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SainsTech Innovation Journal
Published by Universitas Qamarul Huda Badaruddin
ISSN : -     EISSN : 26155400     DOI : https://doi.org/10.37824/sij
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering
Information Technology and Computer Science Information System, Network, Information Retrieval, Natural Language Processing, Data Mining, Machine Learning, Image Processing, Computer Vision, Data Science, Software Development Civil Engineering Geological Engineering, Structural Engineering, Project Management, Road Structure
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 96 Documents
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PENERAPAN SEARCH ENGINE OPTIMIZATION (SEO) SEBAGAI STRATEGI PEAMASARAN MOTION GRAPHIC BAGI KONTRIBUTOR MICROSTOCK UNTUK MENINGKATKAN PENJUALAN ASET DIGITAL Rozy Saputra; Joni Saputra; Dedi Satriawan Kusnayadi; M. Afriansyah M. Afriansyah
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1261

Abstract

Perkembangan industri kreatif digital menyebabkan meningkatnya penggunaan platform microstock sebagai media pemasaran aset digital, termasuk motion graphic. Namun, meningkatnya persaingan pada platform microstock menyebabkan banyak aset digital sulit ditemukan oleh calon pembeli karena kurang optimalnya penerapan metadata seperti judul, kata kunci, deskripsi, dan kategori. Penelitian ini bertujuan untuk menganalisis penerapan Search Engine Optimization (SEO) sebagai strategi pemasaran motion graphic bagi kontributor microstock untuk meningkatkan penjualan aset digital pada platform Adobe Stock. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan metode eksperimen semu (quasi experiment) menggunakan desain One Group Pretest-Posttest. Data penelitian diperoleh melalui observasi dashboard Adobe Stock, dokumentasi performa aset, serta penerapan strategi SEO pada metadata aset motion graphic. Penerapan SEO dilakukan melalui optimasi judul, kata kunci, deskripsi, dan kategori aset dengan mempertimbangkan analisis keyword intent. Hasil penelitian menunjukkan bahwa penerapan SEO mampu meningkatkan visibilitas aset motion graphic yang ditunjukkan melalui peningkatan jumlah unduhan dan pendapatan (earning) setelah dilakukan optimasi. Kata kunci: Search Engine Optimization, Motion Graphic, Microstock, Adobe Stock, Pemasaran Digital
Design and Construction of an IoT-Based Automatic Chicken Coop Door Systemwith Scheduled Time Control and Remote Control Parhanudin Parhan; Ahmad Fatoni Dwi Putra; Nuraqilla Waidha Bintang Grendis
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1303

Abstract

This article discusses the design and development of an Internet of Things (IoT)-based automatic system with scheduled time controland remote access control. The Internet of Things (IoT) is a system consisting of hardware, software, and internet technology designed to facilitate human interaction and enable devices with IP addresses to utilize internet networks as a communication medium. IoT can be used to monitor various physical parameters, such as temperature, humidity, pressure, and motion, depending on the application requirements. Designing systems using IoT-based technology can simplify daily human activities, as demonstrated in this study, which applies IoT methods to design an automatic chicken coop door with scheduled time control and remote access capabilities.The proposed method successfully produced an automatic chicken coop door design that operates effectively using IoT technology. All major components, including the ESP8266 microcontroller, RTC module, servo motor, I2C LCD, breadboard, and the Blynk application, were successfully interconnected and operated in an integrated manner according to their respective functions. The test results indicate that the component integration performed smoothly, where the ESP8266 microcontroller was able to process time data, control servo motor movement, display information on the LCD, and receive as well as execute commands directly from the application. Therefore, this device can serve as an efficient solution to support themodernization of poultry farming through IoT-based technology
Performance Analysis of K-Nearest Neighbors and Naive Bayes Algorithms in Stunting Risk Classification in Toddlers Using Public Dataset Nurhikmayani; Syahrani Lonang; Ahmad Fatoni Dwi Putra
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1354

Abstract

Stunting is a chronic nutritional problem in toddlers that can affect physical growth, cognitive development, and children's health quality in the future. This study aims to analyze and compare the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in classifying stunting risk in toddlers using a public dataset from Kaggle. The research was conducted through several stages, including data preprocessing, data cleaning, normalization using the Min-Max method, data balancing using SMOTE-ENN, splitting training and testing data, and parameter optimization using Grid Search. Model evaluation was carried out using a Confusion Matrix with accuracy, precision, Recall, F1-score, ROC Curve, and AUC Score metrics. The results showed that the KNN algorithm performed better than the Naïve Bayes algorithm in classifying stunting risk. The KNN algorithm produced higher accuracy, precision, Recall, and F1-score values, as well as more optimal ROC-AUC values for each classification class. Based on these evaluation results, the KNN algorithm was considered more effective and stable in detecting stunting risk in toddlers compared to the Naïve Bayes algorithm. Therefore, the KNN algorithm can be used as an effective method to support early stunting risk detection based on Machine Learning.
Sentiment Analysis of Positive and Negative User Reviews for TikTok and YouTube Applications on the Google Play Store Using Naïve Bayes and Support Vector Machine (SVM) Algorithms RAKYATOL HASANAH; Syahrani Lonang; Ahmad Fatoni Dwi Putra
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1358

Abstract

The development of digital technology has increased the use of social media applications such as TikTok and YouTube, resulting in a large number of user reviews on the Google Play Store. These reviews can be utilized to determine user satisfaction through sentiment analysis. This study aims to analyze the sentiment of user reviews on TikTok and YouTube applications using the Naïve Bayes and Support Vector Machine (SVM) algorithms, as well as to compare the performance of both algorithms. The research data were obtained through a web scraping process consisting of 20,000 reviews, including 10,000 TikTok reviews and 10,000 YouTube reviews. The data then underwent preprocessing, sentiment labeling, splitting into training and testing datasets, and classification using the Naïve Bayes and Support Vector Machine (SVM) algorithms. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score as evaluation metrics. The results showed that the Naïve Bayes algorithm outperformed SVM. For the TikTok application, Naïve Bayes achieved an accuracy of 80.30%, precision of 80.20%, recall of 80.30%, and F1-score of 80.20%, while SVM achieved an accuracy of 78.70%, precision of 78.60%, recall of 78.70%, and F1-score of 78.50%. For the YouTube application, Naïve Bayes achieved an accuracy of 78.40%, precision of 78.00%, recall of 78.40%, and F1-score of 77.90%, while SVM achieved an accuracy of 77.50%, precision of 77.20%, recall of 77.50%, and F1-score of 76.70%. Based on these results, the Naïve Bayes algorithm demonstrated better performance in classifying user review sentiments on TikTok and YouTube applications.
Comparison of Discharge Using Manning's and Strickler's Methods in the Primary Canal of Gebong Weir, West Lombok Regency Muhamad Yamin; Agus Winardi; Wirriyanti Isnasari; Masitha Maghfirah
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1373

Abstract

This study aims to analyze and compare the flow discharge in the primary canal of Bendung Gebong using the Manning and Stickler methods. The research was conducted to determine the differences in discharge calculation results and to identify the most suitable method for stone masonry channels. The research data were obtained through direct field measurements, including the top width, bottom width, water depth, and channel bed slope. The measured channel dimensions consisted of a top width of 10 m, bottom width of 7 m, water depth of 1.40 m, and channel bed slope of 0.001. The method used in this study was open channel hydraulic analysis by calculating hydraulic parameters such as wetted area, wetted perimeter, and hydraulic radius. Furthermore, discharge calculations were carried out using the Manning and Stickler equations. The analysis results showed that the wetted area was 11.9 m², the wetted perimeter was 11.10 m, and the hydraulic radius was 1.07 m. The discharge calculated using the Manning method was 13.10 m³/s, while the Stickler method produced 12.97 m³/s. The comparison results indicated a discharge difference of 0.13 m³/s with a percentage difference of 0.99%. The relatively small difference indicates that both methods provide nearly similar results under stone masonry channel conditions. The Manning method produced a slightly higher discharge than the Stickler method and was considered more practical for irrigation channel hydraulic analysis.
Classification of Child Stunting Status Using the K-Nearest Neighbor (KNN) Algorithm Based on Toddler Growth Data in East Lombok Regency, West Nusa Tenggara Asno Azzawagama Firdaus; Arif Himawan; Junaedi; Anggun Sindiana; Istianah; Baiq Selviana Pertiwi; Baiq Wangi Narsih; Kartika Yundia; Ahmad Azhari
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1351

Abstract

Stunting is a major nutritional issue in Indonesia that significantly impacts children's physical growth, cognitive development, and future quality of life. Childhood stunting can be identified using nutritional status indicators—such as weight-for-age (W/A), height-for-age (H/A), and weight-for-height (W/H)—specifically by observing a Z-score below -2 for the H/A indicator. The growing volume of anthropometric data on children under five necessitates a rapid and objective method for identifying stunting status. This study aims to classify children's stunting status using the K-Nearest Neighbor (KNN) algorithm based on growth data from children under five in East Lombok Regency, West Nusa Tenggara. The dataset comprises records for 3,416 children, including information on gender, age, weight-for-age (W/A), weight-for-height (W/H), and height-for-age (H/A). Data preprocessing involved removing duplicates, handling missing values, transforming categorical data, and applying Min-Max normalization. The data was split into 70% training data and 30% testing data, with the KNN algorithm applied using k = 5. Model evaluation was conducted using a confusion matrix. The results demonstrate that the KNN model achieved an accuracy rate of 83.15%, indicating a strong capability to classify stunting status based on the children's anthropometric characteristics. These findings confirm that the KNN algorithm can serve as a tool for healthcare professionals to identify stunting more rapidly, objectively, and efficiently, thereby supporting early prevention and management efforts.

Page 10 of 10 | Total Record : 96

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