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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Majalah Ilmiah UPI YPTK CSRID
Muhammad Reza Putra
Universitas Putra Indonesia YPTK
Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Social Sciences

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Sistem Pendukung Keputusan Pemilihan Pabrik Obat Menggunakan Metode Electre (Metode Elimination Et Choix Traduisant La Realite) pada Rumah Sakit Permata Hati Berbasis Web Muhammad Reza Putra; Faradila Mustika; Eka Praja Wiyata Mandala
Majalah Ilmiah UPI YPTK Vol. 27 (2020) No. 1
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jmi.v27i1.28

Abstract

Demikian pesatnya permintaan akan obat-obatan membuat Rumah Sakit Permata Hati memiliki kecenderungan untuk memilih lebih dari satu pabrik obat ,Hal ini disebabkan pemenuhan aspek antisipasi pada kebutuhan obat yang sering berubah dengan kondisi yang kurang menentu. Keadaan ini membawa Rumah Sakit Permata Hati kebingungan dalam memilih pabrik obat yang akan dijadikan sebagai pemasok pada rumah sakit khususnya kepada dokter yang memberikan keputusan pada permasalahan pemilihan pabrik obat, karena proses pemilihan pabrik merupakan bagian yang penting di dalam aktivitas pembelian karena berdampak pada kualitas dan ketersediaan bahan baku, efisiensi biaya pengeluaran, dan kelancaran sirkulasi keuangan rumah sakit. Untuk mengatasi hal tersebut diperlukan suatu sistem pendukung keputusan (SPK) yang dapat mengakomodasi kriteriakriteria. Proses pemilihan pabrik dilakukan dengan metode Electre (elimination et choix traduisant la realite), yaitu dengan melakukan outranking menggunakan indifference, defference dan threshold. Adapun keluaran dari sistem pendukung keputusan (SPK) ini berupa perankingan dalam pemilihan pemasok pabrik obat terbaik dan informasi yang dipilih secara objektif bagi pengambil keputusan yang dapat dijadikan acuan untuk mengambil keputusan dalam proses pemilihan pabrik obat
Perancangan dan Pembuatan Teknologi Augmented Reality sebagai Media Pembelajaran Aksara Minang di SDN 01 Patamuan Berbasis Android Andre Irawan; Randy Permana; Muhammad Reza Putra
Majalah Ilmiah UPI YPTK Vol. 26 (2019) No. 2
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jmi.v26i2.51

Abstract

Augmented reality merupakan sebuah teknologi yang dapat menggabungkan antara dunia nyata dengan dunia virtual. Seiiring berjalannya waktu augmented reality dikembangkan pada berbagai bidang contoh nya yaitu bidang pendidikan. Aksara minang merupakan sebuah kebudayaan tulisan yang berasal dari alam minangkabau yang ditemukan pada kitab tambo alam. Aksara minang sendiri terdapat 15 buah huruf aksara dan lima tanda untuk tanda baca. Dengan memanfaatkan teknologi augmented reality ini dapat menjadikan sebuah pembelajaran mengenai aksara minang
Machine learning classification analysis model community satisfaction with traditional market facilities as public service Hadi Syahputra; Musli Yanto; Muhammad Reza Putra; Aulia Fitrul Hadi; Selvi Zola Fenia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1744-1754

Abstract

Traditional markets are public service facilities that can be utilized by thecommunity. The market function is used place where sellers and buyers meetin conducting transactions. This study aims to build a machine learningclassification analysis model in measuring community satisfaction withtraditional market facilities. The analytical methods used include Fuzzy.multiple linear regression (MRL), artificial neural network (ANN), anddecision tree (DT). Fuzzy is used to generate a pattern of rules in determiningthe level of satisfaction. MRL serves to measure and test the correlation ofrules that have been formed. The ANN method is used to carry out theclassification analysis process based on learning. In the final stage. DT is usedto describe the decision tree of the analysis process. This study presents theresults of machine learning analysis which is very good in determiningsatisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzylogic which can develop a classification rule pattern of 32 patterns. MRL alsoshows a significant correlation level of 81.1% based on the indicator variables.Overall, the machine learning classification analysis model can provideknowledge to be considered in the management of traditional markets aspublic service facilities.
Automated Fruit Image Classification Based on HSV Features, Morphological Segmentation, and Extreme Learning Machine Agung Ramadhanu; Halifia Hendri; Wahyu Saptha Negoro; Mardison Mardison; Larissa Navia Rani; Sofika Enggari; Muhammad Reza Putra
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 1 (2026): Februari 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.1.2026.135-147

Abstract

Fruit image classification plays a crucial role in smart agriculture, particularly in automating sorting and quality control processes. This study proposes a fruit classification system by integrating HSV color space conversion, adaptive thresholding, morphological segmentation, and the Extreme Learning Machine (ELM) algorithm. The dataset consists of three fruit classes—apple, pineapple, and watermelon—with a total of 480 images, divided into 360 training samples and 120 testing samples. Image preprocessing involves resizing, HSV conversion, noise reduction through morphological operations, and feature extraction based on color and shape characteristics. The extracted features are used to train and test an ELM model. To improve classification performance and address potential overfitting in traditional ELM, this study introduces a new development called the Extended Extreme Learning Machine (EELM). The key innovation lies in modifying the calculation of the output weights βj, where a regularization term is introduced using ridge regression to stabilize learning and improve generalization. Experimental results show that the proposed system achieves 100% accuracy on the training data and an average accuracy of 83.3% on the testing data. The system also demonstrates robustness in handling varying lighting conditions and fruit shapes. These improvements enable EELM to better handle noisy or complex data by preventing over-reliance on randomly initialized hidden layer parameters. Consequently, EELM demonstrates improved reliability, making it more suitable for deployment in resourceconstrained real-world environments such as mobile or embedded systems.
Co-Authors Andre Irawan Aulia Fitrul Hadi Eka Praja Wiyata Mandala Faradila Mustika Hadi Syahputra Halifia Hendri Larissa Navia Rani, Larissa Mardison Mardison Ramadhanu, Agung - Selvi Zola Fenia Sofika Enggari Wahyu Saptha Negoro Yanto, Musli