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All Journal Civil Engineering Forum Teknik Sipil Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan YUME : Journal of Management Jurnal Litbang Sukowati : Media Penelitian dan Pengembangan SULUH: Jurnal Abdimas Manajemen Agribisnis: Jurnal Agribisnis Dinasti International Journal of Digital Business Management Jurnal Pengabdian Masyarakat Teknik Maharaat: Jurnal Pendidikan Bahasa Arab Jurnal Pengabdian kepada Masyarakat Nusantara Rabbani: Jurnal Pendidikan Agama Islam Devotion: Journal of Research and Community Service Berajah Journal Journal of Economics and Business Letters International Journal of Social Science, Education, Communication and Economics International Journal Of Science Education and Technology Management (IJSETM) Jurnal Talenta Sipil Mafaza : Jurnal Pengabdian Masyarakat Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri Prosiding Seminar Nasional Teknik Sipil UMS Community Services and Social Work Bulletin JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah APPA : Jurnal Pengabdian Kepada Masyarakat Intelmatics INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER) Permata : Jurnal Pendidikan Agama Islam Jurnal Studi Islam dan Sosial Jurnal Pengabdian Masyarakat Widya Wiwaha EL-HAYAH ILLAT-J
Abdul Rochman
Universitas Muhammadiyah Surakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Intelmatics

 1 
Implementation of Trend Analysis to Fulfill Needs of Consumer Housing with Least Square Method Anggara, Denny; Pratiwi, Dian; Rochman, Abdul
Intelmatics Vol. 4 No. 1 (2024): Januari-Juni
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v4i1.17495

Abstract

In this era there are a lot of real estate that stands with attractive prices, facilities and home designs. Real Estate is a land and buildings on it and arranged in an orderly manner. With the rapid growth of society, it is very influential in buying ready-to-live houses that match the criteria and prices they want. However, many people have difficulty in finding a house ready to live in that fits the criteria and price range they want. Therefore, by making the Trend Analysis Application for Ready-to-occupy Houses, it can provide alternative choices to users according to the criteria that buyers want, such as having a garage, garden, building area, having how many floors and so on as well as the price they want. This application was developed using the Least Square Method Trend Analysis Method for datasets of ready-to-occupy houses collected from one housing estate agency. The steps carried out by the researchers started from collecting housing data, then the data was processed using the least square method with R- studio tools and then formulated into the PHP programming language to become a web program. After the web program was successfully built, researchers conducted a survey to determine the level of user satisfaction with the application, which was 77.8% of the 18 survey participants. Based on the presentation, it can be concluded that this research is feasible in providing home recommendations for customers.
Brain Tumor Detection System Based on Convolutional Neural Network Febrianto, Nanang Dwi; Mardianto, Is; Rochman, Abdul; Najih, Muhammad
Intelmatics Vol. 5 No. 1 (2025): January-June
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i1.22135

Abstract

Early detection of brain tumours is essential to improve the effectiveness of treatment. This study develops a Convolutional Neural Network (CNN) model to detect brain tumours from MRI images. Using a dataset of 4410 images, the model was trained and tested with several CNN architectures, such as EfficientNetB0, InceptionNetV3, ResNet, MobileNet, VGG16, Model 1. Results showed that the best model achieved 97.8% accuracy, thus being able to predict brain tumours with a high degree of reliability. These findings support the application of CNNs in medical detection systems to assist doctors in faster and more accurate diagnosis.
Development of Osteoporosis Prediction System on Femur and Tibia Bones with Convolutional Neural Network Akhdan, Muhammad; Pratiwi, Dian; Rochman, Abdul
Intelmatics Vol. 5 No. 2 (2025): July-December
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i2.23237

Abstract

Osteoporosis and osteopenia are conditions that commonly affect bone health significantly, this is characterized by decreased bone density causing the risk of fractures especially in the femur and tibia. The prevalence rate of these diseases is calculated from 103,334,579 people between the ages of 15 and 105 years, with an overall prevalence of 18.3%. Fast and accurate detection is needed for the first line of defense for osteoporosis patients and potential patients. This study provides the development of a Convolutional Neural network (CNN) model trained to predict osteoporosis and osteopenia from x-ray radiographs of femur and tibia bones. The proposed model has satisfactory performance on all metrics namely average accuracy 90%, average recall 90%, average F1 score 90%. From these performance results, alternative detection methods using CNN can be considered by medical parties or parties who can utilize the first diagnosis of osteopenia to osteoporosis bone disease handling compared to conventional methods.
Co-Authors Adie Dwiyanto Nurlukman Adrian, Firman Ardi Agus Salim Alfarisi, Ibnu Kamal Ali Asroni Alifah, Farah Dina Anggara, Denny Anggi Nurcahyo Dwi Saputro Anto Budi L Astri Kustina Dewi Budi Prasetyo Budi Setyawan Devi Cahyaningtyas Dian Pratiwi Dion Octovianus, Steven Eko Santoso Ernawati, Santi Fadhilah, Rif'atul Fajarrini, Eka Nur Faqih, Achmad Farros Kangsa Deva, Muhammad Fatimah Saktiana, Eka Fauzi Mubarak Febrianto, Nanang Dwi Ilham, M Hijri Indra Lesmana Irianto, Rafif Aryo Is Mardianto, Is Jubaidah, Ida Judijanto, Loso Julieta, Divani Juraifa, Syalwa Anindya Khotimah, Nur Kurniawati, Aprilia Wahyu Mafthuchach, Viniyati Maftuchach, Viniyati Maharani*, Alfina Maharani, Cherinda Mahdalena Mahdalena Maresti, Putri Marfu’ah, Uliyatul Mathori, Muhammad Maulana Ramadhan, Hikmal Moch. Solikin, Moch. Muhammad Akhdan Muhammad Nur Sahid Muslihudin Muslihudin, Muslihudin Nabilah Putri, Fitria Najih, Muhammad Nasikhah, Atiun Nur Faradilla, Aryana Nur Khotimah Handayani Nurchasanah, Yenny Pebrianto, Viky Prakusya Subhan, Bintang Prastiyo, Rudi Pudail, M. Purwanto, Purwanto Putri, Fitria Nabilah ratih ratih Ridoan, Ahmad Rifai, Rahmat Rohman, Muhammad Kholilur Salim Salim Santosa, Rakka Pradyatama Santoso, Gatot Budi Sarfi Hamidi, A. Setiadi, Virginia Suryani Setyawan, Alif Sholeha Juliyani, Annis Sijabat, Panderaja Siti Aisyah Siti Fatimah Styyogo, Lilo Sudjatmiko, Aliem Suharni Suharni Sulaiman, Hafizh Maulana Sulistiyono, Yoga Sulistyo, Achmad Taufiq Suliwati Sumarji Sunani, Sunani Sutryani, Heni Syaifudin Syaifudin Syaifudin, Syaifudin Taslimanurrohim, Andika Ujianto, Muhammad Usman Fauzi, Muhamad Uswatun Chasanah Utami, Puput Retno Wahyu Surya Daru Saputra Yusuf Fadli, Yusuf Zephyr Rafif, Navaly