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All Journal Jurnal Penelitian Teknologi Informasi dan Sains Modem : Jurnal Informatika dan Sains Teknologi Switch: Jurnal Sains dan Teknologi Informasi
Selfira Selfira
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Computer Science & IT

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Penerapan Algoritma Apriori untuk Rekomendasi Asuransi pada Nasabah: (Studi Kasus: Asuransi Jasindo Kota Medan) Amanda Putri Ardana; Akim M.H. Pardede; Selfira Selfira
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 2 No. 3 (2024): SEPTEMBER : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v2i3.2379

Abstract

Jasindo Insurance Company is one of the insurance companies that receives insurance coverage both directly and indirectly, with ownership of 1 share of dwiwarna series A owned by the Republic of Indonesia and 424,999 shares of Series B owned by PT Bahana Pembinaan Usaha Indonesia (Persero). PT Asuransi Jasa Indonesia has several products and choices in choosing which insurance is needed by customers in agriculture, health, education and many more. Due to the large amount of competition in the business world, it requires management to find the right strategy in increasing the use of Jasindo insurance by knowing the relationship between age, gender, marital status, occupation and the type of Jasindo insurance that is widely chosen by customers. In order to find out the use of insurance that is widely used by the community, it is necessary to analyze the data on the use of insurance using the apriori algorithm method to determine the combination between item-sets of transaction data on Jasindo insurance data. Based on the research conducted after experimenting with the above case with a minimum support = 25%, confidence = 100% so that the results of the rule that meets the support and confidence values are obtained, namely if the gender is male, the marital status is Unmarried, then the type of insurance is jasimdo health and jasindo rainbow then giving value is successful with 25% support, 100% confidence.
Pemodelan K-Nearest Neighbor Untuk Identifikasi Pola Kepuasan Mahasiswa Terhadap Pelayanan Kampus (Studi Kasus : STMIK Kaputama) Muhammad Rizky R Ritonga; Marto Sihombing; Selfira Selfira
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 4 (2024): Oktober : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i4.238

Abstract

This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as "Very Good" and "None", challenges were encountered in classifying the "Fair" and "Deficient" classes. The study concludes that KNN is effective in identifying student satisfaction patterns but highlights the need for improvements in accurately classifying these challenging classes. Ultimately, the research underscores the importance of data quality and feature selection in enhancing the performance of classification models for student satisfaction analysis.
Prediksi Jumlah Pendonor Darah di Kabupaten Langkat Menggunakan Metode Regresi Linear Fajar Amalia Putri; Relita Buaton; Selfira Selfira
Switch : Jurnal Sains dan Teknologi Informasi Vol. 2 No. 5 (2024): September : Switch: Jurnal Sains dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/switch.v2i5.189

Abstract

A blood donor is someone who wants to donate their own blood to people in need without any element of coercion from anyone. Predicting the number of blood donors is very important and necessary to find out the number of blood donors in Langkat Regency in 2023-2024, and the prediction results can help PMI Langkat Regency in increasing the number of blood donors. The method applied in this prediction system is Linear Regression, where this analysis determines whether or not each variable is in accordance with the prediction results being tested and estimates that the value of the variable will increase or decrease each month. The prediction system is carried out using the RapidMiner application because this application is very appropriate for producing information output in the form of prediction results for the coming year. The prediction results obtained by testing using the Linear Regression method show increases and decreases every month. There are 11 months where there has been an increase and decrease in the predicted results and are in accordance with the data in 2023, then there is 1 month which has decreased in the predicted results and does not match the data in 2023. From the overall data results, it can be calculated the number of blood donors in Langkat Regency in 2023 and every month. Measuring the error level of prediction results using RMSE, the resulting accuracy level was 83.574%.
Co-Authors Amanda Putri Ardana Buaton, Relita Fajar Amalia Putri Marto Sihombing Muhammad Rizky R Ritonga Pardede, Akim Manaor Hara