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All Journal Journal of Information Technology and Computer Science Paradigma SWAGATI: Journal of Community Service
Manik, Lindung Parningotan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences Other

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Design Pattern Evaluation on A RESTful API Wrapper: A Case Study of Software Integration with An Internet Payment Gateway using Model-Driven Architecture Manik, Lindung Parningotan
Journal of Information Technology and Computer Science Vol. 4 No. 3: Desember 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3391.081 KB) | DOI: 10.25126/jitecs.201943107

Abstract

A proper use of design patterns has proven to be very useful in the development of robust applications over time. In this paper, the design patterns are introduced in the early stage of the software development where model-driven architecture is used as the engineering approach. A RESTful internet payment gateway API (Application Programming Interface) wrapper is selected as the case study. At the beginning, Platform Independent Model (PIM) is created as the domain model. After that, the PIM is transformed into the Platform Specific Model (PSM). Before converting the PSM into the source code, three design patterns such as builder, observer, and factory pattern are added into the model. To evaluate the impacts of implementation, static analysis is used to examine the generated code before and after adding the design patterns. The result shows that the design decision increases cohesion, complexity, coupling, inheritance, and size metrics of the source codes.
Pelatihan Penggunaan Aplikasi Administrasi RT/RW Berbasis Website Pada PKK RW 06 Tegal Parang Mampang Hermaliani, Eni Heni; Gata, Windu; Manik, Lindung Parningotan; Ernawan, Ferda
SWAGATI : Journal of Community Service Vol. 1 No. 2 (2023): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2023v1i2.1100

Abstract

Kehidupan bermasyarakat pada tingkatan paling bawah diatur melalui Permendagri nomor 5 tahun 2007 yang mengatur tentang pembentukan Rukun Warga dan Rukun Tetangga. Untuk dapat menjalankan fungsi dan perannya dengan baik pada revolusi 4.0 ini, pemerintah diharapkan dapat beradaptasi dengan perkembangan teknologi untuk menyelesaikan dan memenuhi kebutuhan masyarakat melalui penggunaan aplikasi berbasis teknologi pada pelayanan publik. Dalam rangka melaksanakan kegiatan tri dharma perguruan tinggi yaitu pengabdian kepada masyarakat, Fakultas Teknologi Universitas Nusa Mandiri menyelenggarakan pelatihan penggunaan aplikasi administrasi RT RW berbasis website yang bertujuan guna memberikan cara dan langkah-langkah penggunaan aplikasi administrasi RT RW yang dapat digunakan oleh pengurus RW 06 dan RT yang berada dibawah RW 06. Pelatihan ini diselenggarakan bekerjasama dengan mitra yaitu PKK RW 06 Kelurahan Tegal Parang Kecamatan Mampang Prapatan. Dengan adanya aplikasi, dapat memberikan pelayanan administrasi kepada masyarakat dengan lebih cepat dan tepat.
Aspect-Based Sentiment Analysis on Indonesian Presidential Election Using Deep Learning Said, Fadillah; Manik, Lindung Parningotan
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 2 (2022): September 2022 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i2.1415

Abstract

The 2019 presidential election is a presidential election that has been a hot topic of discussion for some time, and people have even talked about this topic since 2018 on the internet. In predicting the winner of the presidential election, previous research has conducted research on the aspect-based sentiment analysis (ABSA) dataset of the 2019 presidential election using machine learning algorithms such as the Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN) and produces good accuracy. This study proposes a deep learning method using the BERT (Bidirectional Encoder Representation Form Transformers) and RoBERTa (A Robustly Optimized BERT Pretraining Approach) models. The results of this study indicate that the indobenchmark BERT and RoBERTa base-Indonesian single label classification models on target features with preprocessing produce the best accuracy of 98.02%. The indolem BERT model and the indobenchmark single label classification on the target feature without preprocessing produce the best accuracy of 98.02%. The BERT indobenchmark single label classification model on aspect features with preprocessing produces the best accuracy of 74.26%. The BERT indolem single label classification model on aspect features without preprocessing produces the best accuracy of 74.26%. The BERT indolem single label classification model on the sentiment feature with preprocessing produces the best accuracy of 93.07%. The BERT indolem single label classification model on the sentiment feature without preprocessing produces the best accuracy of 94.06%. The BERT indobenchmark multi label classification model with preprocessing produces the best accuracy of 98.66%. The BERT indobenchmark multi label classification model without preprocessing produces the best accuracy of 98.66%.
Co-Authors Eni Heni Hermaliani, Eni Heni Ferda Ernawan Gata, Windu Said, Fadillah