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Indra Kelana Jaya
Universitas Methodist Indonesia
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Physics Public Health Social Sciences

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Penerapan Metode Support Vector Machine untuk Pengenalan Pola Aksara Batak Toba Efdi Sarjono Panjaitan; Humuntal Rumapea; Indra Kelana Jaya
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2 (SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akun
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp49-55

Abstract

The usage of the Batak Toba script has declined, and its complex forms pose challenges in pattern recognition. This study employs the Support Vector Machine (SVM) method to classify Batak Toba script patterns, utilizing a Histogram of Oriented Gradients (HOG) as a feature extraction technique. The data used comes from various sources, totaling 285 script images. After preprocessing, SVM was applied to separate characters into two main classes, which were further subdivided into subclasses until final classification was achieved. The results show that the combination of HOG and SVM can classify Batak Toba script characters with an accuracy of 89,47%. This research makes a significant contribution to the preservation of the Batak Toba script and has broader potential applications in pattern recognition and image classification.
Analisis Performa Jaringan Saraf Tiruan Backpropagation Menggunakan Optimizer SGD, RMSProp dan Adam untuk Klasifikasi Stunting pada Balita Adi Putra Sinaga; Naikson Fandier Saragih; Indra Kelana Jaya
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp393-401

Abstract

Stunting is a linear growth disorder in toddlers caused by chronic malnutrition during critical developmental periods and remains a significant national health problem in Indonesia. According to the 2022 Indonesian Nutritional Status Survey (SSGI), national stunting prevalence reached 21.6%, surpassing the World Health Organization (WHO) recommended ceiling of 20%. This study evaluates three optimizers—Stochastic Gradient Descent (SGD), RMSProp, and Adam—within a Backpropagation Artificial Neural Network (ANN) for classifying toddler stunting status. The dataset comprises 1,454 anthropometric records (sex, age, and height) collected from UPT Puskesmas Kampung Baru, Medan City, covering 2020–2023. Preprocessing included MinMax scaling (fitted exclusively on training data to prevent leakage) and SMOTETomek resampling to address severe class imbalance. Forty-eight model configurations were evaluated via grid search across a 3-32-1 architecture (ReLU hidden layer, sigmoid output). Evaluation metrics comprised accuracy, precision, recall, F1-score, specificity, AUC, and Matthews correlation coefficient (MCC). RMSProp achieved perfect scores on all metrics (1.000) in the shortest execution time (133.42 s). Adam achieved equivalent classification performance with similarly rapid convergence (135.28 s). SGD attained perfect recall (1.000) only when training data were simultaneously balanced and normalized, empirically demonstrating that non-adaptive optimizers require both interventions to compete with adaptive counterparts.
Optimasi Algoritma Genetika pada Perbandingan ANN dan KNN untuk Klasifikasi Penyakit Jantung Andreas Zai; Lima Hartima Rambe; Reza Ananda Putra; Rika Rosnelly; Tamado Simon Sagala; Indra Kelana Jaya
Majalah Ilmiah METHODA Vol. 15 No. 1 (2025): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol15No1.pp10-23

Abstract

A comparative analysis of genetic algorithm optimization methods on the performance of Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) in heart disease classification shows significant results. The research used a heart disease dataset consisting of 303 samples with 14 attributes. Genetic algorithm optimization produced substantial performance improvements in both models. The optimized ANN model achieved 94.85% accuracy, 93.00% precision, 97.00% recall, and 97.00% ROC AUC, demonstrating excellence in positive case identification. Meanwhile, the optimized KNN model achieved 93.30% accuracy, 92.00% precision, 95.00% recall, and 96.77% ROC AUC, yielding more balanced performance. The genetic algorithm optimization method proves its effectiveness in improving heart disease classification accuracy, where ANN is optimal for applications requiring high sensitivity and KNN is more stable for small datasets.
Co-Authors Adi Putra Sinaga Albert Harianja Ambarita, Indah Andreas Zai Azli, Putri Berlian Juni R Marpaung Dandi Daniel Siregar Darwis Robinson Manalu DEWI SARTIKA Dian Agaventa, Chrissandro Dumayanti, Imelda Sri Efdi Sarjono Panjaitan Ericho Elovando Surbakti Eva Julia Gunawati Harianja Fajar Fati Gratianus Nafiri Larosa Feruche, Melani Gea, Asaziduhu Girsang, Jhon Vebrianto Helen Fransisca Simanungkalit Humuntal Rumapea Imelda Sri Dumayanti Jeudi A. T. P. Sianturi Jimmy F. Naibaho Larosa, Fati G. N. Larosa, FGN Larosa Lima Hartima Rambe Lyna M. N. Hutapea Mariana Tri Susanti Marpaung, Berlian Juni R Marzuki Sinambela Marzuki, Sinambela Melani Feruche Mendarissan Aritonang Mufria J. Purba Naibaho, Jimmy F. Naikson Fandier Saragih Nine Situmeang Nova Soraya Simanjuntak Panjaitan, Efdi Sarjono Pujiastuti, Maria Putra, Reza Ananda Rambe, Lima Hartima Resianta Perangin-Angin Reza Ananda Putra Rika Rosnelly Rika Rosnelly, Rika Rimbun Siringoringo Rumahorbo, Benget Sagala, Tamado Simon Samuel Van Basten Halomoan Manurung Saragih, Naikson F. Silalahi, Arina Prima Silalahi, Veraci Simanullang, Harlen Gilbert Simbolon, Anita Ira Agustina Sinambela Marzuki Siska Narulita Siboro Surianto Sitepu Surianto Sitepu Susanti, Mariana Tri Tamado Simon Sagala Tarigan, Kerista Widya Ompusunggu Yohanna, Margaretha Yolanda Y. P. Rumapea yolanda yulianti pratiwi rumapea Zai, Andreas