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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research 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
Co-Authors Abdullah Muhazir Arief Wahyu Dwi Ramadhanu Zamili Christyn Parsaulyan P. Maibang Daniel Franci Sihombing Delima Sitanggang, Delima 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 Selwa Rajen Siregar, Rosyidah Siti Aisyah Sridewi, Nurmala Sriyuni Sinaga Tommy Tommy Tonazisokhi Giawa Wahyudi Valentino Simamora Yennimar Yonata Laia