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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Surya Medika Jurnal CoreIT 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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PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN MODIFIED K-NEAREST NEIGHBOR UNTUK KLASIFIKASI KELUARGA BERESIKO STUNTING Pratama, Dandi Irwayunda; Insani, Fitri; Yanto, Febi; Afrianty, Iis
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.24698

Abstract

Stunting disebabkan oleh kekurangan gizi kronis, yang menghambat pertumbuhan terhambat pada anak dan dapat memengaruhi kesehatan jangka panjang. Penelitian ini bertujuan untuk mengklasifikasikan keluarga beresiko stunting menggunakan metode K-Nearest Neighbor (K-NN) dan Modified K-Nearest Neigbor (MK-NN). Perbandingan keduanya dilakukan dengan tujuan memberikan gambaran lebih jelas mengenai metode mana yang lebih cocok dalam membantu dalam memilih algoritma yang memberikan hasil yang optimal. Data yang digunakan terdiri dari 23607 data keluarga dan 20 parameter, diperoleh dari Balai Penyuluhan KB (Kampung Berencana) di Kecamatan Tuah Madani. Hasil menunjukkan bahwa MK-NN memberikan performa lebih konsisten pada berbagai nilai k dengan akurasi mencapai 99.28% terutama pada rasio 80:20 dan 70:30. Sebaliknya, K-NN mencapai akurasi maksimum 99.36% tetapi mengalami fluktuasi pada nilai k tertentu. MK-NN juga unggul dalam metrik precisision, recall dan f1-score menunjukkan mampu menghadapi data yang kompleks. Dapat disimpulkan bahwa MK-NN lebih efektif dan stabil dibandingkan K-NN. Penelitian ini menyarankan penggunaan data ekonomi seperti pendapatan dan pekerjaan orang tua pada studi mendatang untuk memberikan hasil klasifikasi yang lebih menyeluruh dan akjrat dalam mendukung kebijakan stunting.
PENERAPAN TEKNIK SMOTE PADA KLASIFIKASI PENYAKIT STROKE DENGAN ALGORITMA SUPPORT VECTOR MACHINE Pasiolo, Lugas; Afrianty, Iis; Budianita, Elvia; Abdillah, Rahmad
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.24731

Abstract

Stroke adalah kondisi darurat medis yang dapat menyebabkan kerusakan otak atau kematian. Deteksi dini dan klasifikasi risiko stroke sangat penting untuk pencegahan dan penanganannya. Penelitian ini menggunakan dataset sebanyak 5110 data untuk meningkatkan akurasi klasifikasi stroke dengan algoritma Support Vector Machine (SVM) pada data tidak seimbang. Teknik Synthetic Minority Over-sampling Technique (SMOTE) diterapkan untuk menyeimbangkan data stroke dan non-stroke, yang dapat meningkatkan performa model. SVM diuji dengan berbagai kernel, yaitu Linear, RBF, Polynomial, dan Sigmoid, serta variasi parameter pada masing-masing kernel untuk mencari konfigurasi optimal. Hasil pengujian menunjukkan penerapan SMOTE meningkatkan akurasi, presisi, dan recall, dengan kernel RBF mencapai akurasi tertinggi 92% pada parameter Cost 100 dan Gamma 1. Temuan ini menunjukkan bahwa penggunaan SMOTE dan optimasi parameter SVM dapat menghasilkan model klasifikasi yang lebih efektif dalam mendeteksi risiko stroke pada data tidak seimbang.
EKSPLORASI FITUR FASTTEXT, TF-IDF DAN INDOBERT PADA METODE K-NEAREST NEIGHBOR UNTUK KLASIFIKASI SENTIMEN Putri, Atika; Agustian, Surya; Jasril, Jasril; Afrianty, Iis
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.24779

Abstract

Sentiment classification is essential for analyzing public opinion, particularly on social media issues. One of the main challenges in sentiment classification is the limited amount of training data, which often affects the model's ability to make accurate predictions. This study examines Kaesang Pengarep's appointment as PSI chairman using feature extraction methods such as FastText, TF-IDF, and IndoBERT, alongside the K-Nearest Neighbor (KNN) algorithm. Optimization steps include adding external data, refining text preprocessing, applying data scaling, and tuning parameters. The baseline model achieved 44% accuracy and 39% F1-score using FastText. After optimization and switching to IndoBERT, the optimal model achieved 57% accuracy and 49% F1-score, showing a 10% improvement. These findings demonstrate that optimizations, such as advanced feature extraction and parameter tuning, significantly impact sentiment classification. Future research could focus on advanced optimization techniques to address data limitations and enhance sentiment analysis performance. Keywords: Sentiment Classification, Model Optimisation, K-Nearest Neighbor, FastText, TF-IDF, IndoBERT.
The Effect of Progressive Muscle Relaxation Exercises on the Sleep Quality of Menapause Women in the Kolaka Health Center Work Area Naim, Rosani; Mariany, Mariany; Saputri, Ekawati; Afrianty, Iis
Poltekita : Jurnal Ilmu Kesehatan Vol. 17 No. 3 (2023): November
Publisher : Poltekkes Kemenkes Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33860/jik.v17i3.3354

Abstract

Menopause is the final phase of a woman's reproduction or it is said that the last menstruation experienced by a woman has a psychological impact, especially sleep disorders.. Four of five women who were surveyed stated that their sleep was often disturbed, especially when it was difficult to fall asleep and woke up feeling it was the middle of the night so they complained of blisters in the morning because the duration of sleep at night was around 4 o'clock. The purpose of this study was to analyze the quality of sleep of mothers before and after carrying out progressive muscle relaxation exercises. Quasy Experiment Method in one group pre test-post test design is the method used with a sample of 30 respondents. The quality of sleep before exercise and after exercise progressive muscle relaxation was measured using a questionnaire. Provision of progressive muscle relaxation exercises in accordance with Standard Operating Procedures (SOP). There were significant differences in the quality of sleep of mothers before and after progressive muscle relaxation exercises on sleep quality of postmenopausal women, namely sleep quality (p value 0.000), sleep latency (p value 0.000), efficiency (p value 0.000), sleep disturbance (p value 0.000), sleep disturbance (p value 0.000), value 0.001), and disturbed activity (0.005). Postmenopausal women's sleep quality improves after doing progressive muscle relaxation exercises is effective. It can be said that the application of progressive muscle relaxation exercises in menopausal women is very good so that the quality of sleep for menopausal women is of high quality.
Prediksi Harga Kelapa Sawit Menggunakan Metode Extreme Learning Machine Hariansyah, Jul; Budianita, Elvia; Jasril, Jasril; Afrianty, Iis
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4858

Abstract

Palm oil is one of the keys to the Indonesian economy and the main commodity for attracting foreign investment. The palm oil and palm kernel industry generates most of the foreign currency from palm oil. The price of palm oil often goes up and down every month resulting in instability in the income received by people who own oil palm plantations. The aim of predicting palm oil prices is to carry out appropriate planning or steps for palm oil business actors. One way to overcome this problem is to make predictions. One method that can make predictions is the Extreme Learning Machine (ELM). ELM is an artificial neural network method used to predict palm oil prices. The ELM method is a feedforward method with a single hidden layer which is better known as a single hidden layer feedforward neural network (SLFNs). In this research, the best implementation was 5 inputs with 20 neurons in the hidden layer with output in the form of palm oil price predictions. Based on the tests carried out, the research produced the smallest error rate of 0.0027111424247658633 using 20 neurons in the hidden layer so that the latest data prediction test results for 5 price rotations in September rotation 1 were 1400.314191, September rotation 2 were 1846.798921, September rotation 3 amounted to 1505.430419, September rotation 4 amounted to 2301.853412, September rotation 5 amounted to 2645.082489 in palm oil price predictions.
Perbandingan Performa Klasifikasi Terjemahan Al-Qur'an Menggunakan Metode Random Forest dan Long Short Term Memory Aftari, Dhea Putri; Safaat, Nazruddin; Agustian, Surya; Yusra, Yusra; Afrianty, Iis
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5156

Abstract

This study focuses on the use of the Qur'an as the primary source of Islamic teachings, aiming to facilitate Muslims' understanding of its content. To achieve this, the classification of translated Qur'anic verses was conducted. Two methods that are rarely used for Qur'anic translation data are Random Forest (RF) and Long Short Term Memory (LSTM) due to their ability to process large and complex data. The data used in this study are translations of the Qur'an that have been classified into 15 topics by previous research, but this study will only focus on 6 topics. The objective of this research is to compare the performance of RF and LSTM in classifying Qur'anic translations into 6 different categories. The results show that in the preaching category, LSTM consistently outperformed RF, with an F1-Score of 57.3% and an accuracy of 96.8%, whereas RF achieved an F1-Score of 49.4% and an accuracy of 97.5%. These findings indicate that LSTM has better performance, especially with proper preprocessing, optimal parameter tuning, and balanced data. This study provides important insights into the development of classification models for Qur'anic translation texts, highlighting the importance of proper preprocessing and parameter tuning.
Implementasi Data Mining Untuk Prediksi Stok Penjualan Keramik dengan Metode K-Means Dinata, Ferdian Arya; Nazir, Alwis; Fikry, Muhammad; Afrianty, Iis
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5200

Abstract

Ceramics has become one goods that consumers show interest in every year, so many companies are interested in selling ceramics. However, ceramic sales must meet and balance changing customer needs as well as problems found regarding ceramic products and customers, such as a lack of stock of ceramic products which results in customers not placing orders and product sales not meeting targets. So it is necessary to group ceramics to anticipate the risks that the company will accept by utilizing the data mining process using past data. This research uses the K-Means method found in data mining. The objective of this research is to group determine sales of brands that have potential for additional stock in the future and to test the data using the DBI (Davies Bouldin Index) which is carried out by testing the distance values between clusters through a series of experiments. This research uses data for the last 1 year from January 2022 to December 2022 with a total of 156 data using 9 attributes, namely brand, item code (FT, WT) and size (40x40, 25x25, 50x50, 25x40, 60x60, 20x40). The results of the research using the K-Means method, the best-selling brand is cluster 2, the best-selling brand is cluster 1 and the best-selling brand is cluster 0. The best-selling brand is HRM, the best-selling brand is VALENSIA and the best-selling brand is MCC. Test results using the DBI method with a validity of 01.013 show that the best cluster is obtained at k=3 using the elbow method. It is hoped that this research will contribute to related companies as support for decision making.
Implementasi Algoritma K-Means dalam Menentukan Clustering pada Penilaian Kepuasan Pelanggan di Badan Pelatihan Kesehatan Pekanbaru Fahrozi, Aqshol Al; Insani, Fitri; Budianita, Elvia; Afrianty, Iis
Indonesian Journal of Innovation Multidisipliner Research Vol. 1 No. 4 (2023): December
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijim.v1i4.53

Abstract

This research discusses the implementation of the K-Means algorithm in determining clustering in customer satisfaction assessments at the Pekanbaru Health Training Agency. Customer satisfaction is the level of a person's feelings to perceive the comparison between the consumer's impression of the level of product and service performance and the customer's or buyer's expectations. The aim of this research is to see the level of customer satisfaction with the Pekanbaru Health Training Agency (Bapalkes) services using K-means clustering and how high the level of customer satisfaction is using the K-means Clustering method. In this research, the data used is Health Training Center customer data from 2019 and 2023. Data was collected through questionnaires distributed via Google form. Creating a rule model for the collected data using the k-means algorithm and rapidminer software. From the research results obtained using the K-Means algorithm in clustering customer data, it can provide customer segmentation results that are in line with expectations, so that the Pekanbaru Health Training Agency can easily understand the characteristics of its customers based on their clusters and their satisfaction. Then, using the elbow and Davies Bouldin methods, we also provide a solution for selecting the right number of clusters so that performance is more optimal and produces more accurate customer segmentation results. From the calculations of the k-means algorithm, it was obtained that the response value was very dominant at 259 who expressed satisfaction and 44 people who expressed dissatisfaction from 303 customers, so that the k-means algorithm used sensitivity and specificity tests, 86% expressed satisfaction and 14% expressed dissatisfaction with services provided by the Pekanbaru Health Training Agency.
Comparison of Triple Exponential Smoothing and Support Vector Regression Algorithms in Predicting Drug Usage at Puskesmas Agnesti, Syafira; Nazir, Alwis; Iskandar, Iwan; Budianita, Elvia; Afrianty, Iis
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3499

Abstract

Drug management is important in managing adequate drug supplies in Puskesmas, to avoid errors in controlling existing drug stock inventory, it is necessary to predict the amount of drug usage by comparing Data Mining methods and Machine Learning methods, using the Triple Exponential Smoothing (TES) and Support Vector Regression (SVR) algorithms. Implementation is done using the Python programming language. The data used is Amlodipine 10 mg and Amoxicillin 500 mg drug data with a period of 42 months, from January 2020 - June 2023. This study aims to determine the best algorithm by comparing prediction error rate using the Mean Absolute Percentage Error (MAPE) method. Based on research that has been conducted on Amlodipine 10 mg and Amoxicillin 500 mg drugs with a division of 80% training data and 20% testing data, the Triple Exponential Smoothing algorithm with an additive model produces MAPE values of 10.36% and 17.50% respectively with the "Good" category. While Support Vector Regression algorithm, with RBF kernel, complexity 1.0, and epsilon 0.1 produces MAPE values of 10.31% and 9.38% in the "Good" and "Very Good" categories, respectively. Based on this, it can be concluded that Support Vector Regression algorithm is better at predicting than the Triple Exponential Smoothing algorithm.
Faktor-Faktor Penyebab Putus Minum Obat Anti TBC di Puskesmas Kolakaasi Kecamatan Latambaga: Factors Causing Out of Taking-Tb Medications at Kolakasi Health Center Latambaga District Bangu, Bangu; Afrianty, Iis; Ayu Lestari, Fajar Vilbra
Jurnal Surya Medika (JSM) Vol. 10 No. 3 (2024): Jurnal Surya Medika (JSM)
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/jsm.v10i3.8964

Abstract

Tuberkulosis adalah penyakit menular melalui kontak langsung dengan penderita, disebabkan oleh kuman Mycobacterium tuberculosis. Putus minum obat adalah Penderita tuberculosi basil tahan asam positif, berturut - turut dua bulan atau lebih tidak minum obat sebelum masa pengobatannya selesai. Angka putus minum obat di Puskesmas Kolakaasi diperkirakan mencapai 40%. Salah satu penyebab putusnya minum obat anti tuberculosis pasien penderita tuberkulosis paru diantaranya efek samping obat anti tuberkulosis seperti:  Hilang nafsu makan (mual), gangguan penglihatan dan gangguan pendengaran. Akibatnya pasien bisa kebal terhadap obat Multi Drugs Resisten. Tujuan: Penelitian ini bertujuan untuk mengetahui penyebab putus minum obat pada penderita tuberkulosis Paru di Wiayah kerja Puskesmas Kolakaasi.   Metode: Penelitian ini bersifat kuantitatif, yaitu penelitian ilmiah yang sistematis terhadap bagian-bagian dan fenomena serta hubungannya. Desain penelitian adalah Cross Sectional, suatu penelitian untuk mempelajari dinamika korelasi antara faktor-faktor risiko dengan efek, dengan cara pendekatan, observasional, pengumpulan data dengan wawancara lansung kepada penderita. Populasi adalah keseluruh pasien tuberculosi basil tahan asam positif sebanyak 63 pasien di puskesmas Kolakaasi selama tahun 2023, dengan teknik total sampling. Pengambilan data menggunakan lembar kuesioner yang disusun oleh peneliti Hasil: Hasil Uji antara efek samping obat tuberkulosis dengan putus minum obat tuberkulosis (p-value 0,000 < α 0,05).  Artinya Ada hubungan yang signifikan antara efek samping obat anti tuberkulosis dengan putus minum obat Kesimpulan: Efek samping obat anti tuberkulosis seperti: Nafsu makan menurun (mual), penglihatan kabur dan gangguan pendengaran menyebabkan putus minum obat anti tuberkulosis pada pasien di Wilayah kerja Puskesmas Kolakaasi kecamatan Latambaga.
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 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 Kurniawan, Saifur Yusuf La Aba Lubis, Anggun Tri Utami BR. Ma'rifah, Laila Alfi Mariany Mariany Maryani Maryani Matondang, Irfan Jamal 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