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All Journal JURNAL ILMIAH INFORMATIKA JURNAL PENDIDIKAN TAMBUSAI JEECS (Journal of Electrical Engineering and Computer Sciences) Nuansa Informatika
Rolly Maulana Awangga
Universitas Logistik dan Bisnis Internasional
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering 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.
Enhancing OCR Accuracy on Indonesian ID Cards Using Dual-Pipeline Tesseract and Post-Processing Rendy Dwi Reksiyano; Syafrial Fachri Pane; Rolly Maulana Awangga
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 2 (2025): December
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i2.3

Abstract

Manual transcription of data from Indonesian identity cards (KTP) remains prevalent in public institutions, often resulting in inefficiencies and human errors that compromise data accuracy. While Optical Character Recognition (OCR) technologies such as Tesseract have been widely adopted. However, the performance on KTP images is still inconsistent due to non-uniform layouts, low contrast, and background noise. This study proposes a dual-pipeline OCR framework designed to enhance the recognition accuracy of Indonesian KTPs under real-world conditions. First, the pipeline performs static region segmentation based on predefined Regions of Interest (ROI), then uses dynamic keyword heuristics to locate text adaptively across varying layouts. The outputs of both pipelines are merged through a voting and regex-based post-processing mechanism, which includes character normalization and field validation using predefined dictionaries. Experiments were conducted on 78 annotated KTP samples with diverse resolutions and quality of images. Evaluation using Character Error Rate (CER), Word Error Rate (WER), and field-level accuracy metrics resulted in an average CER of 69.82%, WER of 80.20%, and character-level accuracy of 30.18%. Despite moderate performance in free-text areas such as address or occupation, structured fields achieved higher accuracy above 60%. The method runs efficiently in a CPU-only environment without requiring large annotated datasets, demonstrating its suitability for low-resource OCR deployment. Compared to conventional single-pipeline approaches, the proposed framework improves robustness across heterogeneous document layouts and illumination conditions. These findings highlight the potential of lightweight, rule-based OCR systems for practical e-KYC digitization and form a foundation for integrating deep-learning-based layout detection in future research.
Co-Authors Dellavianti Nishfi Ilmiah Huda Prianto, Cahyo Rendy Dwi Reksiyano Resa Rianti Roni Andarsyah Syafrial Fachri Pane Zaky Muhammad Yusuf