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Machine Learning Based Prediction of Health Risks in Pregnant Women Rahma Devi; Inggih Permana; Rice Novita; Febi Nur Salisah
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.766

Abstract

Pregnancy is an important phase that requires optimal health monitoring to prevent complications that are risky for both mother and fetus. The high maternal mortality rate in Indonesia emphasizes the importance of early detection of pregnancy risks. The use of machine learning offers an effective predictive approach to quickly and accurately identify pregnancy risks. This study aims to compare the performance of five machine learning algorithms, namely Logistic Regression, Decision Tree C4.5, Random Forest, Support Vector Machine, and Naive Bayes, using the Maternal Health Risk Dataset. The hold-out validation method with data sharing of 80% training data and 20% test data was used in this study. Model evaluation is conducted based on accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest had the best performance with an accuracy of 93%, followed by Decision Tree at 93%, SVM at 82%, Logistic Regression at 76%, and Naive Bayes at 72%. Thus, Random Forest is rated as the most optimal algorithm in predicting pregnancy risk and potentially supporting the development of decision support systems for health workers. This research is expected to be the basis for the development of a machine learning-based decision support system to increase the effectiveness of health services for pregnant women.
Pengukuran Tingkat Capability Level Domain Apo12 (Managed Risk) dan Apo13 (Managed Security) Berdasarkan Framework COBIT 2019 Anggy Julia Wulandari; Febi Nur Salisah
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 2 (2026): Juni: JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i2.4070

Abstract

This study aims to measure the information technology governance capability level at SLB Cendana Rumbai using the COBIT 2019 framework. The study was conducted due to several issues in information technology management, including the absence of structured risk management, the lack of Standard Operating Procedures (SOPs) related to information security, the absence of periodic data backup and recovery mechanisms, and irregular monitoring of system security. This study focuses on the APO12 (Managed Risk) and APO13 (Managed Security) domains, which were selected based on design factor results as the main priorities for information technology management in the school. The research methods employed include observation, interviews, documentation, and questionnaire distribution to relevant stakeholders. The capability level measurement was carried out to determine the current condition of information technology governance (current capability), identify gaps between the current and expected levels, and formulate improvement recommendations. The results indicate that risk management and information security at SLB Cendana Rumbai still require improvement to become more effective, structured, and well-directed. Therefore, improvement recommendations were proposed as guidelines to enhance the quality of information technology governance in supporting the school’s operational activities optimally.
Applying KNN, NBC, and C4.5 Algorithms to Identify Eligibility for Non-Cash Food Aid Rizki Pratama Putra Agri; Inggih Permana Permana; Febi Nur Salisah; Muhammad Jazman; Muhammad Afdal
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/89xvxf70

Abstract

The Indonesian government has implemented the Non-Cash Food Assistance (BPNT) program as an effort to improve people's welfare. However, in its implementation, there are still obstacles in the process of determining the right beneficiaries. Determining the right BPNT recipients is important to ensure that the assistance is received by people who really need it and to prevent budget misuse. This research aims to help the government to easily process data using three classification algorithms, namely K-Nearest Neighbour (K-NN), Naïve Bayes Classifier (NBC), and C4.5 in classifying BPNT recipient data in Air Molek Village, Indragiri Hulu Regency. K-NN, NBC, and C4.5 were chosen because they represent different approaches: K-NN is distance-based, NBC is probability-based, and C4.5 uses decision trees. The stages of the methodology used include data collection, data preprocessing, data splitting (Hold-Out), data balancing and model testing. The results showed that the K-NN algorithm got an accuracy of 70.45%, precision 68.34% recall 72.42%, NBC got an accuracy of 60.58%, precision 58.21%, recall 85.42%, and C 4.5 with an accuracy of 62.56%, precision 59.17%, recall 63.33%. The results of this study can help the government in developing a more objective and data-based decision support system for determining BPNT recipients. The limitation of this research is the use of data that is limited to only one of the data sources.
Design and Development of a Web-Based Quranic Verse Submission Application at Al-Uswah Islamic Boarding School Pekanbaru Megawati Megawati; Harisman Efendi; Febi Nur Salisah; Fitriani Muttakin
Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Vol. 11 No. 1 (2025): Februari
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The process of Quran recitation submission at Al-Uswah Islamic Boarding School in Pekanbaru involves interaction between students and teachers for submitting and evaluating Quran memorization. This research aims to develop a Quran Recitation Submission WebApp based on ReactJS for the frontend and Firebase as the backend. The application was developed using the Rapid Application Development method and tested through Blackbox Testing and User Acceptance Testing (UAT). The development results show that all features function well, with positive feedback from users. The main features of the application include hafiz ranking, online recitation submission, and the ability for teachers to provide feedback to students.
Co-Authors A Anggraini Afdal Muhammad Efendi Anggi Widya Atma Nugraha Anggia Anfina Anggy Julia Wulandari Angraini Angraini Anisa Nirmala, Fitri Anwar, Tengku Khairil Arabiatul Adawiyah Arif Marsal Arif Marsal Arif Marsal Arrazak, Fadlan Bayu Putra Danil Risaldi Darmawan, Reza Dewi Astuti Eki Saputra Eki Saputra Eki Saputra Elin Haerani Endah Purnamasari Esis Srikanti Fachrurozi Fadhilah Syafria Fadil Rahmat Andini Febrian, Dany Fernanda, Ustara Dwi Fiki Fitri Wulandari Fitriah, Ma’idatul Fitriah, Ma’idatul Fitriani Muttakin Fitriani Muttakin Fitriani Muttakin Giansyah, Qhoiril Aldi Gustinov, Mhd Dion Harisman Efendi Hasbi Sidiq Arfajsyah Hendri, Desvita Husaini, Fahri Idria Maita Idria Maita Idria Maita Idriani R, Nova Imam Muttaqin Indah Lestari Indri Dian Pertiwi Inggih Permana Inggih Permana Permana Intan, Sofia Fulvi Jayadi, Puguh Jazman , Muhammad Jazman, Muhammad Kusuma, Gathot Hanyokro Leony Lidya M Afdal M Afdal M. Afdal M. Afdal M. Afdal M.Afdal Maulana, Rizki Azli Mawaddah, Zuriatul Mega wati, Mega Megawati Megawati - Megawati Megawati Megawati Megawati Mona Fronita Muhammad Afdal Muhammad Afdal Muhammad Iqbal Indrawan Muhammad Jazman Muhammad Jazman Muhammad Luthfi Muhammad Luthfi Hamzah Muhammad Munawir Arpan Munzir, Medyantiwi Rahmawita Mustakim Mustakim Muttakin, Fitriani Nabila Putri Nailul Amani Nardialis Nardialis Nasution, Nur Shabrina Naufal Fikri, R. Adlian Nesdi Evrilyan Rozanda Nesdi Evrilyan Rozanda Norhavina Norhavina Nuraisyah Nuraisyah Nurkholis Nurkholis Nurrahma, Intan Puput Iswandi Putra, Adhytia Pratama Putri, Amanda Iksanul Rahma Aliya Rahma Devi Rahmawita M, Medyantiwi Rahmawita, Medyantiwi Rangga Arief Putra Ria Agustina Rice Novita Rice Novita Rizka Fitri Yansi Rizki Pratama Putra Agri Rizki Pratama Putra Agri Rozanda, Nesdi Evrilyan Sanusi Saputri, Setia Ningsih Sari, Gusmelia Puspita Sarjon Defit Setiawati, Elsa Shir Li Wang Shulhan Abdul Gofar Siti Zainah Sulthan Habib Syahri, Alfi Syaifullah Syaifullah Syaifullah Syaifullah Syaifullah Syaifullah Syarif, Yulia Tengku Khairil Ahsyar Tshamaroh, Muthia Uci Indah Sari Winda Wahyuti Wira Mulia, M. Roid Zarnelly Zarry, Cindy Kirana