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All Journal Nuansa Informatika Sistemasi: Jurnal Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Technologia: Jurnal Ilmiah Tematik : Jurnal Teknologi Informasi Komunikasi International Journal of Global Operations Research International Journal of Quantitative Research and Modeling Majalah Bisnis & IPTEK Formosa Journal of Applied Sciences (FJAS) International Journal of Ethno-Sciences and Education Research SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif INTERNAL (Information System Journal) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
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Fakultas Teknik Dan Informatika, Universitas Informatika Dan Bisnis Indonesia
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Journal : Tematik : Jurnal Teknologi Informasi Komunikasi

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Perbandingan Model Klasifikasi C4.5, Naïve Bayes, Support Vector Machine dan K-nearest Neighbor untuk Memprediksi Kelayakan Masyarakat dalam Menerima Bantuan PBI APBD Tutik Ultsa Rahmatika; Nur Alamsyah; Titan Parama Yoga; Budiman
TEMATIK Vol 10 No 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

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Abstract

This research evaluates the eligibility of the community to receive APBD Contribution Assistance (PBI) using four classification algorithms: C4.5, Naïve Bayes, K-Nearest Neighbor, and Support Vector Machine (SVM). There is a problem of inaccurate distribution of assistance, which prompted the selection of these four methods with specific considerations, C4.5 (Decision Tree) is known for its clarity and interpretability, providing an easy-to-understand understanding of the factors that influence classification decisions, Naïve Bayes was selected for its efficiency and speed in training and testing, suitable for large datasets and can be updated quickly with new data, K-Nearest Neighbor (KNN) is used for decision making based on local patterns in the data, useful if the eligibility decision is local or related to the surrounding environment while Support Vector Machine (SVM): Selected for its ability to handle complex and non-linear datasets. The results show that SVM has the highest Weighted Mean Precision, reaching 91.67%, confirming its superiority as the best choice. These findings make a significant contribution to improving the accuracy of determining the eligibility of PBI APBD beneficiaries, supporting targeting accuracy, and ensuring the effectiveness of the assistance program for people in need.
Analisis Perbandingan Sentimen Pengguna Twitter Terhadap Layanan Salah Satu Provider Internet Di Indonesia Menggunakan Metode Klasifikasi Della Puspita Sari; Budiman; Nur Alamsyah
TEMATIK Vol 10 No 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

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Abstract

The Internet is needed for everyday life, whereas in Indonesia there are many internet service providers, one of which is indihome. Sentiment analysis itself aims to classify a text into Negative, Positive and Neutral classes. On the twitter platform, there are many reviews about internet providers, one of which is indihome, because of poor service or just to appreciate the services provided. Based on the calculation of the results obtained 71.1% negative, 21.1% positive and 7.7% neutral. The data obtained is not balanced, therefore the classification process is assisted using Smote. The results of the comparison of the four methods used are Support Vector Machine, Naïve Bayes, Random forest, Decision tree. From the overall comparison, the highest accuracy without smote or using smote is Support Vector Machine with an accuracy level of 89% AUC level of 89% if using smote gets 93% accuracy and 97% AUC level with 80% training data and 20% testing.
Co-Authors Aggi Panigoro Sarifiyono Ahmad Fauzi Ramadhan Ahmad Rizqy Hamdy Amos Duan Nugroho Ari Rizki Fauzi Cahya Miftahul Falah Catherin Rumambo Mogot Pandin Chairul Habibi Chairul Habibi Darsiti . Della Puspita Sari Dirham Triyadi Elia Setiana Fikri Rizqillah Hasani Gelar, Trisna Hasan Nuraripin Imannudin Akbar Imannudin Akbar Irtsa Yasyfa Ismi Nur Muhamad Jilan Hafizh Ferdiansyah Efendi Putra Karlina, Nichi Hana M. Rizki Ramadhan Niqotaini, Zatin Nur Alamsyah Nur Alamsyah Nursyanti, Reni R. Yadi Rakhman A R. Yadi Rakhman Alamsyah Reni Nursyanti Reynaldy Gimnastiar Sophian Ramadhan Suci Fitriani Setiawan Tiara Sartina Jayanti Titan Parama Titan Parama Yoga Titan Parama Yoga Tutik Ultsa Rahmatika Valencia Claudia Jennifer Kaunang Vani Maharani Nasution Venia Restreva Danestiara Venia Restreya Danestiara Wulandari Wulandari Zein Suna Arfigan Said