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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pendidikan Matematika (Kudus) Jurnal Surya Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Jurnal Surya Masyarakat Journal of Soft Computing Exploration Jurnal Komputer dan Teknologi Informasi
Muhammad Sam'an
Department of Informatics, Universitas Muhammadiyah Semarang
Religion Humanities Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Mathematics Public Health Other

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Convolutional neural network hyperparameters for face emotion recognition using genetic algorithm Muhammad Sam'an; Safuan Safuan; Muhammad Munsarif
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp442-449

Abstract

The development of artificial intelligence in facial emotion recognition (FER) is rapidly growing and has been widely applied in various fields. Deep learning (DL) techniques with evolutionary algorithms have become the preferred choice for solving various security, health, gaming, and other related problems. This research proposes the use of a genetic algorithm (GA) as the main method to optimize hyperparameters in the convolutional neural network (CNN) model for FER. The required computation time is approximately 37 hours 57 minutes 55 seconds, with generation 3 taking the longest time at around 16 hours 45 minutes 4 seconds. However, generation 3 achieved an accuracy of 76.11%, which is the highest compared to other generations. The results indicate that the more generations are involved, the higher the achievable accuracy. Furthermore, the proposed CNN-GA model in this study outperforms previous models that have been examined. Thus, this study makes a significant contribution to improving the understanding of using GAs to optimize the performance of CNN models for FER.
Metode Simple Additive Weighting untuk Pemilihan Website dengan Keaktifan Terbaik (Studi Kasus Website Pemerintah Kota Semarang) Muhammad Sholeh Sarwono; Muhammad Sam'an
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 1, No 2 (2023): Sistem Pengambilan Keputusan
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jkti.v1i2.12888

Abstract

Metode Simple Additive Weighting meyakini bahwa pemilihan alternatif dengan nilai tertinggi dari beberapa pilihan yang ada merupakan teknik pengambilan keputusan multi-kritteria yang sederhana dan efektif. Hal ini dikarenakan dalam pemilihannya disertai dengan evaluasi serta pembandingan alternatif lain berdasarkan sejumlah kriteria yang telah ditentukan sebelumnya.. Penelitian ini bertujuan untuk membandingkan website yang terdaftar dalam Pemerintah Kota Semarang dalam mencari website dengan keaktifan terbaik pada Kecamatan Banyumanik melalui metode Simple Additivve Weighting. Hasilnya menunjukkan bahwa website milik Kelurahan Jabungan memiliki tingkat keaktifan terbaik dibandingkan website lain, di mana pada setiap alternatifnya memiliki nilai tertinggi setelah dilakukannya normalisasi data serta perhitungan bobot kriteria pada masing – masing nilai kriteria.
Sistem Klasifikasi Tahu Non-Formalin Menggunakan Metode Random Forest Sefrina Ainun; Muhammad Munsarif; Muhammad Sam'an
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 1, No 2 (2023): Sistem Pengambilan Keputusan
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jkti.v1i2.12861

Abstract

Tahu formalin adalah salah satu jenis makanan yang mengandung bahan-bahan kimia yang dapat mengawetkan daripada tahu tanpa formalin. Penelitian ini bertujuan untuk mengklasifikasikan tahu formalin dan tahu tidak formalin. Penelitian ini menggunakan metode random forest yang merupakan bagian dari algoritma machine learning untuk klasifikasi, Penelitian ini mencoba menerapkan metode random forest pada dataset tahu formalin dengan jumlah dataset public. Setelah dilakukan beberapa tahapan dalam pengujian dengan metode random forest maka diperolah hasil akurasi 89%. Model random forest dikembangkan menjadi aplikasi web deteksi tahu non formalin dan tahu formalin yang berfungsi bagi masyarakat dalam meningkatkan pangan agar bebas konsumsi tahu non formalin.
Implementasi Metode Interpolasi Bilinear Untuk Perbesaran Skala Citra Hasbi Ardianto Pratama; Muhammad Sam’an
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 1, No 1 (2023): ESTIMASI NUMERIK
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/.v1i1.11803

Abstract

Perubahan skala citra merupakan salah satu bidang yang cukup popular hari ini. Banyak aplikasi yang membutuhkan input citra dengan ukuran tertentu. Ukuran citra mempengaruhi hasil dari pengenalan citra tersebut. Metode yang sering digunakan untuk mengatur ukuran citra adalah metode interpolasi. Kualitas citra hasil interpolasi tergantung dari metode interpolasi yang diterapkan. Interpolasi berkaitan erat dengan proses pemetaan piksel-piksel baik secara forward maupun reverse.Kata kunci: Interpolasi Bilinear, Perbaikan Citra, Pembesaran Citra 
Penggunaan Pohon Keputusan dalam Penempatan Naga pada Permainan Dragon City Syafrie Abdunnasir Jawad; Muhammad Sam'an; Safuan Safuan
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 1, No 1 (2023): ESTIMASI NUMERIK
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jkti.v1i1.11910

Abstract

Dalam permainan “Dragon City” terdapat banyak habitat sesuai dengan elemennya. Habitat-habitat tersebut juga mempunyai harga yang berbeda-beda tergantung dari kelangkaan elemennya. Dengan pohon keputusan, proses pemilihan habitat bagi naga baru akan lebih mudah. Pohon keputusan akan memantu pemain menentukan habitat yang paling efisien bagi naga-naga baru.Kata Kunci: dragon city, habitat naga, naga,  pohon keputusan.
Perbandingan Kinerja Akurasi Model Mesin Learning Untuk Prediksi Penyakit Jantung Juyus Muhammad Adinulhaq; Muhammad Sam'an
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 1, No 2 (2023): Sistem Pengambilan Keputusan
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jkti.v1i2.12918

Abstract

This research aims to comprehensively analyze heart disease-related data through Exploratory Data Analysis (EDA), identification of correlations between numerical variables, and cluster analysis to uncover patterns in the data. Furthermore, using various machine learning algorithms, such as Logistic Regression, Support Vector Classifier, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbors, and Gaussian Naive Bayes, a heart disease prediction model was built. The model evaluation shows that Naive Bayes has the highest test accuracy of 90%, followed by RandomForestClassifier and KNeighborsClassifier which have 85% test accuracy. These findings indicate a good ability to predict heart disease, but further analysis is needed to ensure good generalization to unseen data. This research makes an important contribution to the development of heart disease prediction models and can support early detection and appropriate intervention strategies.
Hybrid deep learning model for YouTube spam comment detection Sam'an, Muhammad; Imaddudin, Khrisna
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3313-3319

Abstract

Social media platforms, including YouTube and Facebook, allow users to interact through comments and videos. However, the openness of these platforms also makes them susceptible to spammers engaging in phishing, malware distribution, and advertisement dissemination. In response, our study introduces an innovative technique for detecting features indicative of spam within comments associated with shared videos. The initial phase involves data collection from the University of California, Irvine (UCI) machine learning repository and preprocessing using tokenization and lemmatization. Subsequently, a rigorous feature selection process is executed, and experiments are conducted with various proposed classification models. The performance evaluation demonstrates outstanding accuracy in identifying spam comments on YouTube: convolutional neural network with gated recurrent unit (CNN-GRU) at 95.92%, convolutional neural network with long short-term memory (CNN-LSTM) at 95.41%, convolutional neural network with bidirectional long short-term memory (CNN-biLSTM) at 96.43%, gated recurrent unit (GRU) at 95.41%, long short-term memory (LSTM) at 94.13%, and bidirectional long short-term memory (biLSTM) at 96.94% and convolutional neural network (CNN) at 94.64%. These results highlight the substantial contribution of our approach to spam detection and the fortification of online security.
Feature selection in P2P lending for default prediction using grey wolf optimization and machine learning Sam'an, Muhammad; Safuan, Safuan; Munsarif, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7651

Abstract

Online loan services like peer-to-peer (P2P) lending enable lenders to transact without bank intermediaries. Predicting which lenders are likely to default is crucial to avoid bankruptcy since lenders bear the risk of default. However, this task becomes challenging when the P2P lending dataset contains numer- ous features. The prediction performance could be improved if the dataset fea- tures are relevant. Hence, applying feature selection to remove redundant and irrelevant features is essential. This paper introduces a novel wrapper feature selection model to identify the optimal feature subset for predicting defaults in P2P lending. The proposed method includes two main phases: feature selection and classification. Initially, grey wolf optimization (GWO) is used to select the best features in P2P lending datasets. Then, the fitness function of GWO is as- sessed using ten different machine learning (ML) models. Experimental results indicate that the proposed model outperforms previous related work, achieving average accuracy, recall, precision, and F1-score of 96.77%, 80.73%, 97.52%, and 80.06%, respectively.
Convolution neural network hyperparameter optimization using modified particle swarm optimization Munsarif, Muhammad; Sam'an, Muhammad; Fahrezi, Andrian
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.6112

Abstract

Based on the literature review, a convolutional neural network (CNN) is one of the deep learning techniques most often used for classification problems, especially image classification. Various approaches have been proposed to improve accuracy performance. In CNN architecture, parameter determination is very influential on accuracy performance. Particle swarm optimization (PSO) is a type of metaheuristic algorithm widely used for hyperparameter optimization. PSO convergence is faster than genetic algorithm (GA) and attracts many researchers for further studies such as genetic algorithms and ant colony. In PSO, determining the value of the weight parameter is very influential on accuracy. Therefore, this paper proposes CNN hyperparameter optimization using modified PSO with linearly decreasing randomized weight. The experiments use the modified National Institute of Standards and Technology (MNIST) dataset. The accuracy of the proposed method is superior, and the execution time is slower to random search. In epoch 1, epoch 3, and epoch 5, the proposed method is superior to baseline CNN, linearly decreasing weight PSO (LDWPSO), and RL-based optimization algorithm (ROA). Meanwhile, the accuracy performance of the proposed method is superior to previous studies, namely LeNet-1, LeNet-2, LeNet-3, PCANet-2, RANDNet-2, CAE1, CAE-2, and bee colony. Otherwise, lost to PSO-CNN, distributed PSO (DPSO), recurrent CNN, and CNN-PSO. However, the four methods have a complex architecture and wasteful execution time.
Peningkatan Kompetensi Guru Madrasah Ibtidayah Duren dan Sabilul Huda Bandungan melalui Pelatihan Pembelajaran Berbasis Teknologi Informasi Ilham, Ahmad; Fathurrohman, Akhmad; Sam'an, Muhammad; Safuan, Safuan; Munsarif, Muhammad; Assaffat, Luqman; Kindarto, Asdani; Ramadhani, Arfido; Adinullhaq, Juyus Muhammad; Febrianto, Febrianto; Nurmantoro, Irvan; Ardhani, Yevi Alviatul; Ariyanto, Nova
Jurnal Surya Masyarakat Vol 5, No 2 (2023): Mei 2023
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.5.2.2023.264-269

Abstract

Madrasah Ibtidaiyah (MI) Duren Village and Sabilul Huda Jimbaran Bandungan District Semarang Regency want to produce quality graduates. However, the competence of teachers is still conventional learning aids so the learning process is not optimal. To answer this problem, the Department of Informatics, Faculty of Engineering at Universitas Muhammadiyah Semarang, Indonesia proposed information technology-based learning training activities for madrasah teachers. The purpose program is to strengthen human resources for teachers in MI Desa Duren and Sabilul Huda Jimbaran. The proposed program is divided into three learning schemes, (1) interactive presentation media, (2) online classroom learning, and (3) online learning evaluation. The results of this program are that the participants proved to be able to produce effective, elaborative, and interactive teaching materials based on information technology so that students are not bored and enthusiastic about following lessons in the classroom. It can be cancluded the program with the theme "Strengthening Teacher Competencies Through Information Technology-Based Learning Training" can overcome problems in Madrasah Ibtidaiyah (MI) Duren Village and Sabilul Huda Jimbaran Bandungan District Semarang Regency.
Co-Authors A. Nafis Haikal A. Nafis Haikal Adinullhaq, Juyus Muhammad Ahmad Ilham Akhmad Fathurrohman Akhmad Fathurrohman Ardhani, Yevi Alviatul Arfido Ramadhani Ariyanto, Nova Asdani Kindarto Bayu Surarso Etna Vianita Etna Vianita Fahrezi, Andrian Febrianto Febrianto Febrianto Febrianto, Febrianto Hasbi Ardianto Pratama Ilham, Ahmad Imaddudin, Khrisna Ina Salamatul Mufaricha Irvan Nurmantoro Juyus Muhammad Adinulhaq Juyus Muhammad Adinullhaq Kindarto, Asdani Luqman Assaffat Luqman Assaffat Much Aziz Muslim Muhammad Munsarif Muhammad Sholeh Sarwono Nova Ariyanto Nurmantoro, Irvan Ramadhani, Arfido Redemtus Heru Tjahjana Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Samsudi Raharjo Sefrina Ainun Susilo Hariyanto Syafrie Abdunnasir Jawad Titi Udjiani Yahya Nur Ifriza Yevi Alviatul Ardhani Yosza Dasril