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INTAN NUR FITRIYANI
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Penerapan Metode TOPSIS Untuk Pemberian Bantuan Bedah Rumah Di Nagari Lunang Selatan Fitriyani, Intan Nur; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.738

Abstract

Indonesian government seeks to improve people's welfare by holding various poverty reduction programs, one of which is providing assistance to uninhabitable houses (RTLH). Equitable development of the welfare of Indonesian society must be comprehensive and even, starting from the smallest scope, namely the village. One of the villages in Indonesia that has implemented a program to provide assistance for uninhabitable houses is Nagari Lunang Selatan which is located in Lunang sub-district, Pesisir Selatan Regency, West Sumatra Province. The implementation of the uninhabitable housing assistance program in Nagari Lunang Selatan has so far still used a manual system so it is not effective because the final results are not objective. There are 5 criteria and 10 alternatives as sample data used in this research. These criteria include the number of dependents, total expenses, total income, land ownership status, and condition of the house. For this reason, this research provides a solution by implementing a decision support system for providing assistance for uninhabitable housing using the Technique For Order of Preference by Similarity to Ideal Solution method, known as TOPSIS, the TOPSIS method is suitable for solving semi-structural problems such as the problem of providing assistance for inadequate housing. inhabit. The aim of this research is to produce a system that can facilitate decision making regarding providing assistance for uninhabitable housing. The results obtained from the test calculation process on sample data of 10 alternatives with 5 criteria provide accurate results. From this test, the results obtained for 3 alternatives as recipients of house renovation assistance
Selection of Head of Study Program using Weighted Aggregated Sum Product Assessment (WASPAS) method Ramadani, Ramadani; Fadillah, Riszki; Fitriyani, Intan Nur
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.803

Abstract

Selecting a Head of Study Program is a crucial strategic decision in education, particularly in Vocational High Schools. At the Software Engineering Study Program Vocational School Sitibanun Sigambal, Labuhanbatu, Rantau Prapat, this process becomes highly complex due to the involvement of various criteria, such as Psychotest Scores, IQ Tests, Communication Skills, Cognitive Tests, and Teaching Experience. The Weighted Aggregated Sum Product Assessment (WASPAS) method, which combines the Weighted Sum Model (WSM) and Weighted Product Model (WPM), is utilized to enhance the accuracy and efficiency of decision-making. This method enables a more objective and structured selection process by leveraging information technology. Based on implementing the Decision Support System (DSS) using the WASPAS method, it can be concluded that it is highly effective in determining the best Head of Study Program rankings, considering the complex criteria and the need for accurate decisions. This DSS facilitates the selection process with results that are more objective, transparent, and aligned with the School's needs and priorities, thus aiding in achieving the School's mission of providing high-quality education.
Penerapan Metode K-Means Clustering untuk Klasifikasi Efek Samping Penggunaan Obat ARV pada Pasien HIV di Puskesmas Fadillah, Riszki; Fitriyani, Intan Nur
Jurnal Media Informatika Vol. 6 No. 1 (2024): Jurnal Media Informatika Edisi September - Desember
Publisher : Lembaga Dongan Dosen

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

Abstract

Pola efek samping yang dialami pasien HIV yang menjalani terapi antiretroviral (ARV) menggunakan metode K-Means Clustering. Data yang digunakan berasal dari rekam medis pasien di puskesmas, yang mencakup informasi tentang usia pasien, jenis efek samping, durasi terapi ARV, dan pola penggunaan obat ARV. Metode Elbow dan Silhouette Score digunakan untuk menentukan jumlah cluster optimal, yang menghasilkan tiga cluster dengan tingkat pemisahan yang baik. Cluster pertama mencakup pasien dengan efek samping ringan dan durasi terapi pendek (kurang dari 6 bulan), cluster kedua berisi pasien dengan efek samping sedang dan durasi terapi menengah (6-12 bulan), sementara cluster ketiga meliputi pasien dengan efek samping berat dan durasi terapi lebih panjang (>12 bulan). Hasil clustering ini memberikan wawasan penting untuk perencanaan intervensi medis yang lebih tepat sasaran, seperti pemantauan rutin untuk cluster 1, pendekatan khusus untuk cluster 2, dan perhatian medis intensif untuk cluster 3. Visualisasi data dengan scatter plot mengilustrasikan hubungan antara keparahan efek samping dan durasi terapi, memudahkan pemahaman tentang pola distribusi pasien yang mengalami efek samping ARV. Temuan ini diharapkan dapat meningkatkan kualitas perawatan dan kepatuhan pasien terhadap terapi ARV.
Socialization and Implementation of a Midwifery Education Chatbot at the Rantauprapat City Community Health Center Fadillah, Riszki; Ramadani, Putri; Adawiyah, Quratih; Fitriyani, Intan Nur
International Journal of Community Service (IJCS) Vol. 4 No. 1 (2025): January-June
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijcs.v4i1.1080

Abstract

Improving the quality of maternal healthcare requires an innovative, technology-based approach, particularly in providing midwifery education. This community service project aimed to introduce and train pregnant women at the Rantauprapat City Community Health Center (Puskesmas) in the use of an educational chatbot based on the Recurrent Neural Network (RNN) algorithm. This chatbot was designed to provide fast, relevant, and accessible pregnancy health information. The activity involved coordination with partner health centers, outreach, hands-on training on the use of the chatbot, and evaluation of its effectiveness. The evaluation results showed that more than 90% of participants felt the chatbot helped them understand their pregnancy status, with the majority of questions related to early symptoms, diet, and safe activities during pregnancy. Furthermore, health workers stated that the chatbot could ease the burden of answering repetitive questions from patients. The implementation of this technology has significantly contributed to improving digital-based midwifery literacy and strengthening the role of community health centers as primary health care centers that are adaptive to technological developments. Going forward, the development of additional features and the expansion of local content are expected to strengthen the use of the chatbot on a broader scale.
PREDIKSI METODE PERSALINAN DENGAN BIG DATA DAN ALGORITMA GRADIENT BOOSTING CLASSIFIER Fitriyani, Intan Nur; Fadillah, Riszki; Adawiyah, Quratih; D, Novica Jolyarni
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1557

Abstract

This study aims to develop a prediction model to determine the method of delivery (normal or cesarean) using the Gradient Boosting algorithm based on maternal examination data. This model was evaluated using precision, recall, F1-score, and accuracy metrics. The results showed that the Gradient Boosting model had an accuracy of 48%, with better performance in predicting Normal delivery compared to Caesarean. Although this model is effective, there is an imbalance in precision and recall for the Caesarean class, indicating the need for improvement in identifying cases of cesarean delivery. Comparison with other algorithms such as Random Forest, Logistic Regression, and SVM showed that Random Forest gave the best performance with an accuracy of 55%. To improve performance, this study recommends hyperparameter optimization, application of class balancing techniques, and enrichment of medical features. The developed model has the potential to be used as a tool in medical decision-making related to delivery methods, which is expected to improve the safety of mothers and babies, and reduce dependence on subjective factors in medical decisions.
Perkiraan Pola Permintaan Paspor di Kantor Imigrasi dengan Menggunakan Metode Exponential Smoothing untuk Memaksimalkan Layanan Riszki Fadillah; Fitriyani, Intan Nur; Ramadani, Putri; Mardivta, Hafizhah
JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital Vol. 4 No. 2 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jumintal.v4i2.6789

Abstract

This study aims to analyze the passport application patterns at the Immigration Office and forecast the number of applications for the coming years using the Exponential Smoothing (Holt-Winters) model. The data used includes the number of passport applications from 2022 to 2024. The analysis shows a significant increase in applications in the coming years, with predictions for 2025, 2026, and 2027 indicating a consistent growth pattern. While the model demonstrates good accuracy, the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) calculations indicate overestimation for the 2024 forecast. The application of the Holt-Winters model in forecasting passport applications in the Immigration field is a novel contribution to the literature, as this method is rarely used in this context. The model provides a systematic quantitative approach to predict long-term trends in application data, which is crucial for more efficient service capacity planning. The implications of these findings suggest that, although the model can predict a consistent growth pattern, the overestimation in 2024 highlights the need for model adjustment in the future. Therefore, increasing service capacity through additional staff and optimizing the digital queuing system are strategic steps that should be implemented to handle the projected surge in applications. These measures are essential to ensure efficient service and the Immigration Office's preparedness for the ongoing rise in applications.
Co-Authors Adawiyah, Quratih D, Novica Jolyarni Fadillah, Riszki Gunadi Widi Nurcahyo Mardivta, Hafizhah Ramadani, Putri Ramadani, Ramadani Riszki Fadillah Sarjon Defit