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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; Rini Sovia
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 Nabilla Yasmin; Yuhandri Yuhandri; Gunadi Widi Nurcahyo
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.
Analisis Komparasi Convolutional Neural Network dan Learning Vector Quantization dalam Klasifikasi Khat Arab Digital Sabri T Rahman; Yuhandri Yuhandri; Sumijan Sumijan
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.976

Abstract

Arabic khat is a form of writing that possesses complex visual characteristics, such as variations in letter shapes, stroke thickness, texture, and stylistic differences. This complexity creates challenges in manually recognizing different types of khat. This study aims to analyze and compare the performance of Convolutional Neural Network (CNN) and Learning Vector Quantization (LVQ) methods in classifying five types of Arabic khat digital images, namely Diwani, Farsi, Naskh, Ruqaa, and Tuluth. The dataset was obtained from the Kaggle.com platform. CNN architecture consists of an input layer of 100×100×1, followed by two convolutional layers with 32 and 64 filters of size 3×3, each followed by ReLU activation and max pooling with stride 2. The network then includes a fully connected layer with 64 neurons, a final fully connected layer corresponding to the number of classes, a softmax layer, and a classification layer. CNN training was conducted using 5-fold cross-validation, applying data augmentation in each fold. For the LVQ method, Local Binary Pattern (LBP) was used for feature extraction from 100×100 images with parameters: radius 1, 8 neighbors, cell size [48 48], and L2 normalization. The extracted features were used for training with an initialization of 25 prototypes from 5 classes. The process also employed 5-fold cross-validation. From 40 testing samples, the CNN model achieved an accuracy of 87.5%, while the LVQ model achieved an accuracy of 85%. The CNN algorithm demonstrated better performance in handling the complex visual patterns of Arabic khat. Meanwhile, LVQ showed advantages in architectural simplicity and computational efficiency. This research is expected to contribute to the development of Arabic khat image classification systems and serve as a reference in selecting optimal methods for Arabic khat recognition.
Co-Authors - Hendrick - Khairiazaz AA Sudharmawan, AA Aal, Defrizal Abda Abda Abdul Azis Said Achmad Fauzan Syaputra Ade Dwi Dayani Afifah Cahayani Adha Aggy Pramana Gusman Agung Ramadhanu Agus Perdana Windarto Akbar Iskandar Akbari Wafridh Aldi Muharsyah Alfallah, Fadhly Alifcha Ghazian Alifia Restu Selvanda Allans Prima Aulia Andema, Henky Andre Rahmat Kurniawan Andrean, Fajri Ilhami Angga Putra Juledi Anita Sindar Anjun Dermawan Antoni Antoni Aprilian Gevindo Ardiyan, Destio Arif Budiman Arika Juwita Z Ariza Ikhlas Asyhari, Ahmad Aulia, Allans Prima Auriga, Wira Ayu Prima Siska Bambang Supperianto Billy Hendrik Borianto, B Budayawan, Khairi Budi Jaya Budi Permana Putra Chairul Imam Chairul Imam, Chairul Chandra, Mrs Montesna Dahria, Muhammad Dari, Rahmatia Wulan Darnis, Rahmi Delmayanti, Vera Dendi Ferdinal Deno Yulfa Ardian Desi Laidawati Devi Maryuni Dewi Eka Putri Dian Maharani, Dian Dikki Handoko Djasmayena, Selvia Djesmedi, Dinda Dodi Andre Putra Dolly Indra DWI JULISA UTARI Dwi Narulita Dwika Assrani Dzaki Al Fikri Effendy, Geraldo Revanska Efori Buulolo Eggy Febyanti Edwar Eka Naufaldi Novri Eka Praja Wiyata Mandala Eka Ramadhani Putra Eka Sofianti Elpina, Elpina Sari Dewi Hasibuan Eriyanto, Joko Erizke Aulya Pasel Esa Kurniawan Esa Kurniawan Eska, Juna Eva Rianti Fachrul Ilmawan Fadil Idensia Fahmi Firzada Fajri Ilhami Andrean Fauzan, Yuniko Febri Aldi Febri Hadi Feri Irawan Fernando Ramadhan Fhajri Arye Gemilang Finny Fitry Yani Firna Yenila Firzada, Fahmi Fitra, Ilham Fuad El Khair Gayatri, Satya Gemilang, Fhajri Arye Gunadi Dwi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Hadi Syahputra Hadrila P A Halifia Hendri Harkamsyah Andrianof Hartika Zain, Ruri Hartika Hartomi, Zupri Henra Hasanatul Iftitah Hasni, Salmi Hasri Awal Hendrick, H Hendro Zalmadani Henky Andema Hermanto Heru Rahmat Wibawa Putra Ibnu Luthfi Idir Fitriyanto Idir Idun Ariastuti Ikhlas, Muhammad Ilham Asy'ari Ilham Fitra Indah Dwi Putri Indah Permata Sari Indra Riyana Rahadjeng Irvan Okta Mazhona Iskandar Fitri, Iskandar Ismail Virgo Jaya, Budi Jefdy Kurniawan Jhon Veri Johan Danu Wijaya Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Julius Santony Julius Santony Julius Santony Julius Santony Julius Santony K Kadrahman Kadrahman, Kadrahman Karseno, Doni Khairani, Maisan Dewi Puspa Khairiazaz Kurniawan, Jefdy Laidawati, Desi Larissa Navia Rani, Larissa Lc Granadi Suhaidir Lidia K Simanjuntak Liga Mayola Lova Endriani Zen Lusi Kestina M Ikhsan Setiawan M Ilham Aldyno M Mutia M, Mutia M.Iqbal, M.Iqbal Maharani Maharani, Maharani Majid Rahman Aziz Mardayulis, Mardayulis Mardison Mardison Mardison Meiditra, Irzon Mesran, Mesran Mey Yuki Lestari Mifthahul Rahmi Mohammad Guntur Montesna Muhammad Abrar Masril Muhammad Amin Muhammad Amin Muhammad Arif Zikir Risky Muhammad Ihksan Muhammad Noor Hasan Siregar Mukhlis Santoso Na'am, Jufriadif Nabilla Yasmin Nandra Sunaryo Nasma Yeni Nasution, Annio Indah Lestari Natalia Silalahi, Natalia Negoro, Wahyu Saptha Nelly Astuti Hasibuan Nissa, Ika Ima Nuning Kurniasih Nurdiyanto, Heri Olivia, Ladyka Febby Ondra Eka Putra P, Prihandoko Permana, Randy Petti Indrayati Sijabat Pohan, Yosua Ade Pratama , Abdul Hanif Pratama, Muhammad Harits Pratiwi, Fitri Prestian Ramadhan Prihandoko Prihandoko Prihandoko Prihandoko, P Pulungan, Akhiruddin Purnomo, Nopi Putra, Heru Rahmat Wibawa Putra, Rafi Septiawan Putra, Rezi Elsya Putri, Stefani R Rahmiyanti Rafi Septiawan Putra Ragil Ardiansyah Rahayu, Rita Rahmad Dian Rahmad Dian Rahmansyah, Rizky Rakhmad Kuswandhie Resnawita Retno Devita Riadi, Rahadatul ‘Aisy Riati, Itin Ridho, Ridho Afwan Rifky, Muhammad Rio Andika Malik Ririn Violina Riski Randa Hidayatullah Rita Sari Rita Sari Rivo Stephano Roby Nurbahri Romi Hardianto Romzi Rahman Ronda Deli Sianturi Rovidatul Rubiati, Nur Rusydi, Rezki S Salmiati Sabri T Rahman Sagala, Gamrina Sahat Sonang Sitanggang Sahri, Alfi Said, Abdul Azis Sajida, Mayang Salman Alfarisi Salimu Salmiati, S Samosir, Khairunnisa Saputra, Randy Sari, Fitri P. Sarjon Defit Seni Oknora Firza Septiana Vratiwi Septiana, Vina Tri Setiawan, Adil Setiawan, Adil Silfia Andini Siregar, Diffri Sisi Hendriani Siska, Ayu Prima Soeheri Soeheri Sonang, Sahat Sonia Indhira Sopi Sapriadi Soraya Rahma Hayati Sovia, Rini Sri Amalia Harahap Sri Dewi Sri Dewi Sri Rahmawati Stefani Hardiyanti Putri Stephano, Rivo Subrianto Chandra Sugiarti, Sugiarti Suginam Suhaidir, Lc Granadi Sukardi Sulastri Sulastri Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Sunaryo, Nandra Supriyanto, Boby Surya Darma Nasution Suryani, Vivi Sutiksno, Dian Utami Syafri Arlis Syafrika Deni Rizki Syafril Syafril Syahid Hakam Abdul Halim Syahputra, Afriadi Syaiffullah, Afif Syaljumairi, Raemon Syaputra, Eka B. Tajuddin, Muhammad Takyudin, Takyudin Tamin, Zulfiqar Taufik Nur Zam Zam Teddy Winanda Teguh Junaidi Teri Ade Putra Tessa Y M Sihite Toti Sri Mulyati Tri Agusti Farma Triyolla Ivandina Tukino, Tukino Uthama, Rayhan Veri, Jhon Very, Jhon Virgo, Ismail Vratiwi, Septiana Wanto, Anjar Wendi Boy Wenni Afrodita Willy Eka Septian Winanda, Teddy Winarto Winarto Wira Apriani Wira, M Wira Sanjaya Wirahmadayanti, Isna Yanti, Salma Nofri yanto, heri Yanto, Musli Yanto, Musli Yendi Putra Yendi Putra Yeni, Nasma Yolla Rahmadi Helmi Yosua Ade Pohan Yuda Irawan Yuda, Fitra Yuda Yudha Aditya Fiandra Yudha Aditya Fiandra Yundari, Yundari Yuniko Fauzan Yusma Elda Yusmaity Zalmadani, Hendro ZH, Lina Alfaridah. Zufari, Faisal Zupri Henra Hartomi