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All Journal TEKNIK INFORMATIKA Jurnal Simantec Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal IPTEK Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JOIV : International Journal on Informatics Visualization INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] PROMINE Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Telematika Jurnal Teknologi Terpadu TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Tika Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer
Muchamad Kurniawan, Muchamad
Institut Teknologi Adhi Tama Surabaya
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Klasifikasi Jenis Jerawat pada Data Citra Jerawat Wajah Menggunakan Convolutional Neural Network Putri, Chatarina Natassya; Qornain, Wafi Dzul; Bamahri, Fakhirah; Yuliastuti, Gusti Eka; Kurniawan, Muchamad
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5231

Abstract

Acne is a condition caused by pilosebaceous inflammation which affects 85% of skin conditions in adolescents and adults. Acne has an impact on the psychological and social health of sufferers. To treat acne, it is necessary to know the right type of acne so that sufferers can treat the type of acne according to how they are treated. This research was carried out to classify the types of acne in facial acne images using the Convolutional Neural Network (CNN) method. Based on previous research, it shows that the use of CNN is considered effective and appropriate in increasing classification accuracy. This research uses a dataset of acne types from Kaggle with a total of 351 data, divided into 5 classes, namely acne fulminans, acne nodules, fungal acne, papules and pustules which will be tested using 2 different optimizers, namely Adam and RMS- prop. From the results of this test, the highest accuracy was 100% using the Adam optimizer and the RMS-prop optimizer test obtained the highest accuracy value of 80%.
Segmentasi Citra Wajah dengan Implementasi Adaptif Threshold- Integral Image Habibah, Maryam Ummul; Kurniawan, Muchamad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021853840

Abstract

Segmentasi wajah merupakan bagian penting dalam pengolahan citra digital untuk mengetahui objek wajah dalam citra sebelum dilakukan pendeteksian ekspresi wajah. Adaptif Threshold – Integral Image adalah salah satu teknik segmentasi berbasis pixel-based, yaitu local thresholding. Penelitian ini bertujuan untuk memisahkan objek wajah manusia dan background -nya. Citra wajah yang akan digunakan nanti citra di dalam ruangan (indoor) dan di luar ruangan (outdoor) dengan resolusi gambar 300x400 piksel. Pada penelitian ini juga mencari nilai parameter S (kernel) dan T (threshold) yang terbaik dengan melakukan 16 kali percobaan. Dan didapatkan hasil terbaik, yaitu citra di dalam ruangan (indoor) nilai S=1/2 dan T=50, serta citra di luar ruangan (outdoor) nilai S=1/30 dan T=30. Segmentasi citra wajah dengan menggunakan metode Adaptif Threshold – Integral Image robust (kuat) terhadap intensitas cahaya tinggi dan rendah dengan mengatur nilai parameter S (kernel) dan T (Threshold) maka metode ini mampu memisahkan objek wajah dan background -nya. Dari hasil uji coba threshold menggunakan metode Adaptif Threshold – Integral Image terhadap citra di dalam ruangan (indoor) dan di luar ruangan (outdoor) menghasilkan thresholding yang baik dengan mempertimbangkan nilai parameter S (kernel) dan T (threshold) memberikan hasil dengan tingkat akurasi yang tinggi, yaitu citra di dalam ruangan (indoor) sebesar 96.72%, dan citra di luar ruangan (outdoor) sebesar 93.59%. AbstractFace segmentation is an important in digital image processing to find out the object's face in the image before detecting facial expressions. Adaptive Threshold - Integral Image is a pixel-based segmentation technique, which is local thresholding. This study is intended to split the object of a human face and its background. Face images that will be used later in indoor and outdoor with an image resolution of 300x400 pixels. This study also searched for the best S (kernel) and T (threshold) parameter values by performing 16 experiments. And the best results are obtained, name the image in the room (indoor) the value of S = 1/2 and T = 50, and the image outside the room (outdoor) the value of S = 1/30 and T = 30. Face image segmentation using the Adaptive Threshold - Integral Image robust method of high and low light intensity by setting the S (kernel) and T (Threshold) parameter values, this method is able to split the face object and its background. From the results of the threshold trial using the Adaptive Threshold - Integral Image method for indoor and outdoor images produces a good thresholding by considering the values of the S (kernel) and T (threshold) parameters to give results with a high degree of accuracy, that is indoor images of 96.72%, and outdoor images of 93.59%.
Klasifikasi Tingkat Kematangan Buah Pisang Menggunakan Metode Cnn Arsitektur Vgg19 Arinal Haq, Fatahillah; Kurniawan, Muchamad; Bagus S, Dadang; Wicaksono, Mukhlis Adi; Sandi Alala, Pratama
Jurnal Tika Vol 9 No 2 (2024): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

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

Abstract

Bananas are abundantly available in Indonesia, rich in nutrients, and hold high economic value. However, the post-harvest sorting process still relies on inconsistent human judgment, resulting in losses for farmers. Therefore, this research proposes the use of Convolutional Neural Network (CNN) to classify the ripeness of bananas based on color. The dataset consists of 450 banana images with three ripeness classes: raw, ripe, and overripe, sourced from Kaggle. Data augmentation is performed using Image Data Generator. CNN is designed using the VGG-19 architecture and trained using both Adam and SGD optimizers. The research results show the highest accuracy of 100% with the lowest loss of 0.02 when using the Adam optimizer with 20 epochs. The SGD optimizer also yields 100% accuracy with a loss of 0.04 at epoch 20. The research conclusion indicates that CNN with the VGG-19 architecture can be used for banana ripeness classification with high accuracy rates. For future developments, the model will be enhanced with layer adjustments and preprocessing to improve accuracy and reduce data loss.
SISTEM DETEKSI PENYAKIT PADA OTAK DENGAN PENDEKATAN KLASIFIKASI CNN DAN PREPROCESSING IMAGE GENERATOR Kurniawan, Muchamad; Abdullah, Ryan Gading
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4371

Abstract

In today's digital era, artificial intelligence technology has become an important part of various human activities, including in the healthcare sector. One of its focal points is the detection of brain diseases, which have significant implications for health and medical expenses. This study addresses the issue of accuracy in brain disease detection through the utilization of Convolutional Neural Network (CNN) methodology and preprocessing Image Generator. Previous research suggests that CNN with preprocessing Image Generator has the potential to enhance detection accuracy. The research employs the Computed Tomography (CT) of the Brain dataset from Kaggle, comprising 259 data points categorized into three classes: aneurysm, tumor, and cancer. Experimental findings indicate that the CNN method with preprocessing Image Generator yields higher accuracy in both training and testing phases, with reduced complexity. In conclusion, this method holds promise for more effective detection of brain diseases
Pemodelan Dataset Tambang Terbuka pada PT. United Tractors Semen Gresik dengan Metode Artificial Neural Network Kurniawan, Muchamad; Fanani, Yazid; Agustini, Siti; Wachid, Aldi
PROMINE Vol 12 No 1 (2024): PROMINE
Publisher : Program Studi Teknik Pertambangan, Fakultas Sains dan Teknik, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jp.v12i1.3311

Abstract

The Mining industry in Indonesia plays a vital role as a source of state income and an integral part of the industrial progress of the nation. The majority of the mining industry in Indonesia employs open-pit mining. One of the weather factors that can be an obstacle in open-pit mining is rainfall. Therefore, this research focused on modelling data from rainfall, working hours and production outcomes. It applied the Artificial Neural Network algorithm with an input layer consisting of two neurons, a hidden layer with two neurons, and an output layer. The data on Rainfall working hours, and production results were trained to produce a model that, later on, will be used to predict the value of production results. For model testing, this study uses two parameters, namely learning rate and epoch. From 90 times of testing, the best model was obtained with a learning rate value of 0.3 and an epoch of 1000 which resulted in an RMSE error of 0.004838259401280330
Implementing K-Nearest Neighbors (k-NN) Algorithm and Backward Elimination on Cardiotocography Datasets Kurniawan, Muchamad; Yuliastuti, Gusti Eka; Rachman, Andy; Budi, Adib Pakar; Zaqiyah, Hafida Nur
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1996

Abstract

Having a healthy baby is a dream for mothers. Unfortunately, high maternal and fetal mortality has become a vital problem that requires early risk detection for pregnant women. A cardiotocograph examination is necessary to maintain maternal and fetal health. One method that can solve this problem is classification. This research analyzes the optimal use of k values and distance measurements in the k-NN method. This research expects to become the primary reference for other studies examining the same dataset or developing k-NN. A selection feature is needed to optimize the classification method, particularly for improving accuracy results. This study used the cardiotocography dataset from cardiotocograph examinations related to fetal conditions. The cardiotocography dataset consisted of 2,126 records with 22 features and variables. It also had three classification classes, normal, suspect, and pathological, from the Universal Child Immunization Machine Learning Repository website. It employed the K-Nearest Neighbor (k-NN) method and the backward elimination feature with ordinary least squares regression. The test in this research applied the scenarios of three distance calculations, i.e., Euclidean distance, Manhattan distance, and Minkowski distance, as well as four variations of k values. Evaluation of each scenario indicated the accuracy of the confusion matrix and execution time. This study compared K-Nearest Neighbor (k-NN) and Backward Elimination methods with K-nearest neighbor (k-NN) without selection features. The best accuracy of the Backward Elimination and K-Nearest Neighbor (K-NN) methods was 91%, as was the K-Nearest Neighbor (k-NN) method without selection features. Both had similar k values (k = 3) and Manhattan distance. The backward elimination method reduced the number of features from 22 to 14. Meanwhile, the execution times of the Backward Elimination and K-Nearest Neighbor (k-NN) methods got better results as each distance averaged 26.54, 19.23, and 68.09 seconds. K-Nearest Neighbor (k-NN) execution times without selection features were 26.83, 19.39, and 68.84, respectively. In conclusion, backward elimination did not increase accuracy because it yielded the same accuracy. However, backward elimination and K-nearest Neighbor (k-NN) produced faster results, with differences of 29%, 16%, and 75%, respectively.
Co-Authors Abdullah, Ryan Gading Andy Rachman Ar, Nanda Hafiz Arinal Haq, Fatahillah Arum Indah Sari Bagus S, Dadang Bamahri, Fakhirah Budi, Adib Pakar Fahrezi Kusuma, Andra Fanani, Yazid Farida Farida Farida Farida Faruq, Umar Al Fawaz, Ahmad Gusti Eka Yuliastuti Habibah, Maryam Ummul Hadimarta, Tommy Ferdian Hendro Nugroho, Hendro Ilmi, Hilmi Maulana Istighfariansyah, Risaldi Maftahatul Hakimah, Maftahatul Misbahul Munir Moch. Kalam Mollah Moneter, Mochamad Rizky Muhammad Aditya Kushardiawan Muhima, Rani Rotul N, Muhammad Dinarta Nabila Dayu Mega Anjani Naili Saidatin Nanik Suciati Nurlaily, Firdausiyah O.T., Pambudi Pakarbudi, Adib Pambudi, Oktavian Tegar Pratikto, Dimas Priska Amelia de Jong Putri, Chatarina Natassya Qornain, Wafi Dzul Rewina, Anggita Eka Ritonga, Mario Franko Ezra Hasiholan Rosetya Wardhana, Septiyawan Sandi Alala, Pratama Setyawati, Indah Siti Agustini, Siti Sulistyowati Sulistyowati Ubaidillah, M. Yahya Wachid, Aldi Wardhana, Septiyawan Rosetya Wicaksono, Mukhlis Adi Yunanda, Sita Fara Zaqiyah, Hafida Nur