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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Query : Jurnal Sistem Informasi JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Journal of Computer Networks, Architecture and High Performance Computing JURNAL MITRA PRIMA Data Sciences Indonesia (DSI) Jurnal INFOTEL
Amir Mahmud Husein, Mawaddah Harahap, Amir
Program Studi Teknik Informatika, Fakultas Teknologi Dan Ilmu Komputer Universitas Prima Indonesia
Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Veterinary

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K-Means Clustering Algorithm Approach in Clustering Data on Cocoa Production Results in the Sumatra Region Mawaddah Harahap; Arief Wahyu Dwi Ramadhanu Zamili; Muhammad Arie Arvansyah; Erwin Fransiscus Saragih; Selwa Rajen; Amir Mahmud Husein
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4199

Abstract

Cocoa agricultural production in Indonesia is currently very low while demand continues to increase every year, so it is very important to build a model that can categorize cocoa farming data. The main objective of this research is to analyze agricultural data using data mining techniques that specifically use the K-Means Clustering algorithm, and Gaussian Mixture Models. In this research, we used quantitative research because it measure number-based data. The results of cocoa production so far still depend on land area, then the number of cocoa trees has a significant effect on the amount of production so it is very important for the government and researchers to develop technologies that can increase cocoa production yields where the demand for cocoa is currently very high in demand worldwide because it can classify the cocoa quality from good quality to poor quality. Based on testing the K-Means Clustering and Gaussian Mixture Model algorithms on data on cocoa production in four provinces, namely North Sumatra, West Sumatra, Lampung and Aceh which were optimized by the Silhouette method, it produced cluster values ​​of 2, 3 and 4. second with a value of 59.8%.
Automatic detection of covid-19 based on CT Scan images using the convolution neural network Mawaddah Harahap; Masdiana Damanik; Linda Wati; Wahyudi Valentino Simamora; Isnaeni Khairani Sipahutar; Amir Mahmud Husein
JURNAL INFOTEL Vol 13 No 4 (2021): November 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i4.689

Abstract

The 2019 coronavirus pandemic (Covid-19) has been declared a health emergency by WHO with the death rate steadily increasing worldwide, various efforts have been made to deal with this pandemic, from prediction to receiving medical imaging. CT Scan and chest X-Ray images have been proven to be accurate to help medical personnel diagnose COVID, in this paper, we propose a convolutional neural network (CNN) approach and the DenseNet transfer learning model series which aims to understand and find the best classification for COVID or Non-COVID detection. On CT scan chest images, we made two special models in the Descent series, then compared the CNNs in both models by calculating the Accuracy, Precision, Recall, and F1-Score values and presented the results in the confusion matrix. The testing framework is carried out on CNN and the first model of the DenseNet series uses adam optimization, the input function is 244x244x3, the soft-max function is applied as an activity with losses across entropy categories, epoch 50, and batch size for training and testing 16 while validation uses batch size 8, the EarlyStopping function also determined, From the test results, the CNN model is superior to the Densenet series of the first model with an accuracy of about 0.76 (76%), when testing the second model, we carried out the shifting, zooming process and changed the input function to 64x64x3, epoch 30 by adding 4 layers. The second model approach produces better accuracy than CNN and the first DenseNet series, but not as good as expected, based on the test results on the second model produces an accuracy of 0.90 (90%) on Densenet169, Densenet121 around 0.88 (88%) and last Densenet201 is about 0.83 83%), so it is superior to simple CNN models
Pendekatan Data-Centric untuk Mengurangi Shortcut-Learning pada Klasifikasi Rontgen Dada: Segmentasi Paru sebagai Panduan Pembelajaran Amir Mahmud Husein; Fachrul Lubis; Abdullah Muhazir
Data Sciences Indonesia (DSI) Vol. 6 No. 1 (2026): Article Research Volume 6 Issue 1, Juni 2026
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v6i1.8075

Abstract

Klasifikasi rontgen dada dengan pembelajaran mendalam menunjukkan kinerja tinggi pada dataset, tetapi model dapat memanfaatkan petunjuk di luar paru dan rapuh saat format citra, penanda, atau sumber data berubah. Pendekatan data-centric membatasi pembelajaran pada wilayah paru. Eksperimen pada COVID-19 Radiography Database (citra dan mask paru). Dibandingkan skema: baseline citra penuh (M0), klasifikasi berbasis mask paru (M1), serta multi-tugas segmentasi paru dan klasifikasi (M2). Evaluasi memakai macro-F1, balanced accuracy, dan metrik per kelas. Perilaku model diaudit melalui saliency-in-lung ratio Grad-CAM, serta diuji dengan gangguan area non-paru: watermark, border atau noise, dan pergeseran kontras. Reliabilitas probabilitas diuji dengan temperature scaling dan selective prediction. Mask paru memberi kinerja lebih merata lintas kelas, fokus paru lebih konsisten, dan degradasi lebih kecil saat gangguan non-paru. Kalibrasi menyelaraskan confidence dengan akurasi empiris; selective prediction menunjukkan trade-off menahan kasus berketidakpastian tinggi. Pendekatan ini relevan sebagai dasar metodologis sistem pendukung keputusan yang dapat diaudit dan prasyarat validasi sebelum penerapan operasional
PENDEKATAN DEEP LEARNING UNTUK PEMANTAUAN AKTIVITAS BELAJAR MENGAJAR SISWA PADA LINGKUNGAN PEMBELAJARAN KELAS Ripka Nduru; Rina Andayani Rotua; Aldio Simamora; Edwin Todo Pardamean Sinaga; Amir Mahmud Husein
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7982

Abstract

This study aims to develop a student learning activity monitoring system based on deep learning and computer vision using YOLOv12n, DeepSORT, MediaPipe FaceMesh, and the Fuzzy Mamdani method. The study was conducted on eighth-grade students of SMP Swasta Deli Murni Suka Maju using classroom learning activity videos that had undergone data selection and preprocessing. The proposed system was designed to detect students, track their identities, and analyze their attention levels automatically and in real-time based on eye conditions and head position. YOLOv12n was employed for student detection, DeepSORT for identity tracking, MediaPipe FaceMesh for extracting facial features, including the Eye Aspect Ratio (EAR) and head position, while the Fuzzy Mamdani method was utilized to classify students' attention levels. System evaluation was performed by comparing the prediction results with manually annotated ground truth and was assessed using the Confusion Matrix, accuracy, precision, recall, and F1-score. The experimental results demonstrate that the proposed system is capable of performing multi-student detection, identity tracking, and automatic attention level analysis. The evaluation achieved an accuracy of 90.20%, indicating that the integration of YOLOv12n, DeepSORT, MediaPipe FaceMesh, and the Fuzzy Mamdani method provides reliable performance for classifying students' attention levels in a smart classroom environment.
Klasifikasi Buah Guava Menggunakan Computer Vision Adya Zizwan Putra; Mawaddah Harahap; Amir Mahmud Husein; Allwin Simarmata
Data Sciences Indonesia (DSI) Vol. 3 No. 2 (2023): Article Research Volume 3 Issue 2, December 2023
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v3i2.4006

Abstract

Penelitian ini bertujuan untuk mengembangkan sebuah sistem klasifikasi buah guava menggunakan teknologi computer vision. Klasifikasi buah guava yang akurat dan otomatis dapat membantu dalam proses identifikasi buah guava yang baik kualitasnya dan dapat digunakan dalam industri pertanian, perdagangan buah-buahan, serta penelitian lanjutan. Metode yang digunakan dalam penelitian ini melibatkan beberapa langkah. Pertama, dilakukan pengumpulan data citra buah guava yang meliputi variasi jenis guava yang berbeda serta berbagai kondisi pencahayaan dan latar belakang. Data citra tersebut kemudian diolah dan dipreproses untuk mengurangi derau dan meningkatkan kualitas citra. Setelah proses ekstraksi fitur selesai, dilakukan pelatihan model klasifikasi menggunakan data citra buah guava yang telah diklasifikasikan secara manual oleh ahli. Model klasifikasi yang terlatih kemudian diuji menggunakan data citra buah guava yang belum pernah dilihat sebelumnya untuk mengukur tingkat akurasi dan performa sistem. Hasil penelitian ini diharapkan dapat menghasilkan sistem klasifikasi buah guava yang akurat dan dapat diandalkan. Dengan menggunakan teknologi computer vision, proses identifikasi buah guava dapat dilakukan secara cepat dan otomatis. Keberhasilan penelitian ini dapat memberikan kontribusi dalam meningkatkan efisiensi industri pertanian dan perdagangan buah-buahan, serta memberikan landasan bagi penelitian lebih lanjut dalam bidang pengolahan citra dan pengenalan pola.
Klasifikasi Tumor Otak pada gambar Magnetic Resonance Images (MRI) dengan Pendekatan Pembelajaran Mendalam Rahmad Rudiansyah; Amir Mahmud Husein
Data Sciences Indonesia (DSI) Vol. 4 No. 1 (2024): Article Research Volume 4 Issue 1, June 2024
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v4i1.4265

Abstract

Tumor otak adalah salah satu penyakit paling mematikan dan kompleks yang mempengaruhi jutaan orang di seluruh dunia, sehingga klasifikasi tumor otak yang akurat sangat penting untuk pengobatan yang efektif. Diagnosis dan pengobatan tumor otak sangat menantang, dan kurangnya diagnosis yang akurat dan tepat waktu sering kali menyebabkan hasil akhir yang buruk bagi pasien. Metode diagnostik saat ini, seperti MRI dan CT scan, seringkali tidak memadai untuk klasifikasi tumor otak secara akurat. Keputusan diagnostik sangat bergantung pada interpretasi pemindaian magnetic resonance imaging (MRI). Dalam penelitian, penerapan berbagai model CNN VGG16, Xception, MobileNet dan ResNet50 digunakan untuk klasifikasi tumor otak pada kumpulan dataset 4 kelas yaitu glioma, meningioma, notumor dan pituitary. Semua model di uji dengan berbagai percobaan eksperimental dan hasil pengujian menunjukkan bawah model Xception menghasilkan akurasi terbaik dibandingkan model lainnya.
Co-Authors Abdullah Muhazir Aldio Simamora Allwin Simarmata Arief Wahyu Dwi Ramadhanu Zamili Christyn Parsaulyan P. Maibang Daniel Franci Sihombing Delima Sitanggang, Delima Edwin Todo Pardamean Sinaga Em Manuel Laia Erwin Fransiscus Saragih Fachrul Lubis Ferdy Riza Isnaeni Khairani Sipahutar Jeckson Edition Sitorus Jefri Poltak Hutabarat Jimmy Ciawi Lilis Suryani Sitanggang Linda Wati Masdiana Damanik Mawaddah Harahap Mawaddah Harahap Melda Sinaga Muhammad Arie Arvansyah Muhammad Khoiruddin Harahap Putra, Adya Zizwan Rahmad Rudiansyah Rina Andayani Rotua Ripka Nduru Selwa Rajen Siregar, Rosyidah Siti Aisyah Sridewi, Nurmala Sriyuni Sinaga Tommy Tommy Tonazisokhi Giawa Wahyudi Valentino Simamora Yennimar Yonata Laia