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Comparative Analysis of Naïve Bayes and K-Nearest Neighbor (KNN) Algorithms in Stroke Classification Iswara, Ida Bagus Ary Indra; Anandita, Ida Bagus Gede; Dahul, Maria
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4395

Stroke, also known as cerebrovascular, is a type of Non-Communicable Disease (NCD). The symptoms of this disease arise due to a blockage (ischemic) or rupture (hemorrhagic) of a blood vessel that disrupts blood flow to the brain. This condition causes a lack of oxygen and nutrients to brain cells, resulting in damage and potentially death. This research aims to compare the use of Naive Bayes and K-Nearest Neighbor (K-NN) algorithms in classifying stroke diseases. The research process involves data collection, data validation, data preprocessing, data reading, data transformation, data splitting, model implementation, classification evaluation, application of Naive Bayes and K-Nearest Neighbor (K-NN) algorithms, and comparative analysis of results. The variables used in this study include: gender, age, hypertension, heart disease, ever married, work type, residence type, avg glucose level, bmi, smoking status, stroke. Sugar, BMI, Smoking Status, Stroke. Based on the experiments conducted, it was found that the Naive Bayes algorithm achieved an average accuracy rate of 91.67%, while the K-Nearest Neighbor (K-NN) algorithm achieved an average accuracy rate of 95.59%. Therefore, it can be concluded that the K-Nearest Neighbor (K-NN) algorithm has a higher average accuracy rate than the Naive Bayes algorithm, with a percentage difference in accuracy of 3.92%.

Penerapan Metode Single Exponential Smoothing Dalam Peramalan Penjualan Barang Ginantra, Ni Luh Wiwik Sri Rahayu; Anandita, Ida Bagus Gede
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 3, No 2 (2019): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

The technology of buying and selling goods in managing goods in and out will provide convenience for the management in managing stock data, financial control and profit calculation that will be immediately known by stakeholders. Forecasting method is a method that is able to analyze several factors that are known to influence the occurrence of an event with a long grace period between the need for knowledge of an event to occur in the future and the time the event has occurred in the past. In a retail company, if this forecasting method is applied in the planning of goods management, the company will be assisted in the process of planning the sale of goods which is currently still being done by predicting the amount of sales of goods that will come without any calculation, causing excessive purchases of goods that can affect the stock of goods. Single exponential smoothing method is a development of the single moving averages method where the forecasting method is done by repeating calculations continuously using the latest data and each data is weighted. The single exponential smoothing method considers the weight of the previous data by giving weight to each data period to distinguish the priority of data. The single exponential smoothing method is a method used in short-term forecasting that is usually only 1 month ahead which assumes that the data fluctuates around a fixed mean value without consistent trends or growth patterns. The accuracy of the application of the single exponential method in forecasting sales of goods in this study with an alpha value of 0.1 on the MAPE calculation average is 2%.

Penerapan Metode Single Exponential Smoothing Dalam Peramalan Penjualan Barang Ginantra, Ni Luh Wiwik Sri Rahayu; Anandita, Ida Bagus Gede
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 3, No 2 (2019): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v3i2.162

The technology of buying and selling goods in managing goods in and out will provide convenience for the management in managing stock data, financial control and profit calculation that will be immediately known by stakeholders. Forecasting method is a method that is able to analyze several factors that are known to influence the occurrence of an event with a long grace period between the need for knowledge of an event to occur in the future and the time the event has occurred in the past. In a retail company, if this forecasting method is applied in the planning of goods management, the company will be assisted in the process of planning the sale of goods which is currently still being done by predicting the amount of sales of goods that will come without any calculation, causing excessive purchases of goods that can affect the stock of goods. Single exponential smoothing method is a development of the single moving averages method where the forecasting method is done by repeating calculations continuously using the latest data and each data is weighted. The single exponential smoothing method considers the weight of the previous data by giving weight to each data period to distinguish the priority of data. The single exponential smoothing method is a method used in short-term forecasting that is usually only 1 month ahead which assumes that the data fluctuates around a fixed mean value without consistent trends or growth patterns. The accuracy of the application of the single exponential method in forecasting sales of goods in this study with an alpha value of 0.1 on the MAPE calculation average is 2%.

Visual Analysis of Marketplace Sales Data for Strategic Decision Making Using Tableau Dewi, Ni Luh Putu Trisna Kantina; Nilawati, Ni Ketut Utami; Anandita, Ida Bagus Gede
TECHNOVATE: Journal of Information Technology and Strategic Innovation Management Vol. 1 No. 3 (2024): July 2024
Publisher : PT.KARYA GEMAH RIPAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52432/technovate.1.3.2024.156-169

PT Akselerasi Sumber Berkah is a local company engaged in the production and distribution of beauty and health products, under the brand name Beaudelab. This company was established in 2019. PT Akselerasi Sumber Berkah is located in West Denpasar, Bali. In processing sales data, the company uses Microsoft Excel. The weakness of the data or information generated is still in the form of tables that do not display more informative information in the form of graphs, making it difficult for companies to see sales developments and other information in a short time. Therefore, in this study, a sales data visualization using tableau was built to assist companies in processing table data into information in the form of graphs so that it does not take a long time to see the company's sales development. The research method used in this research is the nine steps kimball method. In this research through the stages of analyzing company data, designing a data warehouse, extract transform load process, implementing data visualization, and testing the system. This system was tested using the user acceptance test method and has obtained results with a percentage of 93% or strongly agree so that this sales data visualization is in accordance with the needs of the company. The results of this study are in the form of 3 pages that display company information in the form of graphs to assist in decision making.

Comparative Analysis of Naïve Bayes and K-Nearest Neighbor (KNN) Algorithms in Stroke Classification Iswara, Ida Bagus Ary Indra; Anandita, Ida Bagus Gede; Dahul, Maria
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4395

Stroke, also known as cerebrovascular, is a type of Non-Communicable Disease (NCD). The symptoms of this disease arise due to a blockage (ischemic) or rupture (hemorrhagic) of a blood vessel that disrupts blood flow to the brain. This condition causes a lack of oxygen and nutrients to brain cells, resulting in damage and potentially death. This research aims to compare the use of Naive Bayes and K-Nearest Neighbor (K-NN) algorithms in classifying stroke diseases. The research process involves data collection, data validation, data preprocessing, data reading, data transformation, data splitting, model implementation, classification evaluation, application of Naive Bayes and K-Nearest Neighbor (K-NN) algorithms, and comparative analysis of results. The variables used in this study include: gender, age, hypertension, heart disease, ever married, work type, residence type, avg glucose level, bmi, smoking status, stroke. Sugar, BMI, Smoking Status, Stroke. Based on the experiments conducted, it was found that the Naive Bayes algorithm achieved an average accuracy rate of 91.67%, while the K-Nearest Neighbor (K-NN) algorithm achieved an average accuracy rate of 95.59%. Therefore, it can be concluded that the K-Nearest Neighbor (K-NN) algorithm has a higher average accuracy rate than the Naive Bayes algorithm, with a percentage difference in accuracy of 3.92%.

Pendampingan Instagram Marketing dalam Membangun Ketrampilan Pemasaran Digital dan Brand Awareness Produk UMKM Suandana, Ni Putu Widantari; Aditama, Putu Wirayudi; Sandhiyasa, I Made Subrata; Prabhawa , I Kadek Angga Surya; Atmaja, Ketut Jaya; Sarasvananda , Ida Bagus Gde; Anandita, Ida Bagus Gede
Jurnal KOMET Vol 1 No 1 (2024): Jurnal Komet: Kolaborasi Masyarakat Berbasis Teknologi : Volume 1 Nomor 1, Juni 2
Publisher : Yayasan Sinergi Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/komet.v1i1.11

UMKM di Desa Geluntung, Bali memiliki produk unggulan contohnya produk keripik, meskipun populer secara lokal, menghadapi tantangan dalam memanfaatkan Instagram untuk memperluas jangkauan pasar dan meningkatkan brand awareness. Keterbatasan pengetahuan digital, manajemen konten yang kurang efektif, pemanfaatan fitur Instagram yang tidak optimal, dan pengukuran performa yang lemah adalah beberapa tantangan utama yang dihadapi. Untuk mengatasi masalah ini, kegiatan pelatihan dan pendampingan dalam pemasaran digital melalui Instagram dilakukan. Metode pelaksanaan meliputi pengaturan profil bisnis, pembuatan konten yang menarik, pemanfaatan fitur-fitur Instagram seperti Stories dan Highlights, serta analisis data melalui Instagram Insights. Hasil kegiatan menunjukkan peningkatan pemahaman dan keterampilan digital, serta peningkatan engagement dan brand awareness produk UMKM.

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