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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) 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) KLIK: Kajian Ilmiah Informatika dan Komputer Media Publikasi Penelitian Kebidanan Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Kepada Masyarakat Meambo TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang Jurnal Ilmiah Ilmu Kesehatan

Iis Afrianty

Universitas Sembilan Belas November Kolaka

Author-ID : 2711542

Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Preeclamsia Perbedaan Kadar Interleukin 6 Serum dan Kadar HsCrp Pada Ibu Hamil Preeklampsia Burhanuddin, Yuniarti Ekasaputri; Syahrianti, Syahrianti; Afrianty, Iis
Window of Health : Jurnal Kesehatan Vol 4 No 3 (Juli 2021 )
Publisher : Fakultas Kesehatan Masyarakat Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33368/woh.v4i03.547

ABSTRACT Levels of Interleukin 6 (IL-6) and hs CRP (high sensitivity C Reactive Protein) increased maternal preeclampsia. Increased hsCRP levels induced by IL6. This study aims to know the differences IL6 serum levels and levels of hsCRP In Preeclamtic pregnancy and Normal pregnancy. The research method using cross sectional study involving 84 pregnant women (42 Preeclamtic pregnancy and 42 Normal Pregnancy) in RSKDIA Siti Fatima and RSKDIA Pertiwi Makassar. Data regarding chronological age and BMI were recorded on all subjects, and hs-CRP and IL-6 concentration was measured by ELLISA method after drained the blood from cubity vein. Preeclamptic pregnancy patients diagnosed by the obstetrician after fullfilled the hospital criteria. Preeclampsia pregnancy was defined as a rise in systolic blood pressure â‰¥140 mmHg and or diastolic blood pressure â‰¥90 mmHg with proteinuria â‰¥300 mg/I for 24 hours urine sample. Layer analysis test was used to compare the characteristic subjects in the group of preeclampsia pregnancy and normal pregnancy group.One Way ANOVA test is used to look at differences in levels of IL-6, hsCRP levels in preeclamtic pregnancy and normal pregnancy. The results showed higher levels of IL-6 preeclamptic woman compared with normal pregnancy group (average difference = 308.8 with a value of p = 0.000 <0.005 and the average difference between the serum levels of IL-6 group of pregnant women with severe preeclampsia different from normal pregnant women mean = 295.5 with a value of p = 0.000 <0.005) and hsCRP levels were also higher in preeclamtic pregnant woman compared to normal pregnant women group (average difference = 0.85 with p = 0.001 <0.005).

Determinan Persalinan dengan Metode Sectio Caesarea di Rumah Sakit Umum Daerah Kabupaten Muna Iis Afrianty; Ika Lestari Salim; Yuniarti Eka Saputri B; Maryani Maryani
Surya Medika: Jurnal Ilmiah Ilmu Keperawatan dan Ilmu Kesehatan Masyarakat Vol 16, No 2 (2021)
Publisher : STIKes Surya Global Yogyakarta

ABSTRACT Background of Study: Delivery through a surgical process through incisions in the abdominal wall and uterine wall to give birth to a fetus is called Sectio Caesarea (SC) delivery. Methods of delivery by CS globally increased from 2000 with 12% of total births (16 million from 131.9) to 2015 to 21% of total births (29.7 million from 140.6 million). In Indonesia, according to Riskesdas in 2018, women aged 15-54 years reached 17.6% of the total number of deliveries. This shows that Indonesia has also experienced an increase in the SC number because in 2013 the SC number only reached 9.8%. Many factors that cause this SC action include premature rupture of membranes, preeclampsia, bleeding, fetal malposition, fetal distress, uterine rupture, CPD and dystocia. Objective to determine the determinants of indicators of Sectio Caesarea (SC)Methods: The type of research used in this study is quantitative. The research method is an analytic survey. in January to April 2021 with a total sample of 153. The type of data taken in this study is secondary data, namely data taken from medical records from the medical record unit.Results: Determinant indicators of caesarean section delivery, namely Chepalopelvic Disproportion (CPD) as many as 56 mothers or 36.8%, then 30 (19.7%) Premature Rupture of Membranes (PROM), Fetal dystocia 17 (14%) mothers, fetuses Macrosomia was 14 (9.2%), placenta previa was 11 (7.2%) and the last was preeclampsia/eclampsia 5 (3.3%).Conclusion: Based on the research objectives, it was concluded that the most common determinant of caesarean section delivery indicators was Chepalopelvic disproportion (CPD). Further research is needed on Maternal Body Mass Index and interpretation of fetal weight on the incidence of CPD and the long-term effects of SC Keywords: Sectio Caesarean , Determinant, CPD, Labor, Maternal Health

Pengaruh Pemberian Holothuria Scabra Terhadap Kadar Docosahexaenoic Acid Pada Air Susu Ibu Dengan Persalinan Preterm: The Influence Of Holothuria Scabra On The Level Of Docosahexaenoic Acid In Breast Milk To Preterm Birth Iis Afrianty; Yuniarti Eka Saputri
Media Publikasi Penelitian Kebidanan Vol. 2 No. 1: MARET 2019
Publisher : Institut Teknologi Kesehatan dan Bisnis Graha Ananda

Bayi preterm membutuhkan Air Susu Ibu (ASI) dengan kadar Docoxahexaenoic Acid (DHA) yang lebih tinggi untuk mengimbangi kekurangan DHA yang dapat mengurangi resiko gangguan inflamasi. Penelitian bertujuan untuk mengetahui pemberian Holothuria scabra terhadap kadar DHA pada ASI dengan persalinan preterm. Jenis penelitian quasi eksperimental dengan rancangan pre-posttest with control group design. Sampel dalam penelitian ini ibu postpartum dengan persalinan preterm sebanyak 40 orang yang dibagi menjadi 2 kelompok. Ibu postpartum yang diberikan kapsul Holothuria scabra adalah kelompok intervensi dan ibu postpartum yang tidak diberikan kapsul Holothuria scabra adalah kelompok kontrol. Pengambilan sampel ASI pre-test masing-masing kelompok pada hari ketujuh kemudian diambil kembali tujuh hari kemudian sebanyak 3 cc. Pemberian intervensi dilakukan selama 7 hari/sampel dengan dosis 3 kali sebanyak 2 kapsul sehari. Sampel ASI akan diperiksa dengan Human DHA ELISA kit. Analisa yang digunakan menggunakan Uji Mann Whitney dan Uji Wilcoxon. Hasil penelitian menunjukkan ada perbedaan kadar DHA antara kelompok intervensi dan kelompok kontrol dengan p-value 0,006. Setelah intervensi diperoleh rata-rata peningkatan 187.02 ug/ml pada kelompok intervensi dan kelompok kontrol sebesar 7.05 ug/ml. Nilai maximum kadar DHA terdapat pada kelompok intervensi dengan nilai 1151,04 ug/ml. Dari hasil penelitian dapat disimpulkan bahwa kelompok yang diberikan kapsul Holothuria scabra lebih efektif peningkatan kadar DHA dibandingkan tanpa diberikan kapsul Holothuria scabra. Kapsul Holothuria scabra dapat membantu mempengaruhi peningkatan kadar DHA pada ASI dengan persalinan preterm.

Mitigasi Bencana Pesisir: Pemberdayaan Komunitas Nelayan Sipatuo melalui Penanaman Mangrove di Kelurahan Tahoa, Kabupaten Kolaka, Sulawesi Tenggara Hasidu, La Ode Abdul Fajar; Bantun, Suharsono; Saleh, Ramlah; Afrianty, Iis; Aba, La; Sety, La Ode Muhamad; Hasria, Hasria; Arif, Arif Prasetya; Arianto, Arianto; Yulianti, Eva Tri; Kamaruddin, Anggi Ashari; Alghi, Anugerah Febryan; Safar, Muhammad; Hamid, Fanul
DHARMA RAFLESIA Vol 21 No 2 (2023): DESEMBER (ACCREDITED SINTA 5)
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/dr.v21i2.30751

Wilayah pesisir sering kali menjadi sasaran bencana alam, yang dapat berdampak serius pada komunitas nelayan. Pengurangan ekosistem mangrove di pesisir dapat meningkatkan risiko banjir, erosi pantai, dan gelombang tinggi. Oleh karena itu, dalam program kegiatan Kosabangsa (Kolaborasi Sosial Membangun Masyarakat), pengabdian masyarakat ini bertujuan untuk membantu komunitas nelayan Sipatuo di Kelurahan Tahoa, Sulawesi Tenggara dalam menghadapi masalah bencana di wilayah pesisir yang merupakan tempat bermukim mereka. Kegiatan penanaman pohon mangrove dilakukan sebagai cara untuk melindungi pantai dan meningkatkan pemahaman komunitas tentang pentingnya pohon mangrove. Masyarakat dilibatkan dalam sosialisasi dan penanaman mangrove. Hasilnya sangat positif, dengan pemahaman masyarakat meningkat sekitar 18%, dan hampir semua orang ikut berpartisipasi. Ini menunjukkan bahwa kegiatan ini sangat efektif dalam memberikan pengetahuan kepada Masyarakat pesisir. Kegiatan pengabdian ini penting dalam upaya pelestarian lingkungan pesisir di wilayah Sulawesi Tenggara. Perlu diadakan penelitian untuk melihat dampak jangka panjang dari upaya mitigasi bencana pesisir dan bagaimana pelestarian mangrove dapat membantu komunitas dalam menghadapi bencana pesisir.

Penerapan Neural Network dengan Menggunakan Algoritma Backpropagation pada Prediksi Putusan Perceraian Zulastri, Zulastri; Afrianty, Iis; Budianita, Elvia; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2437

The high divorce rate has a negative impact on couples who will file for divorce and also has an extreme impact on children such as psychological disorders of children. The magnitude of the impact of divorce, it is necessary to predict the divorce decision. In this study, the application of the backpropagation method to predict divorce decisions was carried out. The data used is data on divorce decisions from the Pekanbaru Religious Court from 2020 - 2021 totaling 779. The dataset obtained is not balanced with 724 accepted classes and 55 rejected classes, balancing is done by reducing excess classes. The parameters used in this study build 3 architectural models [6-7-1], [6-9-1], [6-12-1], learning rate (0.01, 0.03, 0.09), max epoch and data sharing (70:30), (80:20), (90:10). The results of this study indicate that the best architectural model is in the network architecture [6-9-1] learning rate 0.09 epoch 300 dataset distribution 80% training data and 20% test data the accuracy value is 80% and the Mean Squared Error (MSE) is 0.1402. In this study, the backpropagation method was successful in predicting divorce decisions.

Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology Harni, Yulia; Afrianty, Iis; Sanjaya, Suwanto; Abdillah, Rahmad; Yanto, Febi; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3625

One approach to handling large of data dimensions is feature selection. Effective feature selection techniques produce the essential features and can improve classification algorithms. The accuracy performance results can measure the accuracy of the method used in the classification process. This research uses the Learning Vector Quantization (LVQ) 1 method combined with Gain Ratio feature selection. The data used is male and female skull bone measurement data totaling 2524. The highest accuracy results are obtained by LVQ 1, which uses a Gain Ratio with a threshold of 0.01 with a learning rate = 0.1, which is 92.01%, and the default threshold weka(-1.7976931348623157E308) with a learning rate = 0.1, which is 92.19%. In comparison, previous research that did not use gain ratio or that did not use GR only had the best results of 91.39% with a learning rate = 0.1, 0.4, 0.7, 0.9. This shows that LVQ 1 using the Gain Ratio can be recommended to improve the performance of the Skull dataset compared to LVQ 1 without Gain Ratio.

Pengaruh Image Enhancement Contrast Stretching dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning Yanto, Febi; Hatta, M Ilham; Afrianty, Iis; Afriyanti, Liza
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4233

Kidney tumors are the third most common after prostate and bladder tumors, accounting for around 208,500 cases (2%) of all cancer cases globally. Renal Cell Carcinoma constitutes 85% of these cases, transitional cell cancer 12%, and other types 2%. In Indonesia, the incidence is 3 per 100,000 people, with a male-to-female ratio of 3.2:1. Ultrasound, CT scans, and MRI are used to detect, diagnose, and assess kidney tumors, with CT scans being crucial for evaluating complex lesions, both cystic and solid. This study uses the Image Enhancement Contrast Stretching technique to improve CT-Scan image quality for deep learning classification using the EfficientNet-B0 architecture. The dataset is split into training, validation, and testing sets in an 80:20 ratio. Hyperparameters include Adamax and RAdam optimizers with learning rates of 0.01, 0.001, and 0.0001. The highest performance was achieved using the Image Enhancement Contrast Stretching technique with the RAdam optimizer and a learning rate of 0.01, resulting in 100% accuracy, precision, recall, and F1-score. For the original dataset using the Adamax optimizer with a 0.01 learning rate, the highest performance was 99.12% accuracy, 98.28% precision, 100% recall, and 99.13% F1-score. This technique significantly enhances the performance of kidney tumor classification models.

Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin dalam Antropologi Forensik Menggunakan Metode Support Vector Machine Rahayu, Siti Sri; Afrianty, Iis; Budianita, Elvia; Syafria, Fadhilah
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4046

Classification of skull bones by sex is part of human biological profile identification in forensic anthropology that aims to determine whether the skeleton belongs to a male or female. The most popular method for determining sex from bones is DNA analysis. However, under some conditions such as burnt, damaged, or very dry skeletal remains, DNA analysis cannot provide accurate results. So forensic anthropology is developing by utilizing the help of machine learning technology. This research shows the performance of Support Vector Machine in classifying skull bones based on gender. The skull parameter data used is data collected by Dr. William Howells from craniometric measurements consisting of male and female data with a total of 2524 data and 82 features, namely bizygomatic breadth, glabello-occipital lenght and others.Â In building the skull bone classification model, the Support Vector Machine kernels used are linear, RBF, and polynomial. Based on the test results, the best accuracy was obtained in each kernel function, namely the linear kernel obtained the best accuracy of 88.14% with C = 2. For the RBF kernel, the best accuracy was 91.30% at C = 2, Î³ = 'auto'. For the polynomial kernel, the best accuracy was 88.14% at C = 1 and 2, Î³ = 1 and 2, d = 1. The evaluation results show that the Support Vector Machine model with the RBF kernel has proven to be the optimal choice in skull bone classification compared to other kernels, based on accuracy, precision, recall, and CrossValidation measurements reaching values above 90%. These results indicate that the skull bone classification model based on gender using Support Vector Machine is recommended in forensic anthropology.

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

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

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

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 Baehaqi Bangu, Bangu Burhanuddin, Yuniarti Ekasaputri Butar-Butar, Rio Juan Hendri Dewi Nasien Dinata, Ferdian Arya Elvia Budianita Fadhilah Syafria 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 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 Sety, La Ode Muhamad Siti Sri Rahayu Suharsono Bantun Surya Agustian Suwanto Sanjaya Syahrianti Syahrianti 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

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