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SINTECH (Science and Information Technology) Journal
Published by STMIK STIKOM Indonesia
ISSN : 25987305     EISSN : 25989642     DOI : -
Core Subject : Science,
Computer Science & IT
SINTECH (Science and Information Technology) Journal merupakan jurnal yang dikelola dan diterbitkan oleh Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK STIKOM Indonesia, dengan e-ISSN 2598-9642 dan p-ISSN: 2598-7305. SINTECH Journal diterbitkan pertama kali pada bulan April 2018 dan memiliki periode penerbitan sebanyak dua kali dalam setahun, yaitu pada bulan April dan Oktober. Bidang keilmuan dari SINTECH Journal mencakup bidang ilmu : Data analysis, Natural Language Processing, Artificial Intelligence, Neural Networks, Pattern Recognition, Image Processing, Genetic Algorithm, Bioinformatics/Biomedical Applications, Biometrical Application, Content-Based Multimedia Retrievals, Augmented Reality, Virtual Reality, Information System, Game Mobile, dan IT Bussiness Incubation.
Arjuna Subject : -
Articles 166 Documents
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Densely Connected dan Residual Convolutional Neural Network Untuk Estimasi Jumlah Keluarga Tingkat Desa Dengan Citra Satelit Siregar, Jodi jhouranda; Kurnia, Anang; Sadik, Kusman
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1191

Abstract

Indonesia conducts a population census every ten years to collect population data. Variables such as family count are collected to provide general population information for policy making and sampling frames. Indonesia as an archipelagic country with an area of 8.3 million km2 will require a lot of resources to collect such data. In the age of big data, satellite imagery has become more available and inexpensive. In this study, we used West Java as a case study for applying deep learning to estimate family counts at the village level. Sentinel-2 and SPOT-67 data are used to model family counts. Using xgboost, we regress the family count with the softmax probability, resulting from family density classification using deep learning (densenet121 and resnet50 ) as the input. With an R2 of 0.93 and a MAPE of 19%, the regression model provides a good prediction of the number of families in the census. Regarding the input data, Sentinel-2 is sufficient to accomplish this task as there is no significant difference from the modeling results with higher resolution images (SPOT 6-7). The input level in the form of a segment of the estimation area and using structured auxiliary variables also deliver better predictions
Analisis Penerapan Metode Association Rule Mining Untuk Transaksi Penjualan di Toko Bangunan Dengan Algoritma Apriori Anggraini, Diah; Sanjaya, Ucta Pradema; Sa’ida, Ita Aristia
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1193

Abstract

In improving the quality of service to customers, UD. Lasmi Jaya store is asked to be able to handle problems that often arise, among others, lack or absence of (out of stock) stock of building goods that are very popular, less strategic layouts, assist in decision making to develop marketing strategies and promote better products and more. Therefore, in this problem, there must be utilization of sales transaction data for processing using a priori algorithms so that it can provide new knowledge that can be utilized by shop owners. From the results of research that has been carried out from 300 sales transaction data from January 1, 2022 - June 30, 2022 with a comparison of a minimum transaction of 15 or a minimum of 5% support and a minimum of 40% confidence with a minimum of 12 transactions or a minimum of 4% support and a minimum of 30% confidence, associations the final results found were more at least 12 transactions or minimum support 4% and minimum confidence 30% because if the minimum support value and minimum confidence value were lower then the association value found would be more, so for determining stock of goods or layout of goods or as promotion and others so that it will make it easier for the owner to manage their sales so that they can grow and provide satisfaction
Pengembangan Sistem Rekomendasi Melalui Pendekatan Web Semantik dan Simple Additive Weighting (SAW) Pramartha, Cokorda; Jayadi, I Putu Indie Surya; Atmaja, I Dewa Made Bayu
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1216

Abstract

Indonesia is one of the countries that have the largest growth after China and India in the use of mobile phones. eMarkerter published that the number of mobile phone users in Indonesia increased by 37.1% from 2016 to 2019 and in 2019 it reached 92 million users. This study aims to develop a mobile phone recommendation system with a semantic web approach using the Simple Additive Weighting Method (SAW). The prototype system available online at http://mobile.oss.web.id that has 3 main features, namely searching, browsing, and recommendation features. The process of searching, browsing, and recommending the system using information that has been stored in Ontology. Meanwhile, the recommendation mechanism uses the SAW method as a method of calculating the weight of each cellphone and the weight of the criteria. Evaluation of functional requirements system using Black-Box testing approach, while evaluation of non-functional requirement systems using Technology Acceptance Model (TAM) approach by involving 32 respondents. The final result of the evaluation shows that the system functionality is running well as expected. Moreover, the evaluation of the non-functionality of the system showed that on average the respondents involved in the study agreed that the system developed was useful and easy to use
Perbandingan Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Stroke Azhar, Yufis; Firdausy, Aidia Khoiriyah; Amelia, Putri Juli
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1222

Abstract

Data mining is often called knowledge Discovery in Database (KDD). Data mining is usually used to improve future decision making based on information obtained from the past. For example for prediction, estimation, association, clustering, and description. Stroke is the second most deadly disease in the world according to WHO. The sufferer has an injury to the nervous system. Because of this, health experts, especially in the field of nursing, need special attention. Currently, the development of the Industrial Revolution Era 4.0 is collaborating in the fieldsof technology and health science so that it becomes something useful by using Machine Learning. There are so many benefits that are used in predicting several diseases that can be anticipated. In this study the dataset is dividedinto 2 parts, namely training data and testing data using split validation. Based on the results of the test that have been carried out in this study, the algorithm that has the highest accuracyvalue on balanced data is Logistic Regression with an accuracy rate of 75.65%, while for unbalanced data, the algorithm that has the highest accuracy results is Logistic Regression, Random Forest, SVM, and KNN with an accuracy rate of 98.63%. This testing process is carried out to identify stroke with data mining algorithms
Penerapan Metode E-Service Quality Terhadap Pengukuran Tingkat Kepuasan Penggunaan Marketplace Parwita, Wayan Gede Suka; Indradewi, I Gusti Ayu Agung Diatri; Ariantini, Made Suci; Ginantra, Ni Luh Wiwik Sri Rahayu; Putra, I Kadek Andika
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1236

Abstract

Intense competition makes various existing markets must be able to provide the best and satisfaction for users to win the existing competition. In the review of the JD.ID application during versions 6.3 and 6.4 there were several user complaints that led to system and service quality problems. Service quality is one of the factors supporting the success or failure of an information system to provide satisfaction to its users. The purpose of this study was to determine how the influence of electronic service quality (e-service quality) on user satisfaction in the JD.ID application. The type of analysis used in this study is simple regression analysis with descriptive analysis to describe a generalization or explain the research subject based on the dimensions of e-service quality, so that an overview of the effect of e-service quality on user satisfaction can be obtained. The processed data was obtained from distributing questionnaires by using the google form. The results showed that the majority of JD.ID marketplace users in Badung Regency were women. The test results show that e-service quality which consists of dimensions of efficiency, system availability, fulfillment, privacy, responsiveness, compensation, and contact has a significant influence on user satisfaction and has a strong correlation, meaning that the higher the service quality JD has. ID, the higher the level of satisfaction of JD.ID users. It can be said that the service quality of JD.ID is quite good in providing user satisfaction.
Analisis Sentimen Pada Pembelajaran Daring Di Indonesia Melalui Twitter Menggunakan Naïve Bayes Classifier Sarasvananda, Ida Bagus Gede; Selivan, Diana; Radhitya, Made Leo; Putra, I Nyoman Tri Anindia
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1241

Abstract

Education is one of the areas most affected by the covid-19 pandemic. Education during the pandemic must continue. To reduce the spread of covid-19 and learning activities can run as usual, the government, in this case the Ministry of Education and Culture, has implemented a distance education system in Indonesia. In addition, the response from the community is very important for an evaluation of the applied online learning. With the implementation of the policy regarding online learning in Indonesia, it is necessary to conduct a sentiment analysis to find out how the responses, opinions, or comments from the public and online learning actors related to online learning are currently being implemented. So the author conducted a research entitled Sentiment Analysis on Online Learning in Indonesia Through Twitter Using the Naïve Bayes Classifier Method to measure student responses regarding online learning during the covid -19 pandemic in Indonesia. The results of the accuracy of this study is 99.8% and the classification error is 0.12%. Of the total data entered, 83 tweets or 20% were included in the positive class, the negative class was 317 tweets or 80%.
Optimasi Parameter Support Vector Machine Dengan Algoritma Genetika Untuk Analisis Sentimen Pada Media Sosial Instagram I Putu Dedy Wira Darmawan; Gede Aditra Pradnyana; Ida Bagus Nyoman Pascima
SINTECH (Science and Information Technology) Journal Vol. 6 No. 1 (2023): SINTECH Journal Edition April 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i1.1245

Abstract

Social media is an online media that users use to interact with each other by expressing themselves by giving comments, and one example is Instagram. All the collected comments will form a public opinion. This opinion can be used with sentiment analysis to become information. The method commonly used to carry out sentiment analysis is classification using machine learning. One of the machine learning that is often used is the Support Vector Machine (SVM). However, on non-linear problems such as sentiment analysis, SVM requires the kernel to map vectors into high-dimensional spaces to solve non-linear problems. The problem faced in using the kernel is to choose the optimal parameters for the classification model to produce a good classification model. This study proposes a new approach to obtain optimal parameters for SVM using Genetic Algorithm (GA). This study designed an SVM-GA classification model from the data collection, processing, classification, and evaluation stages. The results showed that the best accuracy produced with parameters optimized with the genetic algorithm was 81.6%, or an increase of 2.4% from the SVM sentiment analysis method without GA optimization.
Perbandingan Metode Pembobotan TF-RF Dan TF-ABS Pada Kategorisasi Berita Di BDI Denpasar I Kadek Wahyu Dananjaya; I Gusti Ayu Agung Diatri Indradewi
SINTECH (Science and Information Technology) Journal Vol. 6 No. 1 (2023): SINTECH Journal Edition April 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i1.1252

Abstract

BDI Denpasar is a government agency tasked with carrying out training and education for human resources of animation, crafts and art. BDI Denpasar in managing news classes in the Kabar Insan Oke service still uses conventional methods. Therefore an automatic news classification module is needed. This study was made to compare the performance level of news classification at BDI Denpasar using K-NN classification with the TF-RF and TF-ABS term weighting methods. Methods that have a high level of performance will be implemented in the news classification module. This research was carried out by collecting news documents, text preprocessing, term weighting, classification, model validation and testing. The K-NN classification uses the n_neighbhor (k), namely k=3, k=5, k=7 and k=9 using a dataset of 324 documents containing 7 classes taken from BDI Denpasar website. Based on the results of the tests performed, TF-RF method obtained a higher performance at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%. TF-ABS method with the highest performance value is found at k=9 which obtains 70% accuracy, 63% precision and 70% recall. So the method that will be implemented in the news classification module is TF-RF at k=5 with an accuracy of 71% with a precision of 73% and a recall of 71%.
Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network Khairunnisak Khairunnisak; Gilang Miftakhul Fahmi; Didit Suhartono
SINTECH (Science and Information Technology) Journal Vol. 6 No. 1 (2023): SINTECH Journal Edition April 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i1.1258

Abstract

Abstract In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium. One of the media for hiding messages is pictures. However, steganography techniques can still be detected by steganalysis techniques. Steganalysis is a technique for analyzing hidden messages in steganography. Therefore this study applies image processing techniques with the Generative Adversarial Network algorithm model, which aims to manipulate images so that steganalysis techniques cannot detect hidden messages. Proof of the results of applying the Generative Adversarial Network algorithm using a web-based application containing message hiding and extraction functions. The results obtained are that the Generative Adversarial Network algorithm can be applied to create mock objects, and images can revive based on training data which is a model for how the algorithm works. In addition, the results of testing the Generative Adversarial Network algorithm were successfully applied to image steganography which functions to prevent steganalysis techniques from trying to detect messages in images. Future research is expected to be able to select steganographic images other than the results from the training data model according to the original size chosen randomly according to the selection of the user.
Pengembangan Sistem Prediksi Bantuan Program Keluarga Harapan (PKH) Berbasis Machine Learning I Wayan Supriana Supriana; Made Agung Raharja; I Made Satria Bimantara
SINTECH (Science and Information Technology) Journal Vol. 6 No. 1 (2023): SINTECH Journal Edition April 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i1.1297

Abstract

The Family Hope Program (PKH) is a poverty alleviation program which is one of the government's strategies in reducing the poverty line. This program provides cash social assistance to poor families who are included in the list of beneficiary families with a focus on education and health. The purpose of implementing the PKH program is not only to reduce poverty and increase human resources but to break the poverty chain. The implementation of PKH in its realization experienced many obstacles that caused the program not to be on target, this was because the data verification process was not yet effective and was still carried out manually. A process is needed to digitize the distribution and realization of the family of hope program. Through this research, a system was developed that can predict the value of PKH beneficiary assistance. The system developed is based on machine learning with a prediction model using Artificial Neural Network (ANN) and Backpropagation learning algorithm. Parameters in the learning system using PKH assessment as many as 8 indicators from the data of PKH beneficiaries in Tabanan Regency. Based on the prediction model testing using two data treatments, namely with and without preprocessing data. Parameters treated with data on numeric attributes and categories provide optimal values with an R2 Score of 0.695824 with a number of hidden layers of 500 and a max epoch of 375

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