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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal ULTIMA InfoSys Jurnal Teknologi Sistem Informasi dan Aplikasi IKRA-ITH ABDIMAS Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Sistem Informasi
Rido Dwi Kurniawan
Pradita University
Computer Science & IT

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SENTIMENT ANALYSIS OF INDONESIAN ELECTION 2024 USING THE K-NEAREST NEIGHBOR METHOD Kurniawan, Rido Dwi; Muliawan, Joshua
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1934

Abstract

The abstract of this research discusses the analysis of Indonesian public sentiment regarding the 2024 election as observed via Twitter. Sentiment graph Research uses the Natural Language Processing method and the K-Nearest Neighbor algorithm to classify sentiment as positive, neutral, or negative. The current era of globalization influences the rapid progress of information technology circulating in society, one of the intermediaries is through the social media Twitter. Twitter can be used as a means of conveying opinions regarding suggestions, criticism and public opinion. Currently social media has a big impact on building public political sentiment and preferences. The social media I took is Twitter so that people's Tweets related to elections can be used to see a picture of public opinion. There are various opinions of Twitter users with positive, neutral and negative sentiments. However, classifying sentiment from Twitter users requires quite a lot of time and effort due to the large number of tweets found. The aim of this research is to conduct a public sentiment analysis of public opinion regarding the 2024 election. Data was collected in October and December 2023. The results show that positive sentiment dominates with 76%, followed by neutral sentiment at 16%, and negative 6%. This analysis helps understand public opinion regarding the 2024 election on social media, especially Twitter.
Enterprise Architecture Design Using TOGAF ADM: The Case of KotaKita Prawira, Dimas Yudha; Kurniawan, Rido Dwi; Indrajit, Richardus Eko; Dazki, Erick
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 2 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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Abstract

One of the benefits of implementing enterprise architecture is creating harmony in between business and information technology for the needs of organization, beside that information system architecture is also a management practice and technology that is useful to improve company performance by allowing them to see themselves in terms of a holistic and integrated view of strategic direction, business practices, information flows and technological resources (Wiranti et al., 2020). The application of enterprise architecture is inseparable from how to plan an organization but also in designing enterprise architecture. In carrying out the design required a complete and easy methodology. TOGAF (The Open Group Architecture Framework) as a framework for building generic enterprise architectures provides complete and easy-to-understand and implement stages. Currently there are not many applications or systems that provides local city information, communication and social media (events, tourist sites, business listings, communities, so on and so forth). Although there is, the application or system does not provide information in an informative manner, the information is scattered in various sources. KotaKita is a product which will be build by startup organization to fill the gap and providing the information on the locality of areas throughout Indonesia, KotaKita can be used as a means of communication of local regional social communities. The Implementation of enterprise architecture (EA) in startup organizations is not a must, but the implementation of enterprise architecture will be very useful in building an architectural blueprint that can be used to make better plans and decisions.
Pelatihan Transformasi Digital: Peningkatan Literasi Digital melalui Pembuatan Learning Management System Tirta jatik di Yayasan YPIK PAM Jaya, Kota Bekasi, Jawa Barat Kurniawan, Rido Dwi
IKRA-ITH ABDIMAS Vol. 9 No. 2 (2025): Jurnal IKRAITH-ABDIMAS Vol 9 No 2 Juli 2025
Publisher : Universitas Persada Indonesia YAI

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Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi digital di Yayasan YPIK PAM Jaya, Kota Bekasi, melalui perancangan dan pengembangan sebuah Learning Management System (LMS) bernama Tirta Jatik. Latar belakang pengembangan ini adalah kebutuhan untuk mentransformasi proses pembelajaran di era digital dengan menciptakan platform terpusat yang fleksibel dan interaktif. Metode yang digunakan dalam pengembangan sistem ini adalah Software Development Life Cycle (SDLC), yang saat ini berada pada tahap perencanaan. Sistem dirancang untuk empat jenis pengguna: Admin, Pembicara, Peserta, dan Tamu, dengan fitur-fitur seperti manajemen kursus, forum diskusi, sistem pesan, dan notifikasi. Hasil dari tahap awal ini mencakup perancangan arsitektur informasi, Use Case Diagram, dan Activity Diagram yang mendefinisikan alur interaksi pengguna dengan sistem. Pengembangan telah mencapai tahap implementasi awal, di mana beberapa kendala seperti integrasi login media sosial telah diidentifikasi untuk penyelesaian lebih lanjut. Diharapkan, LMS ini dapat menjadi solusi efektif untuk mendukung dan meningkatkan kualitas pembelajaran digital di lingkungan yayasan
Utilizing a Data Warehouse to Analyze the Effects of Sales Type, Product Type, and Price on Net Profit in an F&B Outlet Putri, Allegra Aretha; Aurelia, Nadine; Veronika, Vera; Wiriady, Fransiska Eka Putri; Kurniawan, Rido Dwi; Sari, Muh. Masri
ULTIMA InfoSys Vol 16 No 2 (2025): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v16i2.4513

Abstract

This research aims to investigate how sales type, product type, and price influence net profit in an F&B outlet using a Data Warehouse system. A quantitative method was applied by integrating daily transaction data into a Data Warehouse architecture built through an Extract, Transform, and Load (ETL) process, allowing the data to be more organized and easier to analyze. To address heteroskedasticity and obtain more reliable coefficient estimates, multiple linear regression with HC3 robust standard errors was used. The results show that price and sales type have a significant and positive effect on net profit, while product type shows mixed effects depending on the category. The regression model, with an R² value of 0.994, indicates that these three variables explain most of the variation in net profit. Overall, the findings highlight how structured data processing through a Data Warehouse can support profitability analysis and improve decision-making in F&B operations.
The Influence of Operating Costs on Revenue and Cash Balance: Data Warehouse Analysis Anthony, Revan; Ernesto, Brian; Prasetya, Jonathan Ansell; Subrata, Kenneth Marchelino; Sari, Muh. Masri; Kurniawan, Rido Dwi
Jurnal Ilmiah Sistem Informasi Vol. 5 No. 1 (2026): January: Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/qwahp256

Abstract

Penelitian ini menganalisis pengaruh biaya operasional terhadap omzet dan saldo kas pada sebuah perusahaan Food and Beverage (F&B) sektor minuman dengan memanfaatkan data warehouse sebagai basis integrasi dan pengolahan data keuangan. Permasalahan utama yang diangkat adalah kurangnya pemanfaatan data keuangan bulanan secara analitis sehingga hubungan antara biaya operasional, kinerja penjualan, dan kondisi saldo kas belum terpantau dengan baik. Tujuan penelitian ini adalah menguji pengaruh biaya operasional terhadap omzet dan saldo kas, serta menilai hubungan antara omzet dan saldo kas melalui analisis statistik yang terstruktur. Data penelitian terdiri dari 21 observasi bulanan periode Januari 2024–September 2025, yang diperoleh dari laporan keuangan internal dan diproses ke dalam data warehouse melalui prosedur extract–transform–load (ETL). Analisis dilakukan menggunakan statistik deskriptif, uji asumsi klasik, regresi linier sederhana, dan korelasi Pearson dengan bantuan Microsoft Excel dan IBM SPSS Statistics. Hasil penelitian menunjukkan bahwa biaya operasional berpengaruh positif dan signifikan terhadap omzet, menandakan bahwa pengeluaran operasional yang bersifat produktif masih mampu mendorong peningkatan pendapatan. Sebaliknya, biaya operasional berpengaruh negatif dan signifikan terhadap saldo kas sehingga dapat menurunkan likuiditas bila tidak dikendalikan. Adapun hubungan antara omzet dan saldo kas bersifat positif sedang namun belum signifikan, sehingga peningkatan omzet tidak selalu langsung meningkatkan saldo kas akhir. Penelitian ini menegaskan pentingnya pengendalian biaya operasional dan pemanfaatan data warehouse untuk menghasilkan informasi keuangan yang lebih terstruktur serta mendukung keputusan manajerial pada usaha F&B.
Analisis Faktor Penentu Kategori Harga Rumah di Kota Tangerang Selatan Menggunakan Web Crawling dan Regresi Logistik Multinomial Muhammad Iyad Irviansyah; Claresta, Vanesya; Anabella, Marshanda; Saputra, Muhammad Rifqo; Kurniawan, Rido Dwi; Sari, Muh. Masri
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 9 No. 3 (2025): IKRAITH-INFORMATIKA Vol 9 No 3 November 2025
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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Abstract

This study aims to identify factors influencing housing price categories in South Tangerang City using digital data obtained through web crawling from online property platforms. The research addresses how physical attributes, facilities, and location affect the probability of a house belonging to a specific price category. Data were automatically collected via web crawling, and after data cleaning and validation, 1,264 housing records were retained for analysis. Housing prices were classified into four categories—Economical, Standard, Luxury, and Exclusive—using a quartile-based approach. Multinomial Logistic Regression (MLR) was applied to model relative probabilities based on land area, building area, number of bedrooms, number of bathrooms, garage availability, and district location. The results indicate that land area, building area, number of bathrooms, and garage availability significantly influence housing price categories, while the number of bedrooms and district location are not significant after controlling for physical characteristics. The model is statistically significant and achieves a classification accuracy of 64.8%. The main contribution of this study lies in the integration of web crawling and Multinomial Logistic Regression for housing price classification, offering a data-driven framework to support housing market analysis and automated property valuation systems.
Co-Authors Ahmad Baehaqi, Ahmad Alfa Yohannis Anabella, Marshanda Anthony, Revan Atmodjo, Dwi Atmojo, Wahyu Tisno Aurelia, Nadine Basit, Muhamad Subhi Claresta, Vanesya Daniel Hasiholan Tinambunan Dazki, Erick Deasy Olivia, Deasy Edison Siregar, Master Ega Wachid Radiegtya Ernesto, Brian Harianja, Richad Januponsa Dio Firizqi Mayatopani, Hendra Muhammad Iyad Irviansyah Muliawan, Joshua Prasetya, Jonathan Ansell Prathama, Dhion Angga Prawira, Dimas Yudha Putri, Allegra Aretha Refgiufi Patria Avrianto Refgiufi Patria Avrianto Richardus Eko Indrajit Saputra, Muhammad Rifqo Sari, Muh. Masri Siregar, Master Edison Subrata, Kenneth Marchelino Veronika, Vera Wiriady, Fransiska Eka Putri