J-SAKTI (Jurnal Sains Komputer dan Informatika)
J-SAKTI adalah jurnal yang diterbitkan oleh LPPM STIKOM Tunas Bangsa yang berfokus di bidang Manajemen Informatika. Pengiriman artikel tidak dipungut biaya, kemudian artikel yang diterima akan diterbitkan secara online dan dapat diakses secara gratis. Topik dari J-SAKTI adalah sebagai berikut (namun tidak terbatas pada topik berikut) : Artificial Intelegence, Digital Signal Processing, Human Computer Interaction, IT Governance, Networking Technology, Optical Communication Technology, New Media Technology, Information Search Engine, Multimedia, Computer Vision, Information System, Business Intelligence, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Computer Network Security, Natural Language Processing, Business Process, Cognitive Systems, Software Engineering, Programming Methodology and Paradigm, Data Engineering, Information Management, Knowledge Based Management System, Game Technology.
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<![CDATA[Prototype Sistem Buka Tutup Pintu Air Otomatis Menggunakan Prakiraan Cuaca ]]>
<![CDATA[Fazrin Muhammad Rizaldi]]>;
<![CDATA[Alun Sujjada]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.529
<![CDATA[Improvements in managing agriculture are urgently needed at this time. Along with the many occurrences of crop failure due to the rainy season which causes flooding in the rice fields. The lack of supervision of the irrigation system makes the water discharge when the rainfall is high, causing the rice plants to be damaged by the flow of water. With this, it is necessary to regulate the immigration channel to prevent crop failure and flooding. Along with this problem, a prototype of an Arduino-based automatic sluice system was made. This prototype has 2 functions, the first is to regulate when the floodgates in the reservoir operate with reference to the water level using an ultrasonic sensor. The sensor signals the Arduino to be processed. The output signal from the Arduino instructs the relay to activate and makes the solenoid work to open or close the water line. The second function is to manage the reservoir which has a water gate leading to the rice field area. This floodgate works with a time setting that can be set as desired. The prototype of the floodgate in irrigation is driven by a 12V DC motor and locks the floodgate when it is opened using a servo motor. With the work of these 2 functions, it can make it easier to regulate the water level in the reservoir and the management of irrigation channels in rice fields.]]>
<![CDATA[Sentimen Analisis Kegiatan Trading Pada Ap-likasi Twitter dengan Algoritma SVM, KNN Dan Random Forrest ]]>
<![CDATA[Neng Resti Wardani]]>;
<![CDATA[Sudin Saepudin]]>;
<![CDATA[Cecep Warman]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.497
<![CDATA[This study aims to find out how people comment on trading activities that are currently busy. As we know that lately there have been cases of trading involving affiliates, many people feel that they have been deceived by these activities. From this case, we conducted research using data collection methods regarding trading, which were taken from the Twitter social media platform using the Orange application. The data obtained through the scraping process will then be filtered to separate positive and negative sentiments, so that the data ready for sentiment analysis is 1,400 tweets. Data were analyzed using three methods, namely Random Forest, KNN, and SVM (Support Vector Machines). The results obtained from the research conducted which has 3 variables, namely positive sentiment has a value of 29%, negative is 10%, and neutral has a value of 62%. To analyze sentiment data from Twitter the author uses 3 classification methods and produces an accuracy value of KnN of 0.999, Random forest 0.994 and Naïve SVM 0.992. Based on the results of the analysis that has been carried out regarding trading activities, people think that not all trading is illegal and fraudulent because many sites are still legal]]>
<![CDATA[Analisis Penggunaan Metode ACPO (Association of Chief Police Officer) pada Forensik WhatsApp ]]>
<![CDATA[Riski Yudhi Prasongko]]>;
<![CDATA[Anton Yudhana]]>;
<![CDATA[Imam Riadi]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.520
<![CDATA[The development of smartphone technology is increasing, causing cyber crime to increase from year to year. One of the smartphone applications used by criminals is WhatsApp. The WhatsApp application is one of the most widely used insta messaging applications, especially in Indonesia. Violations such as hate speech, defamation, and fraud are common on the WhatsApp social network. This study was conducted with the aim of finding forensic evidence of cyberbullying behavior on the WhatsApp insta messaging application using the Association of Chiefs of Police (ACPO) method. This forensic phase involves planning, arresting, analyzing and presenting to search for digital evidence of cybercrimes using Belkasoft Evidence Center and HashMyFiles software. Digital evidence on smartphones can be found using case scenarios with 13 parameters that have been generated. The results of this study indicate that the Belkasoft Evidence Center digital forensic software is 81.92%, while HashMyfiles can detect the authenticity of digital evidence 79.96%.]]>
<![CDATA[Analisis Tingkat Kepuasan Masyarakat Terhadap Layanan Pemberdayaan Potensi Sumber Kesejahteraan Sosial ]]>
<![CDATA[Muhammad Bagus Rizki]]>; Magdalena A. Ineke Pakereng
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.488
<![CDATA[Along with the increasing number of people who need social assistance, companies engaged in their fields are required to improve the quality of their services, one of which is the Semarang Regency Social Service. Determination of the level of service satisfaction of the Semarang Regency Social Service is done by distributing questionnaires to the public who visit or use the Semarang Regency Social Service's service facilities. The results of the questionnaire are then described in the Cartesian Importance-Performance Analysis (IPA) diagram and the Customer Satisfaction Index (CSI) value which shows overall user satisfaction. The Cartesian IPA diagram shows that the attributes of aspects and requirements that must be met, the time given, the amount of the tariff, to the quality and service facilities provided by the community at the Semarang Regency Social Service. The overall level of service satisfaction is indicated by a CSI value of 83.59, which means that users or the public are very satisfied with the services provided by the Semarang Regency Social Service]]>
<![CDATA[Story Generator Bahasa Indonesia dengan Skip-Thoughts ]]>
<![CDATA[M Mustofa]]>;
<![CDATA[Dhomas Hatta Fudholi]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.479
<![CDATA[Currently, there are many studies that want computers to be able to imitate human creativity in stringing words into writing like a writer. This study aims to use the RNN algorithm to produce automatic story writing in Indonesian. The main contribution in this research is the creation and evaluation of the RNN algorithm based on the skip-thoughts model using an Indonesian language dataset. The skip-thoughts model consists of an encoder in the form of single GRU layer with 500 hidden units, and two decoders with single GRU layer each with 500 hidden units. The function of the encoder is to do the word mapping process from the input sentence, while the decoder predicts the sentence before (previous decoder) and the sentence after (next decoder) from the input sentence. The dataset used in the model training is in the form of stories in Indonesian with the genres of folklore and short stories. The model training process is run in 100 epochs, using the ADAM optimizer to get the optimal model. Based on the results of the assessment of respondents who have a background as writers, the folklore model shows a fairly good rating (average score of 65) for the S-P-O-K criteria, and a low rating for criteria of linkage between sentences (average score of 38) and the context of the whole story (average score of 32). The short story of life model shows a good rating (average score of 73) for the S-P-O-K criteria, and a low rating for the linkage between sentences criteria (average score of 48), and the context of the whole story (average score of 42). Based on the results of the assessment, the skip-thoughts model used in the Indonesian story generator has worked well, but it can still be improved by increasing the number of training datasets for each story genre used, as well as being more specific in determining the genre in order to obtain story integrity better.]]>
<![CDATA[Sistem Pendukung Keputusan Metode SMART Dalam Penentuan Pemberian Bantuan Sosial Berdasarkan DTKS Di Desa Bebengan ]]>
<![CDATA[Ahmad Nurhidayat]]>;
<![CDATA[Aji supriyanto]]>;
<![CDATA[Eddy Nurraharjo]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.511
<![CDATA[The level of welfare of the people in Indonesia is still low which results in the number of poverty is high. In an effort to reduce poverty in Indonesia, the government provide social assistance to provide capital or meet needs community food. There is a lot of help to make the shortage of help not right on target. In this effort, this research builds a support system decision by referring to the Integrated Community Welfare Data (DTKS) as the criteria. The DTKS data only contains BPS data for 2019 and not yet there periodically. Therefore, DTKS is needed to verify the poor restrictions on covid 19. This system aims to make it easier for Bebengan village officials in deciding the provision of social assistance to the community. methods that can be used The method used in this system is the SMART (Simple Multi Attribute Rating) method Technique). The SMART method is a fast multi-criteria retrieval technique and easy to use. To determine potential recipients of social assistance based on 10 criteria, namely: house conditions, water sources, income, electricity voltage, Education, employment, air resources, cooking fuel, age, dependents. Based on these 10 criteria, a decision support system is drawn up with: using intelligent methods, Decision Support System is a system computers that process data into information and decisions. From the criteria that given that the mother of the wife gets the top ranking as the top alternative with value 93.]]>
<![CDATA[Komparasi Analisis Bukti Digital Tiktok Lite Menggunakan Metode National Institute of Justice ]]>
<![CDATA[Imanuel Gilbert Rian Mailangkay]]>;
<![CDATA[Abdul Hadi]]>;
<![CDATA[Elia Zakharia]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.407
<![CDATA[With the development of communication and information technology today, there are many applications to send information to each other or share information. Tiktok Lite or commonly called the light version of Tiktok is the same application as Tiktok but is more friendly for users who have low smartphone specifications. With all the features that TikTok Lite has, such as the feature of creating, editing, and sharing short video clips complete with filters and accompanied by music as support and sending text messages or chatting. This can have a negative impact on the use of Tiktok Lite because it can provide opportunities to commit crimes, especially cyberbullying. In overcoming this cyberbullying act, digital forensic analysis is needed on the Tiktok Lite application. The method used is the National Institute of Justice (NIJ), with the flow of Identification, Collection, Examination, Analysis, and Reporting. Based on the research results, the MOBILedit Forensics Express tool gets a percentage of 0% in the search for digital chat evidence and account data on the Tiktok Lite application on smartphones that have not been and have been rooted, while the Belkasoft Evidence Center and Magnet Axiom tools get a 100% percent in the search for digital evidence. chat and account data on the Tiktok Lite application on a smartphone that has been rooted.]]>
<![CDATA[Analisis Sentimen Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization Terhadap Pelaksanaan Program Merdeka Belajar Kampus Merdeka ]]>
<![CDATA[Erina Undamayanti]]>;
<![CDATA[Teguh Iman Hermanto]]>;
<![CDATA[Ismi Kaniawulan]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.502
<![CDATA[During the MBKM program running at several universities in Indonesia, several problems occurred, namely the implementation of the curriculum that did not have a reference, the disbursement of pocket money given was not on schedule, the policies of each partner were different, and the existence of the covid-19 pandemic. The way to find out public opinion or opinion about the MBKM program is to summarize public opinion on Twitter social media. This study aims to analyze the results of the classification of twitter users opinions on the MBKM program in Indonesia through sentiment analysis using the Naive Bayes method based on Particle Swarm Optimization. The research metodology carried out in this study was through the stages of data crawling, text preprocessing, feature extraction, classification, and evaluation. The data used in this study are 428 data. The results of the research in the form of sentiment analysis obtained are positive sentiments of 61.92%, it can be concluded that the MBKM program can be well received by the Twitter user community, especially students. Although there are some negative sentiments that appear around 38.08%. The results of this study can be used as a reference for the MBKM policy development team, especially the Kemendikbud POKJA team, because this program can provide benefits and experiences for students while the results of this research can be used as evaluation material for the team in the future to be even better]]>
<![CDATA[Sistem Informasi Trouble Ticket (SIKET) Untuk Layanan Gangguan Jaringan Berbasis Web ]]>
<![CDATA[Sapta Musta Wijaya]]>;
<![CDATA[Wida Prima Mustika]]>;
<![CDATA[Andi Sanjaya]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.525
<![CDATA[Network service radio trunking PT.Handal Exa Teknologi often also gets various reports and complaints from users, currently users are still making reports and complaints using email and through group chat applications such as whats app , in the process of reporting this disturbance the helpdesk team is still doing manual recording. The purpose of this study is to create a web-based information system facility to process data on service interruption reports and be able to solve problems, can provide convenience for users to report service disruptions by using the website and maintain company service to users to be able to respond quickly in processing the handling of service disturbances, from the purpose of this research to produce a trouble ticket web-based ticket that can assist companies in data processing various reports and complaints of disturbances so that service to users becomes maximal and effective. The research method used in this study is based on qualitative methods. While the development of the system uses the waterfall model. The software used are: Windows Pro Home 10, Visual studio code, and Xampp.Information System Trouble Ticket -based Web at PT. Reliable Exa Technology that improves service to customers in terms of responding quickly to complaints that want to be reported and can be a medium for information on data disturbances that have occurred]]>
<![CDATA[Optimasi Analisis Sentimen Pada Twitter Olshop Tokopedia Menggunakan Textmining Dengan Algoritma Naïve Bayes & Adaboost ]]>
<![CDATA[H Hartati]]>;
<![CDATA[Deni Hermawan]]>;
<![CDATA[M. Akhsanal]]>;
<![CDATA[Zailani Wahyudi]]>;
<![CDATA[Angga Ariyanto]]>;
<![CDATA[Dedi Dwi Saputra]]>
<![CDATA[ J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER ]]>
Publisher : <![CDATA[STIKOM Tunas Bangsa Pematangsiantar ]]>
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DOI: 10.30645/j-sakti.v6i2.493
<![CDATA[Sentiment Analysis or commonly called Opinion Mining is the process of understanding, extracting and processing textual data automatically to obtain sentiment information contained in a sentence of opinion or opinion on a problem or object by someone, whether it tends to have a positive or negative opinion. This study aims to classify tweet data into 2 classifications, namely positive and negative. In this study, Indonesian text is used on Twitter social media in the form of tweets related to Tokopedia. Public opinion contained in the tweet can be used as material to find out whether tweets on Twitter, especially on Tokopedia, are classified as positive or negative. The data used consists of 1,000 tweet data. This dataset comes from the tweets of Tokopedia customers written on the Tokopedia twitter account. In text mining techniques, “transform case”, “tokenize”, “token filter by length”, “stemming” are used to build classifications. Gataframework is used to help during the preprocessing and cleansing process. RapidMiner is used to help create sentiment analysis in comparing three different classification methods, on Tokopedia's tweet data. The method used to compare in this research is the Naïve Bayes algorithm and the Naïve Bayes algorithm which is added with the Synthetic Minority Over-sampling Technique (SMOTE) feature and the Naïve Bayes algorithm is added with the Synthetic Minority Over-sampling Technique (SMOTE) feature which is optimized with Adboost. . The Naïve Bayes algorithm added with the Synthetic Minority Over-sampling Technique (SMOTE) feature, which was optimized with Adboost, got the best score. With 94.95% accuracy, 90.86% precision, 100.00% recall and 0.950 AUC]]>
