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All Journal Jurnal Informasi dan Teknologi Jurnal Sistem Informasi Triguna Dharma (JURSI TGD)
Hafizah
STMIK Triguna Dharma
Computer Science & IT

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Stunting Sistem Cerdas Mendiagnosa Stunting pada Anak Menggunakan Mesin Inferensi Tugiono; Afdal Alhafiz; Hafizah
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 4
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i4.237

Abstract

Stunting is an unresolved nutritional problem in Indonesia. Stunting is a physical growth disorder characterized by a decrease in growth speed and is the impact of nutritional imbalances. Stunting will cause long-term impacts, namely disruption of physical, mental, intellectual, and cognitive development. Children who are stunted until the age of 5 years will be difficult to repair so that it will continue into adulthood and can increase the risk of offspring with low birth weight. Low economic factors cause people to think twice about consulting on stunting with doctors or nutritionists. In addition, the reluctance to come to the Puskesmas is an indicator of the low level of public awareness of children's health. Whereas the Puskesmas is a place that provides information about the problem of stunting. This is one of the causes of the delay in reducing the prevalence of stunting. An expert system is a system that seeks to adopt human abilities or knowledge into computers, so that computers can work in solving a problem like an expert or someone who has knowledge in a particular field. Utilization of expert systems with certainty factor methods and forward chaining inference engines will greatly help facilitate the community in making an early diagnosis of stunting in children, so that people can take initial treatment if their child is diagnosed with stunting. With this facility, of course, the risk of increasing stunting in children can be reduced and even prevention is carried out.
Jaringan Syaraf Tiruan Dalam Pengenalan Pola Aksara Batak Simalungun Menggunakan Convolutional Neural Network Muhammad Fadil; Hafizah; Azlan
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 2 (2026): EDISI MARET 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i2.11644

Abstract

Aksara Batak Simalungun merupakan warisan budaya tak benda yang mulai terpinggirkan di era digital. Penelitian ini bertujuan mengembangkan sistem pengenalan pola aksara Batak Simalungun secara otomatis menggunakan algoritma Convolutional Neural Network (CNN). Dataset terdiri dari 570 citra tulisan tangan dari sepuluh responden, masing-masing menulis 19 huruf sebanyak tiga kali. Gambar diambil menggunakan kamera ponsel dan melalui tahapan praproses seperti resizing, konversi ke RGB dan normalisasi. Arsitektur CNN dirancang khusus dengan lapisan konvolusi, RelU, pooling, Flatten dan fully connected layer. Hasil pengujian menunjukkan model mampu mengenali aksara dengan akurasi tinggi, yaitu sebesar 91,98%, serta nilai presisi, recall, dan f1-score yang baik. Penelitian ini menunjukkan bahwa CNN efektif dalam mengenali pola visual kompleks dan berpotensi digunakan untuk pelestarian budaya melalui aplikasi edukatif.
Co-Authors Afdal Alhafiz Azlan Muhammad Fadil Tugiono