cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Sularno
Contact Email
soelarno@unidha.ac.id
Phone
+6281377008616
Journal Mail Official
dio@unidha.ac.id
Editorial Address
padang
Location
Unknown,
Unknown
INDONESIA
Jurnal Sains dan Teknologi
Published by CV ITTC Indonesia
ISSN : -     EISSN : 28077393     DOI : 10.47233
Core Subject : Science, Education, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering
Jurnal Sains Dan Teknologi (JSIT), merupakan Jurnal Penelitian dan Kajian Ilmiah yang diterbitkan CV.ITTC - INDONESIA dan dikelola langsung oleh Webinar.Gratis dan Even.Gratis yang terbit 3 (tiga) kali dalam setahun. Penyunting menerima kiriman naskah hasil kajian dan penelitian untuk bidang, Teknik Elektro, Teknik Sipil, Teknik Mesin, ,Teknologi Informasi.
Arjuna Subject : Umum - Umum
Articles 58 Documents
 1   2   3   4   5 
Search results for , issue "Vol. 5 No. 3 (2025): September-Desember" : 58 Documents clear
Analisa Kapasitas Jalan Jembatan di Desa Bulukandang – Ketan Ireng Kabupaten Pasuruan Salsabila, Nisa; ., Khofifah
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3485

Abstract

-Transportation is the cornerstone of infrastructure that improves the economy and mobility. Bridge road capacity is aparameter used to assess the dimensional strength of the infrastructure to serve stressful traffic. The aim of the research isto determine the capacity of the bridge road in Bulukandang - Ketan Ireng village, to compare this capacity with thecurrent traffic volume.The Bridge Road Capacity Analysis Study in Bulukandang - Ketan Ireng Village, Pasuruan Regencyused quantitative research using numerical calculations and analyzed statistically based on the 1997 Indonesian RoadCapacity Manual (MKJI).The results of the discussion on the analysis of the capacity of the bridge road in Bulukandang -Ketan Ireng Village are that the capacity of the Bridge Road in Bulukandang - Ketan Ireng Village, Pasuruan Regency is 8- 10 tons. The capacity of the Bridge Road. The traffic volume that occurs on the Bridge Road in Bulukandang - KetanIreng Village occurs on weekends or Saturday afternoons with a total of 154 pcu/hour. Meanwhile, the lowest trafficoccurred in the morning on Monday with a total of 102 pcu/hour
Perancangan dan Implementasi Smart Brooding System untuk Kandang Ayam Broiler Berbasis Arduino Nano Sanhaji, Ganis; Ihsan, Muhammad; Sumantri, Cyndi Odilia; Nareswari, Natasya; Suherman, Rizky Herdyan
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3543

Abstract

Broiler chicken farming is one of the important livelihoods in Indonesia which is generally carried out traditionally.Problems often experienced by broiler chicken farmers are the temperature and humidity conditions that are not idealduring the brooding period, which is one of the important factors in increasing broiler chicken production. The purpose ofthis study is to control and monitor the temperature and humidity in broiler chicken coops during the brooding period. Tocontrol and monitor the temperature and humidity, a device was designed for use in chicken coops. The device uses anArduino Nano as a microcontroller, a DHT22 sensor, and a PWM dimmer module as a temperature and humidityregulator, and an I2C LCD as a display for temperature and humidity values. The results of sensor readings on thedesigned device will be compared with sensor readings on a standard tool that has been calibrated daily for 21 days.Controlling the temperature and humidity of broiler chicken coops with an automatic system has proven effective, with anaverage measurement error of 0.65% temperature and 4.48% humidity, so that the cage environment remains in optimalconditions for chicken growth. The results showed that cage A (closed house with automatic control) produced a higheraverage chicken weight of 967.9 grams compared to cages B and C. Overall, stable temperature, humidity, cagecleanliness, and feed and vitamin management were the main factors determining the success of broiler chicken farming.
Prediksi Jumlah Obat Menggunakan Jaringan Syaraf Tiruan RNN pada Data Penjualan Bulan Juli Billah, Ana; Yolanda, Aulia; Khairani, Dea; Putra, Dimas Triono; Syif, Helma Tiara
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3588

Abstract

Accurate drug quantity predictions are crucial in inventory management at pharmacies or hospitals to ensure sufficientdrug availability and avoid overstocking or stockouts. However, these predictions are often difficult to make due tocomplex and dynamic drug sales patterns. This study aims to predict drug sales volume using Recurrent Neural Network(RNN) and Long Short-Term Memory (LSTM). The dataset was collected from pharmacy sales records in July. Theresearch stages included data preprocessing, normalization, constructing a time series dataset with a window size of 3,and splitting into training (80%) and testing (20%) datasets. The models were trained for 100 epochs with a batch size of10. The results show that the RNN model achieved a Root Mean Squared Error (RMSE) of 338.16, while the LSTM modelproduced an RMSE of 433.44. This indicates that RNN outperformed LSTM in predicting drug sales on a simple dataset.The findings suggest that RNN can serve as an alternative method to support drug stock planning to ensure betterdistribution and availability
Implementasi Metode Adaptive Neuro-Fuzzy Inference System (ANFIS) untuk Prediksi Status Gizi Balita Studi Kasus Wilayah Kabupaten Blitar Kusuma, Mochammad Rizky; Chulkamdi, Mukh Taofik; Lestanti, Sri
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3620

Abstract

This study aims to develop a predictive model for the nutritional status of toddlers using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on anthropometric data. According to WHO data (2023), global nutritional problems, such as wasting and stunting, are alarming, with 45 million toddlers experiencing wasting and 149 million experiencing stunting. In Indonesia, the prevalence of stunting was recorded at 24.4%, higher than the WHO threshold of 20%. In Blitar Regency, the prevalence of stunting also increased from 14.3% (2022) to 20.3% (2023), a contributing factor being the manual recordingsystem at community health centers (Puskesmas) and integrated health posts (Posyandu). This study used data from 5,000 toddlers from the Kanigoro Community Health Center and Gogodeso Integrated Health Post (Posyandu), with 70% of the data allocated for training and 30% for testing. Model evaluation was conducted using three metrics: Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The evaluation results demonstrated the best prediction accuracy in the MSE for weight/age, height/age, and weight/height, indicating stable data vriation and sensitivity to outlier detection. This prediction system was implemented using MATLAB GUIDE, making it practical for use by healthcare professionals. The results of this study can support efforts to accelerate stunting reduction through faster and more accurate predictions of toddler nutritional status
Penerapan Algoritma K-Means Clustering dalam Segmentasi Citra Sayuran: Wortel, Kol, dan Terong Berbasis Matlab atiqah, sri; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3622

Abstract

The process of identifying vegetable quality faces a major challenge due to its reliance on manual inspection, which istime-consuming, inconsistent, and highly dependent on the observer’s subjectivity. This study aims to examine theapplication of the K-Means Clustering algorithm in the digital image segmentation of three types of vegetables—carrots,cabbages, and eggplants—to evaluate the algorithm’s ability to separate the main object from the background and assessidentification accuracy based on shape and texture features. The research employs an exploratory method with aconceptual prototype approach. The dataset consists of 30 digital images (10 for each vegetable type) obtained throughdirect image acquisition under controlled lighting conditions. All images were processed using MATLAB R2023a andconverted from the RGB color space to the CIELab (Lab) color space* prior to segmentation using the K-Meansalgorithm. After segmentation, shape features (area, perimeter, eccentricity) and texture features based on the Gray LevelCo-occurrence Matrix (GLCM) were extracted. Quantitative analysis was conducted to evaluate the segmentationaccuracy and the effectiveness of object separation. The results show that the K-Means algorithm successfully separatedthe main objects from the background with 100% accuracy and high consistency. This approach is considered feasible asan initial model for an automatic identification system for agricultural commodities based on digital imagery, withpotential for further development through dataset expansion and comparison with other algorithms.
Implementasi Image Processing untuk Klasifikasi Citra Sapi, Gajah, dan Iguana dengan K-Means Maharani, Filsha Rifi; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3626

Abstract

The rapid technological developments have made significant contributions in various fields, but the main problem faced in animal research and conservation is the limitations of manual identification methods that are time-consuming and prone to human error. In addition, visual differences between species often cause difficulties in the process of accurately classifying animal images. This study aims to develop an automatic classification system based on the K-Means Clustering method in identifying three animal species, namely cattle (Bos taurus), elephants (Loxodonta africana and Elephas maximus), and iguanas (Iguanidae). The research method includes several main stages, namely image acquisition, preprocessing by converting RGB to LAB color space, image segmentation using the K-Means Clustering algorithm, and extraction of shape and texture features with Eccentricity, Energy, and Homogeneity parameters. The dataset used consists of 30 images, 10 for each species. The results were analyzed using a confusion matrix to measure the level of classification accuracy. The results showed that the system was able to classify all images with an accuracy level of 100% without any misclassification between classes. Confusion matrix analysis reinforced these findings by demonstrating fully correct identification for all samples. These findings demonstrate the effectiveness of the K-Means Clustering method in grouping animal images with striking visual differences and offer potential applications in conservation and intelligent farming systems.
Implementasi Pengolahan Citra untuk Klasifikasi Jenis Bunga Matahari, Mawar, dan Tulip Menggunakan Algoritma K-Means Clustering ., Ulfa; Ramadhanu, , Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3627

Abstract

Manual identification and classification of ornamental flower varieties is time-consuming and highly dependent onindividual expertise, resulting in identification errors that impact the production value chain and operational efficiency ofthe horticulture industry. This research aims to implement an automated classification system for three types ofornamental flowers (sunflower, rose, and tulip) using K-Means Clustering method with visual feature analysis to improveidentification accuracy and computational efficiency. The research methodology includes acquisition of 210 high-qualitybalanced flower images (70 samples per class), preprocessing with RGB to HSV color space transformation, segmentationusing K-Means with k=3, and extraction of 10 multi-dimensional features encompassing morphology, color, and GLCMtexture. The dataset was divided into 80% training and 20% testing using stratified sampling with K-Fold CrossValidation. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The researchresults demonstrate overall accuracy of 88.89% with sunflower achieving F1-score of 0.98 (0% error), rose 0.86 (14.3%error), and tulip 0.85 (19% error). Aspect ratio, solidity, and mean red channel intensity proved to be the mostdiscriminative features. Misclassification predominantly occurred in the rose-tulip pair (71.4%) due to red spectrum coloroverlap and morphological variation. K-Means algorithm demonstrated optimal balance between accuracy,computational efficiency (0.3s/image), and interpretability, although it has limitations on low feature separability. Thisstudy is limited to a small dataset (210 images) and controlled conditions, requiring real-world validation for bettergeneralization
Klasifikasi Jenis Kendaraan (Helikopter, Mobil, Motor) Menggunakan Metode K-Means Clustering pada Pengolahan Citra Nurjannah, Farah; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3631

Abstract

Digital image-based vehicle type classification still faces obstacles because the identification process is generally done manually, so it takes a long time and has the potential to result in object recognition errors. This condition indicates the need for an image processing-based automation system that is able to recognize vehicle types accurately and efficiently. This study aims to develop a vehicle image classification system (helicopters, cars, and motorcycles) using the K-Means Clustering method to improve identification accuracy based on visual characteristics. This study was conducted with a quantitative approach through four main stages, namely image preprocessing (RGB to LAB conversion and size normalization), segmentation using the K-Means Clustering algorithm, extraction of shape features (metric, eccentricity) and texture (contrast, correlation, energy, homogeneity) based on Gray Level Co-occurrence Matrix (GLCM), and evaluation of accuracy using a confusion matrix. The research dataset consists of 30 vehicle images divided equally for each class. The results show that the combination of the K-Means Clustering method and GLCM feature extraction is able to classify three types of vehicles with an accuracy level reaching 100%. These findings prove that the K-Means method is effective for vehicle image recognition automation, and can be used as a basis for developing artificial intelligence-based visual identification systems in the future.
A Implementasi K-Means Clustering dalam Segmentasi Citra Hewan pada Kucing, Kambing, dan Burung Delvi, Syerlin Aprilia; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3632

Abstract

Image segmentation is one of the most important challenges in digital image processing because it determines the successof separating the main object from the background so that visual information can be further analyzed. The problem ariseswhen the object has complex color, texture, and shape characteristics, as in animal images that often have color patternssimilar to their surroundings, making object boundaries difficult to distinguish clearly. This study aims to apply the KMeans Clustering method in the process of animal image segmentation—specifically for cats, goats, and birds—and toevaluate its effectiveness in identifying and separating the main object from the background. The method used is the KMeans Clustering algorithm, an unsupervised learning technique that groups image pixels based on color similarity in theRGB color space through an iterative process until centroid stability is achieved and clusters representing different imageregions are formed. The results show that the K-Means method can produce good segmentation performance for imageswith uniform lighting and simple backgrounds but experiences a decrease in accuracy when the object’s color is similar toits environment. Overall, this algorithm is effective, simple, and can serve as a foundation for developing automatedanimal image identification and classification systems
Penerapan K-Means Clustering untuk Klasifikasi Citra Aksesoris Ekstraksi Warna dan Tekstur GLCM Zubaidah, Rima Puti; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3633

Abstract

The main problem in accessory image recognition lies in the similarity of physical shapes among objects such as bracelets, necklaces, and earrings, which often causes difficulties in the automatic classification process. This study aims to develop an accessory image classification system capable of accurately grouping objects based on a combination of color and texture features using the K-Means Clustering algorithm. The method used includes several preprocessing stages such as resizing images to ensure uniform dimensions and normalizing pixel values to achieve consistent data scales. Color features were extracted using RGB and HSV histograms to represent color variations, while texture features were obtained through the Gray Level Co-occurrence Matrix (GLCM) method with four parameters: contrast, correlation, energy, and homogeneity. All extracted features were then combined and analyzed using the K-Means algorithm with k=3, corresponding to the number of accessory categories. The results show that combining color and texture features produces a more optimal cluster separation compared to using single-feature extraction. The K-Means algorithm successfully grouped accessory images according to their respective categories with high consistency. These findings have potential applications in digital catalog management systems and product recommendation systems on e-commerce platforms.

Page 2 of 6 | Total Record : 58

 1   2   3   4   5 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 6 No. 1 (2026): Januari - April Vol. 5 No. 3 (2025): September-Desember Vol. 5 No. 2 (2025): Mei - Agustus Vol. 5 No. 1 (2025): Januari-april Vol. 4 No. 3 (2024): September - Desember Vol. 4 No. 2 (2024): Mei - Agustus Vol. 4 No. 1 (2024): Januari - April Vol. 3 No. 3 (2023): September - Desember Vol. 3 No. 2 (2023): Mei - Agustus Vol. 3 No. 1 (2023): January-April Vol. 2 No. 3 (2022): September - Desember Vol. 2 No. 2 (2022): Mei - Agustus Vol. 2 No. 1 (2022): January-April Vol. 1 No. 3 (2021): Jurnal Sains dan Teknologi Vol. 1 No. 2 (2021): Jurnal Sains dan Teknologi (JSIT) Vol. 1 No. 1 (2021): Jurnal Sains dan Teknologi (JSIT) More Issue