cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Radiyan Rahim
Contact Email
jsit@rcf-indonesia.org
Phone
+6281267426503
Journal Mail Official
jsit@rcf-indonesia.org
Editorial Address
Jl. Garuda III Blok C/10 Komplek Pondok Permai, Kel. Limau Manis Salatan, Kec. Pauh, Kota Padang, Provinsi Sumatera Barat.
Location
Kota padang,
Sumatera barat
INDONESIA
Jurnal Sains Informatika Terapan (JSIT)
Published by Riset Sinergi Indonesia
ISSN : -     EISSN : 28281659     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
The scope of this journal is all about Computer Science that are: 1. Artificial Intelligence 2. Computer System 3. Data Mining 4. Information System 5. Decision Support System (DSS) etc.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 188 Documents
 15   16   17   18   19 
Penerapan Data Mining Untuk Memprediksi Harga Udang Vaname Di Pasar Lokal Menggunakan Algoritma Decision Tree Gugun, Gunawan
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.800

Abstract

This research aims to predict the price of vaname shrimp in the local market of Padang City using the Decision Tree algorithm. The data used includes historical records of shrimp prices from 2024 to 2025, along with supporting variables such as production volume, feed prices, weather conditions, pond type, and market conditions. The research methodology includes data collection, preprocessing, algorithm implementation, model evaluation, and system development. The results show that the Decision Tree model can predict shrimp prices with an accuracy of 88.89%. The most influential factors in determining prices are weather conditions and feed prices. A web-based prediction system was also developed using PHP and MySQL to facilitate users in accessing price predictions interactively. This research is expected to assist farmers and traders in making better business decisions, optimize inventory management, and improve the efficiency of the vaname shrimp supply chain in the local market.
Rancang Bangun Sistem Pendeteksi Kantuk Untuk Keamanan Berkendara Berbasis Website Zikri, Ahmad
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.801

Abstract

Traffic accidents are often caused by drowsy drivers, which significantly reduce concentration, alertness, and reaction time while driving. This study aims to design and develop a web-based drowsiness detection system using a camera as the primary visual sensor. The system integrates digital image processing and artificial intelligence techniques to analyze the driver’s eye and mouth movements in real time with a high level of accuracy. When early signs of drowsiness are detected, the system provides an instant alert through a web-based interface that can be accessed both locally and remotely. Experimental results show that the system can detect drowsiness with 92% accuracy, an average response time of 1.2 seconds, and stable performance under various lighting and environmental conditions. The implementation of this system proves to be effective, reliable, and low-cost in preventing accidents caused by fatigue. Therefore, this innovative technology offers a practical, efficient, scalable, and affordable solution to enhance road safety and driver awareness for both private and commercial vehicles.
Penerapan Data Mining Memprediksi Penjualan Obat Menggunakan Metode K-Nearest Neighbor (Studi Kasus : Apotek Difana) Febrialdo, Raecky Meyzal; Wendra, Yumai
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.804

Abstract

The development of information technology in the digital era has provided opportunities for various sectors, including the pharmaceutical industry, to improve operational efficiency and service quality. One of the main challenges faced by pharmacies is inventory management, where both stockouts and overstocks often occur due to the limitations of conventional prediction methods. This study aims to apply data mining techniques using the K-Nearest Neighbor (KNN) algorithm to predict drug sales at Apotik Difana. KNN is chosen because it is simple yet effective in recognizing sales patterns from historical data. A web-based prediction system was developed to facilitate accessibility and usability for the pharmacy. The scope of this study focuses on historical sales data without considering external factors, and only KNN is used without comparison to other algorithms. The results are expected to assist the pharmacy in determining the right type and quantity of drugs, optimizing inventory management, reducing losses from expired drugs, and improving customer service quality. Furthermore, this research provides a theoretical contribution to the development of data mining in sales prediction and offers a practical, technology-based solution for pharmaceutical inventory management.
Sistem Pendeteksi Kebocoran Gas Berbasis Arduino Menggunakan Nodemcu Dan Bylink Chan, Kardi
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.805

Abstract

The rapid advancement of Internet of Things (IoT) technology has significantly contributed to the development of smart home systems that improve safety, comfort, and efficiency in modern households. This study aims to design and implement a gas leak detection system based on Arduino, integrated with NodeMCU ESP8266 and the Blynk application as a real-time monitoring and notification platform. The system employs an MQ-2 sensor to detect LPG gas concentration in the surrounding air. When the gas concentration exceeds a certain threshold, the system automatically activates a buzzer and LED indicator as a local alarm, while simultaneously sending a notification to the user’s smartphone via the Blynk application. Experimental results demonstrate that the system responds rapidly, with a delay of less than one second, and maintains good detection accuracy and network stability. Furthermore, the system is cost-efficient, user-friendly, and capable of remote monitoring, making it an effective solution for enhancing home safety. The integration of Arduino, NodeMCU, and Blynk provides a reliable and scalable IoT-based framework that can be expanded for future smart home security applications.
Penerapan Data Mining Untuk Prediksi Permintaan Hasil Pertanian Beras Menggunakan Metode Fp-Growth Berbasis Website Duta Apyuma, Rabel
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.811

Abstract

This research aims to implement the FP-Growth algorithm in predicting rice demand through a web-based system at Toko Pangan Kita in Pesisir Selatan Regency. The FP-Growth method is applied to identify frequent purchasing patterns from transaction data, thereby supporting business owners and warehouse managers in making strategic decisions related to stock management and distribution. The system was developed using PHP and MySQL and equipped with features such as registration, login, and password reset to ensure user management and security. The analysis process was conducted through two approaches, namely the manual implementation of FP-Growth via programming and validation using RapidMiner. The results show that the FP-Growth algorithm can generate association rules with varying support and confidence values, such as the rule Ir 42 > Solok with 36% support and 79% confidence, which indicates a very strong relationship. In conclusion, this prediction system proves effective in analyzing rice sales data at Toko Pangan Kita and provides strategic insights for business management, thus enhancing stock management efficiency, improving customer service, and supporting more accurate rice distribution planning.
Penerapan Data Mining Untuk Prediksi Peningkatan Jumlah Orang Dengan Gangguan Jiwa (ODGJ) Di Kota Padang Menggunakan Metode K-Means yogi ilhamdi
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.814

Abstract

This study aims to apply the K-Means algorithm to predict the risk of an increase in the number of People with Mental Disorders (ODGJ) at Prof. Dr. HB. Saanin Mental Hospital. The focus is on clustering patient data based on primary diagnoses such as biological, psychological, and social factors to identify patterns that can predict low, medium, and high-risk increases. The research is motivated by the difficulties faced by relevant parties in analyzing ODGJ data in Padang City. The method used is data mining, specifically the K-Means algorithm, to identify patterns of increased ODGJ risk. The analyzed data includes inpatient and outpatient records for the year 2024. The results show that the K-Means algorithm can effectively cluster sub-districts in Padang City based on their risk of increase, with high-risk areas identified as Koto Tangah, Kuranji, and Lubuk Begalung. Testing using RapidMiner yielded results consistent with manual analysis. The implementation of a developed web-based system provides convenience for administrators in efficiently managing patient data, with features for recording the number of patient admissions. The findings of this study are expected to contribute to the development of information systems in the health sector and serve as a reference for future research.
Rancang Bangun Alat Deteksi Kebakaran Otomatis Berbasis Internet Of Thing Dan Wireless Di Dapur Rumah Makan Soponyono Arrafi, Muhamad
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.824

Abstract

This research aims to design and implement an automatic fire detection device based on Internet of Things (IoT) and wireless technology, applied in the kitchen of Rumah Makan Soponyono. The system is developed to detect fire and smoke early using a flame sensor and an MQ-2 gas sensor connected to an ESP8266 microcontroller as the main controller. Sensor data are processed by the microcontroller and transmitted via the internet in the form of real-time notifications through the Telegram application. This allows the restaurant owner to receive immediate alerts even when not on-site. The prototype also includes a buzzer, LED, and 16x2 LCD as local warning indicators. The experimental results show that the system can detect fire at a distance of 20 cm and smoke at 3 cm, with a response time of less than two seconds. Once fire or smoke is detected, the system automatically sends notifications to Telegram and activates the local alarm. This IoT-based system provides an effective solution to enhance kitchen safety, minimize fire risks, and contribute to the application of IoT technology in early warning and fire prevention systems.
Penerapan Data Mining Untuk Prediksi Harga Kayu Manis Menggunakan Algoritma Regresi Linear Sederhana ( Studi Kasus : Usaha Dagang Munang ) Putra, Aris
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.825

Abstract

The development of information technology has enabled more accurate data analysis methods in the business sector, one of which is through data mining techniques. This research aims to predict cinnamon prices using the Simple Linear Regression algorithm at Usaha Dagang Munang. The main problem faced by traders is the unpredictable price fluctuation caused by various external factors such as market demand, weather conditions, and currency exchange rates. Using historical price data from 2015 to 2025, this study applies the Simple Linear Regression method to develop a data-driven prediction model. The analysis process was carried out using RapidMiner software, which produced a regression equation with a positive coefficient value of 1172.727, indicating an upward trend in price each year. The results show that the Simple Linear Regression algorithm can provide accurate price estimations and serve as a useful reference for farmers and traders in determining sales strategies. The application of data mining has proven effective in supporting data-based decision-making in the agricultural sector, particularly in the cinnamon commodity market.
Implementation Machine Learning Algorithms To Predict The Financial Resilience Of Companies Based On Financial Statements Putri, Nadya Andhika; Lahara, Nico Gawa
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.835

Abstract

This study explores the application of machine learning algorithms to predict the financial resilience of companies based on their financial statements. In an era of data-driven decision-making, traditional financial analysis methods may fall short in providing timely and accurate insights. By leveraging advanced machine learning techniques, such as regression models, decision trees, and neural networks, this research aims to create predictive models that can effectively forecast a company's financial health. The study utilizes historical financial data, including balance sheets, income statements, and cash flow reports, to train and test various machine learning models. The findings highlight the potential of machine learning in identifying patterns and trends within financial data that may not be readily apparent through conventional methods. The results can provide valuable tools for financial analysts, investors, and company managers to assess and mitigate financial risks, enhancing decision-making processes and strategic planning
Pengembangan Aplikasi Point Of Sale Berbasis Android Pada UMKM Nyemil Beauty Menggunakan Metode Agile Mulyani, Laras Niti; Iqbal, Muhammad; Islamaya, Adinda; Rati; Rachmadianti, Fionalita
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.837

Abstract

Nyemil Beauty, UMKM kuliner kontemporer, menghadapi tantangan serius karena pencatatan transaksi manual yang tidak efisien, memperlambat operasional, rentan kesalahan, dan risiko kehilangan data inventaris. Urgensi ini mendorong studi untuk merancang dan mengembangkan aplikasi Point of Sale (POS) berbasis Android.Aplikasi ini bertujuan mengotomatisasi pengelolaan transaksi, data produk, dan laporan penjualan UMKM. Pengembangan menggunakan metode Agile, dengan perancangan meliputi analisis kebutuhan, penyusunan diagram, dan implementasi menggunakan Android Studio, bahasa Kotlin, serta basis data SQLite.Fitur utama aplikasi mencakup pencatatan produk dan transaksi, serta pembuatan laporan penjualan digital. Hasil pengujian pengguna menunjukkan penerimaan positif: 41,87% sangat baik, 55,25% baik, dan 2,5% cukup. Aplikasi POS berbasis Android ini berhasil meningkatkan efisiensi dan akurasi transaksi penjualan di Nyemil Beauty, terbukti mudah digunakan, andal, dan menyediakan solusi permanen atas masalah pencatatan manual.

Page 18 of 19 | Total Record : 188

 15   16   17   18   19 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025) Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025) Vol. 4 No. 1 (2025): Jurnal Sains Informatika Terapan (Februari, 2025) Vol. 3 No. 3 (2024): Jurnal Sains Informatika Terapan (Oktober, 2024) Vol. 3 No. 2 (2024): Jurnal Sains Informatika Terapan (Juni, 2024) Vol. 3 No. 1 (2024): Jurnal Sains Informatika Terapan (Februari, 2024) Vol. 2 No. 3 (2023): Jurnal Sains Informatika Terapan (Oktober, 2023) Vol. 2 No. 2 (2023): Jurnal Sains Informatika Terapan (Juni, 2023) Vol. 2 No. 1 (2023): Jurnal Sains Informatika Terapan (Februari, 2023) Vol. 1 No. 3 (2022): Jurnal Sains Informatika Terapan (Oktober, 2022) Vol. 1 No. 2 (2022): Jurnal Sains Informatika Terapan (Juni, 2022) Vol. 1 No. 1 (2022): Jurnal Sains Informatika Terapan (Februari, 2022) More Issue