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All Journal Jurnal Keperawatan PROtek : Jurnal Ilmiah Teknik Elektro Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Promotif: Jurnal Kesehatan Masyarakat Jurnal INSYPRO (Information System and Processing) Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informasi Kesehatan Indonesia (JIKI) Journal of Health Sciences Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Ainet : Jurnal Informatika Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) Babali Nursing Research Madaniya J-PEN Borneo : Jurnal Ilmu Pertanian Tangible Journal Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Joutica : Journal of Informatic Unisla Al-Khidmah Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer PELS (Procedia of Engineering and Life Science) Jurnal Informatika Progres Media Bina Ilmiah International Journal of Health and Information System (IJHIS) Jurnal Abdimas Jatibara Pangulu Abdi: Jurnal Pengabdian Kepada Masyarakat VISA: Journal of Vision and Ideas Jurnal Intelek Dan Cendikiawan Nusantara ARUS JURNAL SAINS DAN TEKNOLOGI Jurnal Intelek Insan Cendikia International Journal of Research in Counseling Journal of Muhammadiyah’s Application Technology Kajian Ilmiah Mahasiswa Administrasi Publik (KIMAP) Jurnal Medis Umum
Titin Wahyuni
Informatika, Universitas Muhammadiyah Makassar
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Pendeteksi Objek Huruf Lontara Untuk Literasi ke Teks Latin Zainuddin, Mohammad Ramadhan; Rahman, Fahrim Irhamna; Wahyuni, Titin
Journal of Muhammadiyah’s Application Technology Vol. 5 No. 1 (2026)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/nth00674

Abstract

Kemajuan teknologi komputer telah mendorong inovasi dalam sistem pengenalan karakter otomatis, termasuk aksara Lontara’. Penelitian ini bertujuan mengevaluasi efektivitas algoritma deteksi objek YOLOv8 dalam mengenali dan mengklasifikasikan karakter aksara Lontara’ dengan akurasi tinggi. Studi dilakukan di Universitas Muhammadiyah Makassar menggunakan metode eksperimen berbasis pembelajaran mesin. Dataset yang digunakan terdiri dari gambar digital karakter Lontara’ yang telah diberi label secara manual. Data dibagi menjadi tiga bagian: 70% untuk pelatihan, 20% untuk validasi, dan 10% untuk pengujian model. Hasil evaluasi menunjukkan kinerja model sangat optimal, dengan akurasi sebesar 98,2%, presisi 98,1%, dan recall mencapai 100%. Capaian ini menandakan sistem memiliki efisiensi dan reliabilitas tinggi dalam mengenali serta mengklasifikasikan karakter aksara Lontara’ dalam berbagai kondisi visual. Temuan ini mendukung potensi implementasi model dalam dunia nyata. Sebagai pengembangan lebih lanjut, disarankan untuk memperluas variasi dataset agar model lebih mampu melakukan generalisasi. Selain itu, eksplorasi algoritma yang lebih modern atau pendekatan hibrida dengan teknik deep learning lain dapat meningkatkan kinerja dan ketahanan sistem terhadap situasi operasional yang kompleks. KATA KUNCIPengenalan Aksara Lontara’, YOLOv8, Deep Learning, Literasi. ABSTRACT: Rapid advancements in computer technology have driven innovation in automatic character recognition systems, including for the Lontara script. This study aims to evaluate the effectiveness of the YOLOv8 object detection algorithm in accurately recognizing and classifying Lontara characters. The research was conducted at Universitas Muhammadiyah Makassar using an experimental method based on machine learning. The dataset consisted of digital images of Lontara characters, which were manually labeled. The data was divided into three subsets: 70% for training, 20% for validation, and 10% for testing the model. The evaluation results showed that the model performed very well, achieving an accuracy of 98.2%, a precision of 98.1%, and a perfect recall of 100%. These results demonstrate the system’s high efficiency and reliability in recognizing and classifying Lontara characters under various visual conditions. The findings support the model's feasibility for real-world implementation. For future research, it is recommended to increase dataset diversity by involving more participants and image sources to enhance generalization capabilities. Additionally, exploring more advanced algorithms or hybrid approaches that combine multiple deep learning techniques may further improve the system’s performance and robustness in more complex operational scenarios.Keywords:Lontara Script Recognition, YOLOv8, Deep Learning, Literacy
Optimizing Early Disease Detection among Adolescents through a School-Based Health Screening Program at SMA Al-Rifa’ie Malang Masyfufah, Lilis; Setiawan, Muhammad Yusuf; Triyono, Erwin Astha; Wahyuni, Titin
Jurnal Abdimas Jatibara Vol 4, No 2 (2026): Jatibara Vol.4 No.2 Februari 2026
Publisher : STIKES Yayasan RS.Dr.Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29241/jaj.v4i2.2542

Abstract

The development of health information technology has undergone a significant transformation. One important aspect that has changed is the method of screening or early detection of diseases, which is often carried out using applications that are easily accessible to the public. Given that cases of tuberculosis, hypertension, low vision, and mental health issues are on the rise due to lifestyle changes. This activity was carried out to introduce the health screening application owned by the government for widespread use in the community. The Methods Community service activities were carried out by giving presentations to the target audience (Al-Rifa'ie High School students) and asking them to practise screening using the introduced application. The Results showed 76% of participants were unaware of the existence of screening applications for these diseases. The provision of health information related to screening through presentations and practical demonstrations was effective in ensuring that participants understood the material because they participated actively.
Co-Authors A.MUHAMMAD SYAFAR Achmad Yanu Aliffianto Adi Malik Muhammad Mutsuhito Aditya, Dwi Martha Nur Adrianingsih, Rizka Agustiawal Agustiawal Agustin Dwi Syalfina Ahmad Faisal Ahmad Risal Aiman , Ailul Alfina Aisatus Saadah Alfina Aisatus Saadah Amelia, Azarine Nahdah Amir Ali Anang Sulistyo ANDI AGUNG DWI ARYA BULU Andi Yusri andi Yusri Anita Dahliana ardi24, ardiansyah_01 Arfandi, Viki Fahril Arianti, Kencana Indah Arshy Prodyanatasari Arvianda Asep Indra Syahyadi Aswad, Muh. Akhwan Adam Baba, Haedir Bakti, Riski Yusliana Bakti, Rizki Yusliana Bambang Nudji Bisono, Eva Firdayanti Cantika Aprilia Santi Chatarina Umbul Wahyuni Cholifah . Cholifah, Cholifah Christine Christine Dewi, Syamrilla Djalil, Sony Achmad Dzakki Adam, Ahmad Wildan Erwin Astha Triyono Fachrim Irhamma Rahman Fachrim Irhamna Rachman Fachrim Irhamnah Rachman Fadhillatul Lailia, Salsabilla Fahmi Ramadhan S Fahrim Irhamna Rahman Firdaus , Abidatu Zahrotul Firman Firman Fitrianti, Dwi Framz Hardiansyah Haidul, Haidul Halisah Duli, St Nur Haruna, Hanjas Hidayanti, Sukria Hidayat, Andra Dwitama Ilmiyah Rosyiari, Ahniyatul Indriani, Lis Jaelan Usman, Jaelan Kamal, Safutri Kazman Riyadi Khafi, Moh. Zainul khairat, arikal Krisnita Dwi Jayanti Krisnita Dwi Jayanti, Krisnita Dwi La Ode Taufik Ismail Listiawan, Nadhila Lukman LUKMAN ANAS Lukman Lukman Maharani, Eva Ratih Masyfufah, Lilis Masyfufah, Lilis  Maulia, Rizky Maylina Surya Wirawati Pribadi Mone, Ansyari Muh. Akhwan Adam Aswad Muhadi, Muhadi Muhammad Faisal Muhyiddin A.M Hayat Mujadilah, Siti Muslimah, Nurul Aulia Mustakim Mustakim Nadhila Listiawan Naila, Faiqotun Nandy Rizaldy Najib Natsir, Fitra M. Nisha, Khairun Nova Mellania Novianti, Siti Nur Alam Nurfadilla, Destiani Irma Octavia, Winda Dwi Pandin, Maria Yovita R. Pribadi, Maylina Surya Wirawati Puspadewi, Intan Putra, Yunior Bimasekti Rachman, Fachrim Irhamnah Rahman , Fahrim Irhamna Rahman, Fahrim Irhamna RAHMANIA Rahmania Rahmawati, Ayu Isnaini Ramadhan S, Fahmi Reski Awalia Retnowati Prihandini Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Rinaldy, Muh Rosyiari, Ahniyatul Ilmiyah S. Kuba, Muhammad Syafa'at Salsabila, Damai Arsila Sari, Selvi Permata Sa’adah, Alfina Asiatus Setiawan, Mohammad Yusuf Setiawan, Muhammad Yusuf Setiawan, Tommy Reynaldy Shafira Trisnanda Fatimatus Zahra Siti Fatimatuz Zahroh Siti Mujanah Slamet Riyadi Sri Hastati Sukmantoro, Agung Anjar SULASTRI Suryadinata, Rivan Virlando Sutha, Diah Wijayanti Syamsuri, Andi Makbul Syarifuddin, Nur Annisa TANTRI INDRABULAN Uddin , Ardiansyah Umi Khoirun Nisak Wibawa. Ar, Arya Wilda Faida, Eka xss, aa xx Yulianita, Novi Eka Zainuddin, Mohammad Ramadhan