Article Per Year (5 Year)
p-Index From 2020 - 2025
1.116
P-Index
This Author published in this journals
All Journal Proceeding Seminar LPPM UMP Tahun 2014 International Journal of Advances in Intelligent Informatics AlphaMath: Journal of Mathematics Education PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika International Journal of Artificial Intelligence Research MAJU : Jurnal Ilmiah Pendidikan Matematika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Proceedings Series on Physical & Formal Sciences Prosiding University Research Colloquium Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Journal of Medives: Journal of Mathematics Education IKIP Veteran Semarang
Malim Muhammad
Universitas Muhammadiyah Purwokerto
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
Better Teaching and Learning Model Character (BTL-C) to Establish Students' Pedagogical Competence Joko Purwanto; Sigit Sugiyanto; Malim Muhammad
Jurnal Pendidikan Matematika IKIP Veteran Semarang Vol 6 No 2 (2022): Journal of Medives : Journal of Mathematics Education IKIP Veteran Semarang
Publisher : Urogram Studi Pendidikan Matematika, Universitas IVET

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (388.158 KB) | DOI: 10.31331/medivesveteran.v6i2.2185

Abstract

Creating a model study of Better Teaching and Learning Character (BTL-C) can help students learn with greater passion, activity, and results while assessing the model study's validity, effectiveness, and applicability. These studies are research & Development studies (R&D). Three phases of research were carried out: an examination of the BTL-C model by theoretic and professional review. Empirical testing supports the BTL-C model's empirical validity. Phased implementation to determine whether the BTL-C paradigm is useful and effective. The study's findings show that the developed model study of BTL-C integrates character items into mathematics items and is provided at the network's ICARE step of connection. A validator has verified the validity of this model, and it is backed up with test-field validation data. This methodology has also been tested regarding how well it helps students develop their character and increase their passion, activity, and learning outcomes. Implementing the BTL-C model in practice has also been evaluated through observations of the lecturer's capacity for learning in the classroom, lecturer responses to model-use amenities, and student acceptance of the lecturer-applied model. The final results show that the generated model study for the BTL-C has met all the necessary criteria and is both practical and effective. Keywords: pedagogy competence, BTL-C, ICARE, effective
Pelatihan Software Geogebra dalam Meningkatkan Motivasi Belajar dan Pemahaman pada Materi Segiempat di SMP Muhammadiyah 1 Purwokerto Malim Muhammad; Lukmanul Akhsani
Prosiding University Research Colloquium Proceeding of The 8th University Research Colloquium 2018: Bidang Pendidikan, Humaniora dan Agama
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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

Abstract

Kegiatan ini bertujuan untuk memberikan pelatihan kepada Siswa kelas 8G ICT SMP Muhammadiyah 1 Purwokerto untuk mengatasi daya tangkap keterampilan spasial dan meningkatkan kemampuan teknologi informasi dan komunikasi khusunya software matematika, dalam hal ini penggunaan software GEOGEBRA sebagai media pembelajaran geometri bangun ruang sisi datar segiempat. Bagi sekolah, Memiliki guru-guru yang tidak hanya memiliki kemampuan dalam teoritis tapi memiliki kemampuan praktis di bidang teknologi komputer yang berkualitas dalam upaya peningkatan kualitas proses pembelajaran. Bagi guru-guru SMP Muhammadiyah 1 Purwokerto sebagai sumbangan pemikiran dan pelatihan peningkatan kemampuan praktis penggunaan software GEOGEBRA sebagai media pembelajaran dalam upaya peningkatan kualitas proses pembelajaran, sehingga dapat meningkatkan kemampuan pemahaman konsep siswa menjadi lebih baik. Bagi siswa SMP Muhammadiyah 1 Purwokerto, khususnya siswa kelas 8G ICT, meningkatkan kemampuan teknologi informasi dan komunikasi khusunya software matematika, dalam hal ini penggunaan software GEOGEBRA sebagai media pembelajaran dalam menyelesaikan materi geometri bangun ruang sisi datar khususnya segiempat.
Item Based Collaborative Filtering Based on Highest Item Similarity Muhammad, Malim; Sugiyanto, Sigit
International Journal of Artificial Intelligence Research Vol 6, No 1.2 (2022)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (306.72 KB) | DOI: 10.29099/ijair.v6i1.310

Abstract

The popularity of movies has increased in recent years. There are thousands of films produced each year. These films make it challenging for movie lovers to pick the ideal film to see. We propose a recommendation system that strives to offer guidance in selecting films.  Depending on the method employed, recommendation systems can be categorized into three groups: collaborative filtering, content-based filtering, and hybrid filtering. In this work, collaborative filtering, one of the methods frequently used in recommendation systems was used. There are two ways to the Collaborative Filtering approach: User-Based Collaborative Filtering (UBCF) and Item-Based Collaborative Filtering (IBCF). There are two methods for finding similar items or users: Cosine and Pearson similarities. The Cosine similarity approach is one way to determine how similar two items are. Additionally, the Pearson Correlation Coefficient approach, which determines similarities between objects by calculating linear correlations between two sets, is the most widely employed. This study aims to determine which system produces the highest item similarity in IBCF and predicted ratings to actual ratings using 90% training and 10% testing data. The data set taken from MovieLens.org consists of 943 users from 1664 movies with 99392 ratings. The MovieLens data collection will be analyzed with the RStudio and the R package recommenderlab. The results reveal that the IBCF with Cosine similarities shows the number of items recommended n top-rated movies to each user for 10 movies. The IBCF can identify the most recommended films and creates a frequency distribution of items.
Optimalisasi Penggunaan GeoGebra sebagai Media Pembelajaran Geometri di SMP Muhammadiyah Sokaraja Muhammad, Malim; Sugiyanto, Sigit
Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 8, No 3 (2025): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v8i3.8579

Abstract

Pembelajaran geometri tiga dimensi di tingkat SMP, khususnya materi Bangun Ruang Kubus dan Balok, seringkali menghadapi kendala akibat rendahnya kemampuan siswa dalam memahami konsep spasial seperti volume dan luas permukaan. Berdasarkan identifikasi kebutuhan mitra di SMP Muhammadiyah Sokaraja, ditemukan bahwa para guru mengalami kesulitan dalam menyampaikan materi secara konkret karena keterbatasan media visual dan rendahnya pemanfaatan teknologi pembelajaran. Salah satu solusi potensial adalah penggunaan perangkat lunak GeoGebra yang memungkinkan visualisasi bangun ruang secara interaktif. Namun, mayoritas guru belum memiliki pengetahuan dan keterampilan yang memadai dalam mengoperasikan aplikasi tersebut. Untuk menjawab kebutuhan tersebut, tim pengabdian dari Universitas Muhammadiyah Purwokerto menyelenggarakan pelatihan GeoGebra kepada 22 guru matematika. Kegiatan ini mencakup teori, praktik, serta evaluasi melalui pre-test dan post-test. Hasil pelatihan menunjukkan peningkatan rata-rata skor sebesar 25,3 poin, yang mencerminkan peningkatan signifikan dalam pemahaman dan kesiapan guru menggunakan GeoGebra dalam pembelajaran. Kegiatan ini menjadi solusi nyata atas permasalahan mitra dan memiliki potensi untuk direplikasi di sekolah lain guna memperkuat integrasi teknologi dalam pembelajaran matematika.
Optimization hybrid weighted switching filtering (OHWSF) using SVD and SVD++ for addressing data sparsity Muhammad, Malim; Gunardi, Gunardi; Danardono, Danardono; Rosadi, Dedi
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1796

Abstract

Recommender systems are crucial for filtering vast amounts of digital content and providing personalized recommendations; however, their effectiveness is often hindered by data sparsity, where limited user-item interactions lead to reduced prediction accuracy. This study introduces a novel hybrid model, Optimization Hybrid Weighted Switching Filtering (OHWSF), to overcome this challenge by integrating two complementary strategies: Hybrid Weighted Filtering (HWF), which linearly combines predictions from SVD and SVD++ using a weighting parameter (α), and Hybrid Switching Filtering (HSF), which dynamically selects predictions based on a threshold rating (θ). The OHWSF framework introduces a tunable optimization mechanism governed by the parameter σ₁ to adaptively balance weighting and switching decisions based on actual rating deviations. Unlike existing static or manually tuned hybrid methods, the proposed model combines dynamic switching with weight optimization to minimize prediction error effectively. Extensive experiments on four benchmark datasets (ML-100K, ML-1M, Amazon Cell Phones Reviews, and GoodBooks-10K) demonstrate that OHWSF consistently outperforms traditional collaborative filtering (UBCF, IBCF), matrix factorization techniques (SVD, SVD++), and standalone hybrid models across all evaluation metrics (MAE, MSE, RMSE). The model achieves optimal performance within the range of α = 0.6–0.9 and θ = 1.0–1.5, demonstrating robustness across varying sparsity levels. Notably, OHWSF achieves up to 742.16% MAE improvement over the UBCF model, with significantly reduced training time compared to SVD++. These findings confirm that OHWSF significantly improves prediction accuracy, scalability, and adaptability in sparse data environments. This research contributes a flexible, interpretable, and efficient hybrid recommendation framework suitable for real-world applications.
Co-Authors Ahmad Ahmad Danardono, Danardono Dedi Rosadi Fitrianto Eko Subekti Gunardi Gunardi Gunawan Gunawan Joko Purwanto Kusno Kusno Lukmanul Akhsani Reni Untarti Sigit Sugiyanto Sigit Sugiyanto