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INDONESIA
Jurnal Sistem Informasi dan Informatika
Published by Universitas Dharma Andalas
ISSN : -     EISSN : 29859735     DOI : 10.47233/jiska
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Materials Science & Nanotechnology
Jurnal Sistem Informasi dan Informatika (JISKA) merupakan jurnal yang diterbitkan oleh Program Studi Sistem Informasi Universitas Dharma Andalas dengan nomor E-ISSN : 2985-9735. Jurnal JISKA Volume 1 Nomor 1 terbit pada bulan Januari 2023 dan dapat diterbitkan tepat waktu. Jurnal JISKA direncanakan untuk terbit dalam rentang waktu 6 bulan yang artinya dua kali dalam setahun yaitu setiap bulan Januari dan Juli. Jurnal ini berisi artikel yang mencangkup bidang ilmu komputer dan teknologi informasi yang dimaksudkan sebagai media dokumentasi dan informasi ilmiah yang sekiranya dapat membantu para dosen, staf dan mahasiswa dalam menginformasikan dan mempublikasikan hasil penelitian, opini, tulisan dan kajian ilmiah lainnya kepada masyarakat ilmiah. Melalui jurnal ini kami mengundang peneliti dan pembaca untuk submit artikel pada Jurnal Sistem Informasi dan Informatika secara online. Kami pula mengucapkan banyak terima kasih kepada semua pihak yang telah membantu penerbitan jurnal ini. Terakhir harapan kami semoga jurnal ini dapat membantu semua pembaca baik dosen maupun mahasiswa serta para peneliti di bidang ilmu komputer dan teknologi informasi dalam mengembangkan ilmu komputer dan teknologi informasi demi kemajuan bersama.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 45 Documents
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Pemanfaatan Media Sosial untuk Branding (Studi Deskriptif pada Vamelania Laundry) Y, Hendrico
Jurnal Sistem Informasi Dan Informatika Vol 4 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/jiska.v4i1.2459

Abstract

Building branding on social media isn't just about posting great photos or diligently creating Stories. It's about how the audience perceives the Vamelania Laundry brand among millions of other content. Think of social media as a digital living room where you can chat with potential customers. Branding is the promise and feeling people remember when they hear a business name. Branding is creating a persuasive message that effectively attracts consumers' attention. Based on these facts, Instagram isn't just a place to show off photos in the world of branding; it's a Visual Showcase and Connection Center. Due to its highly visual nature, this platform plays a crucial role in shaping audience perceptions of a brand. Vamelania Laundry, a pioneer in machine-based laundry services, offered by the piece and by the kilo in 2007, promoted its services using Instagram. The purpose of this study was to determine the branding activities carried out by Vamelania Laundry on its Instagram social media account and to determine the factors that led to Instagram being chosen as an active branding medium. This research method used was descriptive qualitative, which is a fact-finding method by collecting data in the form of words and images, not numbers. Therefore, this study will contain several data excerpts to illustrate the presentation of the report. Researchers concluded that Vamelania Laundry utilizes Instagram effectively, as evidenced by its diverse branding activities and its ability to utilize various available features.
Implementasi Protokol Redis Pub/Sub Menggunakan Python untuk Sistem Monitoring Suhu IoT Secara Real-Time Sammir, Hadadd; Hamdi, Khairil; ., Isnardi
Jurnal Sistem Informasi Dan Informatika Vol 4 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/jiska.v4i1.2486

Abstract

This research implements the Redis Publish/Subscribe (Pub/Sub) mechanism using the Python programming language for real-time transmission of IoT temperature sensor data. The primary focus of this study is to address latency challenges in data distribution for logging and alerting system requirements. The system is designed with an architecture where temperature data is published to a Redis channel and simultaneously received by multiple subscribers. One subscriber unit is responsible for recording data into a database for historical analysis, while another unit validates temperature thresholds to trigger instant alerts upon detecting anomalies. Test results demonstrate that the use of Redis Pub/Sub effectively achieves decoupling between data senders and receivers, thereby enhancing system scalability. This architecture proves capable of distributing information with low latency and high efficiency. This study concludes that Redis Pub/Sub is a reliable solution for IoT monitoring systems that require rapid response and seamless data synchronization between monitoring functions and preventive actions.
Sistem Toko Online Berbasis PHP Menggunakan Framework Bootstrap: Desty’s Pastry Saputra, Muhammad Farhan; Wijaya, Anggi Hadi; ., Ipriadi
Jurnal Sistem Informasi Dan Informatika Vol 4 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/jiska.v4i1.2493

Abstract

“Desty’s Pastry” previously relied on WhatsApp-based ordering and manual record-keeping, resulting in frequent order errors, limited market reach, and the absence of modern payment and notification features. This study aims to address these challenges by developing a web-based online store to improve operational efficiency and enhance customer experience. The system was built using the Waterfall development model, with requirements collected through interviews and direct observations. PHP 8.2, Bootstrap 5.3.5, and MySQL were used as core technologies, supported by Data Flow Diagrams and Entity Relationship Diagrams for system design. The resulting system provides key features including product catalog management, user authentication, shopping cart functionality, order processing, and an administrative dashboard, along with digital payment integration through QRIS. Black-box testing using Equivalence Partitioning showed that core functionalities such as registration, product selection, cart management, and order processing performed correctly with accurate data handling. Overall, the system successfully resolves initial operational issues and offers a scalable solution for SMEs adopting digital sales platforms.
Analisis Hierarchical Clustering untuk Segmentasi Pelanggan pada Dataset Mall Customers Maissy Angelica Pakpahan; Sirlia Sahid; Mika M.F Simanullang; Rifqi Putra Winanda
Jurnal Sistem Informasi Dan Informatika Vol 4 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/jiska.v4i1.2615

Abstract

Penelitian ini bertujuan untuk menganalisis segmentasi pelanggan menggunakan metode Hierarchical Clustering pada dataset Mall Customers. Tujuan utama penelitian adalah mengelompokkan pelanggan berdasarkan kemiripan Annual Income dan Spending Score. Metode penelitian meliputi preprocessing data menggunakan normalisasi Z-score, perhitungan jarak Euclidean, serta proses clustering menggunakan metode Ward linkage. Penentuan jumlah cluster optimal dilakukan dengan menggunakan beberapa metrik evaluasi seperti Silhouette Score, Calinski-Harabasz Index, dan Davies-Bouldin Index. Hasil penelitian menunjukkan bahwa jumlah cluster optimal adalah lima dengan performa clustering yang baik ditunjukkan oleh nilai Silhouette yang tinggi dan Davies-Bouldin yang rendah. Setiap cluster merepresentasikan segmen pelanggan yang berbeda seperti pelanggan dengan pendapatan tinggi dan belanja tinggi maupun rendah. Hasil ini dapat digunakan sebagai dasar strategi pemasaran yang lebih efektif.
Penerapan Random Forest untuk Klasifikasi Diagnosis Kanker Payudara Berbasis Dataset WBCD Naufal Aqiilah Asra; Maulana Al Nouri; Tia Risky Yasmin Saketang; Repi Meilani Putri
Jurnal Sistem Informasi Dan Informatika Vol 4 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/jiska.v4i1.2633

Abstract

Breast cancer is one of the most critical global health challenges, with Indonesia recording 66,271 new cases in 2022 according to GLOBOCAN data published by the International Agency for Research on Cancer (IARC/WHO). Early and accurate detection is essential to improving patient survival rates, yet conventional diagnosis remains time-consuming and dependent on expert availability. This study implements the Random Forest algorithm to classify breast cancer diagnosis using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset from the UCI Machine Learning Repository. The dataset consists of 569 samples with 30 numerical features extracted from fine-needle aspirate (FNA) cell images, labeled as benign or malignant. Data preprocessing involved removing non-predictive columns, converting categorical labels to binary format, handling outliers using IQR Clipping, and applying StandardScaler normalization. The dataset was split into 80% training and 20% testing using stratified splitting, with the Random Forest Classifier configured using 100 decision trees and class_weight=balanced to handle class imbalance. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics alongside confusion matrix analysis and 5-Fold Stratified Cross Validation. The model achieved 97.37% accuracy on the test set, with zero False Positive predictions, meaning no benign patient was misdiagnosed as malignant. Cross-validation confirmed generalization ability with a mean accuracy of 96.31%, indicating no overfitting. Feature importance analysis identified area_worst, concave points_worst, and perimeter_worst as the most dominant features, consistent with the clinical morphological characteristics of malignant cancer cells. These findings demonstrate the strong potential of Random Forest as a reliable and interpretable tool for supporting breast cancer diagnosis.

Page 5 of 5 | Total Record : 45

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