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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Surya Medika Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Window of Health : Jurnal Kesehatan Bunda Edu-Midwifery Journal (BEMJ) Manuju : Malahayati Nursing Journal Poltekita : Jurnal Ilmu Kesehatan Building of Informatics, Technology and Science Surya Medika: Jurnal Ilmiah Ilmu Keperawatan dan Ilmu Kesehatan Masyarakat Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Media Publikasi Penelitian Kebidanan Malcom: Indonesian Journal of Machine Learning and Computer Science JIKA Indonesian Journal of Innovation Multidisipliner Research Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Kepada Masyarakat Meambo TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang Indonesian Journal of Innovation Multidisipliner Research Jurnal Ilmiah Ilmu Kesehatan
Iis Afrianty
Universitas Sembilan Belas November Kolaka
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Convolutional Neural Network Model for Sex Determination Using Femur Bones Nasien, Dewi; Adiya, M. Hasmil; Afrianty, Iis; Farkhan, Mochammad; Butar-Butar, Rio Juan Hendri
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1711

Abstract

Forensic anthropology is the critical discipline that applies physical anthropology in forensic education. One valuable application is the identification of the biological profile. However, in the aftermath of significant disasters, the identification of human skeletons becomes challenging due to their incompleteness and difficulty determining sex. Researchers have explored alternative indicators to address this issue, including using the femur bone as a reliable sex identifier. The development of artificial intelligence has created a new field called deep learning that has excelled in various applications, including sex determination using the femur bone. In this study, we employ the Convolutional Neural Network (CNN) method to identify the sex of human skeleton shards. A CNN model was trained on 91 CT-scan results of femur bones collected from Universiti Teknologi Malaysia, comprising 50 female and 41 male patients. The data pre-processing involves cropping, and the dataset is divided into training and validation subsets with varying percentages (60:4, 70:30, and 80:20). The constructed CNN architecture exhibits exceptional accuracy, achieving 100% accuracy in both training and validation data. Moreover, the precision, recall, and F1 score attained a perfect score of 1, validating the model's precise predictions. The results of this research demonstrate excellent accuracy, confirming the reliability of the developed model for sex determination. These findings demonstrate that using deep learning for sex determination is a novel and promising approach. The high accuracy of the CNN model provides a valuable tool for sex determination in challenging scenarios. This could have important implications for forensic investigations and help identify victims of disasters and other crimes.
Benchmarking Various Machine Learning Models to Detect Lung Cancer Afrianty, Iis; Afriyanti, Liza
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.38590

Abstract

This study benchmarked and evaluated the performance of various machine learning techniques to detect lung cancer using public datasets. The techniques used include Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, C4.5, Bayesian Network, Reptree, Naive Bayes, and P.A.R.T. Evaluation was carried out using metrics such as Accuracy, F-measure, Precision, TPR, ROC, FPR, PRC, and MCC. The results showed that the Support Vector Machine algorithm performed best on balanced dataset distribution, while Random Forest showed stable performance on unbalanced datasets. This study confirms the importance of selecting appropriate algorithms and data distribution to improve lung cancer detection.
Sistem Prediksi Produksi Kelapa Sawit Berbasis Gradio Menggunakan Algoritma Regresi Linear Berganda Matondang, Irfan Jamal; Budianita, Elvia; Syafria, Fadhilah; Afrianty, Iis
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.994

Abstract

The instability of oil palm production often leads to discrepancies between production targets and actual outputs, thereby necessitating an accurate prediction model to support operational planning. This study aims to develop an oil palm production prediction model and to identify the most influential variables affecting production outcomes as a basis for data-driven decision-making. The model was developed using the Multiple Linear Regression method based on historical data from 2020–2024, consisting of 60 monthly observations with variables including number of trees, land area, rainfall, number of fruit bunches, and plant age. The research stages included data preprocessing, variable selection through testing several feature combinations, model development, and performance evaluation using the coefficient of determination (R²), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). The results indicate that the combination of number of trees, land area, number of fruit bunches, and plant age produced the best performance, with an R² value of 0.85 on the training data and 0.81 on the testing data. The MAE values were 125,307 kg and 176,984 kg, the MSE values were 28,870,838,455 kg² and 52,809,954,662 kg², and the RMSE values were 169,914 kg and 229,804 kg, respectively. Based on the regression coefficients, the number of fruit bunches was identified as the most dominant variable, with a coefficient value of 637,720 kg. The model was subsequently implemented using the Python Gradio library in the form of an interactive interface to support production planning effectiveness and minimize the risk of inaccurate decision-making in oil palm plantation management.
Co-Authors Adiya, M. Hasmil Afriyanti, Liza Aftari, Dhea Putri Agnesti, Syafira Al Rasyid, Nabila Alfaiza, Raihan Zia Alghi, Anugerah Febryan Aprima, Muhammad Dzaky Arianto Arianto Arif, Arif Prasetya Ayu Lestari, Fajar Vilbra Azhima, Mohd Baeda, Abd. Gani Baehaqi Bangu, Bangu Burhanuddin, Yuniarti Ekasaputri Butar-Butar, Rio Juan Hendri Dewi Nasien Dinata, Ferdian Arya Elvia Budianita Fadhilah Syafria Fahrozi, Aqshol Al Farkhan, Mochammad Febi Yanto Fitri Insani Fitri, Anisa Gusti, Siska Kurnia Guswanti, Widya Hamid, Fanul Hariansyah, Jul Harni, Yulia Hasibuan, Aldiansyah Pramudia Hasidu, La Ode Abdul Fajar Hasria Hasria, Hasria Hatta, M Ilham Ika Lestari Salim Jasril Jasril Kamaruddin, Anggi Ashari Khair, Nada Tsawaabul Kurniawan, Saifur Yusuf La Aba Lubis, Anggun Tri Utami BR. Ma'rifah, Laila Alfi Mariany Mariany Maryani Maryani Matondang, Irfan Jamal Mhd. Kadarman Muhammad Fikry Muhammad Irsyad Naim, Rosani Nasus, Evodius Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Ode Abdul Fajar Hasidu, La Ode Muhammad Sety, La Pasiolo, Lugas Pratama, Dandi Irwayunda Putri, Atika Putri, Widya Maulida Rahmad Abdillah Ramadhani, Astrid Rasmiati Rasyid Rosmiati Rosmiati Safar, Muhammad Saleh, Ramlah Saputri, Ekawati Saputri, Ekawati Saputri, Sety, La Ode Muhamad Siti Sri Rahayu Suharsono Bantun Surya Agustian Susanti, Risqi Wahyu Suwanto Sanjaya Syahrianti Syahrianti Teluk, Grace Tedy Tukatman Tukatman Tulak, Grace Tedy Vitriani, Yelfi Yuhanah Yuhanah Yulianti, Eva Tri Yuniarti Eka Saputri Yuniarti Eka Saputri B Yusra, Yusra Zabihullah, Fayat Zulastri, Zulastri