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All Journal JURNAL ILMIAH INFORMATIKA JURNAL PENDIDIKAN TAMBUSAI Nuansa Informatika
Rolly Maulana Awangga
Universitas Logistik dan Bisnis Internasional
Computer Science & IT Social Sciences

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Clean Arsitektur Zaky Muhammad Yusuf; Rolly Maulana Awangga
Jurnal Pendidikan Tambusai Vol. 7 No. 2 (2023): Agustus 2023
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Abstract

Clean Architecture adalah sebuah konsep arsitektur perangkat lunak yang berfokus pada pemisahan antara lapisan bisnis dan teknologi dalam aplikasi. Dalam pengembangan aplikasi restoran menggunakan Golang, penggunaan Clean Architecture sangat dianjurkan karena dapat membantu pengembang dalam membuat aplikasi yang mudah di-maintain dan scalable. Dalam arsitektur ini, aplikasi dibagi menjadi beberapa lapisan, antara lain lapisan presentasi, lapisan bisnis, dan lapisan penyimpanan data, masing-masing dengan tugas dan tanggung jawab yang terpisah. Dengan penggunaan Golang dan Clean Architecture, pengembangan aplikasi restoran dapat dilakukan dengan lebih efektif dan efisien.
ANALISIS SENTIMEN PERBANDINGAN LAYANAN JASA PENGIRIMAN KURIR PADA ULASAN PLAY STORE MENGGUNAKAN METODE DECISION TREE DAN RANDOM FOREST Dellavianti Nishfi Ilmiah Huda; Cahyo Prianto; Rolly Maulana Awangga
JURNAL ILMIAH INFORMATIKA Vol 11 No 02 (2023): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v11i02.7952

Abstract

Courier delivery is a crucial aspect of the e-commerce industry, and customer satisfaction with delivery services can significantly impact a company’s reputation, whether positive or negative. Therefore, sentiment analysis of customer reviews on the Play Store platform can provide valuable insights into the performance and acceptance of various courier delivery services available. This Study aims to conduct sentiment analysis on reviews of courier delivery services using two classification methods: Random Forest and Decision Tree. The first step in this research is data pre-processing, which includes text cleaning, tokenization, and the removal of irrelevant words. Subsequently, relevant features are extracted from the review texts using suitable feature extraction methods. Both Random Forest and Decision Tree methods are implemented to classify reviews from three companies: Pt X, Pt Y, and Pt Z, into two sentiment categories: positive and negative.The performance of both methods is evaluated using standard evaluation metrics. Furthermore, it is expected that this research will provide valuable information to the three e-commerce companies and courier service providers in improving the quality of their services based on customer feedback. Additionally, it can serve as a reference for consumers in choosing a courier delivery company that suits their needs.
Penerapan PCA dan Algoritma Clustering untuk Analisis Mutu Perguruan Tinggi di LLDIKTI Wilayah IV Resa Rianti; Roni Andarsyah; Rolly Maulana Awangga
NUANSA INFORMATIKA Vol. 18 No. 2 (2024): Nuansa Informatika 18.2 Juli 2024
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v18i2.211

Abstract

The Internal Quality Assurance System (SPMI) is a guideline used by universities to assess the quality of performance and implementation of higher education internally. SPMI is very important to be considered by universities in order to compete positively with other universities, both at home and abroad, as well as to improve the management and implementation of higher education in the institution. In this study, three machine learning algorithms are applied, namely K- Means, Mean Shift, and DBSCAN, to cluster SPMI data. The methods used include Principal Component Analysis (PCA) to reduce data complexity without losing important information, and three clustering algorithms to group universities based on similarity of quality indicators. The K-Means algorithm clusters data based on distance to the nearest centroid, Mean Shift identifies clusters based on data density, and DBSCAN clusters data based on density and is able to handle outliers and irregularly shaped clusters. The results show that Mean Shift produces the best cluster with Silhouette Score 0.566, Davies- Bouldin Index 0.648, and Calinski-Harabasz Index 971.07. The K-Means algorithm provides quite good results with Silhouette Score 0.466, Davies-Bouldin Index 0.757, and Calinski-Harabasz Index 757.06. Meanwhile, DBSCAN has lower performance with Silhouette Score 0.216, Davies-Bouldin Index 1.045, and Calinski-Harabasz Index 105.67. This research provides the results of identifying universities that need special attention and helps in strategic planning for quality improvement so that they can carry out guidance more effectively and contribute to the development of a quality assurance system for higher education in Indonesia.
Co-Authors Dellavianti Nishfi Ilmiah Huda Prianto, Cahyo Resa Rianti Roni Andarsyah Zaky Muhammad Yusuf