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All Journal Foristek JOURNAL OF APPLIED INFORMATICS AND COMPUTING Technomedia Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal ScientiCO : Computer Science and Informatics Journal Jurnal Algoritma Jurnal Gramaswara: Jurnal Pengabdian kepada Masyarakat Jurnal Kreatif Online Innovative: Journal Of Social Science Research Madani: Multidisciplinary Scientific Journal Nanggroe: Journal Of Scholarly Service Information Technology Education Journal Nanggroe: Journal of Scholarly Service The Indonesian Journal of Computer Science Jurnal MediaTIK
Chairunnisa Ar. Lamasitudju
Teknik Informatika, Fakultas Teknik, Universitas Tadulako
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Perancangan Proses Bisnis Sistem PPDB Dengan Fokus User Experience Pada CV Mitra Global Techno (Studi Kasus TK IT AL Qolam) Pratama, Septiano Anggun; Lamasitudju, Chairunnisa Ar; Hendra, Syaiful; Putri, Fadiah Suryani
Foristek Vol. 13 No. 2 (2023): Foristek
Publisher : Foristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54757/fs.v14i2.304

Abstract

Admission of New Students (PPDB) is one of the crucial stages or processes in the world of education, including at the Kindergarten level. A PPDB process that focuses on effective user experience is very important to maintain the reputation of educational institutions and provide a positive experience to parents of prospective students. The Al Qolam IT Kindergarten PPDB process is still carried out manually or conventionally, so it takes quite a long time for parents to process the registration form and return the registration form to the data processing process by the school operator. Considering these problems, designing the PPDB business process with a focus on user experience using BPMN (Business Process Model and Notation), it is hoped that this will create a simpler, more comfortable and intuitive registration experience for parents of prospective students and accuracy in data processing.
Optimasi Penjadwalan Rapat Berbasis Web Untuk Mengurangi Konflik Jadwal Menggunakan Kombinasi Algoritma Greedy dan Decision Tree Ahmad, Tjoet Muty; Lamasitudju, Chairunnisa Ar.
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3080

Abstract

The manual meeting scheduling process has a high potential for scheduling conflicts. These conflicts include delays in information, meeting room clashes, or meeting time clashes. These problems are caused by the absence of a system and visualization for managing meeting schedules. To streamline the scheduling process, a website-based meeting scheduling information system was developed for activities that are carried out routinely with high frequency, using a combination of two algorithms that can overcome these problems. The Greedy algorithm is used to detect conflicts, and the rule-based Decision Tree algorithm is used to provide alternative time or room suggestions when schedule conflicts occur. The results of blackbox testing and usability testing prove that the application of these algorithms makes the system more effective and provides the right workflow for this system. This research contributes to the development of an effective meeting scheduling system and integrates two algorithms as a new solution in managing the scheduling process.
Performance Comparison of Multilayer Perceptron (MLP) and Random Forest for Early Detection of Cardiovascular Disease Setiawan, Dita Widayanti; Lapatta, Nouval Trezandy; Amriana, Amriana; Nugraha, Deny Wiria; Lamasitudju, Chairunnisa Ar.
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10826

Abstract

Cardiovascular disease is a disorder of the heart and blood vessels that can lead to heart attacks, strokes, and heart failure, so early detection is essential. This study compares Multilayer Perceptron (MLP) and Random Forest for risk classification in a Kaggle dataset containing 70,000 samples with balanced targets. Pre-processing included age conversion, outlier cleaning, standardization, and feature selection based on feature importance. Both models were optimized using RandomizedSearchCV and evaluated using accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, and k-fold cross-validation. The results show that the accuracy of MLP is 73.90% and Random Forest is 74.23% with an AUC of 0.80 for both. Random Forest is more stable across all folds and performs better on the negative class, while MLP is slightly more sensitive to the positive class. Independent t-test and Mann-Whitney U tests show p>0.05, indicating that the difference in performance is not significant. The most influential features were diastolic blood pressure, age, cholesterol, and systolic blood pressure. The non-clinical Streamlit prototype demonstrated the model's potential for education and initial decision support.
Co-Authors Ahmad, Tjoet Muty Amriana Amriana Andhyka, Andhyka Andi Hendra Anita Candra Dewi Ashadi, Nini Rahayu Deny Wiria Nugraha Dewi, Shabrina Syntha Dival Maulana, Muhammad Dwi Shinta Angreni Dwiwijaya, Kadek Agus Harani, Makruf Hendra Jaya Laala, Jonathan Zebina Laila, Rahma Makmur, Elfira Miftah Miftah Miftah Miftah Miftah, Miftah Mohammad Yazdi Pusadan Muhammad Asriadi Ngemba, Hajra Nini Rahayu Ashadi Nini Rahayu Ashadi Ninik Rahayu Ashadi Nouval Trezandy Lapatta Nuridayanti, Nuridayanti Nursalim, Moh. Agung Pramadinda, Alda Nur Priska, Salsa Dilah Pusadan, Yazdi Putri, Fadiah Suryani Rahmah Laila Rifki, Moh Rinianty, Rinianty Ryfial Azhar, Ryfial Saleh, Muhammad Taufik Septiano A Pratama Septiano Anggun Pratama Setialaksana, Wirawan - Setiawan, Dita Widayanti Shabrina Syntha Dewi Siti Fitriana Siti Rahmawati Sulaiman, Dwi Reski Anandari Sulaiman, Dwi Rezky Anandari Syahrullah Syahrullah Syaiful Hendra Widiani, Ni Nengah Widyati, Made Ayu Sri Wirdayanti Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori