Garuda - Garba Rujukan Digital

PENGARUH PEMBERIAN TERAPI JUS WORTEL TERHADAP PENURUNAN TEKANAN DARAH PADA PENDERITA HIPERTENSI DI RW. 018 KEL. MEKARJAYA KEC. SUKMAJAYA KOTA DEPOK TAHUN 2012 Sari, Anggi Puspita; Herlina, Santi
Jurnal Keperawatan Widya Gantari Indonesia Vol 1 (2014): Jurnal Keperawatan Widya Gantari Indonesia
Publisher : Fakultas Ilmu Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52020/jkwgi.v1i0.837

Hypertension is one of the deadliest diseases in the world which is often called the silent killer. The disease is dangerous because it deals with the cardiovascular, circulatory system is functioning to provide and circulate oxygen supply and nutrients to all tissues and organs are needed in the process of metabolism. Even the estimated number of people with hypertension will increase to 1.6 billion by 2025. This study aims to determine the effect of carrot juice therapy to decrease blood pressure in hypertensive patients. This Quasi-experimental study used a sample of 20 respondents, each intervention group and control group 10 respondents. Data collected and analyzed to meet the criteria using univariate and bivariate using T-test, which consists of test Paired Samples T-test and Independent t-tests. The results showed that there is influence of carrot juice therapy to decrease blood pressure in hypertensive patients, with a value of p = 0.000 for systolic and p-value = 0.001 for diastolic (p-value < 0.05). The results also indicate that there are significant differences between blood pressure reduction in the intervention group and control group (p-value < 0.05). This research is expected to be useful as an input to the world of nursing, family, and especially the client to learn more about nonpharmacological treatment of blood pressure reduction.

Edukasi Literasi Digital terhadap Perkembangan Anak pada TPA Al Ihsan Wati, Embun Fajar; Sari, Anggi Puspita
SENADA : Semangat Nasional Dalam Mengabdi Vol. 2 No. 1 (2021): SENADA: Semangat Nasional Dalam Mengabdi
Publisher : Perkumpulan Dosen Periset Indonesia

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

Teknologi digital sudah menyebar ke seluruh lapisan masyarakat tetapi sebagian besar masyarakat belum mampu menggunakan teknologi tersebut secara baik khususnya terhadap anak-anak. Penggunaan teknologi digital yang tidak tepat yang diberikan kepada anak-anak bisa menimbulkan efek yang tidak baik bagi kelangsungan kehidupan individu dan sosial mereka. Oleh sebab itu literasi digital selayaknya diberikan edukasi agar dapat mendidik kepribadian bangsa terutama generasi penerus bangsa. Literasi digital merupakan dasar pengetahuan yang didukung oleh teknologi informasi yang saling terhubung dengan tujuan untuk memahami bagian-bagian penting dalam literasi digital dan prosedur literasi terhadap anak. Untuk itu kami dari tim Dosen Fakultas Teknik dan Informatika akan mengadaan pengabdian masyarakat mengenai literasi digital di TPA Al Ihsan Duri Kosambi Jakarta Barat dengan harapan pada kegiatan ini, anak-anak mampu menggunakan teknologi digital dengan baik dan benar, mampu memilih mana yang baik dan mana yang buruk. Adapun metode pelaksanaan kegiatan ini dimulai dari persiapan dan pelaksanaan. Persiapan dimulai dari observasi lokasi, wawancara, sampai mencari referensi untuk persiapan acara. Kemudian pelaksanaan nya dilakukan dengan cara memaparkan materi melalui video anak-anak yang disesuaikan dengan tema, storytelling dan diakhiri dengan pemberian games. Kegiatan ini juga akan dipublikasikan dalam bentuk press release di media online.

Improved Naive Bayes Algorithm with Particle Swarm Optimization to Predict Student Graduation Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 6 (2024): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i6.338

Timely graduation is very important for educational institutions such as universities, especially for students. Because it can prove that the University and students are able to undergo the learning process theoretically and practically. But many students do not pay attention to graduation, especially those who are already working or married. Therefore, analysis is needed to predict student graduation so that solutions can be found by the University. Data mining was chosen as a method to process data to get new information. The algorithm used in data mining is Naïve Bayes. The research stages include loading data into excel, cleaning empty data, selecting databases related to graduation and taking data from 300 students majoring in Informatics Engineering. The next stage is data transformation by categorizing student data, namely personal data attributes (gender, age, marital status, job status) and academic data (grade). Data testing, application of Naïve Bayes algorithm and accuracy testing were carried out with Rapis Miner software version 10.3.001. The results of data processing with Rapid Miner using the Naïve Bayes algorithm are shown with the Confusion Matrix and ROC Curve. The results of confusion matrix from data processing with Naïve Bayes in the form of accuracy, precision, and recall have the same result of 100%. The percentage of the Confusion Matrix indicates that the model created can classify correctly and accurately. The ROC curve depicted with AUC yields a value of 1, which means that the test showed excellent results

Prediction of Student Graduation using the K-Nearest Neighbors Method Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 3 (2023): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i3.318

Predictions on the accuracy of student graduation are designed to support study programs in guiding students so that they can graduate on time. The number of student graduations will influence the university's accreditation score. Graduation predictions can provide very useful information in decision-making; therefore, research was conducted on student graduation data. This data will be processed using the K-Nearest Neighbor method. The dataset used consisted of 150 students majoring in informatics engineering. The variables included gender, age, marital status, grade, and job status. The research methodology used in this study consists of 6 stages: Data Collection, Data Selection, Preprocessing, Transformation, Testing, and Evaluation. In the preprocessing or cleaning stage, the data can be fully utilized because all fields have been filled in correctly. Meanwhile, in the transformation stage, the data is categorized as follows: age (young: 19-24, old: 25-50) and grade (large: 3-4, small: 1-2.9). The K-Nearest Neighbor (KNN) method can predict student graduation rates. The KNN method, processed with the RapidMiner 9.9 tool, obtained an average accuracy of 100%. Based on the results of 100% accuracy and an AUC value of 1, it can be concluded that the KNN method is highly accurate in classifying graduation data for the 150 students.

Imbalanced Data NearMiss for Comparison of SVM and Naive Bayes Algorithms Gunawan, Wawan; Devianto, Yudo; Sari, Anggi Puspita
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.485

The study aims to improve the diagnosis, management, and prevention of HIV/AIDS by using classification algorithms. The dataset used consists of 707,379 records and 89 columns. Data preprocessing includes removing irrelevant attributes, handling inconsistencies, and balancing the data using the NearMiss method, resulting in a balanced proportion of reactive and non-reactive HIV cases. Once the data is balanced, it is split into several ratios: 60:40, 70:30, 80:20, and 90:10. The classification models used in this study are Naive Bayes and SVM. The models are evaluated using the metrics Accuracy, Precision, Recall, and F1-Score. The results show that the SVM model achieves the highest accuracy of 82.6% with a 90:10 data split at a 6-fold value, and 82.2% with a 60:40 data split at a 5-fold value. On the other hand, Naive Bayes achieves the highest accuracy of 61.1% with a 60:40 data split.

Prediction of Student Graduation using the K-Nearest Neighbors Method Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 3 (2023): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i3.318

Predictions on the accuracy of student graduation are designed to support study programs in guiding students so that they can graduate on time. The number of student graduations will influence the university's accreditation score. Graduation predictions can provide very useful information in decision-making; therefore, research was conducted on student graduation data. This data will be processed using the K-Nearest Neighbor method. The dataset used consisted of 150 students majoring in informatics engineering. The variables included gender, age, marital status, grade, and job status. The research methodology used in this study consists of 6 stages: Data Collection, Data Selection, Preprocessing, Transformation, Testing, and Evaluation. In the preprocessing or cleaning stage, the data can be fully utilized because all fields have been filled in correctly. Meanwhile, in the transformation stage, the data is categorized as follows: age (young: 19-24, old: 25-50) and grade (large: 3-4, small: 1-2.9). The K-Nearest Neighbor (KNN) method can predict student graduation rates. The KNN method, processed with the RapidMiner 9.9 tool, obtained an average accuracy of 100%. Based on the results of 100% accuracy and an AUC value of 1, it can be concluded that the KNN method is highly accurate in classifying graduation data for the 150 students.

Improved Naive Bayes Algorithm with Particle Swarm Optimization to Predict Student Graduation Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 6 (2024): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i6.338

Timely graduation is very important for educational institutions such as universities, especially for students. Because it can prove that the University and students are able to undergo the learning process theoretically and practically. But many students do not pay attention to graduation, especially those who are already working or married. Therefore, analysis is needed to predict student graduation so that solutions can be found by the University. Data mining was chosen as a method to process data to get new information. The algorithm used in data mining is Naïve Bayes. The research stages include loading data into excel, cleaning empty data, selecting databases related to graduation and taking data from 300 students majoring in Informatics Engineering. The next stage is data transformation by categorizing student data, namely personal data attributes (gender, age, marital status, job status) and academic data (grade). Data testing, application of Naïve Bayes algorithm and accuracy testing were carried out with Rapis Miner software version 10.3.001. The results of data processing with Rapid Miner using the Naïve Bayes algorithm are shown with the Confusion Matrix and ROC Curve. The results of confusion matrix from data processing with Naïve Bayes in the form of accuracy, precision, and recall have the same result of 100%. The percentage of the Confusion Matrix indicates that the model created can classify correctly and accurately. The ROC curve depicted with AUC yields a value of 1, which means that the test showed excellent results

Pengaruh Rasio Keuangan Terhadap Pembiayaan Bermasalah PT Bank Muamalat Tbk Sari, Anggi Puspita; Muhammad Alan Nur; Budi Sukardi
Jurnal Ilmu Perbankan dan Keuangan Syariah Vol. 5 No. 1 (2023)
Publisher : Program Studi Perbankan Syariah Fakultas Ekonomi dan Bisnis Islam UIN Datokarama Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24239/jipsya.v5i1.191.67-82

Tujuan dari studi penelitian ini ialah untuk menguji apakah dampak pengaruh dari rasio keuangan terhadap pembiayaan bermasalah PT Bank Muamalat Tbk dengan indikator seluruh rasio keuangan meliputi rasio keuangan likuditas (FDR), solvabilitas (ROA), (ROE), (NIM) dan biaya operasional (BOPO) serta rentabilitas (CAR). Populasi penelitian dengan mengacu pada laporan keuangan pada PT Bank Muamalat Tbk tahun 1998 hingga 2021 menggunakan metode purposive sampling untuk pengambilan sampel. Dalam studi penelitian ini data diolah dengan alat analisis yaitu aplikasi olah data Eviews 12 menggunakan Metode Least Square (LS) yang menghasilkan nilai parameter model penduga yang lebih tepat, apakah model tersebut menyimpang dari asumsi klasik ataupun tidak serta merupakan teknik peramalan yang menggunakan data deret waktu untuk mengidentifikasi suatu tren tertentu. Digunakan dalam studi penelitian ini, untuk mengolah data dan menguji Asumsi Klasik menggunakan uji regresi linear berganda (Multikolinearitas, Autokorelasi, Heteroskedastisitas, Normalitas, dan Linearitas) serta regresi linier berganda juga diuji. Temuan penelitian ini menunjukkan bahwasanya likuiditas tidak berpengaruh terhadap NPF Nett, rentabilitas berpengaruh terhadap NPF Nett pada variabel ROA, ROE, NIM. Sedangkan variabel BOPO pada rentabilitas tidak memiliki pengaruh terhadap NPF Nett. Serta solvabilitas terbukti signifikan berpengaruh terhadap NPF Net. Dalam hal ini, CAR yang menilai kecukupan modal yang dimiliki suatu bank untuk menentukan solvabilitas berpengaruh terhadap tingkat berapa pembiayaan bermasalah yang terjadi di PT Bank Muamalat Tbk.

How Does Bank Syariah Indonesia's Financial Performance Measure Up Using The RGEC Method's Bank Health Assessment? Sari, Anggi Puspita
Jurnal Keuangan dan Perbankan (KEBAN) Vol. 3 No. 1 (2023): Juli-Desember
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jkk.v3i1.7764

Banks are required to conduct periodic self-assessment of their health level and take effective corrective measures using an assessment of factors including risk profile, good corporate governance (GCG), earnings, and capital, abbreviated as RGEC. The RGEC method is what banks currently use to assess the health level of banks because it is a refinement of previous methods. The degree of bank health as determined by the RGEC technique is the subject of this quantitative descriptive study, which analyzes Bank Syariah Indonesia's health for the years 2019 to 2022. The study's secondary data sources are the yearly financial reports of Islamic Commercial Banks covering the years 2019 to 2022. The following findings are derived from the debate of Bank Syariah Indonesia's Islamic banking sharia for the 2019–2022 evaluation period, The non-performing financing ratio (NPF Nett in composite position 1 may be considered to be in very good health. Conversely, Bank Syariah Indonesia's FDR ratio values in composite positions 1 and 2 might be considered highly healthy. Bank Syariah Indonesia's GCG ratio in composite position 2 may be considered healthy. The Bank Syariah Indonesia's earnings evaluation, or its capacity to turn a profit, received a composite score of 3 to 1, indicating that it is both fairly healthy and extremely healthy from 2019 to 2022. Bank Syariah Indonesia's bank capital was evaluated from 2019 to 2022, and it received a composite evaluation of 1, meaning it is in excellent condition.

The Influence of Macroeconomic Factors on Economic Growth on Indonesia's Receipt and Distribution of ZIS Funding Sari, Anggi Puspita; Sukardi, Budi
ZISWAF ASFA Vol 2 No 1 (2024): ZISWAF ASFA Journal (May 2024)
Publisher : ASFA Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69948/ziswaf.19

Indonesia has significant potential for distributing zakat, which can effectively address issues such as poverty and inequality in the nation. The ratio of ZIS funds received and disbursed by BAZNAS RI serves as the dependent variable in this analysis, which utilizes secondary data from January 2017 to December 2021, illustrating the quantitative approach employed in this investigation. Macroeconomic variables that affect economic growth, such as GDP, interest rates, inflation, poverty, and total money in circulation (JUB), are the independent variables. This study utilized multiple linear regression analysis with the Least Squares (LS) methodology. The classical assumption test consists of many linear regression tests such as Multicollinearity, Autocorrelation, Heteroscedasticity, Normality, and Linearity. The amount of Money in Circulation (JUB) that decreases the poverty rate, and the effect of decreasing the unemployment rate in Indonesia are the findings of the study that will affect the feasibility of channeling ZIS funds in Indonesia. Therefore, it can be concluded that changes in the quantity of GDP, interest rates, and inflation will not affect the receipt or distribution of ZIS funds in Indonesia.

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