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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Surya Medika 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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Question Answering System pada Chatbot Telegram Menggunakan Large Language Models (LLM) dan Langchain (Studi Kasus UU Kesehatan): Question Answering System on Telegram Chatbot Using Large Language Models (LLM) and Langchain (Case Study: Health Law) Lubis, Anggun Tri Utami BR.; Harahap, Nazruddin Safaat; Agustian, Surya; Irsyad, Muhammad; Afrianty, Iis
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1378

Abstract

Di bidang kesehatan, peraturan yang diterapkan dikenal sebagai hukum kesehatan, yang bertujuan untuk melindungi kepentingan pasien dan meningkatkan standar praktik medis. Pada tahun 2023, Indonesia menerapkan UU No 17 Tahun 2023 tentang Kesehatan, mencakup hak pasien, standar layanan, dan partisipasi masyarakat. Omnibus Law ini diharapkan menyelesaikan masalah kesehatan dan melindungi penyedia layanan. Penelitian ini bertujuan untuk mengembangkan Question Answering System (QAS) berbasis chatbot yang terintegrasi dengan Telegram. Metode yang digunakan adalah Langchain dan Large Language Models (LLM). Langchain digunakan untuk memfasilitasi pembangunan chatbot, sementara LLM adalah jenis model AI yang menggunakan pendekatan pembelajaran mesin untuk menghasilkan teks yang serupa dengan bahasa manusia. Sumber data yang digunakan sebagai basis pengetahuan adalah UU No 17 tahun 2023 tentang kesehatan. Chatbot yang dibangun telah berhasil memberikan jawaban kepada pengguna dengan hasil pengujian menggunakan BERTScore mendapatkan rata-rata nilai precision, recall, f1-score masing-masing sebesar 76%, 80%, 78%. Sedangkan untuk ROUGE-1 sebesar 60%, 45%, 50%, untuk ROUGE-2 sebesar 34%, 25%, 28%,  dan untuk ROUGE-L sebesar 45%,34%,38%.
Penerapan Metode Backpropagation Neural Network untuk Klasifikasi Penyakit Stroke Azhima, Mohd; Afrianty, Iis; Budianita, Elvia; Gusti, Siska Kurnia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1956

Abstract

Stroke is a non-communicable disease that can occur suddenly due to local or global disruption of brain function. The early symptoms of stroke are often difficult to recognize, causing many sufferers not to realize or feel the signs, so the death rate is quite high. This research aims to determine the ability of the Backpropagation Neural Network (BPNN) method in classifying stroke. The dataset used consists of 4891 medical records with stroke and non-stroke classes which include ten relevant variables (gender, age, hypertension, history of heart disease, BMI, blood sugar levels, and so on). This research runs three scenarios with the BPNN architecture model [19:25:1], [19:29:1], and [19:35:1] using a certain combination of variables, namely the comparison of training and testing data (90:10, 80 :20, 70:30), and learning rate 0.1; 0.01; 0.001. Test results with the highest average accuracy level of 96.14% were achieved with an architectural model of [19:29:1], a learning rate of 0.001, and a training and testing data distribution of 80:20. Based on testing, it can be concluded that BPNN is considered capable of classifying stroke
Pengetahuan Dan Sikap Tentang Anemia Mempengaruhi Kepatuhan Minum Tablet Fe Pada Mahasiswi Prodi Keperawatan USN Kolaka Afrianty, Iis; Naim, Rosani
Bunda Edu-Midwifery Journal (BEMJ) Vol. 7 No. 2 (2024): September 2024
Publisher : Akademi Kebidanan Bunga Husada Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54100/bemj.v7i2.221

Abstract

Anemia is a condition where there is a lack of red blood cells or what is commonly known as hemoglobin. Hemoglobin is needed to carry oxygen and if you have too few or abnormal red blood cells, or not enough, there will be a decrease in the blood's capacity to carry oxygen to the body's tissues. Teenage girls will menstruate every month so they are at higher risk of developing anemia due to iron deficiency. Observational Analytical research design with a Cross Sectional approach. The sample for this research was students from the Ninebelas November Kolaka University nursing study program. The technique used was simple random sampling, obtaining a sample of 86 respondents. The research instrument used was a questionnaire about knowledge and attitudes which had been tested for validity and reliability. The statistical test used is Chy Square. The research results showed that there was a significant relationship between knowledge and attitudes towards adherence to consuming blood supplement tablets
Klasifikasi Tulang Tengkorak Manusia Berdasarkan Jenis Kelamin Menggunakan Backpropagation Pada Antropologi Forensik Afrianty, Iis; Mhd. Kadarman; Elvia Budianita; Fadhilah Syafria
Computer Science and Information Technology Vol 5 No 3 (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.v5i3.8235

Abstract

Klasifikasi tulang tengkorak berdasarkan jenis kelamin merupakan langkah utama pada antropologi forensik dalam mengidentifikasi profil sisa-sisa kerangka. Klasifikasi jenis kelamin bertujuan untuk menentukan apakah kerangka tertentu adalah milik laki-laki atau perempuan. Penelitian ini berfokus pada klasifikasi tulang tengkorak berdasarkan jenis kelamin dengan menggunakan teknik pembelajaran mesin tingkat lanjut, khususnya Backpropagation Neural Network (BPNN). Tujuan dari penelitian ini adalah untuk menunjukkan kinerja BPNN. Data yang digunakan dalam penelitian ini diperoleh dari Dr. William Howells, meliputi pengukuran kraniometri dari 2524 sampel tengkorak laki-laki dan perempuan, dengan 86 variabel seperti lebar bizygomatic dan panjang glabello-oksipital. Teknik BPNN digunakan karena kemampuannya untuk memodelkan hubungan yang kompleks dan tidak linier. Kinerja model ini dievaluasi dengan menggunakan metrik standar akurasi. Pembagian data latih dan data uji menggunakan k-fold cross-validation dengan k = 10. Penelitian ini menjalankan dua skenario uji, yaitu menggunakan satu hidden layer dan dua hidden layer. Untuk masing-masing model arsitektur menggunakan learning rate sebagai parameter uji, yaitu 0,1; 0,01; dan 0,001. Hasil penelitian menunjukkan bahwa pendekatan pembelajaran mesin dapat secara efektif membedakan antara tulang tengkorak laki-laki dan perempuan, dengan akurasi rata-rata 92,32% untuk satu hidden layer dan 90,74% untuk dua hidden layer. Hasil tersebut menunjukkan, model klasifikasi tulang tengkorak manusia berbasis gender dengan menggunakan jaringan syaraf tiruan backpropagation sangat disarankan sebagai teknik yang berhasil dalam mengklasifikasikan tulang tengkorak manusia.
Edukasi Pemberian ASI Eksklusif Pada Ibu Nifas di Rumah Sakit Benyamin Guluh Kolaka Afrianty, Iis; Saputri, Ekawati; Rosmiati, Rosmiati; Tukatman, Tukatman; Bangu, Bangu; Baeda, Abd. Gani
Jurnal Pengabdian Meambo Vol. 2 No. 1 (2023): Jurnal Pengabdian Kepada Masyarakat MEAMBO
Publisher : PROMISE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56742/jpm.v2i1.42

Abstract

Air Susu Ibu (ASI) adalah nutris paling baik yang diberikan kepada bayi karena kandungannya terdiri dari banyak zat dan faktor protektif penting yang dibutuhkan untuk pertumbuhan dan perkembangan bayi sehingga angka kesakitan dan angka kematian bayi dapat diturunkan. Pembentukan system imun yang kuat pada bayi didukung oleh Kandungan ASI yang sangat lengkap dan kompleks, dan terdiri dari ratusan molekul bioaktifsehingga bayi terlindungi dari infeksi. Masih rendahnya pencapaian  program  pemberian  ASI eksklusif dapat terjadi karena   beberapa   hambatan, diantaranya  rendahnya  pengetahuan  tentang  manfaat dan tujuan pemberian ASI eksklusif   bisa menjadi penyebab  gagalnya  pemberian ASI eksklusif. Salah satu strategi yang dilakukan pemerintah adalah dengan memberikan penyuluhan atau edukasi kepada ibu hamil. Pengabdian ini bertujuan untuk memberikan edukasi tentang ASI Ekslusif pada ibu nifas, pengabdian ini dilaksanakan di Rumah Sakit Benyamin Guluh Kolaka pada tanggal 18 November 2022 jam 10.30-11.00. Pengabdian ini  adalah upaya rehabilitatif dengan  memberikan ASI Ekslusif  pada ibu nifas diruang nifas RSBG Kolaka. Peserta yang mengikuti edukasi kesehatan berjumlah 12 orang yang terdiri atas ibu-ibu postpartum beserta keluarga yang mendampingi. Peserta cukup  antusias mendengarkan dan menyimak materi yang   diberikan sehingga diharapkan ASI Ekslusif berhasil dilaksanakan.  
Membangun Generasi Sehat dan Cerdas melalui Edukasi Reproduksi Afrianty, Iis; Saputri, Ekawati; Tulak, Grace Tedy; Yuhanah, Yuhanah; Rasyid, Rasmiati
TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang Vol. 2 No. 1 (2025): Juni
Publisher : Perhimpunan Ahli Teknologi Informasi dan Komunikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71234/tenang.v2i1.50

Abstract

This initiative seeks to improve teenagers' comprehension, awareness, and perspectives on reproductive health through participatory and inclusive teaching approaches. The program was executed in phases, encompassing preparation, implementation, assessment, and follow-up, and involved adolescent participants, educational institutions, and parents. The pre-test and post-test results indicated an average knowledge increase of 85%, especially on reproductive anatomy, sexually transmitted disease prevention, and personal hygiene. Interactive conversations, role-play simulations, and individual consultations were beneficial in promoting active involvement and sound decision-making. The backing of educational institutions and guardians was important in guaranteeing the program's longevity. Feedback revealed that 94% of participants deemed the employed strategies highly successful in facilitating their comprehension of the material. The results of this exercise offer suggestions for incorporating reproductive health education into school curriculum and enhancing the program's reach. This effort aims to cultivate a generation that is healthy, knowledgeable, and accountable for their reproductive health.
Klasifikasi Kelayakan Air Minum dengan Backpropagation Neural Network Berbasis Penanganan Missing Value dan Normalisasi Kurniawan, Saifur Yusuf; Sanjaya, Suwanto; Vitriani, Yelfi; Afrianty, Iis
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5871

Abstract

The issue of drinking water quality and its suitability for human consumption represents a significant concern in contemporary society, particularly in the context of maintaining public health. The existing research on the classification of drinking water eligibility has yet to yield conclusive results. The objective of this research is to utilize the backpropagation neural network method to categorize drinking water feasibility data, thereby ensuring that the water consumed meets established safety standards. The data utilized in this study were obtained from an open repository and encompass a total of 3,276 data points. The data set comprises nine water quality parameter attributes, namely pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The data underwent a series of pre-processing steps, including the removal of missing values, the replacement of missing values with the average value of the attribute, and normalization using the MinMax Scaler and Z-score methods. The artificial neural network architecture comprises three principal components: input, hidden, and output neurons. The optimal architecture scenario is [9; 17; 15; 10; 1], comprising nine input neurons, 17 neurons in the initial hidden layer, 15 neurons in the second hidden layer, 10 neurons in the third hidden layer, and a single output neuron. The evaluation results demonstrate that this model effectively classifies drinking water eligibility data with an accuracy rate of 0.6579. However, the results indicate that the accuracy achieved requires further improvement for more reliable applications. These findings illustrate the promising potential of the BPNN method in classifying drinking water quality data.
Penerapan Algoritma FP-Growth dan K-Means Clustering dalam Analisis Pola Asosiasi Berdasarkan Segmentasi Pelanggan Hasibuan, Aldiansyah Pramudia; Insani, Fitri; Nazir, Alwis; Afrianty, Iis
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.7112

Abstract

The pharmaceutical industry has experienced rapid growth, urging companies to leverage sales data effectively to enhance data-driven marketing strategies. However, utilizing sales data remains a challenge for XYZ company, a pharmaceutical distributor. This study aims to analyze customer purchasing patterns by applying the FP-Growth algorithm for association analysis, combined with customer segmentation using the K-Means algorithm based on RFM (Recency, Frequency, Monetary) analysis. The segmentation process resulted in four customer clusters: active and loyal customers (Cluster 1), passive customers (Cluster 2), less active customers (Cluster 3), and new customers (Cluster 4). FP-Growth analysis for each cluster revealed that Cluster 1 generated 10 significant association rules with a minimum support of 0.01 and confidence of 0.7, while Clusters 2, 3, and 4 produced 2, 3, and 4 association rules, respectively, with adjusted parameters. All rules showed a lift value > 1, indicating positive relationships between products. The findings of this study provide strategic insights for companies in designing data-driven marketing approaches, such as more targeted product offerings for loyal customers or retention strategies for passive customers, thereby optimizing sales and increasing profitability in each customer segment.
GAMBARAN KARAKTERISTIK IBU POST SECTIO CESAREA TERKAIT PENYEMBUHAN LUKA Saputri, Ekawati; Afrianty, Iis; Nasus, Evodius
Jurnal Ilmiah Ilmu Kesehatan Vol. 2 No. 1 (2023): Volume 2, Nomor 1, Tahun 2023
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jiik.v2i1.23296

Abstract

Sectio caesarea sebagai metode persalinan yang dilakukan dengan memberikan sayatan terbuka pada dinding rahim sehingga menimbulkan luka pada area perut. Secara global sekitar 21% terjadi persalinan sectio caesarea. Di Indonesia, persalinan sectio caesarea berkisar 17,6%. Penelitian ini bertujuan untuk mengetahui gambaran karakteristik ibu post sectio caesarea terkait penyembuhan luka. Penelitian ini merupakan penelitian kuantitatif deskriptif dengan pendekatan Analisis Data Sekunder (ADS). Jumlah sampel sebanyak 136 dengan menggunakan teknik purposive sampling. Hasil penelitian ini menunjukan bahwa karakteristik ibu post sectio caesarea adalah sebagian besar berusia 20-35 tahun (76,5%) dengan tingkat pendidikan menengah (46,3%) , multipara (69,1%), tidakmemiliki riwayat SC (56,6%) dan tidak menderita anemia (61,8%). Kondisi luka yang dialami oleh ibu post sectio caesarea hampir keseluruhannya adalah luka kering (99,3%). Luka post sectio caesareamengalami penyembuhan dengan baik.
Implementation of Feature Selection Information Gain in Support Vector Machine Method for Stroke Disease Classification Fitri, Anisa; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.116

Abstract

Stroke is a disease with a high mortality and disability rate that requires early detection. However, the main challenge in the classification process of this disease is data imbalance and the large number of irrelevant features in the dataset. This study proposes a combination of Support Vector Machine (SVM) method with Information Gain feature selection technique and data balancing using Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. The dataset used consists of 5,110 data with 10 variables and 1 label. Feature selection was performed with three threshold values (0.04; 0.01; and 0.0005), while SVM classification was tested on three different kernels: Linear, RBF, and Polynomial. Model evaluation was performed using Confusion Matrix and training and test data sharing using k-fold cross validation with k=10. The best results were obtained on the RBF kernel with Cost=100 and Gamma=5 parameters at an Information Gain threshold of 0.0005, with accuracy reaching 90.51%. These results show that the combination of techniques used aims to determine the variables that most affect SVM classification in detecting stroke disease
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 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 Kurnia Gusti, Siska Kurniawan, Saifur Yusuf La Aba Lubis, Anggun Tri Utami BR. Ma'rifah, Laila Alfi Mariany Mariany Maryani Maryani 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