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All Journal Klasikal: Journal of Education, Language Teaching and Science Jurnal Dedikasi Jurnal Pendidikan Terapan Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Security, Computer, Information, Embedded, Network and Intelligence System Micronic: Journal of Multidisciplinary Electrical and Electronics Engineering Prosiding Seminar Nasional Dies Natalis Universitas Negeri Makassar Media Elektrik V-Machine: Vocational and Mechanical Community Service Journal (Jurnal Pengabdian Masyarakat) Journal of Vocational, Informatics and Computer Education
Akmal Hidayat
Universitas Negeri Makassar
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

 1 
Penerapan Machine Learning Pada Mikrokontroler Arduino Mega PRO MINI ATmega2560-16AU Wahyudi; Wulan Purnamasari; Akmal Hidayat; M. Miftach Fakhri
Journal of Embedded Systems, Security and Intelligent Systems Vol 3, No 1 (2022): May 2022
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan Teknologi Informasi dan Komunikasi (TIK) telah mempengaruhi semua aspek yang ada, seperti aspek ekonomi, budaya, politik, sosial, pertahanan keamanan, pekerjaan rumah tangga bahkan dunia pendidikan sekalipun. Perkembangan tersebut banyak berkembang di era industri 4.0 saat ini mulai dari Internet of Things (IoT), Big Data, Argumented Reality, Cyber Security, Artifical Intelegence, Addictive Manufacturing, Simulation, System Integeration dan Cloud Computing. Salah satu perkembangan teknologi yang sangat berkembang saat ini yaitu machine learning atau pembelajaran mesin. Pada penelitian ini berfokus pada metode algoritma K-NN dan sensor warna TCS3200. Pada penelitian yang dilakukan oleh penulis ini menggunaka sensor warna TCS3200 dan Arduino mega 2560 pro mini sebagai perangkat keras yang digunakan. Penelitian ini bertujuan mendeskripsikan penerapan machine learning pada mikrokontroler arduino mega pro mini ATmega2560. Penelitian ini menggunakan metode studi pustaka atau library research dengan berbagai teknik pengumpulan data, selanjutnya melakukan pengujian untuk mengetahui kinerja sistem. Setelah dilakukan pengujian dilakukan analisa untuk mendapatkan kesimpulan akhir dari proses penelitian. setelah dilakukan pengujian sebanyak 20 kali dengan menempelkan pada objek, sistem ini bisa menginisialisasi warna dengan tepat. Dari hasil pengujian algoritma KNN dihasilkan akurasi tertinggi terdapat pada K=5, dimana nilai akurasi yang didapatkan adalah 80%. Sedangkan akurasi terendah terdapat pada k=9, dimana nilai akurasi yang didapatkan hanya 10%.
Optimisasi Sumber Energi Listrik Dari Mesin Pengering Rak Telur Menggunakan Modul Termoelektrik Generator: Indonesia Sudarmanto Jayanegara; Dary Mochamad Rifqie; Samnur; Akmal Hidayat; Muhammad Hasim S
Journal of Embedded Systems, Security and Intelligent Systems Vol 4, No 2 (2023): November 2023
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v4i2.978

Abstract

Energi listrik ialah energi yang kompleks diubah bentuk ke energi yang lain. Energi listrik sangat dibutuhkan oleh manusia untuk membantu pekerjaannya. Energi listrik ini dapat dihasilkan dari konversi energi panas. Energi Panas ini dihasilkan dari plat permukaan cerobong mesin pengering rak telur. Energi panas tersebut diubah menjadi energi listrik dengan menggunakan konsep efek seebeck. Penelitian ini dilakukan untuk mengetahui karakter dari modul TEG sebagai sumber energi listrik dalam memanfaatkan panas pada dinding cerobong suatu mesin pengering rak telur yang menggunakan sekam padi sebagai bahan bakar tungku mesin. Pengujian dilakukan dengan cara memanfaatkan panas cerobong bawah yang terpisah oleh sebuah Heat Excanger (HE) dengan kecepatan blower tungku pembakaran 2600 rpm dan kecepatan blower lingkungan 2800 rpm dengan jumlh termoelektrik yang digunakan sebanyak 36 buah. Hasilnya menunjukkan bahwa modul TEG pada cerobong bawah diperoleh perbedaan temperatur (∆T), perbedaan tegangan (∆V) dan daya (P) masing-masing ∆T 73.25 °C ; ∆V 12.26 Volt ; P 1.312 Watt.
Effectiveness of Digital Intervention on Improving Mental Health Literacy and Health Service Utilization Akmal Hidayat; Nur Aeni Rahman; Zahrotul Ainil Mahfudhah Umar; Nurfaisa Riono; Abd Majid
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7700

Abstract

The purpose of this study is to assess how well the "MindWell" information system facilitates the assessment and tracking of users' mental health. "MindWell" is an online resource created to give consumers quick and simple access to mental health assessments, educational materials, and expert advice. Using cutting-edge web technology, this system offers real-time screening services, ensures user data confidentiality and privacy, and includes features like mental health education, support service referrals, and monitoring. The system's capacity to offer pertinent information and assist users in understanding their mental health disorders was examined, along with input from beta users and a series of trial runs. The findings demonstrated that "MindWell" was successful in raising users' awareness of mental health issues, assisting in the early detection of mental health issues, and giving them access to helpful resources. It is envisaged that the deployment of this system will aid in the pursuit of more widespread and long-lasting improvements in mental health.
Classification of Students' Emotions from Facial Expressions Using CNN to Support Adaptive Learning Akmal Hidayat; Hera Ariska; Iren Kirana; Asmiyah Auliatna; Dian Sri Yuninda; Elvira Nur
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/b1rcm003

Abstract

The integration of affective aspects into adaptive learning systems remains limited, as most educational technologies primarily rely on cognitive performance indicators. However, students’ emotional conditions significantly influence engagement, motivation, and learning outcomes. This study aims to develop and evaluate a Convolutional Neural Network (CNN) model for classifying students’ emotions based on facial expressions to support adaptive learning environments. A quantitative experimental approach was employed. Facial expression image data were preprocessed through face detection, resizing, normalization, and data augmentation before being trained using a CNN architecture with the Adam optimizer and categorical cross-entropy loss function. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that the proposed CNN model achieved an overall accuracy of 90% with an average F1-score of 0.88 across four emotion categories (Happy, Sad, Neutral, and Angry). The confusion matrix indicates that most predictions fall within the correct classification range, although minor misclassifications occurred between low-intensity Sad and Neutral expressions. The stability of training and validation loss curves demonstrates good generalization ability without significant overfitting. These findings indicate that CNN-based facial emotion classification can serve as a reliable component in adaptive learning systems by providing real-time affective feedback. The study contributes to the development of artificial intelligence applications in education by integrating emotional recognition into adaptive instructional design
Co-Authors Abd Majid Adhe Adrean Nur Alif Ahmad Abdillah Fathur Rachman Aminuddin, Amin Amiruddin Amiruddin Andi Muh Achyar AM Andi Shelma Putri Azzahra Anggie Angriani Nanik Anwar Wahid Asmiyah Auliatna Ayu Saputri Bahar Azzahra Eka Azzahra Kusumanigtyas Dary Mochamad Rifqie Della Fadhilatunisa Dewi Fatmarani Surianto Dian Sri Yuninda Dwi Utami Puji Astuti Elvira Nur Fadhilarahman Baso Faisal Faisal Fitriyanty Dwi Lestary Hera Ariska Hisdal Sikma Putra Iren Kirana Iriandhy Iriandhy Irmayana Irmayana Kelvianto Pratama Harum Khaidir Rahman Kurnia Prima Putra Lisah Febrianti M. Miftach Fakhri Marwan Ramdhany Edy, S.Pd.,M.Kom Miftahul Jannah Awaliyah Mudarris Mufti Fathur Rahman Muh Alif Dzulfaqaar M Muh Bhilal Halim Muh. Bhilal Halim Muh. Sunan Jaya Irmawan Muhammad Alwi Muhammad Fajar B Muhammad Fatwa Ma’arij Muhammad Haristo Rahman Muhammad Hasim Muhammad Sadidan Muhammad Ubaidillah Muhammad Yahya Nur Aeni Rahman Nurfadilah Istiqamah Nurfaisa Riono Nurlaela Nurmila Nurmila Puang Intan Maylani Putri Mayang Sari Samnur . Setialaksana, Wirawan - Sudarmanto Jayanegara Syafiqah Al Faiqah Wahyu Hidayat M Wahyudi Wahyudi Wahyudi Wulan Purnamasari Wulan Purnamasari S Zahrotul Ainil Mahfudhah Umar