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KLASIFIKASI WAKAF PRODUKTIF MENGGUNAKAN ALGORITMA ID3 PADA SISTEM INFORMASI ASET DAN KEHARTABENDAAN MAJELIS WAKAF YOGYAKARTA Muhammad Munsarif
(JurTI) Jurnal Teknologi Informasi Vol 4, No 1 (2020): JUNI 2020
Publisher : Universitas Asahan

Abstract – Productive Waqf is one of the waqf management programs developed in the modern era to improve the Islamic economy. The large number of waqf land received by nazir from year to year causes the management of waqf to be more complicated. This is caused by the lack of financial support and based on data from the ministry of religion in April 2020 the area of waqf land is 50,260.89 Ha. The waqf information system that has been developed has not been able to be a solution to the problems of waqf land management. Muhammadiyah which is the oldest Islamic organization that has succeeded in making many charitable efforts using waqf still not able to overcome the existing problems. The amount of waqf that is owned by Muhammadiyah requires an analysis to make a classification of productive and non-productive endowments that can provide information to make decisions in the management of endowment management on an ongoing basis. In this research, the ID3 algorithm is used for waqf classification which can show productive and non-productive categories using data derived from Muhamamdiyah Asset Information System in the Yogyakarta region. The results of the study provide recommendations that are shown in the decision tree produced by ID3 so that it can be concluded that for the management of waqf it is necessary to group and classify productive and non-productive land to create a professional endowment management plan.Keywords: Decision Support System, ID3 Algorithm, Data Mining, Waqf information system, Productive Waqf, Muhammadiyah, Rapidminner.Abstract – Wakaf produktif menjadi salah satu program pengelolaan wakaf yang dikembangkan di era modern dengan tujuan untuk meningkat perekonomian umat islam.Besarnya Jumlah wakaf tanah yang diterima oleh para nadzir dari tahun ke tahun menyebabkan pengelolaan wakaf menjadi lebih rumit.Hal ini di sebabkan tidak adanya dukungan pembiayaan dan pengembangannya.Berdasarkan data dari kementerian agama pada April 2020 luas tanah wakaf 50.260.89 Ha .Sistem informasi wakaf yang telah dikembangkan belum mampu menjadi solusi atas permasalah pengelolaan tanah wakaf .Muhammadiyah yang merupakan organisasi Islam tertua yang telah berhasil membuat banyak amal usaha dengan menggunakan wakaf masih belum mampu mengatasi persoalan yang ada.Besarnya wakaf yang di miliki oleh muhammadiyah membutuhkan sebuah analisa untuk membuat klasifikasi wakaf produktif dan non produktif yang mampu memberikan informasi untuk membuat keputusan dalam perencanaan pengelolaan wakaf secara berkesinambungan. Pada penelitian ini Algoritma ID3 digunakan untuk klasifikasi wakaf yang mampu menunjukan kategori produktif dan non produktif dengan menggunakan data yang berasal dari Sistem informasi aset Muhamamdiyah wilayah Yogyakarta. Hasil penelitian memberikan rekomendasi yang di tunjukkan dalam pohon keputusan yang di hasilkan oleh ID3 ,Sehingga bisa simpulkan bahwa untuk pengelolaan wakaf di butuhkan pengelompokan dan klasifikasi tanah produktif dan non produktif dalam upaya membuat rencana pengeloaan wakaf secara profesional.Kata kunci: Sistem Pendukung Keputusan, Algoritma ID3, Data Mining, Sistem Informasi Wakaf, Wakaf Porduktif, Muhammadiyah, Rapidminner

Analysis of the Integrated E-Mesp 4CS Mobile Instrument Eny Winaryati; Muhammad Munsarif; Iwan Junaedi; Utomo; Alya Dwi Arianty; Asiva Khoirunisa; Kafitra Marna Ibrahim
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12980

The need for valid and contextually relevant supervision instruments remains a key challenge in realizing 21st-century education that integrates the 4Cs skills (critical thinking, creativity, collaboration, and communication) with the Profil Pelajar Pancasila. This study aimed to develop and evaluate the feasibility of the Integrated E-MESp 4Cs Mobile as a digital instrument for academic supervision. Using a research and development (R&D) approach with qualitative data collection, this study conducted Focus Group Discussions, in-depth interviews, and document analyses involving teachers, principals, supervisors, and education experts. The data was analyzed through interactive qualitative procedures, including data reduction, presentation, and verification. The results show that the E-MESp 4Cs Mobile instrument is feasible and relevant to be used as an integrated supervision tool that combines 4Cs indicators with the dimensions of the Pancasila Student Profile. Stakeholders also emphasized the need for standardization aligned with national guidelines and the potential use of artificial intelligence to support data-based supervision and policy recommendations. Overall, the E-MESp 4Cs Mobile instrument demonstrates strong potential to enhance teacher supervision practices, support deep learning, and strengthen the development of humanistic, inclusive, and character-oriented graduates.

Optimizing the compact convolution transformer for enhanced pneumonia detection Muhammad Munsarif; Norshuhani Zamin; Muhammad Sam’an
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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

Pneumonia detection through medical imaging, especially using CT scans or X-rays, presents notable challenges due to the subtle and often unclear signs of the disease. This paper introduces a novel neural network model, the Compact Convolutional Transformer (CCT), designed to address these challenges by optimizing detection accuracy. The CCT model incorporates configuration dropout in its convolutional layers to enhance both robustness and precision.Experiments conducted on a dataset of 5,856 chest X-ray images from pediatric patients aged one to five years demonstrated the model's effectiveness, achieving a remarkable 97% accuracy, 97% recall, 98% precision, and an F1-score of 98%. When compared to state-of-the-art models like DarkNet-53 and VGG-19 + GradCAM, which achieved F1-scores of 97.3% and 95.61% respectively, the CCT model consistently matched or outperformed them, particularly when dealing with smaller and more complex datasets. Even models such as CNN + Bayesian Network, which used larger datasets, only reached an F1-score of 96.3%.These results underscore the superior efficiency and accuracy of the CCT model, highlighting its potential for broader applications in medical diagnostics and image analysis, especially in pneumonia detection.

Hyperparameter optimization of deep residual recurrent fusion models for facial emotion recognition Muhammad Munsarif; Ku Ruhana Ku-Mahamud
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2581-2594

Deep learning facial emotion recognition (FER) is widely applied in healthcare, education, and human–computer interaction. However, many deep learning models suffer from suboptimal hyperparameter configurations that reduce accuracy and stability. This study proposes three deep residual recurrent fusion models that integrates residual blocks with recurrent neural networks (bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), and gated recurrent unit (GRU)) to capture both spatial and temporal features. A systematic hyperparameter optimization strategy was applied, tuning kernel size, filter size, recurrent units, batch size, learning rate, dropout, and weight decay to balance generalization and computational efficiency. The models were evaluated on four benchmark datasets: FER2013, FERPlus, RAF-DB, and CK+. The results show that optimized configurations achieved outstanding accuracy, reaching 99.85% on FER2013, 99.99% on FERPlus, and 100% on RAF-DB and CK+. These findings demonstrate that careful hyperparameter tuning significantly enhances feature extraction, mitigates vanishing gradient and overfitting issues, and improves generalization across diverse datasets. The proposed framework highlights the importance of optimization in advancing robust FER systems for real-world applications.

Pelatihan ChatGBT kepada Guru di Majelis Pendidikan Muhammadiah kota semarang untuk Peningkatan literasi digital Muhammad Munsarif; Muhammad Sam'an; Samsudi Raharjo
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

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.

Peningkatan Kompetensi Guru di Sekolah Menengah Islam Ihsanul Fikri melalui Pelatihan Pengembangan Media Pembelajaran Berbasis Kecerdasan Buatan: Sebuah Studi Mixed-Methods Rima Dias Ramadhani; Ahmad Ilham; Muhammad Sam’an; Akhmad Fathurohman; Safuan Safuan; Muhammad Munsarif; Lukman Assaffat; Asdani Kindarto; Muhammad Wahyu Anggana; Teguh Firmasyah; M. Amin Syaifani; Afan Arga Ahyana; Adi Nugroho; Janu Yogi Kurnia
Jurnal Surya Masyarakat Vol 7, No 2 (2025): Mei 2025
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.7.2.2025.207-214

The training program on Artificial Intelligence-based Learning Media Development (P3MP-AI) represents a strategic initiative to enhance the quality of education at the Ihsanul Fikri Integrated Islamic High School. In the rapidly evolving landscape of information technology, the integration of Artificial Intelligence (AI) technology into education has become an urgent necessity. This endeavor aims to address the personalized learning needs, overcome human resource and time constraints, and enhance the overall quality of education in achieving educational goals. Through the Active and Interactive Learning Method (MPB-AI) approach, educators engage in various practical and interactive activities, enabling them to comprehend and applykecerdasan buatanconcepts practically. Evaluation of this training program demonstrates significant achievements in enhancing participants' understanding and skills, as well as a high level of creativity and diversity in AI-based learning media development practices. Thus, this training program has successfully achieved its objectives in improving the quality of education at the Ihsanul Fikri Integrated Islamic High School. Recommendations for future activities include extending the duration of implementation for optimal goal attainment and developing similar activities periodically to continually enhance educators' abilities in delivering more effective and efficient learning experiences.

Pelatihan ChatGBT kepada Guru di Majelis Pendidikan Muhammadiah kota semarang untuk Peningkatan literasi digital Muhammad Munsarif; Muhammad Sam'an; Samsudi Raharjo
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

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.

Peningkatan Kompetensi Guru di Sekolah Menengah Islam Ihsanul Fikri melalui Pelatihan Pengembangan Media Pembelajaran Berbasis Kecerdasan Buatan: Sebuah Studi Mixed-Methods Rima Dias Ramadhani; Ahmad Ilham; Muhammad Sam’an; Akhmad Fathurohman; Safuan Safuan; Muhammad Munsarif; Lukman Assaffat; Asdani Kindarto; Muhammad Wahyu Anggana; Teguh Firmasyah; M. Amin Syaifani; Afan Arga Ahyana; Adi Nugroho; Janu Yogi Kurnia
Jurnal Surya Masyarakat Vol 7, No 2 (2025): Mei 2025
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.7.2.2025.207-214

The training program on Artificial Intelligence-based Learning Media Development (P3MP-AI) represents a strategic initiative to enhance the quality of education at the Ihsanul Fikri Integrated Islamic High School. In the rapidly evolving landscape of information technology, the integration of Artificial Intelligence (AI) technology into education has become an urgent necessity. This endeavor aims to address the personalized learning needs, overcome human resource and time constraints, and enhance the overall quality of education in achieving educational goals. Through the Active and Interactive Learning Method (MPB-AI) approach, educators engage in various practical and interactive activities, enabling them to comprehend and applykecerdasan buatanconcepts practically. Evaluation of this training program demonstrates significant achievements in enhancing participants' understanding and skills, as well as a high level of creativity and diversity in AI-based learning media development practices. Thus, this training program has successfully achieved its objectives in improving the quality of education at the Ihsanul Fikri Integrated Islamic High School. Recommendations for future activities include extending the duration of implementation for optimal goal attainment and developing similar activities periodically to continually enhance educators' abilities in delivering more effective and efficient learning experiences.

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

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%.

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