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All Journal International Journal of Electrical and Computer Engineering Elektron Jurnal Ilmiah TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SITEKIN: Jurnal Sains, Teknologi dan Industri Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jurnal Mantik Penusa Rang Teknik Journal Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURTEKSI Jurnal Teknologi Informasi dan Pendidikan Systematics Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika C.I.T. Medicom Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Innovative: Journal Of Social Science Research Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science) Jurnal Elektronika dan Teknik Informatika Terapan Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Gunadi Widi Nurcahyo
Universitas Putra Indonesia YPTK Padang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Other

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Journal : Bulletin of Computer Science Research

 1 
Model Deep Learning Berbasis Multilayer Perceptron untuk Identifikasi Demam Berdarah Dengue dan Tifus Nurhadi, Nurhadi; Defit, Sarjon; 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.v5i5.754

Abstract

Dengue Hemorrhagic Fever (DHF) and Typhus/Typhoid are two infectious diseases often found in tropical areas. In Indonesia, data shows that cases of DHF and typhoid are quite high, so a system is needed that can help doctors make faster and more accurate decisions based on blood test results. Based on the previous explanation, this study aims to apply the Deep Learning Multilayer Perceptron (MLP) method to be able to identify dengue fever and typhus. This study uses a Deep Learning-based Multilayer Perceptron approach for accurate classification of Dengue Fever, Typhoid Fever, and Normal cases using clinical blood parameters and selected symptoms. This methodology consists of several stages: dataset acquisition, preprocessing, model architecture design, training, and evaluation. The dataset was taken from Dumai City Hospital medical record data from 2023 to 2024, totaling 379 patient data used to identify Dengue Fever and Typhus using 7 clinical parameters as the main input obtained from laboratory examination results and patient clinical symptoms: Hemoglobin, Leukocyte, Platelet count, Hematocrit level, Headache, Abdominal pain, and diarrhea. Based on the results obtained, the application showed the best performance in classifying Dengue Fever, which is shown through the achievement of the model evaluation metrics as follows. The test results indicate that an increase in the amount of test data is directly proportional to the percentage of classification success achieved by the system. Based on the test results with 10% validation data, 70 % training data, and 20 % test data, the system showed very good performance with an overall accuracy of: 98.68% (Accuracy = 0.9868), which indicates a high level of success in classifying for the three classes, namely Normal, Dengue Fever, and Typhus.
Analisis Algoritma K-Means Clustering untuk Pengelompokan Rekomendasi Judul Proposal Tugas Akhir Mahasiswa Yulihartati, Sandra; Defit, Sarjon; 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.v5i5.755

Abstract

The academic process requires speed and accuracy in processing student data, such as submitting final project titles. In the context of final project title recommendations, many universities have not yet implemented the Data Mining approach optimally. Based on this, this study aims to recommend grouping of student final project proposal titles. The K-Means clustering method can be used in grouping data based on similarities between analyzed objects. With the K-Means method, the student grouping process utilizes grade data from the courses of Rock Mechanics, Drilling and Excavation Techniques, Underground Mining Methods, Reserve Modeling and Evaluation, Explosives and Blasting Techniques, Open Pit Mining, Mine Drainage Systems, Mapping Surveys, and Mineral Resources. The results of K-Means are strongly influenced by the k parameter and centroid initialization. The research variables include data mapping of course grades of students in the Mining Engineering Study Program. Based on the K-Means Clustering Method, it has been able to divide 104 student value data into 3 clusters, namely Natural Resource Exploration (C0), Geomechanics (C1) and Mining Environment (C2). The results of Cluster CO are 60, the results of Cluster C1 are 27 and the results of Cluster C2 are 17. The contribution of this research can provide fast, precise and accurate information in grouping recommendations for student final project proposal titles.
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 A Alfarisdon AA Sudharmawan, AA Abdi Rahim Damanik Afifah Cahayani Adha Afriosa Syawitri Agung Ramadhanu Ahmad Zamsuri, Ahmad Alexyusandria alexyusandria Alfarisdon, A Ali Djamhuri Andi, Muhammad Yusril Haffandi Anggraini, Siska Dwi Anita Sindar Apriade Voutama Ardia Ovidius ardialis Ardiani, Novia Sutra Asyhari, Ahmad Aulia Mardhatilla Ayudia, Dina Ayunda, Afifah Trista Bayu Rianto Billy Hendrik Boy Sandy Dwi Nugraha.H Breinda, Engla Budayawan, Khairi Budiarti, Lela Bufra, Fanny Septiani Candra Putra Cyntia Lasmi Andesti Cyntia Trimulia Damanik, Abdi Rahim Daniel Theodorus Darma Yunita Darmawi Darnis, Rahmi Dedi Irawan Deri Marse Putra Dina Ayudia Dinda Permata Sukma DWI JULISA UTARI Dwi Utari Iswavigra Dyan Mardinata Putra Eka Putra, Dian Elfina Novalia Erizke Aulya Pasel Faisal Roza Fajri Karim Fanny Septiani Bufra Fauzan Azim Fauzi Erwis Febriani, Widya Febrina, Yerri Kurnia Fernando Ramadhan Fitriani, Yetti Fortia Magfira Gaja, Rizqi Nusabbih Hidayatullah Hafid Dwi Adha Handika, Yola Tri Hartati, Yuli Hasni, Salmi Hazlita, H Hendrik, Billy Honestya, Gabriela Humairoh, Putri Idir Fitriyanto Idir Ilham Effendi Indah Savitri Hidayat INTAN NUR FITRIYANI Ipri Adi Ira Nia Sanita Irzal Arief Wisky Jefri Rahmad Mulia Johan Harlan Jufri, Fikri Ramadhan Jufriadif Na`am, Jufriadif Jufriadif Na’am Juliantho, Dwana Abdi Julius Santoni Julius Santony Julius Santony Julius Santony Julius Santony Julius Santony Karim, Fajri Khelvin Ovela Putra Kholil, Muhammad Irvan Larissa Navia Rani Leony Lidya Lidia Sutra Lova Endriani Zen Lubis, Fitri Amelia Sari Lusi Kestina Luth Fimawahib M Mutia M, Mutia M. Almepal Wanda M. Ibnu Pati Mardayatmi, Suci Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Miftahul Hasanah Miftahul Hasanah, Miftahul Miftahul Mardiyah Mike Zaimy Muhammad Amin Muhammad Irvan Kholil Nabila, Tuti Nadia, Nadia Aini Hafizhah Nadya Alinda Rahmi Nasution, Amir Salim Khairul Rijal Nia Nofia Mitra Nissa, Ika Ima Nst, Ely Nurhalizah Nur Azizah Nur, Rofil M Nurdini, Siti Nurhadi Parinduri, Rezti Deawinda Pati, Muhammad Ibnu Pebriyanti, Defi Petti Indrayati Sijabat Puji Chairu Sabila Putra, Akmal Darman Putra, Deri Marse Putra, Dyan Mardinata Putri Humairoh Putri, Stefani Putri, Yozi Aulia Putut Wicaksono, Putut Radillah, Teuku Rafiska, Rian Rahmad Supriadi Rahman, Zumardi Ramadhanu, Agung Riati, Itin Rika Apriani Rika Apriani, Rika Ririn Violina Ritna Wahyuni Rizka Hafsari Rizki Mubarak Roby Nurbahri Roni Salambue Rovidatul Rozakh, Muhammad Rusnedy, Hidayati Rustam, Camila S Sumijan Sabil, Muhammad Sahari Sahari Sahri, Alfi Sajida, Mayang Sandi Alam Sandrawira Anggraini Sani, Rafikasani Santriawan, Aji Sari, Fitri P. Sarjon Defit Sarjon Defit Sarjon Defit Septiana Vratiwi Sharon Sintia Sintia Siregar, Diffri Siregar, Fajri Marindra Sisi Hendriani Siska Dwi Anggraini Siti Nurdini Sovia, Rini Sri Handayani Sri Layli Fajri Stefani Hardiyanti Putri Suci Mardayatmi Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Suri, Melati Rahma Sutra, Lidia Syafri Arlis Tesa Vausia Sandiva Ulfa, Ulia Ulfatun Hasanah Ulia Ulfa Verdian, Ihsan Vratiwi, Septiana W Wahyudi Wahyu, Fungki Wahyudi Wahid Wahyudi Wahyudi Wendi Robiansyah Weri Sirait Widya Febriani Yasmin, Nabilla Yeng Primawati Yerri Kurnia Febrina Yetti Fitriani Yolla Rahmadi Helmi Yoni Aswan Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yuhandri Yunus Yuhandri, Y Yuli Hartati Yulihartati, Sandra Yunita Cahaya Khairani Yunus, Yuhandri Yuyu, Yuhandri