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All Journal Publikasi Pendidikan Jurnal Teknologi dan Manajemen Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) ILKOM Jurnal Ilmiah Jurnal Literasiologi Jurnal Mantik Journal Dynamic SainT Teknika Journal of System and Computer Engineering INFINITY : UKI TORAJA JOURNAL OF INFORMATION TECHNOLOGY J-Icon : Jurnal Komputer dan Informatika TONGKONAN: Jurnal Pengabdian Masyarakat
Michael, Aryo
Universitas Kristen Indonesia Toraja
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Multimedia Psikoedukatif untuk Penguatan Resiliensi Anak Pasca Bencana: Analisis Kebutuhan Program Pengabdian kepada Masyarakat Harmelia Tulak; Aryo Michael; Jemi Pabisangan Tahirs
Publikasi Pendidikan Vol 16, No 1 (2026)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70713/publikan.v16i1.81542

Abstract

Bencana longsor di Toraja Utara berdampak pada kondisi psikososial anak sekolah dasar, yang mengalami kesedihan, ketakutan, dan penurunan semangat belajar, sehingga diperlukan intervensi psikoedukatif yang kontekstual dan berbasis budaya lokal. Kegiatan ini difokuskan pada tahap analisis kebutuhan sebagai langkah awal pelaksanaan program Pengabdian kepada Masyarakat (PkM). Pengumpulan data dilakukan di SDN Buntao dengan melibatkan 14 guru, 1 kepala sekolah, 29 siswa, 1 tokoh adat, dan 1 lurah. Metode yang digunakan adalah deskriptif dengan pendekatan kualitatif. Data diperoleh melalui FGD, wawancara, observasi, dan kuesioner. Hasil analisis menunjukkan bahwa anak-anak membutuhkan dukungan psikososial berbasis media yang menarik dan mudah dipahami. Guru dan masyarakat menilai pentingnya penguatan nilai-nilai budaya lokal seperti gotong royong, musik Toraja, dan simbol rumah adat Tongkonan untuk membangun semangat kebersamaan dan harapan. Oleh karena itu, pengembangan video animasi psikoedukatif berbasis budaya Toraja direkomendasikan sebagai media yang potensial untuk membantu anak-anak pulih secara emosional, sosial, dan spiritual pasca bencana. 
Pengenalan Plat Kendaraan Berbasis Android menggunakan Viola Jones dan Kohonen Neural Network Michael, Aryo
ILKOM Jurnal Ilmiah Vol 8, No 2 (2016)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v8i2.52.95-102

Abstract

Penelitian ini bertujuan untuk membangun sistem pengenalan plat kendaraan roda dua berbasis android.  Penelitian ini dilaksanakan di Kota Makassar Indonesia.  Metode yang digunakan pada penelitian ini adalah pengenalan pola plat kendaraan dengan metode viola jones, kemudian segmentasi karakter plat menggunakan metode morfologi, dan pengenalan karakter plat dengan kohonen neural network.  Hasil penelitian yang diperoleh menunjukkan bahwa pengenalan plat kendaraan menggunakan kohonen neural network berdasarkan pengujian yang dilakukan menunjukkan persentase keberhasilan pengenalan karakter pada plat kendaraan bermotor pada kondisi yang baik sebesar 78,57% edangkan pada plat yang kurang baik sebesar 57,14%.
Classification model of Toraja arabica coffee fruit ripeness levels using convolution neural network approach Michael, Aryo; Garonga, Melki
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.861.226-234

Abstract

The purpose of this study is to design a CNN deep learning algorithm model that can classify the maturity level of Arabica coffee fruit based on image, the resulting model can be applied to a coffee bean sorting device based on artificial intelligence so that problems that exist in the process of sorting arabica coffee fruit that meets the standards can be avoided, to improve the quality of arabica Toraja coffee products. The research began from the collection of data in the form of raw Arabica coffee image Toraja as many as 4000 images of arabica coffee fruit with 4 categories, half-cooked, perfectly ripe, and mature old. CNN basic architecture is created using images with a size of 128x128 pixels, 4 convolution layers using 3x3 filters opening 32, 64, 128, and 256 with ReLU activation, followed by a poll layer with a 2x2 filter. The full connected layer uses 2 hidden layers with dropout layers. The training model was conducted with a 5-fold cross-validation method using epoch 100, 'adam' optimization algorithm with a learning rate of 0.0001, and batch size 10. The success of a model is seen based on the calculation of the confusion matrix. The test results showed that the accuracy rate of the third model using a combination of max polling and average polling performed best with an introduction accuracy of 98.75%, the first model used max polling with an accuracy of 98.25% while the lowest accuracy on the second model used average polling with an accuracy of 97.75%.
PERFORMANCE EVALUATION OF LIGHTWEIGHT DEEP LEARNING MODELS FOR BORAX-CONTAMINATED MEATBALL IMAGE CLASSIFICATION Aryo Michael; Ireve Devi Damayanti
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7462

Abstract

Food safety, particularly concerning the use of illegal additives such as borax in processed meat products like meatballs, remains a critical issue in Indonesia. This study analyzes the performance of several lightweight deep learning models based on Convolutional Neural Networks (CNN) and Transformers to classify images of meatballs containing borax, enabling their deployment on resource-constrained devices such as smartphones. Data collection involved capturing 1,429 images of meatballs with and without borax using a smartphone camera under varying lighting conditions and shooting angles. The five main architectures evaluated were ConvNeXt-Nano, Swin-Tiny, ViT-Tiny, MobileViT-XS, and EfficientNet-B0. Hyperparameter optimization was conducted using Optuna, followed by training with a 5-fold cross-validation scheme. Model evaluation metrics included accuracy, precision, recall, F1 score, and inference speed. The results show that MobileViT-XS was the best-performing architecture, achieving 65.93% accuracy, 0.703 precision, 0.706 recall, 0.659 F1 score, and efficient memory consumption (45.94 MB). These findings indicate that a hybrid approach combining the strengths of CNNs and Transformers can achieve an optimal balance between detection accuracy and computational efficiency. Therefore, such models have the potential to be applied as food safety detection systems on devices with limited resources
Co-Authors Adda, Muhammad Sofwan Amarya Sula Damayanti, Irene Devi Dudung, Marni Fridolin, Fridolin Garoga, Melki Garonga, Melki Gidion Aryo Nugraha Pongdatu Haryati, Berlian Zetikarya Irene Devi Damayanti Ireve Devi Damayanti Jemi Pabisangan Tahirs Juprianus Rusman K, Gabriel Maharani, Fanny Marchelin Nofrianto Pasae P, William P., Setriyanti Padang, Samuel Yakobus Padang, Semuel Yakobus Patalo, Enohk Eightry Piopadang, Helce K. Y. Saladan, Fidryani Salu, Nerwan Srivan Palelleng Tulak, Harmelia Tumanan, Reynaldi Ary