Jurnal Teknologi dan Sistem Komputer

Journal Information
ISSN / EISSN : 2620-4002 / 2338-0403
Total articles ≅ 254
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Latest articles in this journal

Agus Subhan Akbar, R. Hadapiningradja Kusumodestoni
Jurnal Teknologi dan Sistem Komputer, Volume 9; doi:10.14710/jtsiskom.2021.14007

This correct the article "Optimasi nilai k dan parameter lag algoritme k-nearest neighbor pada prediksi tingkat hunian hotel (Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates)" in vol. 8, no. 3, pp. 246-254, Jul. 2020; https://doi.org/10.14710/jtsiskom.2020.13648In the original published version of this article, the placement of Figure 8 and Figure 9 less appropriate which causes the manuscript hard to read. In addition, Table 2 through Table 6 need to be repositioned.The publisher apologizes for these errors. These errors have been corrected online.
Yufis Azhar, Galang Aji Mahesa, Moch. Chamdani Mustaqim
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 15-21; doi:10.14710/jtsiskom.2020.13790

Cancellation of hotel bookings by customers greatly influences hotel managerial decision making. To minimize losses by this problem, the hotel management made a fairly rigid policy that could damage the reputation and business performance. Therefore, this study focuses on solving these problems using machine learning algorithms. To get the best model performance, hyperparameter optimization is applied to the random forest algorithm. It aims to obtain the best combination of model parameters in predicting hotel booking cancellations. The proposed model is proven to have the best performance with the highest accuracy results of 87 %. This study's results can be used as a model component in hotel managerial decision-making systems related to future bookings' cancellation.
Melinda Melinda, Elizar Elizar, Yunidar Yunidar, Muhammad Irhamsyah
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 22-30; doi:10.14710/jtsiskom.2020.13868

The Wiener filter is an adaptive filter which able to produce the desired estimates. Besides, this filter can also suppress noise in digital signal processing. This study aims to segment the fluctuation pattern, which results from data acquisition from a capacitive sensor with the object H2O. The fluctuation pattern to be processed is the High Fluctuation (HF) pattern by dividing the pattern into several segments according to the input frequency. It aims to see in more detail and clearly the state of each segmentation of the pattern. The results will show noise attenuation and suppression after filtering with a Wiener filter. The Signal to Noise Ratio (SNR) value will also be analyzed, which shows that the signal quality is getting better after applying the Wiener filter. Then, the analysis of the Mean Square Error (MSE) results can provide more consistent results with a smaller average error.
Agil Aditya, Betha Nurina Sari, Tesa Nur Padilah
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 1-7; doi:10.14710/jtsiskom.2020.13747

K-medoids clustering uses distance measurement to find and classify data that have similarities and inequalities. The distance measurement method selection can affect the clustering performance for a dataset. Several studies use the Euclidean and Gower distance as measurement methods in numerical data clustering. This study aims to compare the performance of the k-medoids clustering on a numerical dataset using the Euclidean and Gower distance. This study used seven numerical datasets and Silhouette, Dunn, and Connectivity indexes in the clustering evaluation. The Euclidean distance is superior in two values of Silhouette and Connectivity indexes so that Euclidean has a good data grouping structure, while the Gower is superior in Dunn index showing that the Gower has better cluster separation compared to Euclidean. This study shows that the Euclidean distance is superior to the Gower in applying the k-medoids algorithm with a numeric dataset.
Sugriyono Sugriyono,
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 311-316; doi:10.14710/jtsiskom.2020.13874

The existence of outliers in the dataset can cause low accuracy in a classification process. Outliers in the dataset can be removed from a preprocessing stage of classification algorithms. Clustering can be used as an outlier detection method. This study applies K-means and a distance matrix to detect outliers and remove them from datasets with class labels. This research used a dataset of students’ academic performance totaling 6847 instances, having 18 attributes and 3 class labels. Preprocessing applies the K-means method to get centroid in each class. The distance matrix is used to evaluate the distance of instance to the centroid. Outliers, which are a different class, will be removed from the dataset. This preprocessing improves the classification accuracy of the kNN algorithm. Data without preprocessing has 72.28 % accuracy, preprocessed data using K-means with Euclidean has 98.42 % accuracy (an increase of 26.14 %), while the K-means with Manhattan has 97.76 % accuracy (an increase of 25.48 %).
Edwin R. Arboleda, Kimberly M. Parazo, Christle M. Pareja
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 317-322; doi:10.14710/jtsiskom.2020.13744

This study aimed to design and develop a watermelon ripeness detector using Near-Infrared Spectroscopy (NIRS). The research problem being solved in this study is developing a prototype wherein the watermelon ripeness can be detected without the need to open it. This detector will save customers from buying unripe watermelon and the farmers from harvesting an unripe watermelon. The researchers attempted to use the NIRS technique in determining the ripeness level of watermelon as it is widely used in the agricultural sector with high-speed analysis. The project was composed of Raspberry Pi Zero W as the microprocessor unit connected to input and output devices, such as the NIR spectral sensor and the OLED display. It was programmed by Python 3 IDLE. The detector scanned a total of 200 watermelon samples. These samples were grouped as 60 % for the training dataset, 20 % for testing, and another 20 % for evaluation. The data sets were collected and are subjected to the Support Vector Machine (SVM) algorithm. Overall, experimental results showed that the detector could correctly classify both unripe and ripe watermelons with 92.5 % accuracy.
Hendi Santoso, Totok Hestirianoto, Indra Jaya
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 8-14; doi:10.14710/jtsiskom.2020.13725

This study aims to develop a turtle nests real-time monitoring system using the Arduino Uno to measure the temperature and moisture of sand used conveniently for certain applications. Sand temperature measurement uses a DS18B20 waterproof sensor, sand moisture uses SKU:SEN0193, and air temperature and humidity using DHT22. The micro SD card module is used to store data from sensor calculations in real-time and continuously. The measuring instrument was designed to be portable and easy to use. The material used is polypropylene that has dimensions of 11x6x18 cm3. Using the regression linear analysis, there was no significant difference in temperature measurements using the DS18B20 sensor and analog thermometer and sand humidity using an SKU:SEN0193 sensor and analog humidity measuring instrument.
Ade Ramdan, , Endang Suryawati, , Vitria Puspitasari Rahadi
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 289-296; doi:10.14710/jtsiskom.2020.13768

Tea clone of Gambung series is a superior variety of tea that has high productivity and quality. Smallholder farmers usually plant these clones in the same areas. However, each clone has different productivity or quality, so it is difficult to predict the production quality in the same area. To uniform the variety of clones in an area, smallholder farmers still need experts to identify each plant because one and other clones share the same visual characteristics. We propose a tea clone identification system using deep CNN with skip connection methods, i.e., residual connections and densely connections, to tackle this problem. Our study shows that the proposed method is affected by the hyperparameter setting and the combining feature maps method. For the combining method, the concatenation method on a densely connected network shows better performance than the summation method on a residual connected network.
Tamunopriye Ene Dagogo-George, , , Modinat Abolore Mabayoje, Shakirat Aderonke Salihu
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 297-303; doi:10.14710/jtsiskom.2020.13669

Diabetic Retinopathy (DR) is a condition that emerges from prolonged diabetes, causing severe damages to the eyes. Early diagnosis of this disease is highly imperative as late diagnosis may be fatal. Existing studies employed machine learning approaches with Support Vector Machines (SVM) having the highest performance on most analyses and Decision Trees (DT) having the lowest. However, SVM has been known to suffer from parameter and kernel selection problems, which undermine its predictive capability. Hence, this study presents homogenous ensemble classification methods with DT as the base classifier to optimize predictive performance. Boosting and Bagging ensemble methods with feature selection were employed, and experiments were carried out using Python Scikit Learn libraries on DR datasets extracted from UCI Machine Learning repository. Experimental results showed that Bagged and Boosted DT were better than SVM. Specifically, Bagged DT performed best with accuracy 65.38 %, f-score 0.664, and AUC 0.731, followed by Boosted DT with accuracy 65.42 %, f-score 0.655, and AUC 0.724 when compared to SVM (accuracy 65.16 %, f-score 0.652, and AUC 0.721). These results indicate that DT's predictive performance can be optimized by employing the homogeneous ensemble methods to outperform SVM in predicting DR.
Adnan Rafi Al Tahtawi, Robi Kurniawan
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 323-329; doi:10.14710/jtsiskom.2020.13822

In hydroponic cultivation sites, pH control is still carried manually by checking the pH level with a pH meter and providing a pH balancing liquid manually. This study aims to design an automatic pH control system in the Deep Flow Technique (DFT) hydroponic system that uses the Internet of Things (IoT) based Fuzzy Logic Controller (FLC). The SKU SEN0161 sensor detects the pH value as FLC inputs in an error value and its changes. These inputs are processed using Mamdani FLC embedded in the Arduino Mega 2560 microcontroller. The FLC produces an output in a pH liquid feeding duration using the peristaltic pump. The results showed that FLC could maintain the pH value according to the set point with a settling time of less than 50 seconds, both with disturbance by adding pH liquid and without disturbance. The pH value can also be displayed on the website interface system as a monitoring system.
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