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Journal : Bulletin of Computer Science Research

Deteksi Pelanggaran Tata Tertib Siswa Sistem Cerdas Menggunakan Face Recognition dengan Metode Convolutional Neural Network Syafril, Syafril; Yuhandri, Yuhandri; Sovia, Rini
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.753

Abstract

Student disciplinary violations are a social problem increasingly common in schools and can negatively impact students' academic and moral development. This phenomenon requires an effective identification system so that prevention and mitigation efforts can be carried out quickly and accurately. This research aims to develop a student face detection system based on Digital Image Processing (DIP) technology that functions to identify and classify adolescent disciplinary violations. The designed system utilizes a camera as an image acquisition device, then processes it to detect the presence of student faces in real-time. The face detection process is carried out using the Haar Cascade Viola-Jones method, which is known to be able to recognize faces with high speed and accuracy. Once a face is detected, the system continues the analysis process using the Convolutional Neural Network (CNN) method to classify facial expressions and behavioral patterns that could potentially indicate violations. The integration between Haar Cascade and CNN allows the system to work efficiently in identifying signs of negative behavior based on visual data. System testing shows satisfactory results, with a high level of facial detection accuracy and fairly reliable behavior classification capabilities. This technology has the potential to be used as a monitoring tool in the school environment, allowing teachers and school management to quickly identify students who need special attention. With the implementation of this system, it is hoped that schools will be able to provide timely guidance, prevent the escalation of deviant behavior, and create a more conducive learning environment. The use of digital image processing-based technology for detecting and classifying student behavior is a relevant innovation in the modern education era, while also supporting efforts to prevent juvenile disciplinary violations through a systematic and measurable approach.
Analisis Metode Forward Chaining dan Certainty Factor untuk Diagnosa Penyakit pada Ibu Hamil Yasmin, Nabilla; Yuhandri, Yuhandri; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.756

Abstract

The high number of complications that occur during pregnancy and childbirth has the potential to significantly increase the risk of morbidity and mortality in pregnant women. The Maternal Mortality Rate (MMR) reflects the condition of pregnant, delivering, and postpartum mothers, which remains relatively high and is a major concern in the health sector. Based on this, this study aims to develop and evaluate an Expert System based on the Forward Chaining and Certainty Factor methods to diagnose diseases in pregnant women at an early stage, thereby providing fast and accurate medical decision support and minimizing the risk of complications during pregnancy. The Forward Chaining and Certainty Factor methods were chosen for their ability to handle rule-based inference processes and provide certainty level calculations in the diagnosis results. Forward Chaining is used to find solutions based on the symptoms entered by users, while the Certainty Factor helps assign confidence weights to the generated diagnosis. The dataset in this study consists of 30 data samples with 30 types of symptoms experienced by patients as variables. The results show that the Forward Chaining and Certainty Factor methods are capable of producing disease diagnoses in pregnant women with an accuracy rate of 95%. The contribution of this research is to improve the quality of maternal health services through fast and accurate diagnoses by medical personnel and to assist pregnant women in obtaining an initial diagnosis of common diseases during pregnancy.
Co-Authors Afifah Cahayani Adha Agus Perdana Windarto Akbar Iskandar Aldi Muharsyah Aldi, Febri Andrean, Fajri Ilhami Anita Sindar Ardiyan, Destio Arif Budiman Aulia, Allans Prima Aziz, Majid Rahman Budayawan, Khairi Chandra, Mrs Montesna Dahria, Muhammad Devita, Retno Dewi Eka Putri Dikki Handoko Dolly Indra Dwi Narulita Dwika Assrani Effendy, Geraldo Revanska Efori Buulolo Eka Praja Wiyata Mandala Esa Kurniawan Fauzan, Yuniko Febri Hadi Feri Irawan Finny Fitry Yani Firzada, Fahmi Fuad El Khair Gayatri, Satya Gemilang, Fhajri Arye Gunadi Widi Nurcahyo Hartomi, Zupri Henra Hendrick, H Idun Ariastuti Iftitah, Hasanatul Iskandar Fitri, Iskandar Jaya, Budi Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Julius Santony Julius Santony Kadrahman, Kadrahman Kurniawan, Jefdy Lidia K Simanjuntak M Ikhsan Setiawan M, Mutia Maharani Maharani, Maharani Malik, Rio Andika Mayola , Liga Mesran, Mesran Musli Yanto Na'am, Jufriadif Natalia Silalahi, Natalia Nelly Astuti Hasibuan Nuning Kurniasih Nurdiyanto, Heri Permana, Randy Petti Indrayati Sijabat Pohan, Yosua Ade Purnomo, Nopi Putra, Heru Rahmat Wibawa Putra, Rafi Septiawan Putri, Stefani Rahayu, Rita Rahmad Dian Rakhmad Kuswandhie Ronda Deli Sianturi S Sumijan Sagala, Gamrina Salmiati, S Sarjon Defit Sarjon Defit Septiana, Vina Tri Setiawan, Adil Sisi Hendriani Siska, Ayu Prima Soraya Rahma Hayati Sovia, Rini Sri Dewi Stephano, Rivo Sugiarti, Sugiarti Suginam Suhaidir, Lc Granadi Sumijan Sumijan Sumijan Sumijan Sumijan, S Surya Darma Nasution Sutiksno, Dian Utami Syafrika Deni Rizki, Syafrika Deni Syafril Syafril Syaiffullah, Afif Tajuddin, Muhammad Takyudin, Takyudin Tessa Y M Sihite Tukino, Tukino Veri, Jhon Virgo, Ismail Vratiwi, Septiana Wanto, Anjar Wendi Boy Winanda, Teddy Yanto, Musli Yasmin, Nabilla Yendi Putra Yeni, Nasma Yenila, Firna Yolla Rahmadi Helmi Yudha Aditya Fiandra Zikir Risky, Muhammad Arif