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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Surya Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Surya Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Jurnal Komputer dan Teknologi Informasi
Muhammad Munsarif
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Medicine & Pharmacology Public Health Social Sciences Other

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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.
Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters Muhammad Munsarif; Muhammad Saman
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p38-45

Abstract

One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%. 
Enhancing Agricultural Pest Detection with EfficientNetV2-L and Grad-CAM: A Comprehensive Approach to Sustainable Farming Agatra, Denaya Ferrari Noval; Cornella, Barisma Ami; Muza'in, Muhammad; Munsarif, Muhammad; Abdollahi, Jafar; Ilham, Ahmad
Journal of Intelligent Computing & Health Informatics Vol 5, No 1 (2024): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i1.13959

Abstract

In modern agriculture, quickly identifying agricultural pests is essential for maintaining high crop yields and ensuring global food security. In diverse and dynamic agricultural environments, traditional pest detection methods exhibit reduced accuracy, limited scalability, and lack interpretability. In this study, EfficientNetV2-L and Grad-CAM were used to significantly enhance pest detection system performance and transparency. EfficientNetV2-L, a fast and resource-efficient model, excels particularly in computationally constrained environments. Traditional CNN models, including EfficientNetV2-L, are criticized as uninterpretable "black boxes" despite their high accuracy. To address this issue, Grad-CAM was used to generate salient maps that visually show the most influential areas of the input image in the model’s decision-making process. This combination not only provides superior pest detection accuracy but also provides actionable insights into the model’s predictions, which is an important feature for building trust among agricultural practitioners. Our experimental results show a 15% improvement in detection accuracy compared to conventional models, especially in identifying visually similar-looking pest species that are often misclassified. In addition, the enhanced interpretability provided by Grad-CAM has led to a deeper understanding of the model’s behaviour, enabling iterative adjustments and improvements that further enhance the reliability of the system. The practical implications of these findings are significant: this integrated model offers a robust solution that can be seamlessly applied to real-time agricultural monitoring systems. With the early detection and proper classification of pests, this model can be used as a more effective pest management strategy to minimize crop damage and increase agricultural productivity. This research not only advances the technological frontier of pest detection but also contributes to broader goals related to sustainable agriculture and food security. Future research will focus on expanding the applicability of this model across different agricultural contexts, improving its adaptability to different environmental conditions, and further optimizing its performance through advanced techniques such as transfer learning and ensemble methods.
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.
Pelatihan ChatGBT kepada Guru di Majelis Pendidikan Muhammadiah kota semarang untuk Peningkatan literasi digital Munsarif, Muhammad; Sam'an, Muhammad; Raharjo, Samsudi
Jurnal Surya Masyarakat Vol 6, No 2 (2024): Mei 2024
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.2.2024.269-275

Abstract

The development of artificial intelligence (AI)--based learning models has made significant progress alongside the abundance of data. This enables the creation of complex deep-learning models to tackle increasingly intricate tasks. Evolving machine learning algorithms become a key factor in enhancing AI model capabilities. The demand for smart and efficient solutions from the business sector drives the adoption of AI technology, supported by advances in sensor technology, the Internet of Things (IoT), natural language processing (NLP), and image recognition. This article highlights the potential impact of AI model development on the learning experience, especially at the Elementary (SD), Junior High (SMP), and Senior High School (SMA) levels. Implementing AI models in elementary and secondary schools can support student progress assessment, provide material recommendations based on student understanding, and develop skills. The study discusses a teacher training initiative using ChatGPT to understand and utilize artificial intelligence in education. Training results show that teachers can effectively create varied and engaging learning materials using ChatGPT. Despite AI's benefits, cultural and social values remain irreplaceable, such as ethics towards teachers and social interactions among students. In conclusion, digital literacy training for teachers is essential to enhance their ability to develop modern and effective learning models, with AI as a valuable tool in creating dynamic and interactive learning environments.
Penerapan algoritma convolutional neural network (cnn) untuk mengklasifikasi jenis kendaraan Wibowo, Bhanu Lintang; Caesarizky, Ovien Yoga; Hakim, Muhammad Ilham; Munsarif, Muhammad
JURNAL KOMPUTER DAN TEKNOLOGI INFORMASI Vol 2, No 2 (2024): Implementasi Sistem Cerdas
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jkti.v2i2.13938

Abstract

Dengan berkembangnya teknologi kendaraan otonom dan pengawasan lalu lintas cerdas, klasifikasi kendaraan menjadi elemen kunci dalam sistem transportasi modern. Artikel ini mengusulkan suatu pendekatan berbasis Convolutional Neural Network (CNN) untuk meningkatkan ketepatan dan efisiensi dalam klasifikasi jenis kendaraan dari data visual. Metode yang diusulkan menggunakan arsitektur CNN yang mendalam untuk mengekstraksi fitur-fitur penting dari citra kendaraan. Data pelatihan yang luas dan bervariasi digunakan untuk melatih model, yang kemudian diuji pada dataset independen untuk mengevaluasi performanya. Hasil eksperimen menunjukkan bahwa pendekatan ini dapat mencapai tingkat akurasi yang tinggi dalam klasifikasi kendaraan, bahkan dalam kondisi pencahayaan yang bervariasi dan latar belakang yang kompleks. Keuntungan utama dari metode ini adalah kemampuannya untuk secara otomatis mempelajari pola dan fitur- fitur diskriminatif dari citra kendaraan, membuatnya mampu mengatasi variasi dan kompleksitas dalam lingkungan praktis. Hasil dari penelitian ini memberikan kontribusi positif terhadap pengembangan sistem transportasi pintar dan meningkatkan keamanan serta efisiensi lalu lintas.
EfficientNet for Medical Image Classification: Performance vs. Efficiency in Skin Cancer Detection Purbandanu, Muhammad Wigig; Kurniawan, Arif; Yanuarta, Rizky; Munsarif, Muhammad; A. Awoseyi, Ayomikun
Journal of Intelligent Computing & Health Informatics Vol 5, No 2 (2024): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v5i2.14338

Abstract

This study applies EfficientNetB2, a computationally efficient convolutional neural network (CNN), to improve the accuracy of skin cancer detection using the heterogeneous HAM10000 dataset. Skin cancer classification poses challenges, including overfitting and class imbalance, which we address through data augmentation, class weighting, and SMOTE (Synthetic Minority Over-sampling Technique). Our model achieved accuracy of 86%, precision of 0.87, recall of 0.85, and an AUC of 0.90. These results outperform comparable architectures, such as ResNet50 and GoogleNet, while maintaining lower computational complexity. The proposed model demonstrates high precision in detecting actinic keratoses and basal cell carcinoma, which require timely treatment, but faces difficulties in differentiating melanoma from benign nevi because of their similar visual appearance. This study highlights the potential of EfficientNetB2 for real-world deployment in resource-limited settings, such as mobile health applications and telemedicine platforms. Future research will focus on integrating attention mechanisms and exploring cross-dataset validation to enhance model generalizability and performance.
Pemberdayaan Guru melalui Pelatihan Pemanfaatan Kecerdasan Buatan (AI) untuk Meningkatkan Kualitas Pembelajaran di Era Digital Muhammad Munsarif; Muhammad Sam’an; Safuan Safuan
ASPIRASI : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Vol. 3 No. 1 (2025): ASPIRASI : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat 
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/aspirasi.v3i1.1379

Abstract

The development of competent human resources (HR) is a top priority for improving the quality of life in society. In education, teachers' ability to enhance their skills is key to successful learning. However, many teachers struggle to adapt teaching methods to the demands of the digital era, especially in utilizing Artificial Intelligence (AI) technology. AI offers innovative solutions such as personalized learning and adaptive platforms that enhance the effectiveness of teaching and learning processes. This study examines the implementation of AI-based learning models and teacher training programs in Muhammadiyah schools in Semarang. Using the Training of Trainer (ToT) approach, the program equips teachers with skills such as using adaptive learning tools and AI-based evaluation systems. The study employs a mixed-methods approach involving practical training and result evaluation. The findings reveal improved digital literacy and confidence among teachers in using AI for interactive learning. In conclusion, AI has great potential to transform education, but its success depends on comprehensive, ethical, and sustainable strategies. The ToT program serves as a strategic step to empower teachers to face educational challenges in the digital era.
Co-Authors A. Awoseyi, Ayomikun Abdollahi, Jafar Adinullhaq, Juyus Muhammad Agatra, Denaya Ferrari Noval Ahmad Ilham Ahmad Miftakhul Akhmad Fathurrohman Akhmad Fathurrohman Alfi Widianto Alif Hardika Ardhani, Yevi Alviatul Arfido Ramadhani ARIF KURNIAWAN Ariyanto, Nova Asdani Kindarto Caesarizky, Ovien Yoga Cornella, Barisma Ami Edi Noersasongko Edi Noersasongko Eny Winaryati Fahrezi, Andrian Febrianto Febrianto Febrianto Febrianto, Febrianto Galih Ratna Hakim, Muhammad Ilham Ilham, Ahmad Irvan Nurmantoro Javier Rayhan Jian Janata Juyus Muhammad Adinullhaq Kholifatul Afwah Kindarto, Asdani Luqman Assaffat Luqman Assaffat Mardiana Mardiana Meike Amelia Hanifah Moch Arief Soeleman Mochammad Arief Soeleman Muhammad Sam'an Muhammad Saman Muhammad Sam’an Muza'in, Muhammad Nova Ariyanto Nurmantoro, Irvan Pulung Nurtantio Andono Purbandanu, Muhammad Wigig Ramadhani, Arfido Safuan Safuan Safuan Safuan Safuan Safuan Safuan Safuan Salsabila Rahma Sam'an, Muhammad Samsudi Raharjo SAMSUDI RAHARJO Sefrina Ainun Shabrina Mirza Siti Muzdalifah Suwahono Suwahono Tirta Ramadhani Wibowo, Bhanu Lintang Yanuarta, Rizky Yevi Alviatul Ardhani