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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknologi Dan Sistem Informasi Bisnis
I Wayan Rizky Wijaya
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Implementasi PPID Dalam Mendukung Transparansi Informasi di Politeknik Pariwisata Bali Wijaya, I Wayan Rizky; Gunawan, I Made Agus Oka; Dharma, I Gede Teguh Satya
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 4 (2025): Oktober 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i4.2244

Abstract

As a vocational higher education institution in the field of tourism, Politeknik Pariwisata Bali has an obligation to provide the public with access to information that is easy, fast, and accountable. This study discusses the implementation of the Public Information and Documentation Officer (PPID) as the main instrument to support information transparency at Politeknik Pariwisata Bali. The system was developed using the Waterfall method with PHP as the programming language and MySQL as the database. The testing process was carried out using the blackbox testing method to ensure that each system function operates as required. The implementation results show that the developed PPID system is capable of providing services for information requests, complaints, and public aspirations in a more effective and integrated manner.
Agile Project Management pada Pengembangan E-Musrenbang Kelurahan Benoa Bali Dewi, Kadek Cahya; Ciptayani, Putu Indah; Wijaya, I Wayan Rizky
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 6: Desember 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2907.644 KB) | DOI: 10.25126/jtiik.2018561143

Abstract

Pendekatan Agile telah diperkenalkan sebagai upaya untuk membuat rekayasa perangkat lunak yang fleksibel dan efisien. Penelitian ini adalah penelitian studi kasus, dengan mengangkat kasus pengembangan sistem e-musrenbang Kelurahan Benoa Bali. Penelitian bertujuan untuk menerapkan manajemen proyek berbasis agile pada kasus tersebut. Metode pengumpulan data yang digunakan adalah in-depth interview, observasi dan focus group discussion. Hasil penelitian menunjukkan bahwa waktu pengembangan proyek adalah 8 minggu. Proyek menggunakan kerangka kerja Scrum yang membagi proyek menjadi 4 sprint. Evaluasi sistem dilakukan melalui focus group discussion dengan pihak product owner dan pengguna sistem. Dapat disimpulkan bahwa pendekatan agile dapat diterapkan dalam pengembangan e-musrenbang Kelurahan Benoa Bali. Pengguna sistem dapat menerima kehadiran e-musrenbang dan memanfaatkannya dalam proses pengajuan usulan perencanaan pembangunan di Kelurahan Benoa Bali.AbstractAgile Approach has been introduced as an attempt to make software engineering flexible and efficient. The research was case study research, with case of e-musrenbang system development in Benoa Village Bali. The research objectives to implement agile project management in that case. Data collection methods used were in-depth interview, observation and focus group discussion. The results found that the project development time was 8 weeks. The project used a Scrum framework that divided the project into 4 sprints. System evaluation is done through focus group discussion with product owner and system users. It can be concluded that the agile approach can be applied in the development of e-Musrenbang in Benoa Village Bali. System users accepted e-musrenbang presence and utilized it in the process of submitting proposals for development planning in Benoa Village Bali.
Analisis Performa Komparatif Algoritma Machine Learning untuk Deteksi Fraud dalam Transaksi Blockchain Apriyanthi, Ni Putu Eka; Dhewanty, Civica Moehaimin; Ayu, Putu Desiana Wulaning; Nugroho, I Made Riyan Adi; Wijaya, I Wayan Rizky
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2285

Abstract

The decentralized finance (DeFi) and blockchain environment encounters substantial security threats, particularly complex and expensive fraudulent activities. Conventional detection methods frequently prove insufficient when dealing with enormous transaction volumes and datasets characterized by unbalanced class distributions. This research seeks to examine and evaluate the effectiveness of three widely used machine learning techniques Logistic Regression, Random Forest, and XGBoost in identifying fraudulent activities within blockchain transactions. The investigation utilized an Ethereum transaction dataset sourced from Kaggle, where the imbalanced data distribution was addressed through the application of SMOTE methodology. Performance assessment was carried out using precision, recall, F1-score, and ROC-AUC measurements on testing data. The findings demonstrate XGBoost's superiority among the algorithms, delivering an accuracy rate of 99.46%, precision of 99.69%, recall of 97.86%, and ROC-AUC score of 99.97%, while maintaining minimal false positive occurrences (only 1 instance). These results exceeded those achieved by both Random Forest and Logistic Regression models, demonstrating that gradient boosting methodologies excel at detecting intricate fraudulent behaviors. The study's outcomes offer significant contributions toward creating resilient and autonomous fraud detection frameworks. Keywords: Blockchain, Fraud, Machine Learning, Logistic Regression, Random Forest, XGBoost.
Implementasi PPID Dalam Mendukung Transparansi Informasi di Politeknik Pariwisata Bali I Wayan Rizky Wijaya; I Made Agus Oka Gunawan; I Gede Teguh Satya Dharma
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 4 (2025): Oktober 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i4.2244

Abstract

As a vocational higher education institution in the field of tourism, Politeknik Pariwisata Bali has an obligation to provide the public with access to information that is easy, fast, and accountable. This study discusses the implementation of the Public Information and Documentation Officer (PPID) as the main instrument to support information transparency at Politeknik Pariwisata Bali. The system was developed using the Waterfall method with PHP as the programming language and MySQL as the database. The testing process was carried out using the blackbox testing method to ensure that each system function operates as required. The implementation results show that the developed PPID system is capable of providing services for information requests, complaints, and public aspirations in a more effective and integrated manner.
Analisis Performa Komparatif Algoritma Machine Learning untuk Deteksi Fraud dalam Transaksi Blockchain Ni Putu Eka Apriyanthi; Civica Moehaimin Dhewanty; Putu Desiana Wulaning Ayu; I Made Riyan Adi Nugroho; I Wayan Rizky Wijaya
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2285

Abstract

The decentralized finance (DeFi) and blockchain environment encounters substantial security threats, particularly complex and expensive fraudulent activities. Conventional detection methods frequently prove insufficient when dealing with enormous transaction volumes and datasets characterized by unbalanced class distributions. This research seeks to examine and evaluate the effectiveness of three widely used machine learning techniques Logistic Regression, Random Forest, and XGBoost in identifying fraudulent activities within blockchain transactions. The investigation utilized an Ethereum transaction dataset sourced from Kaggle, where the imbalanced data distribution was addressed through the application of SMOTE methodology. Performance assessment was carried out using precision, recall, F1-score, and ROC-AUC measurements on testing data. The findings demonstrate XGBoost's superiority among the algorithms, delivering an accuracy rate of 99.46%, precision of 99.69%, recall of 97.86%, and ROC-AUC score of 99.97%, while maintaining minimal false positive occurrences (only 1 instance). These results exceeded those achieved by both Random Forest and Logistic Regression models, demonstrating that gradient boosting methodologies excel at detecting intricate fraudulent behaviors. The study's outcomes offer significant contributions toward creating resilient and autonomous fraud detection frameworks. Keywords: Blockchain, Fraud, Machine Learning, Logistic Regression, Random Forest, XGBoost.
Co-Authors Civica Moehaimin Dhewanty Civica Moehaimin Dhewanty I Gede Teguh Satya Dharma I Made Agus Oka Gunawan I Made Riyan Adi Nugroho Kadek Cahya Dewi Ni Putu Eka Apriyanthi Ni Putu Eka Apriyanthi Nugroho, I Made Riyan Adi Putu Desiana Wulaning Ayu Putu Indah Ciptayani Wulaning Ayu, Putu Desiana