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All Journal Jurnal Veteriner PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo Riyadhoh : Jurnal Pendidikan Olahraga Jurnal Ilmiah Wahana Pendidikan Prosiding Seminar Nasional Ilmu Teknik Edumaspul: Jurnal Pendidikan
Fachruddin Fachruddin
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences Veterinary Other

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Implementation of Monitoring and Evaluation of Teacher Performance at Madrasah Aliyah Salafiyah Darul Muta’allimin Tanah Merah Aceh Singkil Miranti Adelia Afda; Fachruddin Fachruddin; Makmur Syukri
Edumaspul: Jurnal Pendidikan Vol 6 No 2 (2022): Edumaspul: Jurnal Pendidikan
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33487/edumaspul.v6i2.5101

Abstract

Implementation of monitoring and evaluation of teacher performance, namely providing an assessment of the programs implemented by the teacher in accordance with the plans made and identifying problems that arise in the implementation of school programs so that they can be overcome. At Madrasah Aliyah Salafiyah Darul Muta'allimin Tanah Merah Aceh Singkil there are several phenomena that seem related to the low performance of teachers including; (1) teachers carry out teaching and learning activities not in accordance with a predetermined time allocation, (2) there are still some teachers who make teaching only an obligation without thinking about the interests of the students and the interests of the madrasah, (3) there is a lack of teacher initiative in the form of creativity in activities learning, (4) some teachers prioritize personal interests over the interests of the madrasah. The purpose of this study was to find out implementation, implementation techniques, and constraints in monitoring and evaluating teacher performance at Madrasah Aliyah Salafiyah Darul Muta'allimin Tanah Merah Aceh Singkil. This study uses a qualitative descriptive research method with data collection techniques using observation and interviews. The results of this study indicate that the implementation of monev on teacher performance at Madrasah Aliyah Salafiyah Darul Muta'allimin Tanah Merah has been going well, starting with monev planning by conducting discussions involving teachers by making various considerations so that the implementation of teacher performance monitoring and evaluation runs effectively and efficiently, using various implementation techniques and combining the results, evaluating the program to find out how far the program has been running and reducing the impact of losses from its implementation and improving teacher performance in the future. There are three types of monev implementation techniques for teacher performance at Madrasah Aliyah Salafiyah Darul Muta'allimin Tanah Merah; (1) Observation, that is, the head of the madrasa makes direct visits so that the activities is in progress or the object you want to observe can be seen, (2) Interview, namely the principal conducts monitoring aimed at one person by conducting direct question and answer, (3) FGD, namely monitoring through the process of solving a problem by unifying equations thoughts and addressing the matters referred to that have been mutually agreed upon through group discussions. obstacles in the process of implementing monev at Madrasah Aliyah Salafiyah Darul Muta'allimin Tanah Merah, namely first, there is no schedule allocated for the implementation of monev so that the initiatives taken to cover these obstacles are deliberations and meetings held during breaks, as well as at the end of class.
Optimasi XGBoost Dengan SHAP Untuk Sistem Skrining Penyakit Jantung Clara Zuliani Syahputri; Jasmir Jasmir; Fachruddin Fachruddin
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.147

Abstract

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.
Evolusi Performa Arsitektur Deep Learning melalui Optimasi Bertahap dan Interpretabilitas Grad-CAM untuk Klasifikasi Penyakit Ikan Air Tawar Sasa Kirana Wulandari; Fachruddin Fachruddin; Jasmir Jasmir
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.179

Abstract

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.
Co-Authors Agik Suprayogi Bambang Ismayana Clara Zuliani Syahputri Defrian, Angga Hendri Hendri Intan Permata Sari Jasmir, Jasmir LINDA APRILIA Makmur Syukri Miranti Adelia Afda Novriyandi Hanif Nur Helmi Pareza Alam Jusia Prima Eka Syam putri Rolin Fadilah Hasibuan Sasa Kirana Wulandari SATRIYAS ILYAS Sopan Sofian Sudirman Sirait Zulfakri Zulfakri