a far error value condition so that it can balance the conditions so as not to falls close to the value of the robot state based on ZMP (Zero Moment Point) and CoG (Central of Gravity) as the MPU6050 sensor placement. In this study, for the balance control system using the Arduino MEGA 2560 PRO Board as a complement to the OpenCM 9.04 microcontroller, using 8 load cell sensors to determine the balance point which has been made predictions of pressure from the load cell using a kalman filter also PID control to handle the servo motor. The results from the center point of the robot's pressure have succeeded in determining the center of balance or CoP based on the X coordinates of 0 and the Y coordinates of 0 and the quadrant direction based on the center of gravity, so that the results of the balance system in standing and dancing conditions are based on the center of balance using a load cell, the success rate when standing by 87.5% and balance when dancing by 89%.
Wood Type Identification System using Naive Bayes Classification Yulianto, Muhammad Anas; Fadlil, Abdul
Control Systems and Optimization Letters Vol 1, No 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v1i3.52

Abstract

Wood, a forest product and natural resource, is a raw material used to make household goods. Some types of wood have almost the same pattern or structure. Wood quality varies greatly depending on the tree species and the environmental conditions in which it grows. This makes it challenging to identify the type of wood, especially for wooden furniture users. Therefore, wood classification is essential to ensure that the wood used meets the required quality standards and requirements. Automatic classification of wood using image processing has several advantages and can make it easier for humans. One of the image processing methods for wood classification is the Naïve Bayes method. Feature extraction technique using GLCM using contrast, correlation, energy, and homogeneity attributes. The GLCM methods can be combined to create a system design to distinguish five wood species using an image-based wood type identification system. The study results have successfully designed a system to determine five types of wood using the framework of an image-based wood type identification system. An application system has been produced to distinguish five types of wood using the framework of an image-based wood type identification system with the GLCM feature extraction method and the Naive Bayes classification method. The application system successfully identified wood species with a test accuracy rate of 88%.
Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction Musliman, Anwar Siswanto; Fadlil, Abdul; Yudhana, Anton
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.704

Abstract

In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.
Classification of Stunting in Children Using the C4.5 Algorithm Yunus, Muhajir; Biddinika, Muhammad Kunta; Fadlil, Abdul
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1062

Abstract

Stunting is a disease caused by malnutrition in children, which results in slow growth. Generally, stunting is characterized by a lack of weight and height in young children. This study aims to classify stunting in children aged 0-60 months using the Decision Tree C4.5 method based on z-score calculations with a sample size of 224 records, consisting of 4 attributes and 1 label, namely Gender, Age, Weight, Height, and Nutritional Status. The results of the study obtained a C4.5 decision tree where the Age variable influenced the classification of stunting with the highest Gain Ratio of 0.185016337. Meanwhile, the evaluation of the model using the Confusion matrix resulted in the highest accuracy of 61.82% and AUC of 0.584.
Optimizing Banana Type Identification: An Support Vector Machine Classification-Based Approach for Cavendish, Mas, and Tanduk Varieties Pamungkas, Aji; Fadlil, Abdul
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9145

Abstract

This research focuses on addressing the need for improved efficiency in the agricultural sector, particularly in banana processing in Indonesia, where the demand for bananas is consistently high. To improve the efficiency of banana processing, the research proposes the development of a machine learning based solution for automatic banana type selection. This solution uses image data of three banana types (Cavendish, Mas, and Tanduked) captured by a microscopic camera. The images are subjected to feature extraction, and a Support Vector Machine (SVM) algorithm is used to train the model. The results are implemented in a graphical user interface (GUI). The experimental results show promising results, with an accuracy of 86.67%, a precision of 87.78%, and an error rate of 13.33%, achieved with SVM parameters of C = 1000 and a linear kernel. This automated approach provides a practical and sustainable solution to the labor-intensive manual banana variety selection process, thus increasing the efficiency of the banana processing industry.
JAVANESE SCRIPT HANACARAKA CHARACTER PREDICTION WITH RESNET-18 ARCHITECTURE Sudewo, Egi Dio Bagus; Biddinika, Muhammad Kunta; Fadlil, Abdul
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 2 (2024): Maret 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.3017

Abstract

Abstract: This study aims to train computers to recognize Javanese script characters known as Hanacaraka. The evaluation was conducted on the use of Convolutional Neural Network (CNN) with the ResNet-18 architecture in recognizing these characters. The research objective is to overcome traditional character recognition barriers and improve accuracy. The method employed includes building a CNN model with the ResNet-18 architecture and using diverse datasets. The results show a training accuracy of 100%, validation accuracy of 98.01%, and accuracy, precision, recall, and F1-score each at 100%. This study concludes that the developed model successfully achieves a high level of accuracy and contributes positively to the development of Javanese Hanacaraka character recognition technology. Keywords: convolution neural network (CNN); javanese hanacaraka script; resnet-18           Abstrak: Penelitian ini bertujuan melatih komputer untuk mengenali huruf aksara Jawa Hanacaraka. Evaluasi dilakukan terhadap penggunaan Convolutional Neural Network (CNN) dengan arsitektur ResNet-18 dalam pengenalan karakter tersebut. Tujuan penelitian adalah mengatasi hambatan pengenalan karakter tradisional dan meningkatkan akurasi. Metode yang digunakan mencakup pembuatan model CNN dengan arsitektur ResNet-18 dan penggunaan dataset yang beragam. Hasilnya menunjukkan akurasi pelatihan 100%, validasi 98.01%, dan akurasi, presisi, recall, dan F1-score masing-masing sebesar 100%. Simpulan penelitian ini adalah bahwa model yang dikembangkan berhasil mencapai tingkat akurasi yang tinggi dan memberikan kontribusi positif pada pengembangan teknologi pengenalan karakter Hanacaraka Jawa.Kata kunci: convolution neural network (CNN); huruf aksara jawa hanacaraka; resnet-18 
KLASIFIKASI JENIS KULIT WAJAH MENGGUNAKAN ALGORITMA RANDOM FOREST Irwansyah, Irwansyah; Yudhana, Anton; Fadlil, Abdul
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.423

Abstract

Skin can be considered the largest organ in the human body. Healthy skin is not only good for the body, but alsoenhances the appearance. Good skin care is essential at any age. In the first few decades of life, the skin has aconsiderable supply of elastin and collagen, but it will gradually decrease. In addition, daily lifestyle can also directlyaffect the appearance of human skin. The purpose of the research is to develop a model that classifies facial skin typesbased on physiological data using random forest algorithm and measure the results of accuracy, precision, and recall.This research uses Rapidminer tools and four facial skin types namely dry, combination, normal, and oily. The resultsof random forest research obtained accuracy results of 93.25%. dry precision 98.02%, combination precision 92.94%,normal precision 93.46%, and oily precision 88.79%. While dry recall 99%, combination recall 79%, normal recall100%, and oily recall 95%. The findings of this research can help create a skincare recommendation system that ismore suited to the needs of each individual.
Klasifikasi Citra Kupu-Kupu Menggunakan Convolutional Neural Network dengan Arsitektur AlexNet Maftukhah, Ainin; Fadlil, Abdul; Sunardi, Sunardi
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1004

Abstract

Kurangnya pengetahuan tentang kupu-kupu dapat menimbulkan masalah karena kupu-kupu berperan penting dalam ekosistem. Urgensi dalam penelitian ini terkait dengan bidang biologi yaitu klasifikasi citra kupu-kupu dapat membantu dalam memahami pola migrasi, pola kawin, dan pola perilaku kupu-kupu dalam interaksinya dengan lingkungan sekitarnya. Tujuan dari penelitian ini adalah untuk mengklasifikasikan spesies kupu-kupu. Dataset yang digunakan adalah dataset citra kupu-kupu sebanyak 5.499 dengan total 50 spesies. Metode yang diterapkan adalah convolution neural network (CNN) dengan arsitektur AlexNet. Proses pelatihan menggunakan arsitektur AlexNet diawali dengan input dataset citra, dataset akan diproses terlebih dahulu seperti resizing dan RGB to grayscale.Kemudian lakukan filter atau kernel. Output dari kernel digunakan untuk melakukan pooled convolution. Konvolusi dan pooling dilakukan sebanyak lima kali. Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. Setelah itu, terhubung sepenuhnya. Tahap terakhir adalah citra dapat diklasifikasikan. Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. Setelah itu, terhubung sepenuhnya. Tahap terakhir adalah citra dapat diklasifikasikan. Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. Setelah itu, terhubung sepenuhnya. Tahap terakhir adalah citra dapat diklasifikasikan. Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. Tahap terakhir adalah citra dapat diklasifikasikan. Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu. Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. Tahap terakhir adalah citra dapat diklasifikasikan.Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu. Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. dan hasil terakhir pengklasifikasian citra kupu-kupu. Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. dan hasil terakhir pengklasifikasian citra kupu-kupu.Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200.
Co-Authors Aang Anwarudin Abdul Azis Achmad Nugrahantoro Aditiya Dwi Candra Ahmad Naufal, Ahmad Ahmat Taufik Aji Pamungkas Akrom, Akrom Alfiansyah Imanda Putra Alfiansyah Imanda Putra Alfian Amiruddin, Nanda Fahmi Andrianto, Fiki Anggit Pamungkas Annisa, Putri Anton Yudhana Anwar Siswanto ANWAR, FAHMI ardi, Ardi Pujiyanta Arief Setyo Nugroho Arief Setyo Nugroho Arif Budi Setianto Arif Budiman Arif Budiman Arif Wirawan Muhammad Aris Rakhmadi Asep Ririh Riswaya Asno Azzawagama Firdaus Atmojo, Dimas Murtia Aulia, Aulia Az-Zahra, Rifqi Rahmatika Aznar Abdillah, Muhamad Bagus Primantoro Bashor Fauzan Muthohirin Basir, Azhar Budiman, Dheni Apriantsani Candra, Aditiya Dwi Darajat, Muhammad Nashiruddin Davito Rasendriya Rizqullah Putra Dewi Soyusiawaty Dewi Soyusiawaty Dhimas Dwiki Sanjaya Dian Permata Sari Dianda Rifaldi Dikky Praseptian M Dimas Murtia Atmojo Doddy Teguh Yuwono Dwi Susanto Dwi Susanto Edy Fathurrozaq Egi Dio Bagus Sudewo Eko Budi Cahyono Eko Prianto Eko Prianto Elvina, Ade Ermin Al Munawar Ermin Ermin Esthi Dyah Rikhiana Fahmi Anwar Fahmi Auliya Tsani Fahmi Auliya Tsani Fahmi Fachri Fanani, Galih Faqihuddin Al-anshori Faqihuddin Al-Anshori, Faqihuddin Fathurrahman, Haris Imam Karim Fauzi Hermawan Fiki Andrianto Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Yasin Fitri Muwardi Furizal Gusrin, Muhaimin Gustina, Sapriani Hafizh, Muhammad Nasir Haksono, Muhammad Rizky Hanif, Abdullah Hanif, Kharis Hudaiby Harman, Rika Helmiyah, Siti Hendril Satrian Purnama Herdiyanto, Erik Herman Herman Herman Yuliansyah, Herman Herman, - Ibnu Rifajar Ibrahim Mohd Alsofyani Ibrahim, Rohmat Ihyak Ulumuddin Ikhsan hidayat Ilhamsyah Muhammad Nurdin Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Irjayana, Rizky Caesar Irwansyah Irwansyah Izzan Julda D.E Purwadi Putra januari audrey Jayawarsa, A.A. Ketut Jogo Samodro, Maulana Muhamammad Joko Supriyanto Joko Supriyanto Kamilah, Farhah Kartika Firdausy Khoirunnisa, Itsnaini Irvina Kusuma, Nur Makkie Perdana Laura Sari Lestari, Yuniarti Lin, Yu-Hao Luh Putu Ratna Sundari M. Nasir Hafizh Maftukhah, Ainin Maulana Muhammad Jogo Samudro Mini, Ros Mohd Hatta Jopri Muammar Mudinillah, Adam Mufaddal Al Baqir Muh. Fadli Hasa Muhaimin Gusrin Muhajir Yunus Muhamad Daffa Al Fitra Muhamad Rosidin Muhammad Faqih Dzulqarnain, Muhammad Faqih Muhammad Johan Wahyudi Muhammad Kunta Biddinika Muhammad Ma’ruf Muhammad Nasir Hafizh Muhammad Nur Faiz Muhammad Nurdin, Ilhamsyah Muhammad Rizki Setyawan Mukti, Sindhu Hari Muntiari, Novita Ranti Murinto Murinto - Murinto Murinto Murni Murni Musliman, Anwar Siswanto Mustofa Mustofa Muthorihin, Bashor Fauzan Mutiara Titani Muwardi, Fitri Nasution, Dewi Sahara Nasution, Musri Iskandar Nilam Tri Astuti Nurwijayanti Pahlevi, Ryan Fitrian Ponco Sukaswanto Poni Wijayanti Prabowo Soetadji Prabowo, Basit Adhi Prayogi, Denis Priambodo, Bambang Putra, Fajar R. B Putri Annisa Putri Annisa Putri Purnamasari Putri Silmina, Esi Ramadhani, Muhammad Ramdhani, Rezki Razak, Farhan Radhiansyah Rezki Rezki Rifqi Rahmatika Az-Zahra Rizky Andhika Surya Rochmadi, Tri Roni Anggara Putra Rusydi Umar Rusydi Umar S Sunardi S, Sunardi Saad, Saleh Khalifah Safiq Rosad Saifudin Saifudin Saifullah, Shoffan Saleh khalifa saad Santi Purwaningrum Sarmini Sarmini Septa, Frandika Setyaputri, Khairina Eka Setyaputri, Khairina Eka Setyaputri, Khairina Eka Shinta Nur Desmia Sari Siswahyudianto Siti Helmiyah Sri Winiarti Subandi, Rio Sukaswanto, Ponco Sukma Aji Sulis Triyanto Sunardi Sunardi Sunardi Sunardi, Sunardi Surya Yeki Surya Yeki Syamsiar, Syamsiar Syarifudin, Arma Tole Sutikno Tresna Yudha Prawira Tri Ferga Prasetyo Tristanti, Novi Tuswanto Tuswanto Virdiana Sriviana Fatmawaty Wahju Tjahjo Saputro Wahyusari, Retno Winoto, Sakti Wintolo, Hero Wulandari, Cisi Fitri Yana Mulyana Yana Mulyana Yasidah Nur Istiqomah Yeki, Surya Yohanni Syahra Yossi Octavina Yuantoro, Jody Yulianto, Dinan Yulianto, Muhammad Anas Yuminah yuminah yuminah, Yuminah Yuminah, Yuminah Yuwono Fitri Widodo Zein, Wahid Alfaridsi Achmad Zulhijayanto -