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All Journal Indonesian Journal of Data and Science Journal of System and Computer Engineering Global Abdimas: Jurnal Pengabdian Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Artificial Intelligence in Medical Issues Advances in Computer System Innovation Journal (ACSI Journal)
Syam, Rahmat Fuadi
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Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Other

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PENINGKATAN KOMPETENSI GURU MELALUI IMPLEMENTASI E-ASSESSMENT PADA DINAS PENDIDIKAN KABUPATEN BONE Jeffry, Jeffry; Usman, Syahrul; Aziz, Firman; Anirwan, Anirwan; Sumardi, Sumardi; Ismail, Ismail; Qamal, Qamal; Haris, Almuhajir; Gani, Kahar; Syam, Rahmat Fuadi
GLOBAL ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2024): Mei 2024, GLOBAL ABDIMAS
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/globalabdimas.v4i1.496

Abstract

Penilaian yang efektif merupakan elemen penting dalam meningkatkan mutu pendidikan. Dalam era digital yang terus berkembang, penggunaan teknologi informasi dalam evaluasi pembelajaran telah membawa dampak positif. Salah satu inovasi yang menjanjikan adalah penggunaan E-Assessment, yaitu evaluasi yang dilakukan secara elektronik. Dalam konteks Dinas Pendidikan Kabupaten Bone, pengabdian ini bertujuan untuk meningkatkan kompetensi guru-guru sekolah dasar melalui implementasi E-Assessment. Metode pengabdian ini melibatkan pelatihan dan pendampingan bagi guru-guru dalam penerapan E-Assessment sebagai alat evaluasi pembelajaran. Selain itu, pengabdian juga melibatkan pengembangan modul dan panduan praktis yang menggambarkan langkah-langkah implementasi E-Assessment yang efektif. Pendekatan kolaboratif dan partisipatif digunakan untuk memastikan keterlibatan guru-guru dalam pengembangan dan implementasi E-Assessment. Hasil pengabdian ini menunjukkan peningkatan pemahaman guru meningkat 28%, pengetahuan konsep e-assessment 47%, relevansi e-assessement dalam konteks pendidikan meningkat 88%, dan pengetahuan tentang dampak penggunaan e-assessment meningkat 4%.
Predictive Analysis of Online Course Completion: Key Insights and Practical Implications Riska, Riska; Syam, Rahmat Fuadi
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.168

Abstract

The rapid expansion of online education has brought significant attention to understanding factors that influence student engagement and course completion. This study aims to predict online course engagement using a dataset from Kaggle, encompassing user demographics, course-specific data, and engagement metrics. Employing a Decision Tree model with 5-fold cross-validation, the research identifies key predictors of course completion, including time spent on the course, the number of videos watched, and quiz scores. The model demonstrates robust performance with accuracy, precision, recall, and F1-scores consistently above 92%, indicating its effectiveness in predicting student outcomes. This predictive capability allows educators and online course providers to identify at-risk students early and implement timely interventions to enhance engagement and completion rates. The study's contributions lie in pinpointing critical engagement metrics and validating the use of Decision Trees in educational data mining. The findings align with existing educational theories that emphasize the importance of active engagement for academic success. Practical implications suggest that online platforms should focus on strategies to increase interaction with course content and provide timely feedback. Future research should explore additional datasets and machine learning models to further refine predictive accuracy and broaden the understanding of factors influencing online learning success. This research provides a foundation for developing more effective online education strategies, ultimately aiming to improve student retention and outcomes
Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset Azdy, Rezania Agramanisti; Syam, Rahmat Fuadi; Faizal, Edi; Sumiyatun, Sumiyatun
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.96

Abstract

In this study, titled "Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset," we explore the effectiveness of the Bagging Meta-Estimator in diagnosing lung diseases, focusing on the challenges of imbalanced datasets. Utilizing a dataset segmented and characterized by Hu moments and encompassing categories of Normal, Bacterial Pneumonia, and Tuberculosis, the algorithm's performance was assessed through a 5-fold cross-validation. Results indicated moderate effectiveness with an average accuracy of 60.574%, precision of 60.749%, recall of 59.753%, and F1-Score of 59.416%, highlighting variable performance across folds. These findings suggest that while the Bagging Meta-Estimator has potential in medical imaging, further refinement is needed for consistent and reliable lung disease detection, especially in imbalanced datasets.
Predictive Sparepart Maintenance Menggunakan Algoritma Machine Learning Extreme Gradiant Boosting Regressor Usman, Syahrul; Syam, Rahmat Fuadi
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1418

Abstract

Spare parts are components that make up a single object that has a specific function. In car vehicles, spare parts have the function of maintaining the performance and function of the vehicle. Predictive Spare Part Maintenance is an effort to improve operational efficiency, customer service, and reduce vehicle downtime through the application of analysis and machine learning algorithms to predict spare part replacement times. A machine learning approach can be used to predict maintenance times for car spare parts, where one of the algorithms that can be used is XGBoost Regressor. Through this approach, this research aims to improve service planning by predicting spare part replacement times based on certain indicators, With the implementation of this research, it is hoped that it can increase operational efficiency in automotive after-sales services, increase customer satisfaction, reduce vehicle downtime, and improve overall service planning and most importantly can provide preventive maintenance information to customers. This research provides prediction results with R2-Score values ​​as follows: train data: 93%, Valid: 90%, Test: 90%
PENGEMBANGAN KOMPETENSI SDM MITRA CV SEHAT KERUPUK MELALUI PELATIHAN MACHINE LEARNING DAN ILMU KOMUNIKASI Usman, Syahrul; Nurdyansa, Nurdyansa; Aziz, Firman; Wijaya, Neti Septi; Arafah, Muhammad Nur; Syam, Rahmat Fuadi
GLOBAL ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2024): November 2024, GLOBAL ABDIMAS
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

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

Abstract

CV Sehat Kerupuk, sebagai salah satu usaha kecil dan menengah (UKM) di sektor produksi makanan ringan, menghadapi tantangan dalam pengelolaan produksi dan pemasaran produk akibat keterbatasan teknologi dan strategi komunikasi yang efektif. Untuk mendukung peningkatan daya saing UKM, program pengabdian masyarakat ini dirancang untuk meningkatkan kompetensi Sumber Daya Manusia (SDM) mitra melalui pelatihan teknologi machine learning dan ilmu komunikasi bisnis. Pelatihan meliputi pemahaman dasar tentang machine learning untuk peramalan permintaan dan pengelolaan stok, serta strategi komunikasi bisnis untuk mengoptimalkan pemasaran digital melalui media sosial dan platform e-commerce. Evaluasi pelatihan menggunakan pre-test dan post-test menunjukkan peningkatan pemahaman peserta sebesar 61,3%. Peserta mampu mengimplementasikan teknik peramalan berbasis data penjualan serta mengembangkan strategi pemasaran digital yang lebih efektif. Hasil program ini diharapkan dapat meningkatkan efisiensi operasional CV Sehat Kerupuk, memperluas jangkauan pasar, dan memperkuat daya saing perusahaan. Program ini memberikan kontribusi nyata dalam pemberdayaan UKM melalui adopsi teknologi modern dan pengembangan komunikasi bisnis yang strategis. 
Implementation of Ensemble Deep Learning for Brain MRI Classification in Tumor Detection Syam, Rahmat Fuadi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.236

Abstract

Introduction: Brain tumor detection from MRI images is critical for early diagnosis and treatment planning. While individual deep learning models have shown high accuracy in medical image classification, combining multiple models can potentially enhance performance. This study aims to develop an ensemble deep learning framework using ResNet18 and DenseNet121 to improve the accuracy of brain tumor classification. Methods: A dataset of 7,023 brain MRI images categorized into four classes—glioma, meningioma, no tumor, and pituitary tumor—was used. Pre-processing included resizing to 224×224 pixels, normalization, and augmentation (random flipping and rotation). ResNet18 and DenseNet121 models were fine-tuned separately using the Adam optimizer with a learning rate of 0.001. The ensemble method was implemented by averaging the softmax outputs of both models to generate final predictions. Results: When evaluated individually, ResNet18 and DenseNet121 achieved validation accuracies of 97.72% and 97.79%, respectively. The ensemble model significantly outperformed both, reaching a validation accuracy of 99.36%. This result demonstrates that integrating both architectures effectively reduces misclassification and enhances overall robustness. Confusion matrix analysis confirmed high classification accuracy across all four tumor categories. Conclusions: The proposed ensemble deep learning approach successfully leverages the strengths of ResNet18 and DenseNet121, achieving superior classification accuracy for brain tumor detection in MRI images. This method holds promise as a reliable tool in clinical diagnostic workflows. Future research should focus on integrating additional architectures, advanced augmentation strategies, and hyperparameter optimization to further improve performance
Analysis of Artificial Intelligence-Based Photogrammetry for Calculating the Volume of Bulk Material Stockpiles Syam, Rahmat Fuadi; Usman, Syahrul
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2323

Abstract

This paper presents an automated UAV-based photogrammetric workflow for efficiently and accurately estimating bulk material stockpile volumes, addressing the limitations of conventional manual and LiDAR-based methods. The proposed approach converts UAV video data captured with a 40 MP RGB drone into georeferenced still frames, followed by SIFT and ORB feature extraction and exhaustive matching within COLMAP database. Incremental Structure-from-Motion with bundle adjustment reconstructs a sparse point cloud of 119,424 points and optimized camera parameters, while PatchMatch-based Multi-View Stereo generates a dense cloud of 2.3 million points at a ground sampling distance (GSD) of 0.1 cm. Ground Control Points obtained with RTK-GNSS ensure sub-2 cm georeferencing accuracy. Stockpile volumes are estimated using angle-of-repose height calculations, truncated-pyramid contour integration, and voxel occupancy methods, achieving volume errors of less than 3%. Validation against GPS and terrestrial laser scanning (TLS) references indicates horizontal accuracy of CE90 = 0.208 m, vertical accuracy of LE90 = 0.056 m, and mean reprojection error of 0.19 pixels. The entire process requires only 24 minutes for 199 images, confirming its applicability for industrial monitoring. Overall, the proposed AI-assisted photogrammetric pipeline provides a robust, reproducible, and cost-effective solution for automated stockpile volume measurement, enhancing safety, accuracy, and material management efficiency.
APLIKASI HOUSEKEEPING HOTEL BERBASIS WEB PADA NOVOTEL MAKASSAR GRAND SHAYLA MENGGUNAKAN METODE WATERFALL Putra Yuzi Bachmid, Fadel Muhammad; Usman, Syahrul; Syam, Rahmat Fuadi; Jeffry, Jeffry
Advances in Computer System Innovation Journal Vol. 2 No. 1: April 2024, ACSI Journal
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/acsijournal.v2i1.516

Abstract

Industri perhotelan merupakan salah satu industri yang berkembang pesat dalam dunia bisnis saat ini, maka oleh karena itu suatu hotel yang ingin bersaing ketat dengan hotel lainnya harus memiliki fasilitas, kemudahan, dan struktur manajemen yang lengkap. Namun menurut hasil wawancara dan observasi di Hotel Novotel Makassar Grand Shayla, mereka masih melakukan pengoperasian status kamar secara manual terutama operasional pembersihan kamar dengan informasi yang terdapat celah, sehingga dalam berkomunikasi pertukaran informasi selalu menggunakan WhatsApp. Penulis berinisiatif melakukan penelitian serta penerapan uji coba dengan membuat aplikasi berbasis web yang diharapkan dapat memberikan pengalaman kepada bagian housekeeping, serta supervisor housekeeping dalam melakukan pemantauan room status untuk setiap kamar.Aplikasi ini berbasis web application juga untuk memantau keadaan room status. Aplikasi web ini juga memiliki beberapa fitur diantaranya adalah dapat mengubah status kamar ke dalam beberapa status sesuai keadaan status kamar secara aktual, diharapkan dapat mengubah status pada pukul 2 pagi dengan metode sesuai dengan operasional yang telah berjalan metode penelitian dengan metode waterfall, karena metode penelitian paling tepat digunakan dengan memanfaatkan observasi dan wawancara.
PERAMALAN TRAFIK UNTUK ALOKASI BANDWIDTH JARINGAN SELULER 4G MENGGUNAKAN MODEL HYBRID ARIMA-LSTM Syam, Rahmat Fuadi; Aziz, Firman
Advances in Computer System Innovation Journal Vol. 2 No. 2: Agustus 2024, ACSI Journal
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/acsijournal.v2i2.585

Abstract

Saat ini, ada beberapa teknik prediksi yang sangat membantu bisnis dalam meningkatkan efisiensi. Salah satunya adalah prediksi alokasi bandwidth. Diharapkan metode ini dapat membantu perusahaan telekomunikasi mengurangi biaya, terutama biaya transfer data dari setiap lokasi. Ketidakmampuan untuk mengelola bandwidth yang diperlukan saat ini adalah masalah umum bagi perusahaan telekomunikasi. Kadang-kadang, ada kekurangan bandwidth atau kelebihan bandwidth pada setiap BTS, yang dapat mengurangi keuntungan yang diperoleh perusahaan. Suatu sistem yang dapat mengatur dan memprediksi kebutuhan bandwidth masa depan diperlukan untuk menyelesaikan masalah tersebut. Dengan menggunakan data dari hasil monitoring bandwidth setiap cell, kami mengeksplorasi prediksi kebutuhan bandwidth pada penelitian ini. Data ini berupa baris waktu. Peneliti mengumpulkan data dari November 2019 hingga Januari 2020. Langkah pertama adalah melakukan simulasi prediksi dengan menggunakan metode LSTM. Setelah mencoba beberapa model LSTM model terbaik adalah LSTM (windows=100, 2 lapisan, 100 neuron), dengan hasil RMSE 387.693019. Peneliti menggunakan hasil model untuk melakukan eksperimen dengan model LTSM. Studi ini menemukan bahwa prediksi dalam waktu lima puluh jam menunjukkan tingkat akurasi yang tinggi.
KLASIFIKASI PERSEDIAAN BARANG MENGGUNAKAN SUPPORT VECTOR MACHINE PADA SISTEM PERIODIC INVENTORY Syam, Rahmat Fuadi; Aziz, Firman
Advances in Computer System Innovation Journal Vol. 2 No. 3: Desember 2024, ACSI Journal
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/acsijournal.v2i3.633

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem periodic inventory menggunakan algoritma Support Vector Machine (SVM) untuk mengklasifikasikan barang berdasarkan pola permintaan, guna mengatasi keterbatasan metode manual dan terkomputerisasi tradisional. Metodologi mencakup pengumpulan data inventori, pra-pemrosesan, dan penerapan SVM dengan berbagai kernel (Linear, Polynomial, RBF, dan Sigmoid) untuk klasifikasi. Hasil penelitian menunjukkan bahwa kernel RBF memiliki kinerja terbaik dengan akurasi 92%, diikuti oleh kernel Polynomial dengan akurasi 90%. Temuan ini menekankan efektivitas kernel RBF dalam menangani data non-linear dan potensinya dalam meningkatkan sistem pengelolaan inventori. Implikasi praktisnya adalah klasifikasi inventori yang lebih efisien dan akurat, mendukung pengambilan keputusan dan optimalisasi operasional.
Co-Authors Almuhajir Haris, Almuhajir Anirwan Anirwan Arafah, Muhammad Nur Edi Faizal Firman Aziz Ismail Ismail Jeffry Jeffry Kahar Gani Nurdyansa Putra Yuzi Bachmid, Fadel Muhammad Qamal Qamal Rezania Agramanisti Azdy, Rezania Agramanisti Riska Riska Sumardi Sumardi Sumiyatun Syahrul Usman Wijaya, Neti Septi