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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Transformatika Pendas : Jurnah Ilmiah Pendidikan Dasar JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Ilmiah Pendidikan dan Pembelajaran Community Development Journal: Jurnal Pengabdian Masyarakat Journal Of Human And Education (JAHE) Journal of Data Insights Journal of Computing Theories and Applications Jurnal Statistika dan Sains Data Journal of Islamic Science Community Journal of Learning on History and Social Sciences Journal of Computing and Smart Ecosystems
Amri, Saeful
Universitas Muhammadiyah Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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OPTIMIZATION OF NAÏVE BAYES USING BACKWARD ELIMINATION FOR HEART DISEASE DETECTION Amri, Saeful; Ningrum, Ariska Fitriyana; Arum, Prizka Rismawati
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.44-50

Abstract

Heart disease is the main cause of death in humans. Even though preventive measures have been taken such as regulating food (diet), lowering cholesterol, and treating weight, diabetes, and hypertension, heart disease remains a major health problem. There are several factors that cause heart disease, including age, type of chest pain, high blood pressure, sugar levels, ECG test values, maximum heart rate, and induced angina. To reduce the percentage of deaths due to heart disease, we need a system that can predict heart disease. The algorithm used in this research is a combination of the Backward Elimination and Naive Bayes algorithms to increase accuracy in diagnosing heart disease. According to the results of this research, the Naive Bayes algorithm has an accuracy value of 78.90% and an Area Under Curve (AUC) value of 0.86, which is included in the good classification category. Combining the Backward Elimination and Naïve Bayes algorithms has an accuracy value of 82.31% and an Area Under Curve (AUC) value of 0.88.
Data Visualization Excellence: Google Data Studio Workshop At Sekolah Indonesia Kuala Lumpur Amri, Saeful; Fadlurohman , Alwan; Ningrum, Ariska Fitriyana; Purwanto, Dannu; Amri , Ihsan Fathoni; Wardani, Amelia Kusuma; Dhani, Oktaviana Rahma
Journal Of Human And Education (JAHE) Vol. 5 No. 1 (2025): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v5i1.2178

Abstract

The development of information technology and the entry of the industrial revolution 4.0 era has led to the inseparability of human activities related to the use of technology. In today's rapidly growing information age, data is one of the most valuable assets. The ability to collect, analyze, and interpret data is becoming a very important skill not only in the world of work but also in education. Education is the foundation for preparing future generations for increasingly complex global challenges, and a good understanding of data can provide a significant competitive advantage. In schools, the ability to analyze and interpret data is becoming an invaluable skill for students. Along with the development of technology, data visualization has become an effective method to convey information in a more comprehensible manner. In this context, Google Data Studio offers a powerful and easy-to-use tool for creating interactive dashboards that help in analyzing and presenting data. Indonesian Migrant Workers (TKI) are Indonesian citizens who live and work abroad. TKI provide a large contribution of foreign exchange to the country of Indonesia. However, there are problems in the field of education for children whose parents work as TKI in Malaysia, especially education that is relevant to success in terms of opening their own jobs abroad. This is considered important because to get jobs in government agencies or companies in Malaysia, the children of TKI working in Malaysia must compete with job seekers who are Malaysian citizens. One alternative that can be taken to overcome competition in getting jobs is to create your own jobs. Opening your own jobs is not an easy thing. Knowledge and insight about this are needed which are given early on to the children of TKI in school. By teaching Google Data Studio in the form of data visualization to students, they not only learn how to read and interpret graphs and diagrams, but also how to present their own data in a more interesting and informative way. This ability will be very useful in the future, both in academic and professional environments. By providing insight into Google Data Studio to students in schools, these students have the provisions to be able to read data and have the opportunity to work and get decent jobs. As a Community Service activity with an international scope, this activity takes partners in Malaysia, namely the Indonesian School-Kuala Lumpur - SIKL which is located in Sentul, Kuala Lumpur, Federal Territory of Kuala Lumpur. The Community Service Team of Muhammadiyah University of Semarang is very receptive to criticism and suggestions so that the implementation of Community Service in the future can be even better.
The Impact of Implementing the Independent Curriculum on Elementary School Students' Learning Outcomes Fisabilillah, Muh. Irodat; Ahmadi; Supriadin; Ridwanulhaq, Alfina Fauziah; Masudah, Nurhidayatul; Nur, Indah Manfaati; Haris, M. Al; Amri, Saeful
Jurnal Ilmiah Pendidikan dan Pembelajaran Vol. 9 No. 1 (2025): March
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jipp.v9i1.91548

Abstract

The Independent Curriculum is an essential component in Muhammadiyah Elementary Schools in Semarang, supporting the spirit of learning that has developed over time. However, there is an imbalance in student achievement scores between phases that require data-based tracking. This study aims to evaluate the effectiveness of the Independent Curriculum in improving students' cognitive understanding using the factorial design method. This type of research is quantitative descriptive. The research population is Muhammadiyah Elementary Schools in the independent school category, with a sample of four schools selected using the two-stage cluster random sampling technique. Data collection techniques are observation and questionnaires. The application of analysis methods includes descriptive and inferential statistics. The research results show that the implementation of the Independent Curriculum can significantly improve students' cognitive understanding, as reflected in the increase in the average student achievement scores between the 2022/2023 and 2023/2024 academic years. In addition, the analysis of the elementary school phase shows that the Independent Curriculum can support the development of student competencies in stages according to educational needs in each phase, thereby improving the quality of learning.
Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result Haris, M. Al; Setyaningsih, Laras Indah; Fauzi, Fatkhurokhman; Amri, Saeful
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20267

Abstract

PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.
"DESA CERDAS" SISTEM INFORMASI PELAYANAN KEPENDUDUKAN BERBASIS WEB DI DESA KUMPULREJO KABUPATEN KENDAL Nur, Indah Manfaati; Amri, Saeful; Nur, M. Saifudin; Imron, Ali
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024): Volume 5 No 1 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i1.24191

Abstract

Desa Kumpulrejo adalah salah satu desa di kabupaten Kendal yang memiliki website aktif yang berisi berbagai macam informasi yang berguna bagi masyarakat. Berdasarkan wawancara dengan Kepala Desa Kumpulrejo, Desa ini sudah selayaknya mempunyai sistem pelayanan publik berbasis teknologi. Saat ini pelayanan publik di Desa Kumpulrejo masih kurang efektif. Masyarakat harus meluangkan waktu datang ke balai desa untuk mengurus kelengkapan administrasi, meskipun di kantor desa sudah terdapat komputer. Permasalahan yang dihadapi adalah mobilitas masyarakat yang tinggi menyebabkan pelayanan publik secara manual sangat tidak efektif dan efisien. SDM pemerintah Desa Kumpulrejo yang belum memiliki ketrampilan teknologi informasi. Pemanfaatan data dan informasi di pemerintah Desa Kumpulrejo belum maksimal. Maka Tim PKM memberikan solusi "DESA CERDAS" Sistem Informasi Pelayanan Kependudukan Berbasis Web di Desa Kumpulrejo . Kegiatan pengabdian ini bertujuan untuk menerapkan IPTEK berupa sistem informasi kependudukan yang disesuaikan dengan kebutuhan mitra. Selain itu peningkatan literasi digital serta literasi data juga diprogramkan pada kegiatan pengabdian ini untuk mendukung implementasi “Desa Cerdas” sistem informasi pelayanan kependudukan desa Kumpulrejo
IMPLEMENTASI SISTEM INFORMASI KEMITRAAN BERBASIS CLOUD UNTUK MENINGKATKAN KETERJANGKAUAN DAN AKSESBILITAS BAGI UMKM AISYIYAH JAWA TENGAH Arum, Prizka Rismawati; Amri, Saeful; Ridwan, Mohammad; Puspitasari, Listi; Supriyadin, Supriyadin; Putro, Dwi Purnomo
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 (2024): Vol. 5 No. 5 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i5.35200

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) berperan signifikan dalam pembangunan ekonomi Indonesia, dengan mayoritas pelakunya adalah perempuan. Di Indonesia, 64,5% pelaku UMKM adalah perempuan, yang berkontribusi sebesar 61% terhadap PDB nasional. Namun, perempuan pelaku UMKM menghadapi berbagai tantangan, seperti kurangnya dukungan keluarga, akses perizinan yang sulit, manajemen bisnis yang belum optimal, dan minimnya akses ke pasar. Organisasi Aisyiyah, melalui program Bina Usaha Ekonomi Keluarga Aisyiyah (BUEKA), berupaya mengatasi masalah ini dengan mengembangkan UMKM perempuan melalui pelatihan, peningkatan kapasitas, dan digitalisasi. Pengabdian masyarakat ini mengusulkan solusi berupa Sistem Informasi Kemitraan Berbasis Cloud untuk meningkatkan keterjangkauan dan aksesibilitas bagi UMKM Aisyiyah di Jawa Tengah. Metode yang digunakan mencakup perencanaan, pelaksanaan, dan evaluasi pelatihan dengan pendekatan partisipatif. Hasil implementasi menunjukkan adanya peningkatan pengelolaan kemitraan UMKM, manajemen kasir, dan integrasi layanan digital berbasis cloud yang dapat memudahkan proses jual beli dan meningkatkan kinerja UMKM perempuan di bawah Aisyiyah.
SOSIALISASI "DESA CERDAS" SISTEM INFORMASI PELAYANAN KEPENDUDUKAN BERBASIS WEB DI DESA KUMPULREJO, KABUPATEN KENDAL Manfaati Nur, Indah; Amri, Saeful; Nur, M. Saifudin; Imron, Ali; Rohani, Imam
Journal of Islamic Science Community Vol. 2 No. 2 (2023)
Publisher : Institut Agama Islam Riyadlotul Mujahidin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55380/isc.v2i2.611

Abstract

Desa Kumpulrejo adalah salah satu desa di kabupaten Kendal yang memiliki website aktif yang berisi berbagai macam informasi yang berguna bagi masyarakat. Berdasarkan wawancara dengan Kepala Desa Kumpulrejo, Desa ini sudah selayaknya mempunyai sistem pelayanan publik berbasis teknologi. Permasalahan yang dihadapi adalah mobilitas masyarakat yang tinggi menyebabkan pelayanan publik secara manual sangat tidak efektif dan efisien. SDM pemerintah Desa Kumpulrejo belum memiliki ketrampilan teknologi informasi. Pemanfaatan data dan informasi di pemerintah Desa Kumpulrejo belum maksimal. Sosialisai ini bertujuan untuk menerapkan IPTEK berupa sistem informasi kependudukan yang disesuaikan dengan kebutuhan mitra. Selain itu peningkatan literasi digital serta literasi data juga diprogramkan pada kegiatan pengabdian ini untuk mendukung implementasi “Desa Cerdas” sistem informasi pelayanan kependudukan desa Kumpulrejo. Secara ringkas hubungan antara kegiatan PKM dalam pencapaian IKU Universitas Muhammadiyah Semarang yang akan direkognisi menjadi kegiatan MB-KM. Mitra berkomitmen kerjasama ini akan berkelanjutan sebagai mitra MB-KM dengan menyediakan ruang bagi mahasiswa untuk berkegiatan Magang ataupun Proyek di Desa.
ANALYSIS OF THE THINKING PROCESS OF GRADE XI STUDENTS IN SOLVING MATHEMATICS PROBLEMS REVIEWED FROM THE EXTROVERT AND INTROVERTED PERSONALITIES OF STUDENTS OF SMK PGRI 24 JAKARTA Amri, Saeful
Journal of Learning on History and Social Sciences Vol. 2 No. 7 (2025): Journal of Learning on History and Social Sciences
Publisher : PT. Antis International Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ejlhss.v2i7.1359

Abstract

Objective: I conducted research with the aim of training and determining the skills of students at SMK PGRI 24 Jakarta. Students' ability to think and solve math problems is examined from the perspective of introverted and extroverted students. Method: To make my research easier, I used a qualitative approach. Six Class XI students from SMK PGRI 24 in Jakarta were the subjects of the study; The three participants each represented extroverted and introverted personality types. Questionnaire, interview, and documentation approaches were used to collect data for this study. To collect this data, aspects of the student's personality were considered, along with testing the data and the conclusions drawn from the participants. A triangulation approach was used to collect data for the study. Results: The study found that introverted and extroverted students were at a full level of understanding of the difficulties used in their thought processes as well as carrying out planning, evaluation, and monitoring procedures. Planning steps are followed, which are completed by having students double-check the planning procedures. According to the results of the research interviews, extroverted students were at the level of tacit use, and introverted students were at the level of strategic use. Novelty: Students' ability to think and solve math problems is examined from the perspective of introverted and extroverted students.
AI-Enhanced Coastal Ecosystem Monitoring for Abrasion and Mangrove Decline Detection Using NDVI and CNN Models Muhammad Ivan Ardiansyah; Saeful Amri; Basirudin Ansor; Wendy Sarasjati; Anggry Windasari; Gansar Timur Pamungkas
Journal of Computing and Smart Ecosystems Vol. 1 No. 2 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v1i2.906

Abstract

Coastal ecosystems in Indonesia are increasingly threatened by accelerating abrasion and severe mangrove degradation, especially in Mangunharjo, Semarang, where shoreline retreat continues to endanger local communities and ecological stability. This study aims to develop an AI-driven monitoring framework for detecting coastal abrasion and mangrove loss using Normalized Difference Vegetation Index (NDVI) combined with a Convolutional Neural Network (CNN) classifier. Multispectral data from Sentinel-2 imagery were processed to extract NDVI time-series from 2015 to 2025, followed by image preprocessing, normalization, and CNN-based classification. The model identifies abrasion-affected zones and declining mangrove cover, while the geospatial dashboard visualizes risk levels and restoration priority areas. Experimental results show that the CNN–NDVI model achieves high accuracy in distinguishing stable and abrasion-prone regions, with clear detection of vegetation loss patterns along the western coastline of Mangunharjo. The developed dashboard successfully integrates prediction output, interactive mapping, and AI-assisted recommendations for mangrove restoration. In conclusion, this system demonstrates the potential of combining satellite data, CNN-based analysis, and geospatial visualization to support data-driven decision-making for coastal ecosystem management and sustainable environmental planning.
An Explainable Multimodal Framework for Chest X-Ray Alert Classification Using Radiology Reports and Images Edy Winarno; Indah Manfaati Nur; Abdul Karim; Saeful Amri; Ismi Elya Wirdati; Prajanto Wahyu Adi
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16023

Abstract

Artificial intelligence has the potential to support radiology workflows by assisting in the identification of cases that may require additional clinical attention. However, alert-oriented medical AI systems should provide not only classification outputs but also interpretable evidence that can be reviewed and audited by clinicians. This study develops and evaluates an explainable multimodal framework for binary chest X-ray alert classification using paired radiology reports and chest X-ray images. The text branch employs TF-IDF n-gram features with a class-balanced Logistic Regression classifier, while the image branch fine-tunes a pretrained ResNet18 model. The two branches are integrated through probability-level late fusion using a validation-selected fusion weight. Explainability is implemented in a modality-specific manner: global coefficient analysis is used to identify influential textual cues, while Grad-CAM heatmaps are used to visualize salient image regions. Experiments were conducted on paired samples from the Open-i/IU X-Ray dataset using text-only, image-only, and fusion-based evaluation settings. Additional analyses include case-level complementarity analysis, bootstrap confidence intervals for ROC-AUC, shortcut-feature inspection, and qualitative Grad-CAM auditing. The results indicate that the text modality provides the dominant predictive signal under the current proxy-label setting. Late fusion produced a small descriptive improvement on the test set, increasing accuracy from 0.8533 to 0.8667, F1-score from 0.8817 to 0.8936, and ROC-AUC from 0.8936 to 0.9025 compared with the text-only baseline. However, the observed ROC-AUC improvement was not statistically conclusive based on bootstrap analysis. These findings suggest that the proposed framework is useful as a reproducible and auditable multimodal prototype, while also highlighting important limitations, including proxy-label ambiguity, potential label leakage from radiology reports, limited image-branch contribution, lack of external validation, and the need for stronger explanation and calibration assessment.
Co-Authors Abdul Karim Agata Dwi Putri Putri AHMADI Ali Imron Alwan Fadlurohman Amaliah Sholeha Arafat Anggry Windasari Aprilla Anawai Basman Ariska Fitriyana Ningrum Asriyanti Sawiah Adam Basirudin Ansor Budiono Rahman Dannu Purwanto Dhani, Oktaviana Rahma Dwi Purnomo Putro Edy Winarno Erna Julia Nanga Faninda Aidina Fitri Fauzi, Fatkhurokhman Fisabilillah, Muh. Irodat Fitri Diana Musa Gansar Timur Pamungkas Haris, M. Al Ihsan Fathoni Amri Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur ine fitriani Ismi Elya Wirdati Lea Angelina M. Al Haris Manfaati Nur, Indah Masudah, Nurhidayatul Miftakhiyah Fazza Baita Mohammad Ridwan Muhammad Ivan Ardiansyah Nur, M. Saifudin Prajanto Wahyu Adi Prizka Rismawati Arum Puput Eka Suryani Puspitasari, Listi Putro, Dwi Purnomo Rahma Safira Ridwanulhaq, Alfina Fauziah Rochdi Wasono Rohani, Imam Siti Nurhalisa Sitti Sahara Supriadin Supriadin Supriyadin, Supriyadin Sutrisno Tiani Wahyu Utami Tri Martini Wardani, Amelia Kusuma Wendy Sarasjati