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Hasil Pencarian

Ditemukan 4 dokumen yang sesuai dengan query
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Sri Safitri Ramadhani
"Saat ini internet sudah menjadi kebutuhan bagi seluruh pihak karena kegunaannya di berbagai aspek kehidupan. Hal tersebut memicu bertambahnya jumlah pengguna internet di seluruh dunia, termasuk di Indonesia. Namun, banyaknya jumlah pengguna tersebut tidak dibarengi dengan peningkatan kualitas dan pemerataan akses internet yang baik. Masih ada wilayah di Indonesia yang memiliki kualitas jaringan internet buruk bahkan tidak terjangkau internet. Pada penelitian ini, dilakukan pengelompokan wilayah di Indonesia berdasarkan kualitas jaringan internet menggunakan Algoritma Genetika KMedoids. Pengelompokan ini bertujuan untuk mengumpulkan wilayah dengan karakteristik yang sama berdasarkan kualitas jaringan internet, sehingga dapat dilihat wilayah mana saja yang sudah memiliki kualitas jaringan internet yang baik dan wilayah mana saja yang masih perlu perbaikan. Data yang digunakan adalah data sekunder dari website Speedtest dengan total 84 wilayah. Adapun variabel yang digunakan di antaranya yaitu Mobile Download, Fixed Download, Mobile Upload, Fixed Upload, Mobile Latency, Fixed Latency, Mobile Provider, dan Fixed Provider. Hasil dari penelitian ini didapatkan 6 klaster dengan nilai evaluasi Davies-Bouldien Index sebesar 0,7647. Klaster 1 terdiri dari 19 wilayah dengan kualitas internet kurang baik, klaster 2 terdiri dari 27 wilayah dengan kualitas internet yang standar, klaster 3 terdiri dari 12 wilayah dengan kualitas internet baik, klaster 4 terdiri dari 6 wilayah dengan kualitas internet yang sangat baik, dan klaster 5 terdiri dari 6 wilayah dengan kualitas internet yang cukup baik.

Internet has now become an essential necessity for everyone due to its utility in various aspects of life. This has led to an increase in the number of internet users worldwide, including in Indonesia. However, the growing number of users has not been accompanied by an improvement in the quality and equitable access to internet services. There are still areas in Indonesia with poor internet quality or even lack of internet access. In this study, a clustering of territory in Indonesia based on internet quality was performed using the Genetic Algorithm K-Medoids. The objective of this clustering was to group territories with similar internet quality characteristics, in order to identify territories that already have good internet quality and territories that require improvement. The data used in the study was obtained from the Speedtest website and covered a total of 84 territories. The variables used included Mobile Download, Fixed Download, Mobile Upload, Fixed Upload, Mobile Latency, Fixed Latency, Mobile Provider, and Fixed Provider. The results of the study revealed 6 clusters with a Davies-Bouldin Index evaluation score of 0,647. Cluster 1 consists of 19 territories with poor internet quality, cluster 2 consists of 27 territories with standard internet quality, cluster 3 consists of 12 territories with good internet quality, cluster 4 consists of 6 territories with very good internet quality, and cluster 5 consists of 6 territories with fairly good internet quality."
Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Unversitas Indonesia, 2023
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UI - Skripsi Membership  Universitas Indonesia Library
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Nur Azizah Vidya
"[ABSTRAK
Banyaknya jejaring sosial yang bermunculan. Salah satu jejaring sosial yang marak digunakan adalah twitter. Kegiatan promosi produk melalui twitter sudah mulai digunakan PT XL Axiata Tbk (XL) sejak tahun 2009 melalui akun @XL123. Penggunaan twitter oleh perusahaan telekomunikasi di Indonesia masih dalam tahap penjualan dan promosi.
Namun demikian, analisis yang dilakukan hanya terbatas pada perhitungan jumlah retweet, komentar, dan follower. Analisis belum melihat sejauh mana makna komentar dari pelanggan maupun masyarakat. Hal ini akan mempengaruhi keputusan membeli masyarakat jika komentar yang diberikan negatif, dan sebaliknya komentar positif akan meningkatkan citra perusahaan di mata stakeholder. Hal ini dapat dilihat dari fakta bahwa rating yang diperoleh XL Axiata tidak sesuai dengan ekspektasi brand tersebut, yaitu rating 3 dari 10. Sedangkan ekspektasi yang diharapkan berdasarkan analisa perbandingan jumlah follower dan following, semestinya XL Axiata memiliki reputasi yang bagus yaitu 7-8.
Penelitian ini melakukan perhitungan reputasi dari produk XL Axiata, dan membandingkannya dengan produk Telkomsel dan Indosat. Selanjutnya dilakukan beberapa teknik ekstrak data, analisis sentimen, serta membandingkan tiga algoritma klasifikasi: Naïve Bayes, Support Vector Machine, dan Decision Tree. Tahap evaluasi performansi menggunakan precision, recall, f-measure, dan kurva ROC (AUC). Hasil menunjukkan bahwa model yang dibentuk oleh SVM memberikan performansi yang lebih baik untuk selanjutnya digunakan untuk melakukan perhitungan Net Brand Reputation. Perhitungan NBR dilakukan di produk 3G, 4G, Voice, SMS, dan Internet (data). Berdasarkan perbandingan kelima produk ini, XL Axiata memperoleh rata-rata nilai reputasi yang lebih di dibandingkan Telkomsel dan Indosat yaitu sebesar 24.5%, sedangkan Telkomsel hanya memperoleh 13.2% dan Indosat 19.3%.

ABSTRACT
The internet in Indonesia has grown rapidly, it proved by many social media comes up. One of famous social media is twitter. Campaign product using twitter had been used by XL Axiata since 2009 through account @XL123. Unfortunately, the using of twitter in Indonesia telecommunication company still in the stage of sales and promotions.
However, the analysis only calculated number of retweets, comments, dan followers. Analyzes haven?t seen what is the meaning of those comments, whether be positive or negative for XL brand products. Negative comments giving influence to society buying decision, while positive comments create good reputation to stakeholders. This is showned by a fact that the rating obtained XL Axiata does not correspond to the brand?s expectation, ie rating 3 out of 10. While expectation based on comparative analysis of number of followers and following, XL Axiata should have a good reputation in rate 7-8.
This study not only calculating XL product but also Telkomsel and Indosat for comparative analysis. Hereafter, we extracted features, algorithms and the classification schemes. Evaluation phase using precision, recall, f-measure and ROC curve (AUC). The sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. The result shows model built by SVM is the best result. Using this model, we measure Net Brand Reputation in 5 products which are 3G, 4G, Voice, SMS, and Internet (data). The experiments showned XL Axiata has the highest reputation score rather than Telkomsel and Indosat with average NBR score 24,5%, while Telkomsel only 13.2% and Indosat 19.3%.;The internet in Indonesia has grown rapidly, it proved by many social media comes up. One of famous social media is twitter. Campaign product using twitter had been used by XL Axiata since 2009 through account @XL123. Unfortunately, the using of twitter in Indonesia telecommunication company still in the stage of sales and promotions.
However, the analysis only calculated number of retweets, comments, dan followers. Analyzes haven?t seen what is the meaning of those comments, whether be positive or negative for XL brand products. Negative comments giving influence to society buying decision, while positive comments create good reputation to stakeholders. This is showned by a fact that the rating obtained XL Axiata does not correspond to the brand?s expectation, ie rating 3 out of 10. While expectation based on comparative analysis of number of followers and following, XL Axiata should have a good reputation in rate 7-8.
This study not only calculating XL product but also Telkomsel and Indosat for comparative analysis. Hereafter, we extracted features, algorithms and the classification schemes. Evaluation phase using precision, recall, f-measure and ROC curve (AUC). The sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. The result shows model built by SVM is the best result. Using this model, we measure Net Brand Reputation in 5 products which are 3G, 4G, Voice, SMS, and Internet (data). The experiments showned XL Axiata has the highest reputation score rather than Telkomsel and Indosat with average NBR score 24,5%, while Telkomsel only 13.2% and Indosat 19.3%., The internet in Indonesia has grown rapidly, it proved by many social media comes up. One of famous social media is twitter. Campaign product using twitter had been used by XL Axiata since 2009 through account @XL123. Unfortunately, the using of twitter in Indonesia telecommunication company still in the stage of sales and promotions.
However, the analysis only calculated number of retweets, comments, dan followers. Analyzes haven’t seen what is the meaning of those comments, whether be positive or negative for XL brand products. Negative comments giving influence to society buying decision, while positive comments create good reputation to stakeholders. This is showned by a fact that the rating obtained XL Axiata does not correspond to the brand’s expectation, ie rating 3 out of 10. While expectation based on comparative analysis of number of followers and following, XL Axiata should have a good reputation in rate 7-8.
This study not only calculating XL product but also Telkomsel and Indosat for comparative analysis. Hereafter, we extracted features, algorithms and the classification schemes. Evaluation phase using precision, recall, f-measure and ROC curve (AUC). The sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. The result shows model built by SVM is the best result. Using this model, we measure Net Brand Reputation in 5 products which are 3G, 4G, Voice, SMS, and Internet (data). The experiments showned XL Axiata has the highest reputation score rather than Telkomsel and Indosat with average NBR score 24,5%, while Telkomsel only 13.2% and Indosat 19.3%.]"
2015
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UI - Tugas Akhir  Universitas Indonesia Library
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Ike Iswary Lawanda
Jakarta : CV Sagung Seto, 2015
027 IKE i
Buku Teks SO  Universitas Indonesia Library
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Budi Gunawan
Jakarta: PT. Gramedia Pustaka Utama, 2018
001.95 BUD k
Buku Teks SO  Universitas Indonesia Library