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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Semantik JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Zonasi: Jurnal Sistem Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Prosiding Seminar Nasional Multidisiplin Ilmu Jurnal Sistem Informasi dan Sains Teknologi MEANS (Media Informasi Analisa dan Sistem) Jurnal Pengkajian Dhamma Algor PROFICIO: Jurnal Pengabdian Masyarakat CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia JURNAL PENDIDIKAN INDONESIA: Teori, Penelitian, dan Inovasi INTECH (Informatika dan Teknologi) Abdi-Dharma Jurnal Abdimas Musi Charitas Jurnal Sistem Informasi JSITP JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Jurnal Ilmiah Sistem Informasi MDP Student Conference Journal Of Informatics And Busisnes Jurnal Ilmiah Computer Science Masterpiece: Journal of Islamic Studies and Social Sciences Journal of Technology and System Information Jurnal Riset Teknik Komputer Jurnal Sains Sistem Informasi Inovasi Gagasan Abdimas & Kuliah Kerja Nyata Jurnal Dunia Peternakan Jurnal Riset Sistem Informasi Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi Jurnal Sains dan Teknologi Jurnal Ilmu Komputer dan Informatika Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Jurnal Riset Multidisiplin Edukasi INTECH (Informatika dan Teknologi) Sinergi : Jurnal Ilmiah Multidisiplin Ar-Rasyid: Jurnal Publikasi Penelitian Ilmiah
Andri Wijaya
Sistem Informasi, Sekolah Tinggi Teknik Musi, Palembang 30133
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Analisis Pola Kepuasan Pengunjung Amanzi Waterpark Palembang Menggunakan Algoritma K-Means Clustering Daely, Septia Angelika Gettin; Sanjaya, Aloisius Egi; Wijaya, Andri
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Palembang's tourism sector increasingly relies on online reviews as visitor satisfaction indicators, yet the large volume of unstructured review data complicates manual analysis. This study aims to analyze visitor satisfaction patterns at Amanzi Waterpark Palembang using K-Means Clustering algorithm on 1,812 Google Maps reviews collected through web scraping techniques. The analytical process includes text preprocessing, TF-IDF weighting, TruncatedSVD dimensionality reduction, and clustering with k=5. Research findings identify five visitor experience segments: Family Recreation (12.4%, rating 4.69), General Positive Reviews (8.9%, rating 4.55), Cleanliness & Comfort (7.1%, rating 4.60), Mixed Reviews & Complaints (67.5%, rating 3.99), and English Language Reviews (4.1%, rating 4.57). Critical findings reveal that 67.5% of reviews fall into the cluster with the lowest rating, dominated by complaints regarding pool water cleanliness, operational system complexity, and perceived high prices. Service quality inconsistency is identified through differing cleanliness sentiments across clusters, indicating standards not consistently maintained especially during peak visit periods . This research provides practical contributions in the form of strategic recommendations for cleanliness improvement, payment system simplification, and quality control consistency, while academically enriching the literature on text mining applications in Indonesia's tourism sector.
Perbandingan Akurasi Support Vector Machine dan Random Forest pada Prediksi Diabetes Melitus Ardi Riyadi; Johan Abisay Tambunan; Andri Wijaya
Jurnal Riset Multidisiplin Edukasi Vol. 2 No. 12 (2025): Jurnal Riset Multidisiplin Edukasi (Edisi Desember 2025)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v2i12.1452

Abstract

Diabetes Mellitus (DM) is a chronic metabolic disease that poses a global health threat, with its prevalence increasing every year. Early detection through the application of Data Mining techniques is crucial to prevent severe complications and to support medical practitioners in making faster clinical decisions. This study aims to compare the performance of two popular machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest, in predicting diabetes risk. Unlike previous studies that often utilize complex feature optimization techniques or oversampling methods (such as SMOTE), this research focuses on evaluating baseline performance to observe each algorithm’s pure capability on the standard Pima Indians Diabetes dataset, which consists of 10,004 medical records with 22 clinical attributes. The experiments were conducted using RapidMiner with a 10-Fold Cross-Validation approach to ensure valid and reliable evaluation results. The findings show that the Random Forest algorithm achieved superior performance with an accuracy of 82.19%, while SVM obtained an accuracy of 79.40%. These results confirm that the ensemble learning approach of Random Forest provides better stability in handling clinical data with high variability compared to single-hyperplane methods such as SVM under default parameters. This study is expected to serve as a foundational benchmark for further development of diabetes prediction models in the future.
Analisis Ulasan Aplikasi dalam Google Play Store Menggunakan Model Naive Bayes Chintia Cantika; Mayer Dani Sitompul; Andri Wijaya
Jurnal Riset Multidisiplin Edukasi Vol. 3 No. 1 (2026): Jurnal Riset Multidisiplin Edukasi (Januari 2026) In Press
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v3i1.1497

Abstract

This study aims to analyze user sentiment toward mobile applications based on reviews collected from Google Play Store by applying the Naive Bayes classification model. User reviews represent an important source of information that reflects user experiences, satisfaction levels, and perceived application quality. However, the large volume and unstructured nature of textual reviews make manual analysis inefficient and subjective. Therefore, this research adopts a quantitative approach using text classification based on machine learning to automatically categorize user reviews into positive, negative, and neutral sentiment classes. The research process consists of data collection, text preprocessing, feature extraction, sentiment classification using Naive Bayes, and model performance evaluation. Text preprocessing includes case folding, tokenizing, stopword removal, and stemming to improve data quality. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that positive sentiment dominates user reviews, indicating that the application is generally well received by users, although negative and neutral sentiments remain present and highlight areas that require improvement. The evaluation results demonstrate that the Naive Bayes model achieves reliable performance in classifying sentiment, with balanced evaluation metrics that indicate stable classification capability. These findings confirm that Naive Bayes remains an effective and efficient method for sentiment analysis of application reviews. This study contributes theoretically to sentiment analysis research and practically provides insights that can support application developers in evaluating user feedback and improving application quality.
Perancangan Data Warehouse Penjualan E-Commerce untuk Analisis Tren Produk dan Brand Populer Branchris; Kevin Alexander Yech; Andri Wijaya
Sinergi : Jurnal Ilmiah Multidisiplin Vol. 2 No. 1 (2026): Sinergi: Jurnal Ilmiah Multidisiplin
Publisher : PT. AHLAL PUBLISHER NUSANTARA

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Abstract

This study presents the design of a data warehouse to support analysis of product trends and popular brands in e-commerce sales. The system integrates sales data from multiple sources through the ETL (Extract, Transform, Load) process and applies a star schema model. Using multidimensional analysis (OLAP), insights on monthly trends, brand performance, and product sales variations over time were generated. The results show that the designed data warehouse effectively supports operational decision-making and marketing strategies. Keywords: data warehouse, e-commerce, OLAP, ETL, star schema   Abstrak Penelitian ini membahas perancangan data warehouse untuk mendukung analisis tren produk dan brand populer pada penjualan e-commerce. Sistem dirancang untuk mengintegrasikan data penjualan dari berbagai sumber (pelanggan, produk, transaksi), melalui proses ETL (Extract, Transform, Load), lalu dianalisis menggunakan model star schema. Analisis data menggunakan visi multidimensi (Online Analytical Processing) menghasilkan insight tren per bulan, performa brand, serta variasi penjualan produk dalam periode tertentu. Hasil rancangan ini menunjukkan bahwa data warehouse mampu mendukung keputusan operasional dan strategi pemasaran. Kata kunci: data warehouse, e-commerce, OLAP, ETL, star schema
Klasterisasi Lagu pada Dataset Spotify Berdasarkan Fitur Audio Menggunakan Algoritma K-Means Branchris; Kevin Alexander Yech; Andri Wijaya
Sinergi : Jurnal Ilmiah Multidisiplin Vol. 2 No. 1 (2026): Sinergi: Jurnal Ilmiah Multidisiplin
Publisher : PT. AHLAL PUBLISHER NUSANTARA

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Abstract

Digital music is currently growing rapidly; however, song retrieval systems that rely solely on genres are often inadequate to meet the needs of users searching for music based on specific moods. This study aimed to cluster songs using the Spotify Songs and Artists Dataset from Kaggle based on audio features that reflect mood dimensions—specifically Valence, Energy, and Danceability—using the K-Means algorithm. This approach was selected to uncover hidden patterns and establish more personalized music categories. The research methodology followed the standard CRISP-DM framework, encompassing data preprocessing with Z-Score normalization, determination of the optimal number of clusters using the Elbow Method, and model evaluation. The experimental results demonstrated that the K-Means algorithm successfully grouped the song data into three main clusters ( ) with distinct characteristics: Happy/Cheerful, Sad/Melancholy, and Energetic/Intense. Cluster quality evaluation using the Silhouette Coefficient yielded a score of 0.30. While this score indicates some overlap typical in the music emotion spectrum, the centroid analysis proved that the algorithm effectively separated mood characteristics to support a more relevant music recommendation system. Keywords: K-Means, Spotify, Clustering, Music Information Retrieval, Mood. Abstrak Musik digital saat ini berkembang pesat, namun sistem pencarian lagu yang hanya mengandalkan genre seringkali tidak memadai untuk memenuhi kebutuhan pengguna yang mencari musik berdasarkan suasana hati (mood). Penelitian ini bertujuan untuk mengelompokkan lagu menggunakan Spotify Songs and Artists Dataset dari Kaggle berdasarkan fitur audio yang merefleksikan dimensi mood, yaitu Valence, Energy, dan Danceability, dengan algoritma K-Means. Pendekatan ini dipilih untuk mengungkap pola tersembunyi dan menciptakan kategori musik yang lebih personal. Metodologi penelitian mengikuti kerangka standar CRISP-DM, dimulai dari pra-pemrosesan data menggunakan normalisasi Z-Score, penentuan jumlah klaster optimal dengan Elbow Method, hingga evaluasi model. Hasil eksperimen menunjukkan bahwa algoritma K-Means berhasil membagi data lagu menjadi tiga klaster utama ( ) dengan karakteristik yang distingtif, yaitu klaster Bahagia (Happy/Cheerful), Sedih (Sad/Melancholy), dan Intens (Energetic/Intense). Evaluasi kualitas klaster menggunakan Silhouette Coefficient menghasilkan nilai 0,30. Meskipun nilai ini mengindikasikan adanya irisan (overlap) antar data yang wajar dalam spektrum emosi musik, analisis pusat klaster (centroid) membuktikan bahwa algoritma mampu memisahkan karakteristik mood secara efektif untuk mendukung sistem rekomendasi musik yang lebih relevan. Kata Kunci: K-Means, Spotify, Klasterisasi, Music Information Retrieval, Mood.
Perancangan Data Warehouse (Studi kasus: Analisis Tren Penyakit Menular) Chintia Cantika; Riski Surya Saputra; Andri Wijaya
Sinergi : Jurnal Ilmiah Multidisiplin Vol. 2 No. 1 (2026): Sinergi: Jurnal Ilmiah Multidisiplin
Publisher : PT. AHLAL PUBLISHER NUSANTARA

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Abstract

Infectious diseases such as COVID-19, Tuberculosis, and Malaria remain significant global health challenges. A major obstacle in mitigating these outbreaks is the fragmentation of healthcare data, which leads to delays in analysis and decision-making. This study aims to design a Data Warehouse capable of integrating surveillance data from various heterogeneous sources to support real-time disease trend analysis. The methodology employed is a bottom-up approach utilizing a three-tier architecture. The data integration process is executed through an Extract, Transform, and Load (ETL) mechanism using Pentaho Data Integration to ensure data quality and consistency. Data storage implements a Fact Constellation Schema within a PostgreSQL database, enabling simultaneous multidimensional analysis of infection cases and mortality rates. The result of this research is a prototype of an interactive dashboard based on Tableau, which presents visualizations of geographic distribution (GIS) and temporal trend graphs. This implementation demonstrates that the centralization of healthcare data can facilitate more effective outbreak monitoring and support evidence-based public health policymaking. Keywords: Data Warehouse, Infectious Diseases, ETL, Fact Constellation Schema, Business Intelligence, Data Visualization. Abstrak Penyakit menular seperti COVID-19, Tuberkulosis, dan Malaria masih menjadi tantangan kesehatan global yang signifikan. Salah satu hambatan utama dalam mitigasi wabah ini adalah fragmentasi data kesehatan yang menyebabkan keterlambatan dalam analisis dan pengambilan keputusan. Penelitian ini bertujuan untuk merancang sebuah Data Warehouse yang mampu mengintegrasikan data surveilans dari berbagai sumber heterogen untuk mendukung analisis tren penyakit secara real-time. Metodologi yang digunakan adalah pendekatan bottom-up dengan arsitektur tiga lapisan (three-tier architecture). Proses integrasi data dilakukan melalui mekanisme Extract, Transform, and Load (ETL) menggunakan Pentaho Data Integration untuk menjamin kualitas dan konsistensi data. Penyimpanan data menerapkan Fact Constellation Schema (Skema Galaksi) pada basis data PostgreSQL, yang memungkinkan analisis multidimensi terhadap kasus infeksi dan mortalitas secara bersamaan. Hasil penelitian ini berupa purwarupa dashboard interaktif berbasis Tableau yang menyajikan visualisasi sebaran geografis (GIS) dan grafik tren temporal. Implementasi ini membuktikan bahwa sentralisasi data kesehatan dapat memfasilitasi pemantauan wabah yang lebih efektif dan mendukung perumusan kebijakan kesehatan masyarakat yang berbasis bukti (evidence-based policy). Kata Kunci: Data Warehouse, Penyakit Menular, ETL, Fact Constellation Schema, Business Intelligence, Visualisasi Data.
PREDIKSI PENERIMAAN MAHASISWA MENGGUNAKAN NEURAL NETWORK BERBASIS RAPIDMINER PADA DATA GRADUATE ADMISSION Ayu Elisya Natama Sianturi; Arron Mosses Jhon Hadi; Andri Wijaya
Jurnal Riset Teknik Komputer Vol. 2 No. 4 (2025): Desember : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/q509nv83

Abstract

This study aims to predict graduate admission outcomes using a Neural Network approach implemented in RapidMiner. The dataset was processed through a series of stages, including data cleaning, normalization, and model training, to ensure optimal learning quality. Model performance was assessed using the Root Mean Square Error (RMSE) metric. The resulting RMSE score of 0.054 indicates a low level of prediction error and demonstrates that the constructed model performs reliably. These findings highlight the potential of Neural Networks as an effective analytical tool for estimating student admission likelihood with higher accuracy and supporting data-driven decision-making in the selection process.
Perancangan Data Warehouse Menggunakan Model Star Schema untuk Analisis Penjualan Retail Berbasis PostgreSQL Wijaya, Andri; Novaldi, Alexander
Journal Of Informatics And Busisnes Vol. 3 No. 3 (2025): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i3.3759

Abstract

The retail industry faces significant challenges in managing massive transaction data volumes. Reliance on operational systems (OLTP) often hinders complex historical data analysis. This study aims to design and implement a Data Warehouse using the Star Schema approach to support efficient retail sales analysis. Utilizing the Sample Superstore dataset, the development follows Kimball’s methodology with technical ETL implementation on PostgreSQL. The research produced a Star Schema architecture consisting of one fact table and five dimension tables. Validation testing confirmed complete data integrity during migration. The results demonstrate the system's capability to present rapid multidimensional insights, including positive annual sales trends and profitability disparities across product categories. This implementation proves that PostgreSQL effectively serves as a robust infrastructure for business intelligence, providing a solid foundation for strategic management decision-making.
Perancangan Data Warehouse Menggunakan Metode Nine Step Pada PT. XYZ Shevchenko, Angelus Galang; Maharani, Wianti; Wijaya, Andri
Journal Of Informatics And Busisnes Vol. 3 No. 3 (2025): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i3.3796

Abstract

PT. XYZ operates in the food distribution sector and handles a large number of inbound and outbound goods transactions each day. The high transaction volume creates challengess in managing inventory effectively, highlighting the need for an integrated data management solution. This research aims to develop a data warehouse for PT. XYZ by applying the Kimball Nine-Step methodology. Data were collected through interviews, field observations, and a review of relevant literature. The data warehouse design process includes identifying fact tables and dimension tables related to receiving and shipping activities, followed by the implementation of the Extract, Transform, Load (ETL) process using SQL Server Management Studio 19. The findings indicate that the proposed data warehouse is capable of integrating operational data and presenting inventory information through reports generated using Microsoft Excel. The system supports improved stock control and enables management to make faster and more accurate decisions.
Perancangan Data Warehouse Untuk Analisis Peminjaman Dan Pengembalian Buku Di Perpustakaan Silaban, Bintang Jelita Nasrani; Zalukhu, Indri Feni Asih; Wijaya, Andri
Journal Of Informatics And Busisnes Vol. 3 No. 3 (2025): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i3.3837

Abstract

The current library environment is experiencing rapid data collection, particulary from the daily activity of borrowing and returning books. This vast amount of data could potentially be utilized for analysis, but in practice, library information systems are often used solely for operational purposes. Consequently, opportunities to extract valuable insights from this data are limited. Given the situation, this study attempts to design and implement a data warehouse focused on analyzing book borrowing and returning patterns, utilizing PostgreSQL as the primary platform. The research process involved several stages, starting with data collection, needs analysis, data warehouse model design using the star schema, and implementation into PostgreSQL. Afterward, an ETL (Extraction, Transformation, and Loading) process was performed to able to combine library transaction data into a more structured and ready for analysis. From this data, the system was able to generate various insights, such as patterns of books that were borrowed most, medim, and least.
Co-Authors Aditiya Hermawan Aditya, Putra Adtiya, Setenilaus Afifah Azzahra Agustin, Evelyn Agustio Dwitama Aguswan, Michael Junius Alessandro, Andreas Alexander Chandra Alexius Hendra Gunawan Amat Basri Andreas Alessandro Andreas Alessandro Fernando Putra Andri Wijaya Andronikus G Anggoro, Deo Ardi Riyadi Ardika, Petra Putri Arif Aliyanto Arif Aliyanto Arif Aliyanto Arron Mosses Jhon Hadi Arvin Lawistra Asek, Ambo Ayu Elisya Natama Sianturi Azahra, Khalida Zia Fitrah Azzahra, Afifah Azzahra, Violina Baiturrahman, Ridwan Benny Daniawan Bima Aprianto S Br Hombing, Nova Magdalena Branchris Buchori Asyik Chintia Cantika cia, crecia Crecia Crecia Crecia, Crecia Daely, Septia Angelika Gettin Damayanti, Lily Daniawan, Benny Deo anggoro Dwitama, Agustio Effendy, Ellena Endri Yuliati Enjeli, Margareta Erwin Erwin Filikano, Thomas Gunawan, Andronikus Halim, Ardie Hambali, M Syahbani Hans Rafael Gabriel Turnip IFAH KHADIJAH, IFAH Iskandar Mirza Iskandar Syah Jacqueline Henny P Jessen Laorenza Suwandi Johan Abisay Tambunan Julian Masidin, Nevin JUNAEDI Juni Lapita Hasugian Ketut Agus Wiikananda Kevin Alexander Yech Kevin kevin Kurniawan Maranto, Ardiane Rossi Kusneti, Leni Latius Hermawan Leni Kusneti Lily Damayanti Lusia Komala Widiastuti Maharani, Wianti Marcello, Daniel Maria Bellaniar Ismiati Masidin, Nevin Julian Mayer Dani Sitompul Meilinda Meilinda Meilinda Michael Imanuel Michael Junius Aguswan Muhamad Raka Nur Habibi Muhammad Basri Muhammad Firdaus Muhammad Raka Nur Habibi Mujiyanto Mujiyanto Mutia Maharani Nababan, Clara Nova Magdalena Br Hombing Novaldi, Alexander Nurhadi, M Wiran Jaya Oktarina, Theresia Pamungkas, Martinus Ponco Pratama, Paskalis Arindra Putra, Steven Adi Raditya Rimbawan Raditya Rimbawan O Ratu, Anggitta Rika Solihah, Rika Riski Surya Saputra Rosana Rosana Samuel Dimas Sutikno Sanjaya, Aloisius Egi Seli Septi Putri Septi Putri Azzahra Septia Angelika Gettin daely Setiawan, Ferdy Shevchenko, Angelus Galang Silaban, Bintang Jelita Nasrani Simanjuntak, Welmi Simbolon, Defrianti Sri Andayani Sri Andayani Stefanus Charles Selvianto Stenilaus A Sugiarti, Sabar Sumual, Imanuel Marcell Supriadi, Jonathan Suwitno Suwitno, Suwitno Thomas Filikano Turnip, Hans Rafael Gabriel Verri Kuswanto Welmi Simanjuntak Wikananda, Ketut Agus Wiyono Yakub, Handoyo Yo Ceng Giap Yoel, Yoel Marcelino Pribadi Yohanes Agung Apriyanto Yusuf Kurnia Zalukhu, Indri Feni Asih