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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Media Informatika International Journal of Public Health Excellence (IJPHE) Internet of Things and Artificial Intelligence Journal International Journal of Community Service (IJCS)
Fadillah, Riszki
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Implementasi Data Mining untuk Pemetaan Persebaran Infeksi Human Imunodeficiency Virus di Provinsi Riau Fadillah, Riszki; Sarjon Defit; Sumijan
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6712

Abstract

Based on data released by the Riau Provincial Health Service until October 2022, there were 8034 people living with HIV/AIDS (PLWHA), of which 3,711 were in the AIDS stage. Human Immunodeficiency Virus is a virus that attacks the body's immune system, while Acquired ImmunoDeficiency Syndrome (AIDS) is a collection of diseases caused by the HIV virus due to damage to the immune system in humans, resulting in the body being susceptible to potential diseases. This research aims to map the spread of HIV/AIDS in Riau Province to prevent and control the spread of the HIV/AIDS virus by the relevant agencies. The method used in this research is Fuzzy C-Means to carry out clustering in districts/cities which will then be visualized using a map or with a Geography Informatics System (GIS). The Fuzzy C-Means method is a data grouping technique that uses the existence of each data point in A cluster as determined by the degree of membership. The output from Fuzzy C-Means is a series of cluster centers and several degrees of membership for each data point. The data used in this research is HIV/AIDS data in Riau Province from 1997 to 2023. Based on the results of the tests that have been carried out, the results obtained are 3 clusters, namely the safe zone has 5 districts/cities, the alert zone has 5 districts/cities, and There are 2 districts/cities in the dangerous zone. There needs to be treatment through the Health Service, the AIDS Control Commission, and related Non-Governmental Organizations (NGOs) to prevent and control HIV/AIDS in Riau Province for areas that have a high potential for the spread of HIV/AIDS. The tests that have been carried out obtain a minimum error value of 0.008251 in the 8th iteration with the performance of Fuzzy C-Means being 13.271 in the distance between clusters.
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.
Addict Coffee Barista Recruitment Decision Support System Using the ARAS Method Mesran, Mesran; Fadillah, Riszki; Wahyu, Riski Ferita
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6249

Abstract

Barista is a person who works in a coffee shop as a delicious coffee maker. So it is necessary to recruit baristas who can work in coffee shops and have responsibilities that not only mix coffee but also have skills in processing coffee beans. The problem in the barista recruitment process is the process of determining barista candidates who are only selected individually so that it is less accurate to get barista candidates who have the expected skills so that it can have an impact on opinions on the coffee shop. So the solution is provided through a decision support system, a highly interactive computer-based system that assists in making a decision to utilise data and models in solving unstructured and semi-structured problems. The method used in making these decisions is the Additive Ratio Assessment Method (ARAS). A total of 11 people who will become data samples and five criteria are used as rules for assessing (selecting). The ARAS method is able to provide maximum results to obtain superior barista recruitment with a result of 5,342, namely A1 as the selected alternative in barista recruitment after going through the method application stage.
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

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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.
Penerapan Naive Bayes untuk Identifikasi Keterlambatan Perkembangan Anak Berdasarkan Data Kesehatan pada Program Studi Kebidanan Sirait, Fahruzi; Sakti Tanjung, Rani Darma; Tusakdiyah Harahap, Halimah; Fadillah, Riszki
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

Penelitian ini berfokus pada pemantauan perkembangan anak, yang merupakan aspek penting dalam kesehatan anak, terutama pada masa emas (golden period) perkembangan. Keterlambatan perkembangan anak sering kali tidak terdeteksi secara dini, yang dapat berdampak negatif pada kualitas hidup mereka di masa depan. Penelitian ini bertujuan untuk mengeksplorasi penerapan metode Naive Bayes dalam mengidentifikasi keterlambatan perkembangan anak berdasarkan data kesehatan yang tersedia. Dengan menggunakan pendekatan kuantitatif dan eksperimen, penelitian ini menganalisis data dari rekam medis, hasil pemeriksaan kebidanan, serta informasi tambahan dari orang tua. Metode Naive Bayes dipilih karena kemampuannya dalam mengolah data besar dan memberikan klasifikasi yang akurat dengan cepat. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes dapat digunakan untuk mengklasifikasikan status perkembangan anak ke dalam kategori normal atau terlambat dengan tingkat akurasi yang tinggi. Dengan memanfaatkan sistem informasi kesehatan, tenaga medis dapat lebih mudah mengakses dan menganalisis data kesehatan anak, sehingga memungkinkan deteksi dini terhadap keterlambatan perkembangan. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam meningkatkan efektivitas pemantauan kesehatan anak dan mendukung intervensi yang tepat waktu. Selain itu, temuan ini juga membuka peluang untuk pengembangan lebih lanjut dalam penerapan teknologi informasi di bidang kebidanan dan kesehatan anak, dengan fokus pada peningkatan kualitas layanan kesehatan secara keseluruhan
Perbandingan Algoritma Naïve Bayes, C4.5, dan K-Nearest Neighbor untuk Klasifikasi Kelayakan Program Keluarga Harapan Ramadani, Putri; Fadillah, Riszki; Adawiyah, Quratih; Suerni, Suerni; Al Ghazali, Baginda Restu
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 | DOI: 10.55338/jumin.v6i1.6083

Abstract

Penelitian ini bertujuan membandingkan kinerja tiga algoritma klasifikasi—Naïve Bayes, C4.5, dan K-Nearest Neighbor (K-NN)—dalam menentukan kelayakan penerima Program Keluarga Harapan (PKH) di Rantau Prapat. Dataset terdiri dari 109 data keluarga dengan variabel seperti pendapatan, jumlah tanggungan, status pekerjaan, dan kepemilikan aset. Pengolahan dan analisis data dilakukan menggunakan RapidMiner Studio, dengan evaluasi kinerja berdasarkan akurasi, presisi, recall, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa algoritma C4.5 memberikan kinerja terbaik dengan akurasi 91,8%, presisi 90,7%, recall 92,3%, dan AUC 0,944. Naïve Bayes mencatat akurasi 87,2% dan recall 88,9%, sedangkan K-NN menghasilkan akurasi 89,9% dan recall 91,1%, namun memerlukan komputasi lebih tinggi. Temuan ini menunjukkan bahwa C4.5 lebih efektif dalam mengklasifikasikan kelayakan penerima PKH secara akurat dan efisien. Penelitian ini menegaskan potensi algoritma machine learning dalam mendukung pengambilan keputusan pada program bantuan sosial. Studi lanjutan disarankan untuk memperluas cakupan data dan mengeksplorasi metode klasifikasi lainnya guna optimalisasi distribusi bantuan.
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.
Utilization of RNN Chatbots for Midwifery Education for Pregnant Women at Rantauprapat City Community Health Centers Fadillah, Riszki; Tanjung, Rani Darma Sakti; Tusakdiyah, Halimah; Jolyarni D, Novica; Purwanto, Juni
International Journal of Public Health Excellence (IJPHE) Vol. 4 No. 2 (2025): January-May
Publisher : PT Inovasi Pratama Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55299/ijphe.v4i2.1469

Abstract

The application of information technology in the healthcare sector has been rapidly advancing with the development of artificial intelligence. One of its potential applications is the use of chatbots powered by the Recurrent Neural Network (RNN) algorithm to enhance maternal health education access for pregnant women. Although health information is increasingly accessible, pregnant women often face challenges in obtaining accurate education about pregnancy due to limitations in time, location, and access to medical professionals. Puskesmas, as a primary healthcare center, plays a crucial role but is limited by the number of healthcare workers and operational hours, reducing the effectiveness of maternal health education delivery. Therefore, AI-powered chatbots can provide instant, personalized information that can be accessed anytime and anywhere. In this study, the developed chatbot using the RNN algorithm is capable of processing conversations contextually, providing relevant answers according to the stage of pregnancy and the specific needs of the pregnant woman. The implementation of this chatbot at Puskesmas Kota Rantauprapat is expected to improve the accessibility of maternal health education, reduce anxiety among pregnant women, and minimize the need for physical visits for common questions. The results of this study demonstrate the potential of RNN-based chatbots as an efficient tool in supporting maternal health education through digital platforms.
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.
Co-Authors Adawiyah, Quratih Al Ghazali, Baginda Restu D, Novica Jolyarni INTAN NUR FITRIYANI JOLYARNI D, NOVICA Mesran, Mesran Purwanto, Juni Ramadani, Putri Ramadani, Ramadani Sakti Tanjung, Rani Darma Sarjon Defit Sirait, Fahruzi Suerni, Suerni Sumijan Tanjung, Rani Darma Sakti Tusakdiyah Harahap, Halimah Tusakdiyah, Halimah Wahyu, Riski Ferita