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Results in Journal Jurnal Teknologi dan Sistem Komputer: 318

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

Abstract:
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 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. These placing errors have been corrected online.The publisher apologizes for these errors.
Bambang Ari Wahyudi, Irma Palupi
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 49-55; doi:10.14710/jtsiskom.2020.13877

Abstract:
This research implements the Susceptible, Infected, and Removed (SIR) model to predict the Covid-19 outbreak in Indonesia. The government official data, consisting of infected, dead, and recovered, are used as actual data to interpolate the model through matching data with minimum mean squared error (MSE). The study uses one of the Quasi-Newton search methods, the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) algorithm, to determine the interaction coefficient's optimal value in the model with the minimum MSE value. Based on data as of July 18, 2020, it predicts that the peak of the infected number will be in October 2020 with around 14 % of the total population infected, and the MSE of 18.42 is relative to the period of the actual data. Meanwhile, the basic reproduction rate is calculated to be 2.035 from the model, where it is underestimated about 29 % compared to the relative basic reproduction rate from the provided actual data.
Jans Hendry, Isnan Nur Rifai, Yoga Mileniandi
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 41-48; doi:10.14710/jtsiskom.2020.13858

Abstract:
The Short-time Fourier transform (STFT) is a popular time-frequency representation in many source separation problems. In this work, the sampled and discretized version of Discrete Gabor Transform (DGT) is proposed to replace STFT within the single-channel source separation problem of the Non-negative Matrix Factorization (NMF) framework. The result shows that NMF-DGT is better than NMF-STFT according to Signal-to-Interference Ratio (SIR), Signal-to-Artifact Ratio (SAR), and Signal-to-Distortion Ratio (SDR). In the supervised scheme, NMF-DGT has a SIR of 18.60 dB compared to 16.24 dB in NMF-STFT, SAR of 13.77 dB to 13.69 dB, and SDR of 12.45 dB to 11.16 dB. In the unsupervised scheme, NMF-DGT has a SIR of 0.40 dB compared to 0.27 dB by NMF-STFT, SAR of -10.21 dB to -10.36 dB, and SDR of -15.01 dB to -15.23 dB.
Yufis Azhar, Galang Aji Mahesa, Moch. Chamdani Mustaqim
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 15-21; doi:10.14710/jtsiskom.2020.13790

Abstract:
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

Abstract:
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.
Arwin Datumaya Wahyudi Sumari, Dimas Rossiawan Hendra Putra, Muhammad Bisri Musthofa, Ngat Mari
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 31-40; doi:10.14710/jtsiskom.2020.13779

Abstract:
This study aims to analyze the comparative performance of pandemic dynamics prediction methods on the island of Java, based on data from March to May 2020 covering the provinces of DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java. The prediction uses Knowledge Growing System (KGS) and time series models, namely Single Moving Average (SMA) and Exponential Moving Average (EMA). Based on the Mean Absolute Percentage Error (MAPE) computational results, the EMA method produces a lower error rate than the SMA method with 47.94 % on average. The KGS prediction with a Degree of Certainty (DoC) produced a trend analysis that the pandemic dynamics in DKI Jakarta province will decrease gradually if the current policy is still implemented. Whereas in the other provinces, the KGS predicted the pandemic dynamics trends will still increase.
Agil Aditya, Betha Nurina Sari, Tesa Nur Padilah
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 1-7; doi:10.14710/jtsiskom.2020.13747

Abstract:
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.
Rusliyawati Rusliyawati,
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 56-63; doi:10.14710/jtsiskom.2020.13776

Abstract:
Tire air pressure is very important in driving, providing comfort, safety, and efficiency in fuel consumption. This study aims to create a model that can determine the measurement of tire air pressure. The model was developed based on the Mamdani FIS with five input parameters: load weight (load capacity), weather, mileage, rim diameter, and tire thickness. Mamdani inference generates front and rear tire air pressure. The calculation of tire pressure using the system was compared with a manual that only considers the vehicle load. This comparison shows the difference in the mean size of 1.24% for the front tire pressure and 2.17% for the rear tire. The system can provide recommendations for tire air pressure by considering several parameters in addition to vehicle load.
Sugriyono Sugriyono,
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 311-316; doi:10.14710/jtsiskom.2020.13874

Abstract:
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

Abstract:
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

Abstract:
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

Abstract:
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

Abstract:
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

Abstract:
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.
, Ekky Rino Fajar Sakti, Mochamad Subianto
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 304-310; doi:10.14710/jtsiskom.2020.13726

Abstract:
Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.
Ahmad Taufiq Akbar, Rochmat Husaini, Bagus Muhammad Akbar, Shoffan Saifullah
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 276-283; doi:10.14710/jtsiskom.2020.13625

Abstract:
Blood type still leads to an assumption about its relation to some personality aspects. This study observes preprocessing methods for improving the classification accuracy of MBTI data to determine blood type. The training and testing data use 250 data from the MBTI questionnaire answers given by 250 respondents. The classification uses the k-Nearest Neighbor (k-NN) algorithm. Without preprocessing, k-NN results in about 32 % accuracy, so it needs some preprocessing to handle data imbalance before the classification. The proposed preprocessing consists of two-stage, the first stage is the unsupervised resample, and the second is the supervised resample. For the validation, it uses ten cross-validations. The result of k-Nearest Neighbor classification after using these proposed preprocessing stages has finally increased the accuracy, F-score, and recall significantly.
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 270-275; doi:10.14710/jtsiskom.2020.13668

Abstract:
Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.
Ahmad Syarif Rosidy, , Mochammad Husni
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 263-269; doi:10.14710/jtsiskom.2020.13686

Abstract:
Event organizers in Indonesia often use websites to disseminate information about these events through digital posters. However, manually processing for transferring information from posters to websites is constrained by time efficiency, given the increasing number of posters uploaded. Also, information retrieval methods, such as Named Entity Recognition (NER) for Indonesian posters, are still rarely discussed in the literature. In contrast, the NER method application to Indonesian corpus is challenged by accuracy improvement because Indonesian is a low-resource language that causes a lack of corpus availability as a reference. This study proposes a solution to improve the efficiency of information extraction time from digital posters. The proposed solution is a combination of the NER method with the Optical Character Recognition (OCR) method to recognize text on posters developed with the support of relevant training data corpus to improve accuracy. The experimental results show that the system can increase time efficiency by 94 % with 82-92 % accuracy for several extracted information entities from 50 testing digital posters.
, Erwin Susanto, Doan Perdana, Husneni Mukhtar, Yulius Anggoro Pamungkas, Yakobus Yulyanto Kevin
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 255-262; doi:10.14710/jtsiskom.2020.13591

Abstract:
This study examines the application of a landslide disaster monitoring system based on soil activity information that utilizes humidity, temperature, and accelerometer sensors. An artificial highland was built as the research object, and the landslide process was triggered by supplying the system with continuous artificial rainfall. The soil activities were observed through its slope movement, temperature, and moisture content, utilizing an accelerometer, temperature, and humidity sensors both in dry and wet conditions. The system could well observe the soil activities, and the obtained data could be accessed in real-time and online mode on a website. The time delay in sending the data to the server was 2 seconds. Moreover, the characteristics of soil porosity and its relevance to soil saturation level due to water pressure were studied as well. Kinetic study showed that the water adsorption to soil followed the intraparticle diffusion model with a coefficient of determination R2 0.99043. The system prototype should be used to build the information center of disaster mitigation, particularly in Indonesia.
Gilbert E. Bueno, Kristine A. Valenzuela, Edwin R. Arboleda
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 228-233; doi:10.14710/jtsiskom.2020.13733

Abstract:
Cacao pod's ideal harvesting time is when it is about to be ripe. Immature harvest would result in hard cacao beans not suitable for fermentation, while overripe cacao pods lead to fungal-infected, defective, and poor-quality yields. The demand for high-quality cacao products is expected to rise due to advancing technology in the present. Pre-harvesting needs to provide optimal identification of which amongst the pods are ripened enough and ready for the next stage of the cacao process. This paper recommends a technique to determine the ripeness of cacao. Nine hundred thirty-three cacao samples were used to collect thumping audio data at five different pod's exocarp locations. Each sound file is 1 second long, creating 4665 cacao sound file datasets at 16kHz sample rate and 16-bit audio bit depth. The process of the Mel-Frequency Cepstral Coefficient Spectogram was then applied to extract recognizable features for the training process. The deep learning method integrated was a convolutional neural network (CNN) to classify the cacao sound successfully. The experimental design model's output exhibits an accuracy of 97.50 % for the training data and 97.13 % for the validation data. While the overall accuracy mean of the classification system is 97.46 %, whether the cacao is unripe or ripe.
Graciella Mae L Adier, Charlene A Reyes, Edwin R Arboleda
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 239-245; doi:10.14710/jtsiskom.2020.13734

Abstract:
Civet coffee is considered as highly marketable and rare. This specialty coffee has a special flavor and higher price relative to regular coffee, and it is restricted in supply. Establishing a straightforward and efficient approach to distinguish Civet coffee for quality; likewise, consumer protection is fundamental. This study utilized visible spectroscopy as a non-destructive and quick technique to obtain the absorbance, ranging from 450 nm to 650 nm, of the civet coffee and non-civet coffee samples. Overall, 160 samples were analyzed, and the total spectra accumulated was 960. The data gathered from the first 120 samples were fed to the classification learner application and were used as a training data set. The remaining samples were used for testing the classification algorithm. The study shows that civet coffee bean samples have lower absorbance values in visible spectra than non-civet coffee bean samples. The process yields 96.7 % to 100 % classification scores for quadratic discriminant analysis and logistic regression. Among the two classification algorithms, logistic regression generated the fastest training time of 14.050 seconds. The application of visible spectroscopy combined with data mining algorithms is effective in discriminating civet coffee from non-civet coffee.
Faisal Dharma Adhinata, Muhammad Ikhsan, Wahyono Wahyono
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 222-227; doi:10.14710/jtsiskom.2020.13660

Abstract:
CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.
Nur Choiriyati, Yandra Arkeman, Wisnu Ananta Kusuma
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 234-238; doi:10.14710/jtsiskom.2020.13407

Abstract:
An open challenge in bioinformatics is the analysis of the sequenced metagenomes from the various environments. Several studies demonstrated bacteria classification at the genus level using k-mers as feature extraction where the highest value of k gives better accuracy but it is costly in terms of computational resources and computational time. Spaced k-mers method was used to extract the feature of the sequence using 111 1111 10001 where 1 was a match and 0 was the condition that could be a match or did not match. Currently, deep learning provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this research, two different deep learning architectures, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), trained to approach the taxonomic classification of metagenome data and spaced k-mers method for feature extraction. The result showed the DNN classifier reached 90.89 % and the CNN classifier reached 88.89 % accuracy at the genus level taxonomy.
Mulia Hanif, Maman Abdurohman, Aji Gautama Putrada
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 284-288; doi:10.14710/jtsiskom.2020.13353

Abstract:
Currently, the smart rice box has applied the Internet of Things (IoT) but without prediction of rice runs out which shows the amount of rice consumption. This study applies linear regression to predict the rice runs out in an IoT-based smart rice box and analyzes its performance. The prediction used the dataset obtained by measuring a smart rice box equipped with a load cell weight sensor and Hx711 module. The weight sensor accuracy was an RMSE of between 56 and 170 grams. The linear regression method applied to the smart rice box to predict rice running out has an MSE value of 0.2588 with a prediction window of 43 days. An R-squared value of less than one is obtained with a predictive threshold of 24 days.
, Dina Priliyana, Moh. Eki Riyadani, Nur Iksan, Hari Wibawanto
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 210-216; doi:10.14710/jtsiskom.2020.13590

Abstract:
Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.
Agus Subhan Akbar, R. Hadapiningradja Kusumodestoni
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 246-254; doi:10.14710/jtsiskom.2020.13648

Abstract:
Hotel occupancy rates are the most important factor in hotel business management. Prediction of the rates for the next few months determines the manager's decision to arrange and provide all the needed facilities. This study performs the optimization of lag parameters and k values of the k-Nearest Neighbor algorithm on hotel occupancy history data. Historical data were arranged in the form of supervised training data, with the number of columns per row according to the lag parameter and the number of prediction targets. The kNN algorithm was applied using 10-fold cross-validation and k-value variations from 1-30. The optimal lag was obtained at intervals of 14-17 and the optimal k at intervals of 5-13 to predict occupancy rates of 1, 3, 6, 9, and 12 months later. The obtained k-value does not follow the rule at the square root of the number of sample data.
Rihartanto Rihartanto, Riris Kurnia Ningsih, Achmad Fanany Onnilita Gaffar, Didi Susilo Budi Utomo
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 201-209; doi:10.14710/jtsiskom.2020.13476

Abstract:
Information that can be in the form of text, image, audio, and video, is a valuable asset that needs to be secured from unauthorized parties. This research aims to study the implementation of Vigenere cipher 128 (VC-128) and square rotation to secure text information. The square rotation is applied to increase the security of the encryption results obtained from VC-128. The randomness of the rotation results was measured using Shannon entropy based on the distance between characters, and the Avalanche Effect measured changes in the encryption results compared to the original text. The square rotation can increase the randomness of the VC-128 encryption results, as indicated by an increase in entropy values. The highest increase in entropy of 34.8 % occurs in repetitive texts with the square size that produces optimal entropy was a 9x9 medium-size square. The Avalanche effect for each test data shows inconsistent results ranging from 44.5 % to 49 %.
Andi Nurkholis, Imas Sukaesih Sitanggang
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 192-200; doi:10.14710/jtsiskom.2020.13657

Abstract:
Land suitability evaluation has a vital role in land use planning aimed to increase food production effectiveness. Palm oil is a leading and strategic commodity for Indonesian people, which is predicted consumption will exceed production in the future. This study aims to evaluate palm oil land suitability using a spatial decision tree algorithm that is conventional decision tree modification for spatial data classification with adding spatial join relation. The spatial dataset consists of eight explanatory layers (soil nature and characteristics), and a target layer (palm oil land suitability) in Bogor District, Indonesia. This study produced three models, where the best model was obtained based on optimizing accuracy (98.18 %) and modeling time (1.291 seconds). The best model has 23 rules, soil texture as the root node, two variables (drainage and cation exchange capacity) are uninvolved, with land suitability visualization obtains percentage S2 (29.94 %), S3 (53.16 %), N (16.57 %), and water body (0.33 %).
Merinda Lestandy, ,
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 217-221; doi:10.14710/jtsiskom.2020.13619

Abstract:
Blood donation is the process of taking blood from someone used for blood transfusions. Blood type, sex, age, blood pressure, and hemoglobin are blood donor criteria that must be met and processed manually to classify blood donor eligibility. The manual process resulted in an irregular blood supply because blood donor candidates did not meet the criteria. This study implements machine learning algorithms includes kNN, naïve Bayes, and neural network methods to determine the eligibility of blood donors. This study used 600 training data divided into two classes, namely potential and non-potential donors. The test results show that the accuracy of the neural network is 84.3 %, higher than kNN and naïve Bayes, respectively of 75 % and 84.17 %. It indicates that the neural network method outperforms comparing with kNN and naïve Bayes.
, , Andang Wijanarko, Erich Adinal Adrian
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 178-184; doi:10.14710/jtsiskom.2020.13422

Abstract:
Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the CUHK Face Sketch database consists of 93 photos and 93 sketches, and personal documentation consists of five photos and five sketches. The sketch matching application to training data produces an accuracy of 76.14 %, precision of 91.04 %, and recall of 80.26 %, while testing with sketch modifications produces accuracy and recall of 95 % and precision of 100 %.
Ali Rizal Chaidir, Gamma Aditya Rahardi,
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 185-191; doi:10.14710/jtsiskom.2020.13643

Abstract:
Line following and lane tracking are robotic navigation techniques that use lines as a guide. The techniques can be applied to mobile robots in the industry. This research applied the Braitenberg controller and image processing to control and obtain line information around the mobile robot. The robot was implemented using Arduino Uno as a controller. A webcam was connected to a computer that performs image processing using canny edge detection and sends the data to the robot controller via serial communication. The robot can navigate on the side of the line, and the success rate of the system is 100 % at a turn of 135 ° and 80 % at a turn of 90 °.
, Harrizki Arie Pradana, Dwi Yuny Sylfania, Fransiskus Panca Juniawan
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 171-177; doi:10.14710/jtsiskom.2020.13468

Abstract:
Improved security of short message services (SMS) can be obtained using cryptographic methods, both symmetric and asymmetric, but must remain efficient. This paper aims to study the performance and efficiency of the symmetric crypto of AES-128 and asymmetric crypto of RSA with message compression in securing SMS messages. The ciphertext of RSA and AES were compressed using the Huffman algorithm. The average AES encryption time for each character is faster than RSA, which is 5.8 and 24.7 ms/character for AES and AES+Huffman encryption and 8.7 and 45.8 ms/character for RSA and RSA+Huffman, from messages with 15, 30, 60 and 90 characters. AES decryption time is also faster, which is 27.2 ms/character compared to 47.6 ms/character in RSA. Huffman compression produces an average efficiency of 24.8 % for the RSA algorithm, better than 17.35 % of AES efficiency for plaintext of 1, 16, 45, and 88 characters.
, Hadha Afrisal, Wisnu Dyan Nugroho
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 164-170; doi:10.14710/jtsiskom.8.2.2020.164-170

Abstract:
This research aims to develop a quadrotor control system for maintaining its position and balance from disturbance while hovering. A fast and reliable control technique is required to respond to high maneuverability and high non-linearity of six degrees of freedom system. Hence, this research focuses on designing a Self-Tuning Fuzzy-PD control system for quadrotor’s attitude. The designed control system utilizes input data from the Inertial Navigation System (INS). Then the quadrotor’s attitude is controlled by passing the PWM signal to the flight controller APM 2.6. The result shows that the average absolute error for the roll, pitch, and yaw angles are relatively small, as mentioned consecutively 2.0790, 2.2660, and 1.5280, while the maximum absolute errors are 6.3140, 6.7220, and 3.820.
, Ari Fadli, Arief Wisnu Wardhana
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 157-163; doi:10.14710/jtsiskom.8.2.2020.157-163

Abstract:
Utilization of an in-memory database as a cache can overcome relational database latency problems in a web application, especially when using a lot of join queries. This study aims to model the academic relational data into Redis compatible data and analyze the performance of join queries usage to accelerate access to relational data managed by RDBMS. This study used academic data to calculate student GPA that is modeled in the RDBMS and Redis in-memory database (IMDB). The use of Redis as an in-memory database can significantly increase Mysql database system performance up to 3.3 times faster to display student data using join query and to shorten the time needed to display GPA data to 52 microseconds from 61 milliseconds.
Nathaniel Clarence Haryanto, Lucia Dwi Krisnawati, Antonius Rachmat Chrismanto
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 140-149; doi:10.14710/jtsiskom.8.2.2020.140-149

Abstract:
The architecture of the text-reuse detection system consists of three main modules, i.e., source retrieval, text analysis, and knowledge-based postprocessing. Each module plays an important role in the accuracy rate of the detection outputs. Therefore, this research focuses on developing the source retrieval system in cases where the source documents have been obfuscated in different levels. Two steps of term weighting were applied to get such documents. The first was the local-word weighting, which has been applied to the test or reused documents to select query per text segments. The tf-idf term weighting was applied for indexing all documents in the corpus and as the basis for computing cosine similarity between the queries per segment and the documents in the corpus. A two-step filtering technique was applied to get the source document candidates. Using artificial cases of text reuse testing, the system achieves the same rates of precision and recall that are 0.967, while the recall rate for the simulated cases of reused text is 0.66.
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 150-156; doi:10.14710/jtsiskom.8.2.2020.150-156

Abstract:
The concept of classification using the k-nearest neighbor (KNN) method is simple, easy to understand, and easy to be implemented in the system. The main challenge in classification with KNN is determining the proximity measure of an object and how to make a compact reference class. This paper studied the implementation of the KNN for the automatic transliteration of Javanese, Sundanese, and Bataknese script images into Roman script. The study used the KNN algorithm with the number k set to 1, 3, 5, 7, and 9. Tests used the image dataset of 2520 data. With the 3-fold and 10-fold cross-validation, the results exposed the accuracy differences if the area of the extracted image, the number of neighbors in the classification, and the number of data training were different.
, Ria Rismayati, Muhammad Tajuddin, Ni Luh Putu Merawati
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 133-139; doi:10.14710/jtsiskom.8.2.2020.133-139

Abstract:
One of the strategic plans of the developing universities in obtaining new students is forming a partnership with surrounding high schools. However, partnerships made does not always behave as expected. This paper presented the segmentation technique to the previous new student admission dataset using the integration of recency, frequency, and monetary (RFM) analysis and fuzzy c-means (FCM) algorithm to evaluate the loyalty of the entire school that has bound the partnership with the institution. The dataset is converted using the RFM approach before processed with the FCM algorithm. The result reveals that the schools can be segmented, respectively, as high potential (SP), potential (P), low potential (CP), and very low potential (KP) categories with PCI value 0.86. From the analysis of SP, P, and CP, only 71 % of 52 school partners categorized as loyal partners.
, Ari Kusumaningsih, Yanuar Aliffio
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 127-132; doi:10.14710/jtsiskom.8.2.2020.127-132

Abstract:
Virtual fitting room (VFR) is a technology that replaces conventional fitting rooms. The VFR is not only available in shops, malls, and any shopping center but also in online stores, which makes VFR technology more and more developed, primarily to support online garment sales. VFR become a trending research interest since Microsoft has developed a Kinect tracking system. In this paper, we proposed the interactive 3D virtual fitting room using Microsoft's Kinect tracking and the rigging technique from 3D Modeling Blender and to implement the VFR. VFR manages the progress of virtual fitting that forms the three-dimensional simulations and visualization of garments on virtual counterparts of the real prospective buyer (user). Users can view the clothing animation on the various poses that are following the user body movements. The system can evaluate the user’s match, guiding them to choose the suitable size of the clothes using Euclidean distance.
Damar Wicaksono, Taufiq Kamal
Jurnal Teknologi dan Sistem Komputer, Volume 8; doi:10.14710/jtsiskom.8.2.2020.100-105

Abstract:
Smart agriculture has an emerged concept by using IoT sensors capable of providing various information about their field condition and conducting environmental monitoring to improve the yield of efficient crops. This research aims to develop a microclimate monitoring system in a closed house. The microclimate being monitored is the effective temperature, which is the temperature felt by broilers at that time in a fast area. In this research, IoT has been implemented using WeMos D1 R32 by sending sensor data to observe the effective temperature parameters as actual temperature, humidity, and wind speed into an MQTT cloud server. Microclimate control in the cage is based on effective temperature. The data can be displayed on a 16x4 LCD screen and accessed via an Android smartphone from anywhere and at any time.
, Aris Wahyu Murdiyanto
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 106-112; doi:10.14710/jtsiskom.8.2.2020.106-112

Abstract:
The quality of farmed shrimps has several criteria, one of which is shrimp size. The shrimp selection was carried out by the contractor at the harvest time by grouping the shrimp based on their size. This study aims to apply digital image processing for shrimp clustering based on size using the connected component analysis (CCA) and density-based spatial clustering of applications with noise (DBSCAN) methods. Shrimp group images were taken with a digital camera at a light intensity of 1200-3200 lux. The clustering results were compared with clustering from direct observation by two experts, each of which obtained an accuracy of 79.81 % and 72.99 % so that the average accuracy of the method was 76.4 %.
, Khurniawan Eko Saputro, Sofiansyah Fadli
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 89-93; doi:10.14710/jtsiskom.8.2.2020.89-93

Abstract:
The occurrence of imbalanced class in a dataset causes the classification results to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (positive class) so that the class distribution becomes balanced and leading to better classification results. This study was conducted to overcome imbalanced class problems on the Indian Pima diabetes illness dataset using k-means-SMOTE. The dataset has 268 instances of the positive class (minority class) and 500 instances of the negative class (majority class). The classification was done by comparing C4.5, SVM, and naïve Bayes while implementing k-means-SMOTE in data sampling. Using k-means-SMOTE, the SVM classification method has the highest accuracy and sensitivity of 82 % and 77 % respectively, while the naive Bayes method produces the highest specificity of 89 %.
Misbahuddin Misbahuddin, Muhamad Syamsu Iqbal, Giri Wahyu Wiriasto, L Ahmad, S. Irfan Akbar, Muhammad Irwan
Jurnal Teknologi dan Sistem Komputer, Volume 8; doi:10.14710/jtsiskom.8.2.2020.121-126

Abstract:
Outdoor positioning is one of the important applications in the Internet of things (IoT). The usage of GPS is unsuitable for low-power IoT devices. Alternatively, it can use the LoRa devices. This research aims to find a better method as the fingerprint algorithm for determining the outdoor position using RSS LoRa. The methods used as the fingerprint algorithm were two artificial neural network models, i.e. backpropagation (BP) with four types of training methods and learning vector quantization (LVQ) with two types of training methods. The experiment results show the performance of LVQ1 better than those of LVQ2. Besides, the LVQ1 was also better than the BP method. However, both BP and LVQ2 have a performance that is almost similar to about 70 %. Both of the artificial neural network models, BP and LVQ, can be used as a fingerprint algorithm to determine quite accurate the outdoor object position.
, Gadhing Putra Aditya, Sofyan Arifianto
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 113-120; doi:10.14710/jtsiskom.8.2.2020.113-120

Abstract:
This study aims to analyze the performance and security of the RSA algorithm in combination with the key generation method of enhanced and secured RSA key generation scheme (ESRKGS). ESRKGS is an improvement of the RSA improvisation by adding four prime numbers in the property embedded in key generation. This method was applied to instant messaging using TCP sockets. The ESRKGS+RSA algorithm was designed using standard RSA development by modified the private and public key pairs. Thus, the modification was expected to make it more challenging to factorize a large number n into prime numbers. The ESRKGS+RSA method required 10.437 ms faster than the improvised RSA that uses the same four prime numbers in conducting key generation processes at 1024-bit prime number. It also applies to the encryption and decryption process. In the security testing using Fermat Factorization on a 32-bit key, no prime number factor was found. The test was processed for 15 hours until the test computer resource runs out.
Gabe Dimas Wicaksana, Maman Abdurohman, Aji Gautama Putrada
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 36-43; doi:10.14710/jtsiskom.8.1.2020.36-43

Abstract:
Online multiplayer games require internet networks to play with opposing players more exciting because multiple players can fight each other. The game experiences lag, which is expressed as the quality of experience (QoE), is one of the most common problems for online multiplayer games, causing the games less exciting to play. This study examined the implementation of Message Queue Telemetry Transport (MQTT) as a communication protocol in multiplayer online games using Arduino and compared its performance against HTTP. QoE used data collected using the mean opinion score (MOS) method. The MQTT resulted in an average QoE score of 3.9 (Pingpong) and 4 (TicTacToe) MOS units, while on HTTP 3.8 (PingPong and TicTacToe). The use of the MQTT communication protocol can improve the QoE of multiplayer online game players compared to HTTP.
, R. Andhika Pandu, Rizky Wiradinata, Hari Peni Julianti, Rudy Setiawan
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 69-77; doi:10.14710/jtsiskom.8.1.2020.69-77

Abstract:
One of the causes of the high infant mortality rate in Indonesia is the lack of health support facilities in remote areas, including incubators, to keep the baby's body warm at a specific temperature. This research develops a model and prototype of a mobile incubator to carry and maintain the baby's temperature during emergencies to get further treatment to hospitals that have better facilities than incomplete health clinic facilities. The mobile incubator prototype uses a PID controller system with the optimum gain value Kp 1.501, Ki 0.016, and Kd -1,319 from the results of modeling and tuning in Matlab. The results of the bode plot analysis show that system stability was achieved with a gain margin of 109 dB. The incubator's operational mobility can last up to 59.6 minutes with two 12 V, 5 Ah batteries.
, I Gede Andika
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 94-99; doi:10.14710/jtsiskom.8.2.2020.94-99

Abstract:
The problem of inscription physical damage as one of the historical heritages can be overcome using an image processing technique. The purpose of this study is to design a segmentation application for ancient scripts on inscriptions to recognize the character patterns on the inscriptions in digital form. The preprocessing was carried out to convert images from RGB to HSV. The application used the gray level run length matrix (GLRLM) to extract texture features and the support vector machine (SVM) method to classify the results. The inscription image segmentation was carried out through the pattern detection process using the sliding window method. The application obtained 88.32 % of accuracy, 0.87 of precision, and 0.94 of sensitivity.
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 84-88; doi:10.14710/jtsiskom.8.2.2020.84-88

Abstract:
The government has launched a program to increase the production of catfish by using biofloc ponds. The biofloc ponds can maintain the quality of water biologically to maximize the growth of fish. However, the level of water quality monitoring is generally only divided into good or bad categories so that it cannot represent the condition of fish growth. Therefore, this study aims to get the level of water quality (0–100 %) using the Mamdani fuzzy inference system (FIS) algorithm based on pH, temperature, and dissolved oxygen (DO). The level of water quality was correlated based on catfish growth conditions. The results showed that the range of values of the water quality level for each condition of catfish growth was 100 % for normal-living fish, 83–99 % for stunted fish growth, and < 83% for threatened fish. The FIS algorithm had 89.92 % of accuracy.
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 64-68; doi:10.14710/jtsiskom.8.1.2020.64-68

Abstract:
This study aims to predict Drought Code (DC) in Kabupaten Kubu Raya using a combination of SOM-RBF. The final weight value of SOM was used as a center on the RBF network. The input data variables are rainfall data and air temperature data for three days with three binary outputs to predict DC values. This study also observed the effect of the number of neurons, learning rates, and the number of iterations on the results of the SOM-RBF network training. The smallest MSE of training result from the SOM-RBF network was 0.159933 using 65 neurons in the hidden layer, learning rate 0.007, and epoch 45000. The detection accuracy of SOM-RBF was 91.34 % from 245 test data.
Toni Arifin, Asti Herliana
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 59-63; doi:10.14710/jtsiskom.8.1.2020.59-63

Abstract:
The problem of visual impairment is a serious problem with increasing cases, ranging from visual impairment to the cause of blindness. This study examines the development of an identification application for the classification of patients with eye disorders using the Decision Tree (DT) method, which is optimized using Particle Swarm Optimization (PSO). This study used 311 eye image data, consisting of 233 normal eye images and 78 eye images with glaucoma, cataracts, and uveitis. The feature extraction used Gray Level Co-occurrence Matrix (GLCM), while the feature optimization used the PSO and the learning method used DT. This optimized visual impairment classification application can improve system accuracy to 88.09 %.
Novianti Puspitasari, Joan Angelina Widians, Noval Bayu Setiawan
Jurnal Teknologi dan Sistem Komputer, Volume 8, pp 78-83; doi:10.14710/jtsiskom.8.2.2020.78-83

Abstract:
Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.
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