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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Majalah Ilmiah METHODA
Andy Andy
STMIK TIME
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health

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A comparative MRI-based study of ResNet-152 and novel deep learning approaches for early Alzheimer’s disease classification Kelvin Leonardi Kohsasih; Octara Pribadi; Andy Andy; Daniel Smith Sunario
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 2: April 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i2.27576

Abstract

Alzheimer’s disease (AD) is the leading cause of dementia, making early-stage detection essential for timely intervention. Most prior studies have focused on binary AD classification, which limits sensitivity to disease progression. This study addressed this gap by evaluating whether tailored convolutional neural network (CNN) architectures could improve stage-aware classification using a publicly available magnetic resonance imaging (MRI) dataset containing 35,984 images across four diagnostic categories. The dataset underwent grayscale conversion, resizing, contrast enhancement, normalization, and class balancing prior to model development. Four models were trained and compared: ResNet-152, a custom multiclass CNN, a one-vs-one (OvO) model, and a one-vs-rest (OvR) model. Performance was measured using accuracy, precision, recall, F1 score, and confusion-matrix–based metrics. The custom multiclass CNN achieved the strongest performance, yielding the highest accuracy and balanced results across all evaluation metrics. These findings demonstrate the value of systematically comparing decomposition strategies for multi-stage Alzheimer’s detection and highlight the potential of the proposed approach to enhance early diagnostic support. Future work may incorporate multimodal inputs or hybrid architectures to improve sensitivity to subtle structural changes and further strengthen clinical applicability.
PERANCANGAN WEBSITE E-COMMERCE MULTI CABANG PADA PT. PASAR SWALAYAN MAJU BERSAMA MENGGUNAKAN ALGORITMA JACCARD COEFFICIENT Andy Andy; Agus Maringan Siahaan; Satriya Miharja; Robet Robet; Didik Aryanto
Majalah Ilmiah METHODA Vol. 14 No. 1 (2024): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol14No1.pp25-32

Abstract

PT. Pasar Swalayan Maju Bersama is a company engaged in the supermarket sector and has 3 branches, namely Maju Bersama Glugur, Maju Bersama Merak Jingga, and Maju Bersama Marendal. But in practice, PT. Maju Bersama Supermarkets still have not utilized good marketing media, both internally and externally. On the internal side, the company has not been able to properly integrate the sales processes of its three branches. In addition, the main problem is related to the amount of transaction data stored in the company's storage. Transaction data recorded every day will certainly burden storage if it is not used properly to become useful knowledge for the company. From the description of the problem, it is necessary to develop a multi-branch based system that is implemented on an E-Commerce website. This research also implements the Jaccard Coefficient algorithm so that it can process company data which turns a lot of knowledge into product recommendations for customers. The results of the study show that the Jaccard Coefficient algorithm is proven capable of processing company data into knowledge in the form of product recommendations that are relevant to customers.
Co-Authors Agus Maringan Siahaan Daniel Smith Sunario Didik Aryanto Kelvin Leonardi Kohsasih Octara Pribadi Robet Robet Satriya Miharja